Employer Based Trip Reduction Strategies at Dalhousie University by Bruce Mans

Employer-based trip reduction An examination of current commuting patterns and opportunities to reduce SOV trips at Dalhousie University

Final report December 8, 2010

Bruce Mans B00531853 Dalhousie University School of Planning

for

Patricia Manuel Course instructor

Ahsan Habib

Technical director

Table of contents ii iv

Table of contents

3 4

Introduction Approach and methodology Literature review

List of figures Executive summary

Types of EBTR programs

Barriers to EBTR

Summary of literature review

Analyses 16 Survey Analysis 16

Data preparation

Survey results 23 Socioeconomic results 37 Travel behaviour 62 Programs opinions and preferences 77 Additional employee opinions 85 Spatial analysis 87 Recommendations 20

Current transportation programs at Dalhousie General recommendations Far zone recommendations

Near zone recommendations

87 91

100

109 103

Conclusion References Appendix

List of figures 1 3

Figure 1.1 Figure 1.2

Context map of Dalhousie University Structure of research project

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Figure 2.1 Figure 2.2 Figure 2.3 Figure 2.4 Figure 2.5 Figure 2.6 Figure 2.7 Figure 2.8 Figure 2.9 Figure 2.10 Figure 2.11 Figure 2.12 Figure 2.13 Figure 2.14 Figure 2.15 Figure 2.16 Figure 2.17 Figure 2.18 Figure 2.19 Figure 2.20 Figure 2.21 Figure 2.22 Figure 2.23 Figure 2.24 Figure 2.25 Figure 2.26 Figure 2.27 Figure 2.28 Figure 2.29 Figure 2.30 Figure 2.31 Figure 2.32 Figure 2.33 Figure 2.34 Figure 2.35 Figure 2.36 Figure 2.37 Figure 2.38 Figure 2.39 Figure 2.40 Figure 2.41

Snapshots showing before-and-after spreadsheets. Map of commute zones Modal split of staff and faculty Modal split of students Gender of staff and faculty Gender of students Staff and faculty age, by mode Staff and faculty age, by commute zone Student age, by mode Student age, by commute zone Staff and faculty employment status, by mode Staff and faculty employment status, by commute zone Sample of student respondents, by status Student status, by mode Distribution of staff and faculty, by household income Staff and faculty household income, by mode Staff and faculty household income, by commute zone Staff and faculty modal split, by estimated commute distances Staff and faculty modal split, by commute zone Survey sample, by commute zones Student modal split, by estimate commute distances Student modal split, by commute zone Staff and faculty commute times Student commute times Relationship of distance and time among staff and faculty SOV users Relationship of distance and time among staff and faculty walkers Relationship of distance and time among staff and faculty transit users Relationship of distance and time among student SOV users Relationship of distance and time among student walkers Relationship of distance and time among student transit users Staff and faculty peak travel periods (entire sample) Staff and faculty peak travel periods (SOV vs. Walking) Staff and faculty peak travel periods (near zone and far zone) Student peak travel periods (entire sample) Student peak travel periods, for SOV users Student peak travel periods, for walkers Staff and faculty reasons for SOV use, by percentage Percentage of staff and faculty indicating ‘convenience’ as a reason for SOV use Percentage of staff and faculty indicating ‘need to run errands’ as a reason for SOV use Student reasons for SOV use, by percentage Percentage of students indicating ‘convenience’ as a reason for SOV use

iii

52 53 54 56 57 58 59 60 64 65 66 66 67 68 69 69 70 70 73 73 74 74 76 76 77 78 79 80 81 82 83 86 86

Figure 2.42 Figure 2.43 Figure 2.44 Figure 2.45 Figure 2.46 Figure 2.47 Figure 2.48 Figure 2.49 Figure 2.50 Figure 2.51 Figure 2.52 Figure 2.53 Figure 2.54 Figure 2.55 Figure 2.56 Figure 2.57 Figure 2.58 Figure 2.59 Figure 2.60 Figure 2.61 Figure 2.62 Figure 2.63 Figure 2.64 Figure 2.65 Figure 2.66 Figure 2.67 Figure 2.68 Figure 2.69 Figure 2.70 Figure 2.71 Figure 2.72 Figure 2.73 Figure 2.74

Percentage of students indicating ‘need to run errands’ as a reason for SOV use Percentage of staff and faculty that occasionally use SOV, by mode and commute zone Percentage of staff and faculty that occasionally use SOV, by mode and commute zone Percentage of staff and faculty within 500 metres of transit, by mode and commute zone MetroTransit buses offering direct service to Dalhousie University Students living within 500 metres of transit, by mode Inter-campus travel required for staff and faculty Inter-campus travel required for students Staff and faculty sample ranking of sustainable transportation initiatives Staff and faculty ranking of bicycle infrastructure improvements, by mode Staff and faculty ranking of transit related improvements, by mode Staff and faculty ranking of other initiatives, by mode Staff and faculty ranking of transportation initiatives, by commute zone Student sample ranking of sustainable transportation initiatives Student sample ranking of bicycle-related transportation initiatives Student sample ranking of transit-related transportation initiatives Student sample ranking of other transportation initiatives Student sample ranking of bicycle-related transportation initiatives, by commute zone Percentage of students that support parking as a funding option Percentage of students that support external grants as a funding option Percentage of students that support base budget allocation as a funding option Percentage of students that do support funding sustainable transportation Staff and faculty interest in carpool programs Student interest in carpool programs Percentage of staff and faculty that support a transit pass Percentage of staff and faculty that would use transit with reduced fares Stated cost of a monthly employee transit pass Percentage of staff and faculty that would use transit more with a transit pass Percentage of jobs that are conducive to telecommuting Percentage of staff and faculty interested in telecommuting Percentage of jobs that are conducive to telecommuting Spatial distribution of staff and faculty Spatial distribution of students

71 72 72 72 87 88 88 89 92 93 96 97 98 99

Figure 3.1 Figure 3.2 Figure 3.3 Figure 3.4 Figure 3.5 Figure 3.7 Figure 3.6 Figure 3.8 Figure 3.9 Figure 3.10 Figure 3.11 Figure 3.12 Figure 3.13 Figure 3.14

Percentage of staff and faculty that support parking as a funding option Percentage of staff and faculty that support external grants as a funding option Percentage of staff and faculty that support base budget allocation as a funding option Percentage of staff and faculty that do not support funding sustainable transportation Office of Sustainability Transportation website An example of an abandonned bicycle A Dal rideshare parking spot sign Tiger Patrol van (Source: unews.ca) UBC’s TREK website CarShare HFX vehicle (Source: CarShare HFX) Microsoft shuttle, with wide seats and WIFI services Express shuttle service “mock-up” Active transportation campus plan (IBI group, 2010) An example of a covered bicycle rack (Source: www.uvic.ca)

Executive summary

Executive summary Dalhousie University, located in Halifax, Nova Scotia, is the largest university in Atlantic Canada with over 16,000 enrolled students and is also one of the largest employers in the city with approximately 7,200 employees. As a result, the university generates a lot of trips to and from its campuses. An estimated 4 to 5,000 of these trips are made by single occupancy vehicle (SOV), contributing to traffic congestion, poor air quality, and parking issues in the city. As a result, this research project recommends effective strategies to reduce trips made by SOV based on a comprehensive literature review and an analysis of university commuting behaviour. Literature review Many research papers have identified parking fees as the most important variable determining whether or not commuters will drive to work alone. Free or heavily subsidized parking will perpetuate SOV use, as it significantly reduces associated costs for SOV users. Thus, employers can effectively reduce SOV travel by reducing or eliminating parking subsidies or by eliminating parking altogether. Employer operated transportation services, such as vanpools, buspools or shuttle services, are also an effective strategy for reducing SOV travel, as they are catered specifically for employee schedules and residential locations. Additionally, basic support actions such as marketing and awareness campaigns have been identified as one of the most cost-effective strategies in reducing SOV use. Employer policies supporting compressed work weeks and telecommuting can help employees avoid making trips altogether; however, several research papers indicate that several barriers can influence success rates, including employee willingness, job characteristics, and availability of space at home. Finally, transportation programs must also have clear benefits for both employers and employees in order to be successful. For example, commuters will only switch to sustainable modes if they can compete with SOV in terms of overall cost, convenience, speed, and comfort. Employers, on the other hand, will not consider implementing programs at all unless they are economically feasible or they can solve specific issues plaguing a workplace (such as poor workplace morale or employee retention). Survey results Data from the 2009 Dalhousie Transportation Survey are analysed in order to determine the current travel behaviour at the university. Several key findings are discovered in this analysis. First, results suggest that SOV use increases with age and income and as employment types become more permanent and stable, suggesting that sustainable modes must be convenient, cost and time-effective, and

Executive summary

comfortable in order to persuade SOV users with more responsibilities and financial resources to switch to sustainable modes. Another important issue arising from the analysis is that travel options for commuters living further than 10 kilometres are limited to two alternatives to SOV; carpooling and transit. Further results indicate that transit is not a viable option for many of these commuters, due to significant time and accessibility issues. Therefore, it is not surprising that survey respondents living farther than 10 km from Dalhousie ranked improvements to transit as the most important factor to encourage sustainable transportation. Respondents living closer to the university generally preferred more bicycle-related improvements. Finally, carpooling programs were clearly the least preferred transportation initiative by all respondents, and SOV users in particular. Recommendations Based on evidence found in the literature review and the university travel behaviour analysis, the university should create a consolidated transportation department that will oversee all transportationrelated issues and increase campus awareness and education. Furthermore, it should seriously consider reducing parking subsidies and supply in order to effectively reduce SOV usage. Dalhousie should also encourage active transportation for nearby commuters by improving on-site bicycle and pedestrian infrastructure while advocating for municipal improvements, such as bicycle lanes. Additional recommendations include improving on-site mobility options and encouraging alternative scheduling arrangements through supportive policy. Finally, the university should conduct further research into the feasibility of a shuttle service that can fill areas that are neglected by transit, and work with community partners such as MetroTransit and other large employers to develop such a service. Ultimately, a new program along with the recommended strategies will enable the university to effectively reduce trips made by SOV, thereby reducing greenhouse gas emissions, improving air quality, and resolving future parking issues. These benefits also extend to the community at large by reducing traffic congestion, encouraging active transportation, consuming less land with parking spaces, and improving future travel behaviour, as younger generations practice sustainable commuting habits. With a dedicated effort, Dalhousie University can be a leader in employer-based sustainable transportation practices, both as a large employer in Halifax and as an important post-secondary institution in Canada.

Introduction

Introduction Dalhousie University is a major university in Canada, located in Halifax, Nova Scotia. The university is located in an urban setting, with several campuses located throughout the city. Established in 1818, it is one of the oldest universities in the country and the largest in Atlantic Canada with over 16,000 enrolled students (Dalhousie University, 2010a). Dalhousie is also one of the largest employers in Halifax, employing over 7,200 staff and faculty (Dalhousie University, 2010b). In total, over 23,000 people commute to the university on a regular basis. Although many of these commuters already use a variety of sustainable modes of transportation (including transit, walking or cycling), 39% of faculty and staff and 11% of students regularly drive alone to the university (Dalhousie University, 2009). Based on these recent estimates, approximately 4 to 5,000 trips are made by single occupancy vehicle (SOV), contributing to transportation problems on the peninsula and campus, such as traffic congestion, increasing costs of space management, and environmental concerns.

Figure 1.1 Context map of Dalhousie University

Source:

Google Maps, 2010

Halifax

Sexton Campus Studley Campus Carleton Campus

North

Introduction

There are many reasons why Dalhousie should strive to reduce trips made by SOV. First, when many trips are made by SOV, transportation is almost certainly the single largest impact a university has on the environment (Tolley, 1996). SOVs release more greenhouse gas emissions per person than most other modes, encourage oil dependency, and require expensive infrastructure and extensive maintenance. Second, trips by SOV also cause employers financial and management issues. SOV users require significant amounts of space on campus for parking. As a result, parking lots continue to overflow every year while the costs of building new infrastructure continues to increase due to increasing land value and decreasing land availability (Toor, 2001). Furthermore, parking lots must constantly be managed, introducing other costs including â&#x20AC;&#x153;the salaries and associated overheads for car park attendants; administration costs; asset value for the land used for car parking; taxes paid on the car-parking space; capital costs of establishing the car parks and maintenance and repair costs for them; and fees and other payments to towing companiesâ&#x20AC;? (Tolley, 1996). Indeed, parking comes with an expensive price tag for employers; a cost which is usually not recovered by the revenue generated from them (Dalhousie University, 2009). Finally, Dalhousie can benefit from the social benefits of having a large population of active commuters. Many studies have revealed that active commuting has a direct impact on oneâ&#x20AC;&#x2122;s physical and mental health (Sallis et al, 2004). For example, some studies have shown that participating in active forms of transportation can significantly contribute to a more motivated and positive lifestyle (Field et al, 2001). As a result, active commuting can improve campus vitality and morale. Also, Dalhousie and other universities have a unique opportunity to influence future travel behaviour by promoting active transportation to young, incoming students. In particular, as first-year students transition from secondary school to the more independent lifestyle of post-secondary education, they may be more willing to try new forms of transportation, which they may maintain through adulthood. Essentially, universities can help future individual travel behaviour become more sustainable. It is clear that travel behaviour involving high SOV usage presents serious environmental, economic and social concerns for Dalhousie University. As a result, the primary objective of this research project is to determine how the university can reduce SOV use by encouraging sustainable travel modes.

Methodology

Approach and methodology In order to achieve this objective, this applied research project includes three stages. First, the project identifies and summarizes the most relevant research papers regarding SOV trip reduction programs for employers in a comprehensive literature review. This review summarizes the many different types of programs and their effectiveness and identifies the most important barriers threatening successful implementation. The second stage involves a descriptive analysis of current travel behaviour of Dalhousie commuters based on data from the 2009 Dalhousie Transportation Survey. The primary objective of this stage is to comprehensively characterize and spatially locate SOV users by comparing them to commuters using sustainable modes. This part of the analysis also explores how distance plays an important role in determining modal choice. The final component of analysis includes a spatial analysis used to identify specific areas with high densities of SOV commuters. Based on the information acquired in the first two stages, the third and final stage identifies the most appropriate and effective strategies aimed at reducing single-occupancy vehicle trips. These strategies are divided into three groups. First, general universitywide recommendations are discussed, which are followed by a latter set of recommendations divided by distance. The first set focuses on strategies that will encourage sustainable travel habits for commuters who live within a 10 km buffer of the university, while the latter focuses on programs for commuters outside this buffer. Figure 1.2 Structure of research project

Effective strategies Analysis Literature review

Literature Review

Literature review Transportation demand management (TDM) refers to policies and programs that attempt to influence travel behaviour and automobile demand by reducing vehicle miles travelled. In general, TDM programs are employed by governing bodies (from municipal to national) in order to reduce the environmental impacts of the transportation sector. However, they can also be utilized by other local stakeholders, including landowners, developers, business associations, employers, and institutions (TCRP, 2010). Employer-Based Trip Reduction (EBTR) initiatives (also known as Commute Trip Reduction, Employee Trip Reduction, or Vehicle Trip Reduction) are specific employer and institution-based TDM strategies used by corporations, hospitals, airports, government facilities, universities, and more. They can also be used by a tandem of employers within a single neighbourhood (including business associations and coops). The communal environmental benefits of reducing vehicular traffic are obvious. However, EBTR programs have several other benefits for employers and institutions. By reducing trips by SOV, employers can reduce parking demand and their associated overhead costs, improve employee travel burdens, recruiting and retention, boost productivity and reduce absenteeism (2010). Also, employers can benefit from the positive corporate image associated with the environmental benefits of EBTR. In order to maximize these benefits, employers will typically draw upon a palette of transportation programs, including support actions and resources, transportation services, and financial incentives and disincentives (2010). This section of the research project highlights the most recent research that has been conducted in the field of EBTR. It begins by identifying the different types of EBTR programs that have been researched while outlining their effectiveness and impacts. Finally, it concludes with a discussion on the most important barriers regarding implementation.

Literature Review

Types of EBTR programs There are many different types of EBTR strategies available to employers interested in reducing trips by SOV. The Transit Cooperative Research Program (TCRP) has summarized the range of options into four categories; 1) employer support actions, 2) transportation services, 3) incentives and disincentives, and 4) alternative work arrangements (TCRP, 2010). The following section highlights the most applicable EBRT-related research projects under these categories.

Employer support actions Support actions are basic measures taken by employers that encourage the reduction of SOV trips at the worksite. Support actions involve employers raising awareness, organizing trips, providing information and support, and/or removing barriers associated with sustainable transportation (2010). Several research projects have explored the effectiveness of various employer support actions, including ridesharing, car-sharing, marketing campaigns, guaranteed ride home programs, and other related programs. Also, support actions are typically self-financed. Employers may, however, use financial incentives to promote and encourage initiatives for greater success (2010). Carpooling (also known as ridesharing or liftsharing) refers to increasing the amount of persons travelling in a vehicle (usually owned by an employee). Employers may promote the concept at the workplace or even offer ride-matching services that will combine employees into a single and efficient trip. Perhaps the most significant barrier to carpooling is time. Morency (2007) finds that scheduling conflicts and inflexibility are key barriers to carpooling, contributing to a high proportion of carpooling matches occurring withing the same household. Buliung et al. (2009) confirm this in another study, which identifies spatial proximity to matches as the most important factor in determining carpool success. The researchers also reveal automobile ownership as another important variable. Other studies explore the effects of workplace characteristics on carpooling. One study confirms that workplaces with typical work schedules for the majority of employees experience better carpooling results (Ferguson, 1990). As a result, institutions such as universities and hospitals may have a difficult time promoting effective carpooling programs. Ferguson (1990) also suggests that larger firms have more success with carpooling due to a greater access to potential carpoolers.

Literature Review

Car-sharing is another type of support action that can be implemented by employers. Car-sharing is a service that caters to people who require occasional access to a private vehicle. Car-sharing companies rent out vehicles by the hour or day. Typically, employers may partner with car-sharing service providers in order to increase on-site mobility for their employees. Although car-sharing does not have a direct impact on reducing SOV trips, it can encourage sustainable travel behaviour by giving employees the opportunity to make out-of-office meetings and run errands during business hours while leaving their own car at home. Employers can also benefit by reducing parking demand and the costs of buying company cars (TCRP, 2005). Recent research has also confirmed that car sharing programs can increase transit usage, while reducing SOV trips, vehicle miles travelled, vehicle ownership, and parking demand (Shaheen, 2005). Finally, Abraham (1999) discovered that in order to be successful, car-sharing organizations need to have a diverse fleet of vehicles, and need to be affordable and relatively accessible. Guaranteed Ride Home (GRH) programs provide free or subsidized emergency rides for commuters who used sustainable forms of transportation to get to work. These programs, like car-sharing, encourage sustainable forms of transportation by increasing mobility at the workplace or institution. GRH programs can be created by employers themselves or by regional entities, such as municipalities or transit agencies. Occasionally, GRH programs will require a small annual registration fee to cover to costs of the programs, where the recipient receives a number of travel vouchers in return. In other cases, employers or transit agencies may subsidize the program as a way to increase transit ridership (and to reduce parking demand for employers) (CUTR, 2006). Several studies have confirmed that GRH programs can encourage sustainable commuting. In New York, 16% of express bus riders said they would not use the service without GRH, while 41% of ferry riders also indicated GRH as an important reason for taking transit (Menczer, 2007). Another study in Sacramento confirms that 12% of transit users would stop using transit in the absence of a GRH program (Todreas, 2002). These studies, among others, show that GRH programs can contribute to reducing SOV trips. Larger employers may also hire transportation specialists to support alternative commuting options. An on-site transportation coordinator, for example, will organize these programs and identify sustainable commuting options for employees. Alternatively, employers may also hire or form a Transportation Management Association (TMA), which is an organization that will arrange transportation programs for a group of businesses or facilities in a specific geographical area. One study has shown that employees who utilize travel coordinators reduced their vehicle miles travelled by an average of 54%

Literature Review

(Nakayama and Takayama, 2005). These types of services make it much easier for employees to discover viable alternatives to SOV commuting. Employers may also simply promote alternative forms of commuting through marketing campaigns. Marketing has been found to be particularly effective at universities. The results of one travel behaviour change program at Monash University in Melbourne, Australia reveal that the program influenced nearly one in four students to consider alternative forms of transportation (Rose, 2008). Employers may also organize event days, such as a ‘Ride to Work Day’, as a strategy to raise awareness of travel alternatives. Another Melbourne study researched the effects of a cycle-to-work day and confirmed that more than 25% of the event participants who were first time riders continued to cycle to work five months later (Rose and Marfurt, 2006). Finally, employers can support alternative forms of commuting by retrofitting the workplace for nonSOV commuters. End-of-use facilities, such as bicycle lockers and showers encourage active forms of transportation (Wardman et al, 2007). Nearby or on-site amenities, such as day care, supermarkets, and other shops also reduce the need to drive a vehicle for errands (TCRP, 2010). Preferential parking for carpool vehicles can “be an important incentive if parking is tight, or the parking lot is large and the reserved spaces are near the building entrance” (2010). Finally, employers can support transit users by selling daily and monthly bus passes as an on-site convenience measure in order to facilitate transit usage (2010).

Transportation services In addition to offering support actions, some employers may occasionally offer physical transportation services for its employees. These types of strategies are not common for employers, and are typically used when workplaces are not well served by transit or require lengthy commutes (TCRP, 2010). Services may include vanpools, buspools, shuttles feeder services, contracted transit services, use of company vehicles, and bicycle loan programs. Employer-sponsored vanpools typically involve the purchase or rental of vans by the business for employee use. The fleet of vans are driven by employees who will pick up colleagues from their homes

Literature Review

along the way to work, thereby reducing the need to drive alone. Buspools are similar to vanpools, but are larger vehicles and operated by designated drivers. All types of van and buspool programs require significant investments on behalf of the employer, but are also quite successful in reducing SOV trips. In Chicago, a vanpool program consisting of 252 vans and 2,423 commuters reduced 2,529 trips each day, and almost 120,000 vehicle miles travelled daily (Michael Baker et al, 1997). Another project reveals that most employer-based vanpools (when offered) run at capacity and have a high share of the total modal split of all employees (Wegmann, 1989). Employers also may be interested in van or buspooling in order to reduce on-site parking demand and improve local air quality and congestion (TCRP, 2010). Similar to other carpooling programs, studies have also found that van and buspooling can reduce employee commuting stress, costs, and tardiness while improving morale (2010). Finally, vanpools have been growing consistently in the United States over the past few decades, increasing from 447 programs in 1984 to 3,982 in 2001 (Wambalaba et al, 2004). Employers in areas with high transit service may not require a door-to-door van or buspool service. Transit can often be a viable option for most employees to get to and from work. Occasionally, however, gaps exist in the transit network that can be filled by employer-based feeder shuttles services to make transit more convenient, efficient and attractive for employees to use. Some shuttle arrangements that have been used by employers includes circulating shuttles linking transit terminals, rail stations, and park-and-rides. Circulating shuttles offer flexible scheduling and increase the use of transit and other non-motorized transport (Minerva et al, 1996). Shuttles can also provide additional benefits, as they can potentially link commuters who travel between multiple workplaces or campuses (TCRP, 2010).

Incentives and disincentives Another approach employers can take use is by offering positive incentives, negative disincentives, or both. This type of EBTR strategy can include non-monetary incentives, such as prizes, or awards, or a more straight-forward approach of dollars and cents. The most common types of incentive offered by employees include transit or ridesharing subsidies, but can also involve increased parking prices, travel bursaries, or award programs.

Literature Review

Alternative transportation subsidies are the most common type of incentive offered by employers. Employers may pay for or partially subsidize alternative forms of transportation (such as transit or ridesharing services, and even walking and cycling) in order to reduce SOV travel or reduce parking demand. Typically, most employers offer transit subsidies, as universities in particular have realized their potential. One of the first universities to offer this initiative was the University of Colorado in 1990. After implementing the program, student transit usage increased by 200% by 2001 (Brown et al, 2001). At UCLA in California, transit usage increased by 56% in only a few years, while trips by SOV fell by 20% (Brown et al, 2003). Other types of employers have also had success with subsidized transit passes, although results seem to be dependent on transit availability in the area. In a recent survey regarding EBTR strategies, businesses that offered transit subsidies in areas with low transit availability saw vehicle trips reduced by an average of 2.4%, while areas with high transit availability saw average reductions of 7.6% (TCRP, 2010). Less commonly, employers may offer vanpool or buspool subsidies; though the effectiveness of this strategy is questionable. In a survey conducted by TCRP (2010), six different programs offered a vanpool service with a separate subsidy, and reduced SOV trips by 20.9%. Sixteen other programs offered a similar vanpool service, but without a subsidy and had an average trip reduction rate of 19.8%, indicating that vanpool subsidies are not nearly as effective as offering the service itself (2010). A final type of alternative transportation subsidy includes employers offering their employees financial allowances to walk or bike to work, which has been found to increase in effectiveness with the amount of the allowance (2010). Although subsidies are the most common type of financial incentive, they are not the most effective. Rather, parking control has the “single largest effect on the performance” of EBTR programs (2010). By reducing the supply of free or subsidized parking spots, employers can discourage driving alone to work very effectively. The TCRP identifies that “parking fees together with restrictive parking averages reductions in SOV trips by 27.6%, while restrictive parking overall and priced parking overall both average 24.6%” respectively (2010). The effectiveness of parking control strategies combined with other positive financial incentives, such as alternative transportation subsidies, have been found to be particularly potent. As mentioned earlier, programs that offer transit subsidies in an area with medium to high transit availability can reduce SOV trips by 7.6%. When combined with parking control strategies, however, this effectiveness increases dramatically to 30.4% (2010). Employers can also use parking as

Literature Review

a positive incentive, offering parking discount or preferential spots to carpool vehicles. They may also “offer employees the option to choose cash in lieu of any parking subsidy offered”, which must then be used toward non-SOV modes of transportation (Shoup, 1997). A past study in California reported the effects of this strategy using eight case studies. The results confirm that after commuters “cashed out”, average SOV trips reduced by 17%, carpooling increased by 64%, transit ridership increased by 50%, walking and cycling increased by 33%, while parking demand dropped by 11% (1997). Finally, one other incentive an employer may offer is in-kind incentives. These types of incentives may include awarding employers that do not drive alone prizes based on lotteries or competitions. Potential prizes can include cash in hand, gift certificates, time off work with pay, or other tangible prizes.

Alternative work arrangements The final type of EBTR strategy an employer may take involves the implementation of scheduling modifications that reduce the need to make trips to the workplace. While there are many types of possible scheduling arrangements, only telecommuting and a compressed work week effectively eliminate trips. Other strategies, such as staggered or flexible work hours can help mitigate daily traffic volumes, but do not help reduce trips outright (unless offered exclusively to commuters using alternative modes as an incentive). Finally, these strategies differ with previous strategies in that they involve trip avoidance rather than modal switch. A compressed work week (CWW) is a scheduling scheme when employees may choose to work 40 hours in four days (4/40), eighty hours in 9 days (9/80), or other similar patterns in order to reduce work-related travel. Research of programs that utilize CWW suggest that SOV trips can be reduced up to 15% (Atherton et al, 1982) while reducing an average of 20 vehicle miles travelled per week per participant (Association of Commuter Transportation, 2004). Aside from trip reduction, the variable scheduling of CWW programs can also help mitigate traffic volumes during peak periods, as commuters no longer will travel during peak commuting hours. CWW commuters in the Philippines experienced a 6% reduction of morning commute times and a 9.3% reduction of evening commute times (Sundo and Fujii, 2005). Studies have also recognized the health and environmental benefits that a CWW can offer. For example, Bambra et al (2008) conclude that CWW can improve the work-life

Literature Review

balance without any adverse health effects. However, a CWW is not suitable for every work type. Research has revealed that employees in the craft/production/labour sector had a higher participation rate than the professional/technical sector, while the health sector had the highest participation rate (Zhou and Winters, 2008). This study also concluded that CWW participation rates have been increasing since 1993 (2008). Telecommuting programs involve employees working all or part of the work week at home, or at “teleworking offices”. Also known as teleworking, these programs became a possibility as online communications allowed employees to stay connected to the workplace. Telecommuting outright eliminates trips, so its potential has been heralded by many researchers in the TDM field. As of yet, however, telecommuting has failed to live up to its promise. One potential reason for this lack of success is because the pool of candidates for telecommuting is surprisingly small. First, not every work type is compatible with telecommuting. Jobs in the retail, production, and service sectors are not compatible because they involve face-to-face interactions or a physical presence (Mokhtarian, 1998). Jobs in the administration, professional, clerical, and managerial sectors, however, are well suited for telecommuting (Rathbone, 1992). Second, regardless of whether a work type is compatible, employer consent is still required which can be affected by the “status and power associated with certain types of occupation” (Huws et al, 1990; Mokhtarian, 1998). Third, assuming the job type is compatible and employer consent is acquired, potential telecommuting candidates will also require an adequate amount of space at home for a comfortable workplace. In countries with compact household sizes, this may be a significant barrier to telecommuting (Yen, 2000; Sullivan and Lewis, 1998). Finally, the potential of telecommuting also simply depends on whether or not an employee even wants to telecommute. The preference to telecommute is based on many variables, including “personal and household characteristics, personal attitudes and perceptions towards teleworking and environmental issues, and personal goals and needs” (Dam et al, 2010). Research has found a positive preference toward telecommuting among employees who are highly educated, have a higher than average income, are middle-aged, and are male professionals (Olszewski and Mokhtarian, 1994; Felstead et al, 2002). Another study identifies that employees who have children under the age of 16, have computers at home and are proficient in using them, and have a greater distance between the home and workplace are all more likely to have a stated-preference toward telecommuting (Yen and Mahmassani, 1997).

Literature Review

Despite its long list of criteria, several studies have found the strategy to be quite effective in reducing SOV trips for eligible participants. Henderson et al. (1996) found that telecommuters reduced daily trips by an average of 27% . Other papers that focused on telecommuting to telecentres found that vehicle miles travelled were reduced by 53% (Mokhtarian and Varma, 1998) while another study confirms reductions of 54%, which was less than the 66.5% reductions of telecommuters based at home (Henderson et al, 1996). Indeed, telecommuting can be a powerful tool to reduce SOV trips, although its complicated list of criteria has made it difficult to implement during the past few decades. However, recent surveys have indicated that the participation rate in telecommuting is slowly rising (TCRP, 2010), perhaps because due increasing global fuel prices (Lyons et al, 2008).

Barriers to EBTR There are many potential barriers that may inhibit the implementation and success of EBTR strategies. Based on existing literature, three significant obstacles have been identified. First, since all EBTR programs are instigated by employers, their willingness to participate in such programs will depend on the economic incentives to do so. Second, employees themselves are free to choose their preferred mode of transportation, and will base their decisions upon several variables that typically vary from one to another. Finally, an unsupportive spatial context may also have an indirect affect on EBTR strategies. The following section will highlight existing research regarding each of these different types of barriers.

Employer barriers All employers have a bottom line of economic self-interest. Under this principle, there are only a few scenarios which will motivate employers to consider utilizing EBTR programs. The first type of â&#x20AC;&#x153;motivatorâ&#x20AC;? is a law or regulation that requires employers to offer programs to help reduce SOV travel. Unfortunately, because employers are not self-motivated to reduce SOV trips, these programs are not

Literature review

as effective as programs that are voluntary (TCRP, 2010). Another type of motivator is that employers are faced with a problem, or set of problems, that can be solved by an EBTR program. Such problems include a lack of parking, fatigued employees and poor morale, on-site traffic congestion and air pollution complaints, or a poor corporate image. The final potential motivator for employers is that they see EBTR programs as an economically sustainable alternative to parking investments. Ultimately, if an employers is not influenced by one of these three motivators, EBTR strategies will likely not occur due to their bottom line. One other barrier may also prevent employers from beginning an EBTR program. A lack of awareness or knowledge of EBTR programs can be a significant barrier (2010). Some employers, for example, may face issues that can be solved by EBTR programs. However, if they are not aware of such programs or do not have a good understanding of their effectiveness, this opportunity will not be taken advantage of.

Employee barriers Another agent in determining the success of EBTR programs is commuter participation. Employees make transportation-related decisions based on the utility of all travel options. Included in this decision making process is the factors of travel time, cost, comfort, and convenience. Essentially, commuters will choose whichever mode offers the highest utility. In most cases, the private automobile offers the best utility because â&#x20AC;&#x153;its users have little difficulty accessing it at the beginning of a trip, generally pay no direct cost to use public roadways, and in most cases can park for free or at a substantial discount at the employment siteâ&#x20AC;? (TCRP, 2010). More sustainable modes must adequately compete with these factors in order to be a viable option for commuters. Although free, walking (and to a lesser degree, cycling) requires much more travel time and good health. Transit often requires time to travel to a designated stop and wait for a vehicle, and also requires a fee to use the service when a seat is not even guaranteed. Transit schedules are also often not perfectly suited for individual work schedules and buses can also get caught in traffic congestion. Finally, carpooling requires additional travel time and scheduling must suit a group of employees. When compared to the single-occupancy vehicle, it is difficult to compare these modes in terms of overall utility. As a result, effective EBTR programs must simply improve the utility factors of sustainable modes of transportation to make them more competitive with SOV travel.

Literature review

Finally, another requirement of successful EBTR strategies is employee awareness. The default mode of choice is SOV for most commuters due to its prevalence in todayâ&#x20AC;&#x2122;s society. Without adequate marketing from employers, this lack of awareness may continue, and commuters may default to SOV use. Contextual barriers Assuming employer and employee support, EBTR programs may face a third significant barrier. The regional context can either enhance or hamper EBTR programs, depending on its characteristics. For example, employment sites located in urban areas with integrated land uses, good transit service, engaging and walkable neighbourhoods, and proper bicycle infrastructure will have the advantage of a supportive regional context when convincing employees to use sustainable modes of transportation. On the other hand, employment sites in automobile-oriented areas, with poor active transportation infrastructure and limited service will face the barriers of an unsupportive regional context for EBTR strategies, and perhaps be forced to rely on independent transportation services. Land value and availability also plays a role in determining how the regional context influences the implementation of EBTR programs. For example, employers in areas with high land value and limited availability will likely have higher associated parking costs, thus increasing the likelihood of considering strategies to reduce these costs. Finally, land use type and density can also influence the mode employees use to commute. If a work site is located in a dense, mixed-use location, employees are more likely to be within walking distance of amenities to run errands or buy lunch. On the other hand, if a work site is located in an area with segregated land uses (such as an industrial park), employees will be much more dependent on a personal automobile to run these same errands.

Literature review

Summary of literature review Several key lessons can be learned from the literature regarding employer-based trip reduction programs. First, programs must overcome several obstacles in order to be successful. Therefore, they must first have clear benefits for employers with economic feasibility as a bottom line. EBTR programs must also have clear benefits for its user â&#x20AC;&#x201C; the commuter. Commuters will only abandon SOV travel when sustainable modes can compete in terms of overall utility. Therefore, sustainable modes must be costeffective, convenient, fast, and comfortable. If they cannot compete with SOV in one of these variables, they must offer a reasonable trade-off by exceeding in another category. For example, if a transportation service cannot compete with SOV in terms of speed, it should offer improved cost, comfort, or convenience. This literature review also finds that some types of EBTR programs can be more effective than others. Most research papers have identified parking as one of the most important variables determining modal choice. It seems that free or heavily subsidized parking is one of the key variables in perpetuating SOV travel as it significantly reduces the costs associated with the mode. Research also confirms that employers can most effectively reduce SOV travel by reducing or eliminating parking subsidies. Alternatively, EBTR programs can be even more effective by offering subsidies for other sustainable modes of travel. Employer-based transportation services are another effective strategy for reducing SOV travel. These services can include a vanpool, buspool or shuttle service, and are typically cost-effective and convenient as they are often catered specifically to employee schedules and residential location. On the other hand, physical transportation services may also be the most cost-intensive type of EBTR program. While not as effective as actual transportation services and parking control, support actions are also helpful in reducing SOV travel. Many researchers identify basic sustainable transportation marketing and awareness campaigns as one of the most cost-effective strategies in reducing SOV use. While overall trip reduction rates are not as successful as transportation services, results in several studies are worth noting. Finally, many research papers recognize the potential of alternative scheduling in reducing trip making. Compressed work weeks and telecommuting help employees avoid travelling altogether, although several barriers (employee willingness, job characteristics, availability of space) must be overcome in order for these arrangements to be possible.

Analyses

Analyses The analyses are conducted in two stages, each with its own strategic focus. The first stage is an analysis of the results of the 2009 Dalhousie Transportation Survey. This analysis examines respondents through two separate lenses; one focuses on how primary modal choice plays a role in the survey results and the other focuses on how distance plays a role. The second stage of analysis focuses on the spatial distribution of Dalhousie commuters. This analysis depends on a different source of data. Postal codes for the entire university population of staff, faculty and students have been obtained, geocoded and mapped in ArcGIS in order to identify the spatial distribution of Dalhousie commuters.

Survey Analysis In early 2009, the Office of Sustainability conducted an online transportation survey. This survey was sent to all of Dalhousie’s faculty, staff and students. The purpose of this survey was to gain an understanding of campus travel patterns in order to design and develop effective strategies that will reduce SOV use. The survey was created to examine both the travel patterns of commuters and their attitudes toward specific TDM initiatives. Ultimately, the goal of the survey was to identify trends that should be examined for the campus master plan and additional projects at the university. In order to get a clear understanding of commuting behaviour between two different populations within the university setting, two separate versions were designed; one for students and one for staff and faculty. The student survey included 21 transportation related questions, while staff and faculty were queried on additional employee-specific issues, such as teleworking and compressed work week issues, for a total of 29 questions. Aside from transportation-related questions, both surveys also collected socioeconomic data from respondents, such as age, gender, and income, and approximate residential location (by postal code or nearest intersection). The surveys were sent out in February 2009 and were collected at the end of March. Staff and faculty were sent a link to the survey via email, while students were contacted through their respective faculties and reminded via Dalhousie’s online “Sticky” note service. In total, 5,888 staff and faculty received the survey by email, and in total, 1,613 responded to the survey (for a 27% response rate). The total number of students contacted about the survey is unknown due to the indirect contact method (via faculties). In total, 1,322 students completed the survey for an 8% response rate (based on the total student population of 16,000).

Analyses

Data preparation After the survey deadline, the Office of Sustainability hired a team of students to process the results into spreadsheets for analysis. The results were organized into two Excel files; one for staff and faculty and another for students. Unfortunately, the final products were very disorganized and ultimately unusable for comprehensive analysis and use in GIS. The following issues and limitations were discovered when the data were received: 1. The chosen field values for results were inconsistent from one query to another. For example, some questions created category groups for a respondent to choose from, while others asked for specific proportions. 2. Each question was given its own page within the spreadsheet, making it difficult to compare results among a particular respondent. 3. Question titles were not coded. 4. Field values included text values, and occasionally mixed quantitative data with qualitative data in the same entry. 5. Fields were left blank when respondents did not answer a question. 6. Residential locations were entered as postal code or nearest intersection, with frequent spelling errors and inconsistent formatting, making it difficult to geocode for use in ArcGIS. 7. The survey asked respondents to determine their annual modal choice over a year by proportion, making it difficult to determine the primary mode of transportation. Furthermore, proportions occasionally did not add up, and respondents also sometimes confused walking with using transit. For example, some respondents indicated that they used transit 50% of the time and walked 50% of the time, when they were most likely walking to a transit stop and using the bus as the primary mode. 8. The survey neglected to ask for destination data, assuming that every respondent was travelling to the main campus. In fact, Dalhousie has several campuses on the peninsula which can affect travel behaviour (particularly transit usage).

Analyses

The following improvements have been made to the dataset to allow for analysis and use within ArcGIS: 1. Field results are reorganized into numerical order, and are made consistent from one question to another. Value codes are recorded onto a key. 2. Responses are consolidated onto one spreadsheet for easier comparisons among different fields. 3. Question titles are given short, eight-letter codes and are also included in the key. 4. Qualitative text is removed from fields that only require a numerical response. Any useful comments are stored in a separate text file for future studies. 5. Non responses are given a numerical value of 0, rather than being left blank. 6. The issue of inconsistent residential location values require significant post-processing. As previously mentioned, responses include either postal code or nearest intersection. Of course, these results can be geocoded, however, they must have consistent formatting. For example, in order to be geocoded, postal codes must be entered at X#X#X#, rather than X#X #X# (with a space in the middle). Furthermore, responses that indicate the nearest intersection cause more problems, as any spelling mistakes or street abbreviations (i.e.; St. instead of Street) cause errors in the geocoding process. As a result, significant manual formatting and proofreading is performed. Values are converted into coordinates using an online tool called batchgeo (http:// www.batchgeo.com). 7. The primary modal choice of each respondent was discovered by determining which mode was used the most for each response. Occasionally respondents indicated a 50/50 split between walking and transit. In these cases, transit was chosen as the primary mode. 8. Unfortunately, destination data was not collected in the survey. As a result, one destination is chosen for every respondent. As a compromise, the intersection of Robie Street and University Avenue is chosen, as it is more or less the central location of the university and is situated between the Studley and Carleton campuses.

Figure 2.1 Snapshots showing before-and-after spreadsheets.

Analyses

Survey results

Survey results In order to make better sense of the survey results, the questions are divided into three sections: socioeconomic data, travel behaviour, and program opinions and preferences. Each section analyzes each variable independently while examining student commuters and staff and faculty commuters separately. There are three reasons for this. First, the survey was conducted in two different versions; one for students and a longer edition for staff and faculty. Although the majority of the questions were the same, staff and faculty were surveyed on more questions associated with employee commute options and were asked for slightly different socioeconomic data. Another reason for separate analysis is that the response rate for staff/faculty and students was quite different. As a result, combining the two types for analysis would result in poor analytical protocol. Finally, each respondent type is a different decision making unit. Generally, staff and faculty are at different places in the life stage process than students, and as a result, either respondent type will make transportation-related decisions based on different factors. Each section also includes a summary of the key findings that need be considered during the recommendations stage. The objective of this stage of analysis is to understand the travel behaviour and opinions of the users of each mode by comparing and contrasting their similarities and differences. Particular attention is given to SOV users in order to understand their commute habits and the potential of switching them to other sustainable modes. This stage also examines commuting characteristics based on distance, by geographically separating respondents into two zones: a “near zone” and a “far zone”. These zones are created to analyse how distance plays a role in determining modal choice for respondents. In order to determine how large the near zone should be, preliminary research on existing literature and trends found in this survey has been conducted. A recent study regarding active transportation in Australia discovered that active commute options are limited beyond a certain distance. The study discovered that the significant majority of commuters living beyond 8 km would not use active transportation as a form of commuting (Rose, 2009). Initial results in this survey confirm similar results. Although 8 km was not an option available on the survey, results are still noticeable for respondents living within or beyond a 10 km buffer. Dalhousie cyclists and walkers are very common within a 10 km buffer, but are sparse beyond it. As a result, a 10

Survey results

km buffer zone based on the existing street network is created in ArcGIS, and displayed in Figure 2.2. Data associated with respondents who live within the near zone re-exported into their own spreadsheet for analysis, while data of the respondents in the far zone are exported into another.

Figure 2.2 Map of commute zones

Base map: Halifax Regional Municipality, 2010

North

Far zone

Near zone

Survey results

Sample details After cleaning the data, the remaining total sample size of staff and faculty is 1190 respondents. As shown in Figure 2.3, 37% of respondents indicate SOV as the primary mode choice, making it the most utilized mode of transportation. Twenty four percent walk to work, while a further 18% carpool and 15% use transit. The most under used mode among staff and faculty is cycling to work, with only 5% indicating as such. The remaining total sample size of student respondents is 1236. Students are far less likely to use SOV as their primary mode than are staff and faculty, as shown in Figure 2.4. Only 12% of students use SOV as their primary mode, making it the third most popular mode behind walking (52%) and transit (25%). The most under used modes is both cycling and carpooling respectively, with only 5% each.

Figure 2.3

Figure 2.4

Modal split of staff and faculty

Modal split of students

12%! 25%!

5%!

37%!

5%!

53%!

25%!

15%! 18%!

5%!

Walk

Bike

Transit

Carpool

SOV

Survey results

Socioeconomic results Gender Staff & Faculty The total sample of staff and faculty for the survey reveals that about 65% of respondents are female. With that proportion as a benchmark, the modal analysis reveals that females are more likely to drive alone to work than males, as shown in Figure 2.5. Males, on the other hand, indicate a higher probability to walk or cycle to work. In fact, 57% of staff and faculty cyclists are male, despite a higher proportion of females among the sample size. Gender proportions do not differ between zones, although a slightly higher rate of females live in the far zone than the near zone.

Students Survey results also confirm a high female response rate. About 68% of student respondents are female. This male-to-female ratio remains consistent when comparing respondents by their primary mode of travel, except again for cycling. Similar to staff and faculty, Figure 2.6 reveals that almost 60% of student cyclist respondents were male. Furthermore, gender ratios do not differ between near zone and far zone.

Survey results

Figure 2.5 Gender of staff and faculty Far Zone!

Near Zone!

SOV!

Carpool!

Female Transit!

Bike!

Male

Walk!

Sample! 0%!

25%!

50%!

75%!

100%!

Figure 2.6 Gender of students Far Zone!

Near Zone!

SOV!

Carpool!

Female Transit!

Bike!

Male

Walk!

Sample! 0%!

25%!

50%!

75%!

100%!

Survey results

Age Staff and Faculty Figure 2.7 separates staff and faculty into age cohorts. The majority of the staff and faculty respondents are between 35 and 54 years of age, comprising almost 60% of the sample population, while less than 1% are over the age of 64. A distinct trend also emerges regarding age and modal choice. As cohorts increase with age, they are also more likely to use SOV as their primary modal choice. For example, only 7.9% of respondents in the youngest cohort use SOV as the primary mode. The modal split for SOV then increases substantially to 27.2% for 25-34 year olds and then climaxes at the 40% mark for respondents between the ages of 35 and 64. Finally, almost 55% of respondents over the age of 65 use SOV as their primary mode. The majority of staff and faculty transit users and walkers, on the other hand, are more likely to be in younger age cohorts, while cycling is the most common for respondents between the ages of 25 and 44. Differences in age are also particularly interesting between commute zones. Figure 2.8 reveals that almost 90% of respondents aged 15 and 24 live within 10 kilometres of Dalhousie. Similarly, this ratio changes consistently with age, as older cohorts tend to live farther from Dalhousie than younger cohorts. These findings may indicate that older staff and faculty respondents tend to live farther from the main campus, while younger respondents prefer closer proximation.

Survey results

Figure 2.7 Staff and faculty age, by mode

64 and over!

Walk

55-64!

Bike

45-55!

Transit 35-44!

Carpool

25-34!

SOV

15-24! 0%!

25%!

50%!

75%!

100%!

Figure 2.8 Staff and faculty age, by commute zone

64 and over!

55-64!

Near zone 45-55!

35-44!

Far zone 25-34!

15-24! 0%!

25%!

50%!

75%!

100%!

Survey results

Students As expected, the majority of student respondents are in the youngest age cohorts, with about 62% between the age of 15 and 24, and a further 33% between the ages of 25 and 34. Interestingly, these cohorts have surprisingly different modal splits particularly among SOV users, walkers, and cyclists, which can be seen in Figure 2.9. For the youngest cohort (15-24), about 58% walk to school on a regular basis, while only 7% drive alone and 3% cycle. For the 25-34 age cohort, walking decreases to 44% while SOV and cycling more than doubles to 17% and 8% respectively. This trend continues with the 35-44 age cohort, where SOV again almost doubles to 32% and walking decreases to 30%. Slight differences in age are also experienced among student respondents in the near zone and far zone. Approximately 86% of respondents between the ages of 15 and 34 live in the near zone, which decreases to 61% for the 35 to 44 age cohort, as seen in Figure 2.10. This relationship between age and distance is consistent with findings from staff and faculty.

ďżź

Survey results

Figure 2.9 Student age, by mode

Walk 35-44!

Bike Transit

25-34!

Carpool 15-24!

SOV

0%!

25%!

50%!

75%!

100%!

Figure 2.10 Student age, by commute zone

35-44!

Near zone 25-34!

Far zone 15-24!

0%!

25%!

50%!

75%!

100%!

Survey results

University status The survey also asks respondents to identify their status at the university. For staff and faculty, respondents re asked to identify their employment status (full-time, part-time, contracted workers, etc). For students, respondents are asked to identify their student status (full-time, part-time, undergraduate, graduate, etc).

Staff & Faculty (Employment status) In general, respondents with more â&#x20AC;&#x153;stableâ&#x20AC;? long-term positions are more likely to use SOV as their primary mode. For example, Figure 2.11 indicates that SOV use is reported by over 40% of permanent full-time staff and faculty, which decreases with the stability of employment statuses, to about 20% for part-time contracted employees. On the other hand, respondents with less stable employment types are more likely to walk or use transit to travel to Dalhousie. Employment status amongst staff and faculty respondents also differs between commute zones, as seen in Figure 2.12. The vast majority of respondents in both zones indicate permanent full-time positions, although this type of employment status is much more prevalent in the far zone (86% in the far zone versus 66% in the near zone). Furthermore, other employment types, such as contracted employees and part-time staff and faculty, are represented much more in the near zone.

ďżź

Survey results

Figure 2.11 Staff and faculty employment status, by mode

Walk

Other!

Bike

Part-time contract!

Permanent part-time!

Transit

Full-time contract!

Carpool

Permanent full-time!

SOV 0%!

25%!

50%!

75%!

100%!

Figure 2.12 Staff and faculty employment status, by commute zone

Other!

Part-time contract!

Near zone

Permanent part-time!

Far zone

Full-time contract!

Permanent full-time! 0%!

25%!

50%!

75%!

100%!

Survey results

Students (Student status) The majority of student respondents are either full-time undergraduate or full-time graduate students for a combined 92%. The remaining 8% of respondents are part-time students, continuing education or “other”. Differences in modal choice do not substantially vary between graduate and undergraduate students, although the proportion of cyclists doubles amongst grad students, while continuing education and “other” also have higher proportions of cyclists.

Survey results

Figure 2.13 Sample of student respondents, by status

800

Frequency

600

400

200

0 'u))*+,e undergrad

3art*+,e undergrad

'u))*+,e graduate

3art*+,e graduate

56n+nu7ng edu8a+6n

Other

Figure 2.14 Student status, by mode

Other!

Walk

Continuing education!

Bike

Part-time graduate!

Transit Full-time graduate!

Carpool Part-time undergrad!

SOV

Full-time undergrad ! 0%!

25%!

50%!

75%!

100%!

Survey results

Household income Staff & Faculty Staff and faculty respondents indicate above-average household incomes, as shown in Figure 2.15. Almost 60% of the sample population have an annual household income of over $60,000.

Figure 2.15 Distribution of staff and faculty, by household income

Frequency

400! 300! 200! 100!

! 00 ,0 60 000 0, $4

000 0, $2

Consistent with previous socioeconomic variables, modal split is again indicative of stability and progression. As seen in Figure 2.16, respondents with lower wages report the lowest use of SOV with only a 5% share, while walking represents over 55% of the cohort respondents. As wages increase, so too does SOV use. Just less than 25% of respondents earning between $20,000 and $40,000 indicate SOV usage, which increases steadily to approximately 42% for respondents earning over $60,000. Income levels also fluctuate between commute zones. Although the majority of respondents in both zones have incomes above $60,000, the near zone represents a much higher proportion of salaries under $40,000 (Figure 2.17). In fact, only 4% of respondents in the far zone report salaries below $20,000.

Survey results

Figure 2.16 Staff and faculty household income, by mode

Walk

More than $100,000!

Bike

$60,000-100,000!

Transit

$40,000-60,000!

$20,000-40,000!

Carpool

Less than $20,000! 0%!

SOV 25%!

50%!

75%!

100%!

Figure 2.17 Staff and faculty household income, by commute zone

More than $100,000!

$60,000-100,000!

Near zone $40,000-60,000!

$20,000-40,000!

Far zone

Less than $20,000!

0%!

25%!

50%!

75%!

100%!

Survey results

Summary of socioeconomic results A distinct trend emerges with results from this socioeconomic analysis. Variables associated with advancement through the typical life-stage process (age, employment type, income level) seem to play a role in determining modal choice. Specifically, increasing age appears to be particularly influential in determining SOV use. Furthermore, staff and faculty respondents with more stable employment positions and higher salaries are more likely to use SOV as their primary commute mode. While it is impossible to pinpoint this trend on any specific factor, two probable explanations may be responsible. First, perhaps as a person advances through the life-cycle process and gains more financial resources and stability, they are more free to choose the best commute mode available to them, with SOV offering the best utility. In contrast, respondents with less access to these resources are more constrained by commute costs, and will choose modes that are not as convenient and fast, but are far cheaper (such as walking or cycling). Another possible explanation for this trend is changing commute preferences. As previously mentioned, age plays an important role in determining modal choice. Results reveal that older respondents appear to use SOV more as their primary mode. Perhaps generational differences influence commute preferences. Nonetheless, one critical lesson can be learned from these preliminary results. The utility of sustainable modes of transportation must be able to compete with SOV. In order to persuade commuters with financial resources to make decisions based on overall utility (such as those in this survey), sustainable modes must be convenient, cost and time-effective, and comfortable. Furthermore, enhancing sustainable modes will also help retain current sustainable commuters (perhaps due to a lack of financial resources) and prevent them from switching to SOV when they advance through the life-stage process.

Survey results

Travel behaviour Distance In the survey, respondents are asked to state their estimated commute distance from a range of options. Then, respondents are also divided into two groups (near zone and far zone) based on their indicated residential location. Staff and Faculty Generally, staff and faculty respondents have greater commute distances than students. Naturally, commute distances play a role in determining modal choice. According to Figure 2.18, less than 7% of respondents with estimated commutes less than 2 km indicate SOV as their primary mode, while over 80% walk. As distance increases, SOV becomes more prevalent until reaching a 75% modal share for respondents living between 41 and 50 km from Dalhousie. Walking and cycling also reduce substantially with distance as no walkers and few cyclists commute more than 10 km to work, while transit becomes a common option for commuters living over 6 km. Staff and faculty are well distributed in both zones. As seen in Figure 2.19, modal choice differs from one to the other. While 37% of the total sample of staff and faculty identify SOV as their primary mode, this proportion skyrockets to 58% in the far zone; more than double the share in the near zone at 25%. In contrast, the most common mode of travel within the near zone is walking at 38%.

Survey results

Figure 2.18 Staff and faculty modal split, by estimated commute distances

Over 51 km!

Walk

41-50 km!

Bike

31-40 km! 21-30 km!

Transit

11-20 km!

Carpool

6-10 km! 3-5 km!

SOV

Less than 2km! 0%!

25%!

50%!

75%!

100%!

Figure 2.19 Staff and faculty modal split, by commute zone

Walk Far Zone!

Bike Transit

Near Zone!

0%!

Carpool

25%!

50%!

75%!

100%!

SOV

Survey results

Students Student respondents are far more concentrated around Dalhousie University than staff and faculty, and as a result, have smaller commute distances (Figure 2.20). In fact, approximately half of the entire sample population estimate that they live within 2 km of Dalhousieâ&#x20AC;&#x2122;s main campus, while 80% estimate their daily commute distance is under 10 km in length (Figure 2.21). Again, modal split differs according to commute distance. Although on a different scale, similar trends to staff and faculty are experienced. SOV users are the definite minority for respondents with short commute distances. Out of 615 student respondents living within 2 km of Dalhousie, two use SOV as their primary mode, while walking represents 85%. Similarly, however, SOV use increases with distance, climbing to roughly 30% for student commuters living between 11 and 40 km. Students commuting distances greater than 41 km experience SOV rates greater than 64%. Students are not as well populated in the far zone as staff and faculty. While 1,034 students live in the near zone, only 183 live in the far zone. Nevertheless, modal choice is still quite different in each zone as shown in Figure 2.22. 62% of students in the near zone walk to school and 23% use transit, while under 7% use SOV. In the far zone, only 2% walk or cycle to work and 40% use transit. SOV use increases dramatically to 40% while the remaining 20% carpool on a regular basis.

Figure 2.20 Survey sample, by commute zones

1200!

Frequency

900!

600!

300!

Students!

Staff and Faculty!

Survey results

Figure 2.21 Student modal split, by estimate commute distances

Over 51 km!

Walk

41-50 km!

31-40 km!

Bike

21-30 km!

Transit 11-20 km!

Carpool

6-10 km!

3-5 km!

SOV

Less than 2km! 0%!

25%!

50%!

75%!

100%!

Figure 2.22 Student modal split, by commute zone

Walk Far Zone!

Bike Transit Carpool

Near Zone!

SOV 0%!

25%!

50%!

75%!

100%!

Survey results

Commute time Respondents are also asked to estimate how long their commutes typically last in minutes, based on

Figure 2.23

Figure 2.24

Staff and faculty commute times

Student commute times 500!

400!

! in

-6 51

-5

! in 41

-4

m 31

0 -3 21

! in

! m 10 0-

m 1 r6

! in O

-6

in 51

-5

in 41

-4

m 0 -3

! in m 21

11 -

m 10 0-

0! !

100!

200!

300!

11 -

Frequency

500!

Frequency

one-way travel to or from Dalhousie.

Staff and Faculty Commute time is typically correlated with commute distance, so it is not surprising that staff and faculty respondents living in the near zone typically have shorter commutes than respondents living in the far zone. Commute times also vary according to commute mode. For example, SOV is the fastest mode of transportation. As a result, commute duration is typically shorter relative to commute distance (Figure 2.25). In contrast, walking and cycling are slower modes of travel. Thus, commute durations are typically longer relative to commute distance. Interestingly, transit users report rather long commute durations relative to commute distance (Figure 2.27). The majority of transit users live within 30 km of Dalhousie, yet many commutes can take up to an hour.

Survey results

Figure 2.25 Relationship of distance and time among staff and faculty SOV users Less than 10 min

150

11-20 min

21-30 min

31-40 min

41-50 min

51-60 min

Over 61 min

Frequency

Time 100

Distance 0

Less than 5km

6-10 km

11-20 km

21-30 km

31-40 km

41-50 km

Over 51 km

Figure 2.26 Relationship of distance and time among staff and faculty walkers Less than 10 min

150

11-20 min

21-30 min

31-40 min

41-50 min

51-60 min

Over 61 min

Frequency

Time 100

Distance 0

Less than 5km

6-10 km

11-20 km

21-30 km

31-40 km

41-50 km

Over 51 km

Figure 2.27 Relationship of distance and time among staff and faculty transit users Less than 10 min 60

11-20 min

21-30 min

31-40 min

41-50 min

51-60 min

Over 61 min

Frequency

Time

Distance 0

Less than 5km

6-10 km

11-20 km

21-30 km

31-40 km

41-50 km

Over 51 km

Survey results

Students Commute times are again affected by modal choice. Figure 2.28 and 2.29 reveal that commute times for student SOV users are again typically shorter relative to commute distance, while longer for walkers. Trends regarding commute times for student transit users are similar to staff and faculty. Longer commute times are again observed relative to distance as seen in Figure 2.30.

Survey results

Figure 2.28 Relationship of distance and time among student SOV users Less than 10 min 60

11-20 min

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Frequency

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Figure 2.29 Relationship of distance and time among student walkers Less than 10 min

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Time 400

200

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Figure 2.30 Relationship of distance and time among student transit users Less than 10 min

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Frequency

Time 100

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Survey results

Peak commuting periods The survey also asks all respondents to indicate their arrival and departure times for every day. In order to better analyze peak commuting periods, this analysis focuses on average weekday travel times (Monday through Friday). Furthermore, the times indicated in this analysis are not specific to the minute. If a respondent indicates an 8:00 am arrival time, he or she may arrive anytime between 8:00 and 8:59, and not exactly at 8:00.

Staff and Faculty Figure 2.31 reveals that weekday travel periods for staff and faculty are quite typical. Most respondents arrive between 6:00 and 10:00 am, with 8:00 am the most common arrival time. Departure times are distributed over a similar time period, spanning between 3 and 7:00 pm, with 4:00 pm as the most common departure time. Modal choice also seems to play a role in determining commute times. For example, SOV users tend to start their days earlier than respondents who walk to work (Figure 2.32). Generally, the daily schedules of SOV users are more pronounced than that of walkers.

Figure 2.31 Staff and faculty peak travel periods (entire sample) 700!

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Survey results

Figure 2.32 Staff and faculty peak travel periods (SOV vs. Walking) 250!

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Pronounced schedules are seen further when comparing respondents in two commute groups, as seen in Figure 2.32. Generally, respondents living in the near zone tend to arrive later and depart later. Furthermore, more near zone respondents travel midday, while far zone respondents have more truncated travel times. Figure 2.33 ďżź 400!

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Survey results

Students Weekday peak travel periods for students follow similar patterns to staff and faculty; however, a higher proportion of before and after-hour trips are made by students (Figure 2.34). Most student respondents arrive between 7:00 and 11:00 am, with a distinct 8:00 am peak arrival time. Trip frequency reduces during midday, but is still high. Departure times are distributed over a similar time period, spanning between 1:00 and 6:00 pm, with 4:00 and 5:00 pm as the peak departure times. In the case of students, respondent modal choice does not influence travel times â&#x20AC;&#x201C; most likely a result of their irregular schedules. Figure 2.34 Student peak travel periods (entire sample) 500!

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Survey results

Figure 2.35

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Figure 2.36

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Survey results

Reasons for SOV use Respondents are also asked to indicate the most important factors for choosing to drive alone to work. The survey includes six potential reasons with which respondents may agree or disagree: 1) convenience, 2) joy of driving, 3) need to run errands on trip, 4) affordable parking, 5) drop children off at childcare, and 6) dropped others off along the way. Staff and Faculty Among the sample population, respondents most commonly indicate “convenience” as the most significant factor for driving to work alone (35%). Not far behind is the “need to run errands” reason chosen by 34% of respondents, while childcare is important factor for approximately 10% and “joy of driving” is chosen by 4%. When comparing respondents by their primary mode, these results do vary. For example, SOV users are particularly concerned about the “convenience” factor (see Figure 2.38). Also, among the 4% that chose “joy of driving” in the sample, the majority were SOV users. Alternatively, walkers, cyclists, and transit users all seem to stress the “need to run errands” as a more important reasons to drive (Figure 2.39).

Figure 2.37 Staff and faculty reasons for SOV use, by percentage 40%!

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0%!

Convenience!

Joy of driving!

Need to run errands!

Affordable parking!

Childcare!

Need to drop off others!

Survey results

Figure 2.38 Percentage of staff and faculty indicating ‘convenience’ as a reason for SOV use 80%!

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0%!

Sample!

Walk!

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Transit!

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Figure 2.39 Percentage of staff and faculty indicating ‘need to run errands’ as a reason for SOV use

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Survey results

Students Similar to staff and faculty but to a lesser degree, the total sample of student respondents indicates “convenience” and the “need to run errands” as the most important reasons for deciding to drive alone to school. Among SOV users and carpoolers, “convenience” is also the most important reason (Figure 2.41), while the “need to run errands” is comparatively high for walkers and transit users (Figure 2.42). Figure 2.40 Student reasons for SOV use, by percentage

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Survey results

Figure 2.41 Percentage of students indicating ‘convenience’ as a reason for SOV use 100%! 80%! 60%! 40%! 20%! 0%!

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Walk!

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Transit!

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Figure 2.42 Percentage of students indicating ‘need to run errands’ as a reason for SOV use

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Survey results

Occasional SOV use This analysis is most interested in primary modes indicated by respondents, as these represent the vast majority of trips made to Dalhousie on a regular basis. However, in order to better understand travel behaviour, it is also useful to identify and characterize occasional SOV users. Staff & Faculty For example, 37% of staff and faculty respondents indicate SOV as their primary mode yet almost 54% indicate they will drive sporadically throughout the year. Carpoolers and cyclists, in particular, reveal a high likelihood of occasional SOV trips. The most likely cause for these SOV trips is probably due to abnormal trip details that would make the primary mode difficult to use. In the case of cyclists, adverse weather conditions are also likely to force cyclists to use SOV. Occasional SOV users also differ quite significantly between commute zones. 42% of respondents who live in the near zone will make occasional trips by SOV. In the far zone, this proportion almost doubles to 74%. Figure 2.43 100%!

Percentage of staff and faculty that occasionally use SOV, by mode and commute zone

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Survey results

Students Similar to staff and faculty, student walkers are the least likely to occasionally drive alone, while 26% of transit users and 38% of carpoolers reveal that they may drive alone occasionally. Interestingly, student cyclists do not have similar rates of SOV use as staff and faculty cyclists. Approximately 45% of staff and faculty cyclists report using SOV occasionally, whereas only 19% of student cyclists can say the same. The proportion of occasional drivers is also significantly different amongst near and far zone respondents. Only 20% of respondents in the near zone indicate that they may drive alone occasionally, which is significantly less when compared to over 62% of far zone respondents.

Figure 2.44 Percentage of staff and faculty that occasionally use SOV, by mode and commute zone 100%!

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Survey results

Proximity to transit Transit access for survey respondents is determined through three separate indicators. First, the survey asks respondents to indicate if they live within 500 meters of a transit stop. Second, exact Euclidian distances from residential location and transit stops are measured using ArcGIS. Finally, euclidean distances are also measured from residential locations to transit stops for buses that offer direct service to Dalhousieâ&#x20AC;&#x2122;s main campus. Staff and Faculty The survey reveals that 72% of staff and faculty respondents state they live within 500 metres of a transit stop. Interestingly, this proportion fluctuates significantly between modes involving an automobile (SOV and carpool) and the other modes. For example, over 96% of walkers and cyclists and 88% of transit users indicate that hey live within 500 metres of a transit stop. On the other hand, only 56% of SOV users and 54% carpoolers live within this buffer (Figure 2.45). Analysis within the commute zones also confirms that transit stops seem to decrease in frequency as distance increases. 92% of all near zone respondents live within the 500 metre buffer, while only 37% of far zone respondents can say the same. These findings are a result of stated proximities to transit stops, and as a result, may include some margin of error. However, these trends are confirmed with a spatial analysis in ArcGIS. Euclidean distances between residential locations and transit stops are measured and averaged, and compared between mode choice. Of the total sample population, the average euclidean distance to transit stops is 2961 metres. Cyclists, walkers, and transit users live nearest to transit stops, ranging in average distances from 167 to 571 metres respectively. SOV users, in contrast, live an average distance of 4687 metres from transit stops, while carpoolers live a further 5730 metres.

Survey results

Figure 2.45 Percentage of staff and faculty living within 500 metres of transit, by mode and commute zone 100%!

75%!

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Walk!

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Finally, similar results are found for averaged distances to transit stops for buses offering direct service to Dalhousie (Figure 2.46), with one noticeable variation. Once again, walkers and cyclists are closest to these particular transit stops, with average distances of 407 and 399 metres respectively. Furthermore, respondents that live the farthest from transit stops are SOV users (with an averaged distance of 8754 metres) and carpoolers (10,051 metres). For these four modes, the average distances are roughly doubled when comparing regular transit stops to â&#x20AC;&#x153;Dalhousie directâ&#x20AC;? transit stops. However, this trend is not observed for transit users. As previously mentioned, the average distance for transit users to regular transit stops was 571 metres. This figure more than quadruples to a averaged distance of 2443 metres from direct transit stops, which means many transit users take buses that require transfers to get to Dalhousie.

Survey results

Figure 2.46 MetroTransit buses offering direct service to Dalhousie University

North

Bus route Bus coverage 1

map:1 Halifax Regional Municipality, 2010 0Base 0.5 2 3 4

5 km

Survey results

Students Almost 90% of student respondents state that they live within 500 metres of a transit stop. Similar to the staff and faculty results, Figure 2.47 reveals that only 60% of SOV users and 71% of carpoolers live within 500 metres of a transit stop, which is low when compared to walkers and cyclists (96% each) and transit users (91%). Furthermore, 96% of near zone respondents state they live within 500 metres of a transit stop compared to only 57% in the far zone. The average distance (based on residential location) for student respondents to transit stops is 1,274 metres. Students that walk to school live extremely close to transit stops, with an average distance of only 84 metres. Transit users and cyclists are not much farther, with average distances of 109 and 118 metres respectively. SOV users and carpoolers, on the other hand, live much farther from transit stops, with average distances of 6,088 and 1,901 metres each. These distances also significantly vary by commute zone. Near zone respondents live an average of 93 metres from a transit stop, while far zone respondents live an average 6,813 metres away. For most modes, average distances are again roughly doubled when comparing distances between regular transit stops and â&#x20AC;&#x153;Dalhousie directâ&#x20AC;? transit stops. However, this trend again is not observed for transit users. While the average distance for transit users to regular transit stops is a mere 109 metres, this skyrockets to 1,467 metres when measuring distance to Dalhousie direct transit stops. Also, near zone respondents live an average of 177 metres from any transit stop while far zone respondents live about 13,007 metres away. Figure 2.47 Students living within 500 metres of transit, by mode 100%!

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ďżź

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Survey results

Intra-campus travel Staff and Faculty The final question of the survey regarding commute details is whether or not respondents travel between campuses for work or classes. Results reveal that approximately 33% of staff and faculty respondents travel regularly between campuses. This ratio does not vary substantially from one mode user to another, although walkers and transit users are only slightly less likely to travel between campuses (Figure 2.48). Finally, there is not a large variation in responses between commute zones, as distance is unlikely to influence this type of behaviour. Figure 2.48 Inter-campus travel required for staff and faculty

Far Zone! Near Zone! SOV! Carpool! Transit! Bike! Walk! Sample! 0%!

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Survey results

Students Students are less likely than staff and faculty to travel between campuses for classes. Approximately one in four respondents indicate that inter-campus travel is required (Figure 2.49). Modal choice and distance are seemingly unimportant factors, as little variation is observed between respondents according to primary mode and commute zone.

Figure 2.49 Inter-campus travel required for students

Far Zone! Near Zone! SOV! Carpool! Transit! Bike! Walk! Sample! 0%!

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Survey results

Summary of travel habits Several key findings from this section of the survey analysis are worth noting for the recommendations stage. Perhaps the most important finding is the crucial role distance plays regarding modal choice. Results suggest that commuters are choosing one of two alternative modes: carpooling and transit. When analysed in greater detail, these two alternatives can not compete with SOV in overall utility. In terms of time, transit is slower than most other modes, particularly as travel distance increases. In terms of convenience, transit stops are often not within walking distance for far zone commuters and most require more than one transfer to get to Dalhousie. Issues regarding convenience are particularly important, as the survey results indicate this as the most important reason for using SOV as a mode. As a result, transit (in its current form) is not likely to encourage many SOV users to switch. Recommendations must consider a better form of transit that can improve in overall utility and fill in the areas that do not have proper access to the existing network. It is also worth mentioning that intercampus travel is quite common for most commuters, with approximately one in three respondents indicating as such. As no existing intercampus shuttle service exists, other than regular transit, future recommendations should also consider filling this gap as well.

Survey results

Programs opinions and preferences One of the most interesting aspects of the survey is a series of questions asking respondents about their interest and opinions regarding transportation programs. Respondents are asked what types of initiatives would be the most effective in encouraging and maintaining sustainable commuting habits. Questions about carpool programs, employee transit passes, telecommuting, and compressed work hours re included in this section of the survey.

Sustainable transportation preferences In the survey, respondentâ&#x20AC;&#x2122;s use a Likert scale (1 = no opinion/response, 2 = strongly disagree to 5 = strongly agree) to rate how different transportation measures could motivate more sustainable travel habits. These measures are categorized into three groups: 1) bicycle infrastructure improvements, 2) transit related improvements, and 3) other general programs. In order to determine which initiative is most preferred, the average rating for each initiative is calculated and ranked accordingly.

Survey results

Staff and faculty respondents are asked about a slightly different set of initiatives than students, including the following (differences in bold): Bicycle infrastructure improvements: 1. Bike racks on buses 2. End of use facilities (such as showers, change rooms, etc) 3. More/better bicycle lanes and paths 4. More/better bicycle racks on campus Transit related improvements: 5. Less expensive transit fare 6. More frequent transit service 7. Reduced transfers 8. Bus route/stop closer to home 9. Shorter travel time 10. Tax deductions for transit pass purchases 11. Employee transit pass

Other general program: 12. Guaranteed ride home (GRH) 13. Carpool programs

Students are asked similar questions regarding sustainable transportation preferences; however they do differ slightly. They are: Bicycle infrastructure improvements: 1. Bike racks on buses 2. End of use facilities (such as showers, change rooms, etc) 3. More/better bicycle lanes and paths 4. More/better bicycle racks on campus Transit related improvements: 5. More frequent transit service 6. Bus route/stop closer to home 7. Shorter travel time 8. Tax deductions for transit pass purchases 9. Better transit service

Other general program: 10. Guaranteed ride home (GRH) 11. Carpool programs

Survey results

Staff and Faculty Figure 2.50 reveals how staff and faculty rank transportation initiatives. In total, cycling infrastructure related questions rank the highest. More/better bicycle lanes and paths, in particular, rank the highest with an average score of 3.79, and more/better bicycle racks follow closely behind with 3.70. Furthermore, transit related improvements also rank quite high. More frequent transit service (3.65), less transfers (3.64) and shorter travel time (3.63) are among the more important initiatives. On the other hand, staff and faculty respondents do not seem concerned about transit fares, as less expensive transit fare (3.30) was the least preferred initiative from the entire list, followed closely by carpool programs (3.36).

Figure 2.50 Staff and faculty sample ranking of sustainable transportation initiatives

4.00!

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Survey results

When comparing staff and faculty respondents by their primary mode, outcomes do vary. For example, although SOV users also indicate a preference for bicycling infrastructure, they are less interested in employee transit passes (3.38) and carpooling initiatives (3.20) when compared to the sample population. Perhaps not surprisingly, walkers are less concerned with shorter travel times (3.51), but prefer more frequent transit service (3.62) as a significant initiative. Cyclists rank more bicycle lanes (3.95) the highest out of all the modes but are less concerned about bicycle racks. Similarly, transit users typically rank transit-related initiatives the highest out of all the modes. More frequent transit service (3.75), closer bus stops (3.57), and less transfers (3.63) are important initiatives for these respondents. Guaranteed ride home (3.80) initiatives are also very important to transit users. Finally, carpoolers are the only group to identify carpooling programs (3.51) as a positive initiative. However, similar to the sample population, cycling infrastructure and transit-related initiatives rank as the most important.

Figure 2.51 Staff and faculty ranking of bicycle infrastructure improvements, by mode 4.00!

Sample Walk

3.75!

Bike 3.50!

Transit 3.25!

3.00!

ďżź

Carpool SOV Bike racks on buses!

EOU Facilities!

More bike lanes!

More bike racks!

Survey results

Figure 2.52 Staff and faculty ranking of transit related improvements, by mode 4.00!

Sample

3.75!

Walk Bike

3.50!

Transit 3.25!

Carpool 3.00!

More frequent bus!

Less transfers!

Closer transit stops!

Shorter travel Tax time! deductions on transit pass!

Employee Bus pass!

SOV

Figure 2.53 Staff and faculty ranking of other initiatives, by mode 4.00!

Sample Walk

3.75!

Bike 3.50!

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Carpool SOV

3.00!

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Gaurunteed ride home!

Survey results

Preference for different initiatives also varies between commute zones (Figure 2.54). Cycling infrastructure receives strong rankings from both zones, although respondents in the far zone indicate a much higher preference for transit-related infrastructure.

Figure 2.54 Staff and faculty ranking of transportation initiatives, by commute zone 4.00!

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Survey results

Students Among student respondents, transit-related initiatives receive the highest ranks, as seen in Figure 2.55. Better transit service (3.73), more frequent transit (3.73), shorter travel times (3.67), and closer bus routes/stops (3.66) are also among the higher ranked initiatives, while the single most preferred initiative is more bicycle lanes.

Figure 2.55 Student sample ranking of sustainable transportation initiatives 4.00!

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Survey results

In general, SOV users, transit users, and carpoolers all prefer more transit-related improvements, while walkers and cyclists weigh cycling-related initiatives higher. Figure 2.56 Student sample ranking of bicycle-related transportation initiatives 4.00!

Sample Walk

3.75!

Bike 3.50!

Transit 3.25!

3.00!

Carpool SOV Bike racks on buses!

EOU Facilities!

More bike lanes!

More bike racks!

Figure 2.57 Student sample ranking of transit-related transportation initiatives 4.00!

Sample Walk

3.75!

Bike 3.50!

Transit 3.25!

3.00!

Carpool SOV More frequent bus!

Closer transit stops!

Shorter travel time!

Tax deductions on transit pass!

Better transit service!

Survey results

Figure 2.58 Student sample ranking of other transportation initiatives 4.00!

Sample Walk

3.75!

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Carpool 3.00!

Gaurunteed ride home!

SOV

Carpool programs!

Respondents in the near zone are the strongest proponents of more bicycle lanes, while far zone respondents value transit improvement more (Figure 2.59). Figure 2.59 Student sample ranking of bicycle-related transportation initiatives, by commute zone 4.00!

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ar po ol

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Survey results

Funding preferences for sustainable transportation Survey respondents are also asked for their preferences on how these initiatives could be paid for. The survey includes four different funding options; 1) parking fees, 2) external grants, 3) base budget allocation derived from greenhouse gas emissions costs of Dalhousie commuting, and 4) Dalhousie should not consider funding sustainable transportation initiatives. Respondents are given two choices on how to indicate their preference: agree or disagree. Staff and Faculty Among the sample population of staff and faculty, approximately 63% agreed that parking fees should help fund sustainable transportation initiatives. Less than half agreed on potential external grants or base budget allocations, while almost the entire population (98%) agreed that sustainable transportation initiatives should be funded in one way or another. For the most part, these preferences do not vary significantly when comparing respondents by primary mode. The only exception is regarding funding through parking fees. While over 80% of walkers and cyclists and almost 70% of transit users believe parking fees should pay for sustainable initiatives, SOV users and carpoolers are far less likely to agree. Not surprisingly, almost half of all SOV users and carpoolers reveal that they would not want sustainable transportation to be funded by parking fees. Similar trends exist between commute zones. While almost 70% of respondents in the near zone agree with funding through parking fees, almost half of far zone respondents disagree. Figure 3.1 Percentage of staff and faculty that support parking as a funding option 100%! 75%! 50%! 25%! 0%!

Walk!

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Survey results

Figure 3.2 Percentage of staff and faculty that support external grants as a funding option 100%! 75%! 50%! 25%! 0%!

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Figure 3.3 Percentage of staff and faculty that support base budget allocation as a funding option 100%! 75%! 50%! 25%! 0%!

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Far Zone!

Figure 3.4 Percentage of staff and faculty that do not support funding sustainable transportation 100%! 75%! 50%! 25%! 0%!

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Survey results

Students On a whole, students are more willing than staff and faculty to use parking fees to help finance sustainable transportation initiatives. Over 72% of the student sample support the idea, compared to 63% of staff and faculty. However, only 58% of student SOV users and 68% of far zone respondents agreed. The student sample is indecisive over the idea of external grants or base budget allocation; however, the clear consensus is that sustainable transportation should be funded in some way, with over 96% in agreement.

Figure 2.60 Percentage of students that support parking as a funding option 100%! 75%! 50%! 25%! 0%!

Walk!

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Figure 2.61 Percentage of students that support external grants as a funding option 100%! 75%! 50%! 25%! 0%!

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Near Zone!

Far Zone!

Survey results

Figure 2.62 Percentage of students that support base budget allocation as a funding option 100%! 75%! 50%! 25%! 0%!

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Carpool!

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Near Zone!

Far Zone!

Figure 2.63 Percentage of students that do support funding sustainable transportation 100%! 75%! 50%! 25%! 0%!

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SOV!

Near Zone!

Far Zone!

Survey results

Carpooling programs Implementing carpooling programs is a potential way to reduce trips made by SOV at the university. However, as previously discovered, carpooling programs seem to be one of the least desired initiatives by respondents. This trend is also apparent in another question included in the survey, which asks specifically if SOV users would be interested in carpooling initiatives. Possible options offered in the survey include; 1) Yes, 2) No, 3) Not sure, or 4) Not applicable. Staff and Faculty For the total sample population, the most common response is â&#x20AC;&#x153;not applicableâ&#x20AC;? because walkers, cyclists, and transit users also answered the question. However, when examining SOV users specifically, only 22% of are interested in carpooling initiatives, while 36% are not interested and a further 37% are uncertain (Figure 2.64).

Students Similar to staff and faculty, carpooling programs is one of the least preferred sustainable transportation initiative. When asked specifically, student SOV users echo this general dislike of carpooling programs, as only 17% suggest they are interested, while 31% are not, and a further 49% are not sure (Figure 2.65).

Survey results

Figure 2.64 Staff and faculty interest in carpool programs 100%!

75%!

Not sure

50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone! Far Zone!

Figure 2.65 Student interest in carpool programs 100%!

75%!

Not sure

50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone! Far Zone!

Survey results

Additional employee opinions Employee transit passes A handful of questions on the survey discuss the potential employee transit pass. First, respondents are asked if they support the idea of having an employee transit pass. Figure 2.66 reveals that 85% of the total sample population support the idea, while SOV users and carpoolers are less enthusiastic (each with about 80% in support) and transit users are more positive (96% in support). Respondents in the near zone are also more likely to respond positively to a transit pass (with 87% supporting the idea versus 80% in the far zone).

Figure 2.66 Percentage of staff and faculty that support a transit pass 100%!

75%!

50%!

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

In relation to the previous question, respondents are asked if they would be encouraged to take transit if the cost of a transit pass was reduced. The overall response of the sample population for this question is not as positive, with only 44% indicating they would be encouraged to take transit with lower fares (Figure 2.67). Of course, current transit users are very supportive of the idea of reduced transit fares (particularly because they are already using transit), as 83% support the idea. SOV users and carpoolers are on the other end of the spectrum, with only 30% of each group indicating they would consider switching with reduced fares.

Figure 2.67 Percentage of staff and faculty that would use transit with reduced fares 100%!

75%!

50%!

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

526 respondents answered â&#x20AC;&#x2DC;yesâ&#x20AC;&#x2122; to the previous question. The survey also asks these respondents what price they would be willing to pay for an employee transit pass. Figure 2.68 reveals this group of people would pay an average of $37.32 for an employee transit pass. Transit users suggest the highest price at $40.49, whereas cyclists suggest the lowest at $33.79. Respondents in the far zone will also pay approximately three dollars more for an employee transit pass, recommending a price of $39.47.

Figure 2.68 Stated cost of a monthly employee transit pass $45.00 !

$40.00 !

$35.00 !

$30.00 !

Sample!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

The final employee transit pass question is aimed specifically at SOV users, and asks if they would use transit more often if an employee transit pass was offered. Results suggest that over 46% of SOV users will not switch if an employee transit pass were offered, whereas about 25% will and a further 28% are unsure (Figure 2.69).

Figure 2.69 Percentage of staff and faculty that would use transit more with a transit pass 100%!

75%!

Not sure

50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

Telecommuting The survey asks staff and faculty respondents two questions regarding telecommuting. First, all respondents are asked whether their job was conducive to telecommuting. Responses are divided, with about 45% indicating their job was conducive to telecommuting and 44% indicating the opposite (Figure 2.70). A further 11% of respondents are unsure. These results remain relatively consistent among respondents with different modes and in either commute zone.

Figure 2.70 Percentage of jobs that are conducive to telecommuting 100%!

75%!

Not sure 50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

Second, all respondents are asked if they would be interested in telecommuting as an option. It seems staff and faculty are generally interested in the initiative, with about 60% of the sample population offering positive responses (Figure 2.71). SOV users, in particular, are the most positive about telecommuting, with almost 65% indicating an interest. Distance seems to have a significant impact on respondentsâ&#x20AC;&#x2122; interest in telecommuting as over 67% of respondents in the far zone indicate positive results, compared to 56% in the near zone.

Figure 2.71 Percentage of staff and faculty interested in telecommuting 100%!

75%!

Not sure 50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

Compressed work week The final question regarding trip reduction programs on the survey is about a compressed work week program (CWW). The question simply asks if respondents would be interested in compressing their schedule into a four-day work week. Results suggest the majority of staff and faculty are indeed interested, with almost 62% indicating as such. In particular, SOV users and carpoolers are the most interested, with over 66% of both groups interested in CWW. Furthermore, over 67% of respondents in the far zone are interested in CWW compared to the 57% of respondents in the near zone.

Figure 2.72 Percentage of jobs that are conducive to telecommuting 100%!

75%!

Not sure

50%!

Interested

25%!

0%!

Walk!

Bike!

Transit!

Carpool!

SOV!

Near Zone!

Far Zone!

Survey results

Summary of program preferences and opinions Several important findings emerge regarding the program preferences of survey respondents that should be considered for recommendations. First, there is a clear trend regarding carpooling programs. Respondents (SOV users in particular) ranked carpooling as the least preferred transportation initiative. Second, respondent preferences are divided between commute zones. In general, far zone respondents prefer transit improvements, while near zone respondents are more in favour of bicycle-related improvements. Third, most respondents are interested in alternative scheduling programs, including compressed work weeks and telecommuting. SOV and far zone commuters are particularly interested in the potential of either of these.

Spatial analysis

Spatial analysis In order to determine the spatial distribution of Dalhousie students and staff and faculty, postal code data is obtained from the Office of Sustainability. For the student population, the mailing addresses for all full-time students are included. Similarly, only full-time staff and faculty postal code locations are obtained. Some data preparation is necessary in order to visualize the spatial distribution of Dalhousie commuters. First, postal codes have to be geocoded for ArcGIS utility. BatchGEO is used again for this process. After coordinates are obtained for the population, they are imported into ArcGIS where they are spatially joined with Nova Scotia dissemination area (DA) tracts obtained from Statistics Canada. Because these tracts vary in size, the total amount of Dalhousie commuters have to be divided by the total area in order to determine the density of each DA tract. This final step reveals the spatial distribution of both students and staff and faculty. Distribution Based on the survey results, it is not surprising that students are more concentrated within the peninsula than staff and faculty. Commuters are also concentrated northwest of the peninsula along the Bedford Highway, beginning in Fairview and extending up to Bedford and as far north as Lower Sackville. Pockets of commuters are also observed in Hammonds Plains, Timberlea, and Herring Cove. East of Halifax, most commuters are concentrated in Dartmouth and Cole Harbour, with a small pocket of in Eastern Passage.

5 5

Spatial analysis

Figure 2.73 Spatial distribution of staff and faculty

Persons per km2

Lower Sackville

2 - 10 11 - 50 51 - 100 Bedford

Hammonds Plains

+ 100

Dartmouth Cole Harbour Timberlea Halifax

Eastern Harbour

Herring Cove

25 km

North

Base map: Halifax Regional Municipality, 2010

Figure 2.74 Spatial distribution of students

Persons per km2

Lower Sackville

2 - 10

25 km

11 - 50 51 - 100 Bedford

+ 100

Hammonds Plains

Dartmouth Cole Harbour Timberlea

Halifax

Eastern Harbour

Herring Cove

25 km

North

Base map: Halifax Regional Municipality, 2010

Recommendations

Recommendations Based on the literature review and current travel behaviour at Dalhousie University, several strategies may help reduce SOV trips. However, before any recommendations are made, it is useful to briefly identify transportation programs that currently exist at Dalhousie.

Current transportation programs at Dalhousie Support Actions Dalhousieâ&#x20AC;&#x2122;s current transportation program is seemingly fragmented and unorganized. The few programs that do exist are run by different departments within the university, or run by the municipality. Perhaps the most notable omission is the lack of a website where commuters can research and explore different travel options. Currently, the only online information available is found under a subheading within the Office of Sustainabilityâ&#x20AC;&#x2122;s website (Figure 3.1). Once this website is found, commuters are not offered many useful resources regarding the potential of sustainable travel behaviour. Figure 3.1 Office of Sustainability Transportation website

Recommendations

Dalhousie does have a reserved parking program for commuters participating in a carpool; however it is â&#x20AC;&#x153;poorly advertised and offers only minor incentivesâ&#x20AC;? (Weinberg et al, 2010). Currently, only four of these reserved spots are used (2010). The university does not offer its own ridematching service, but directs commuters interested in carpooling to an external online ridematching service (hrmsmarttrip.ca). Dalhousie has also been chosen as a pilot site for a Guaranteed Ride Home program initiated by the municipality (Dalhousie University, 2010c). This program provides staff and faculty who commute using sustainable modes of transportation (carpool, tran-

Figure 3.2 A Dal rideshare parking spot sign

sit, walking, or cycling) free taxi rides home in the case of emergency, unexpected overtime, or illness. Rides are limited to five per year. Finally, Dalhousie has approximately 550 bike racks located throughout university property (Dalhousie University, 2010d); however, the majority of these racks are old, poorly maintained, have no security services, are rarely covered. Furthermore, it is not uncommon for slots in these racks to be occupied by abandoned bicycles that have been stripped-down and are rusted (Figure 3.3). The university also recently opened a bicycle repair shop on campus.

Figure 3.3 An example of an abandonned bicycle

Recommendations

Physical Services Dalhousie offers after-hours services run in tandem with the Dalhousie Student Union. First, Tiger Patrol is a walk-home and patrol service run by students to increase safety for walkers after hours. This service is operated during the Fall and Winter semesters. The university also operates an after-hours shuttle service. This free service consists of two vans with room for five passengers, and runs on 15 minute intervals between 6:15 to 11:45 pm. These shuttles operate only on the peninsula; one shuttle travels along a southern route terminating at St. Maryâ&#x20AC;&#x2122;s University, while the other shuttle covers the north terminating at the Metro Centre (Dalhousie University, 2010e). A recent report indicates that this service is used by about 30 people per night. This report suggests that this low rate of usership is because of poor routing and little advertising (Kudel, 2009).

Figure 3.4 Tiger Patrol van (Source: unews.ca)

Incentives and disincentives Dalhousie introduced a U-Pass program in 2006, which allows full-time students to ride transit during the academic year (September to April). The program is paid for by a mandatory student fee, most recently costing $118 a year in 2010 (Dalhousie University, 2010f). Unfortunately, no useful transportation data was collected before the implementation of the U-Pass program, making it difficult to determine the effectiveness of the program. However, high transit ridership amongst students in the 2009 Transportation Survey at least suggests that students are taking the bus. The university heavily subsidizes parking. The university owns 2,800 parking spots, of which 2,266 are spots for unreserved parking passes (Weinberg et al, 2010). Under the current budgeting scheme, the revenue generated from the sale of these passes are not enough to cover their expenses. Each unreserved parking spot costs approximately $1,046 per year for maintenance, security, and snow removal (2010). The cost of a parking pass one of these spots is $224.70 for students and $249.84 for staff and faculty, which means that each spot is costing the university approximately $750 each year, for a total deficit of $1.7 million (Dalhousie University, 2010g). This deficit is most likely covered by the student fees of commuters who do not require parking at all. Each parking spot has an average land value of $14,000 â&#x20AC;&#x201C; almost $32 million in land assets (Dalhousie University, 2009). Under this subsidy scheme,

Recommendations

Dalhousie is essentially encouraging commuters to use SOV as their primary modal choice by paying for expensive parking costs. Alternative work arrangements Finally, compressed work weeks are currently permitted by Dalhousie under Section 10.5 of the Human Resources Policy and Procedure Manual (Dalhousie University, 2010h). This section indicates that employees may work additional hours during the week in order to take a day off work, and avoid making a trip to the university. The university does not have a policy on teleworking, but according to the Office of Sustainability, research is being conducted for possible future implementation.

Recommendations

General recommendations Parking Perhaps the most straight-forward approach to reducing SOV usage is to simply stop paying these commuters to do so. Findings from the literature review indicate that strategies involving increasing parking costs are the most effective strategy in reducing trips made by SOV (see page 9). Currently, Dalhousie is encouraging SOV use by paying for a significant amount of parking costs. SOV users only cover approximately 25% of the annual costs per parking spot. The remainder is covered indirectly by all Dalhousie commuters, whether they use SOV or not. Parking subsidies need to be phased out by increasing parking fees consistently every year until parking revenue covers all expenditures. Indeed, reducing subsidies will help make alternative modes more equitable. However, another strategy will help reduce SOV usage within the near zone specifically. Prior to 2004, Dalhousieâ&#x20AC;&#x2122;s Sexton Campus had a parking policy in place that did not allow the sale of parking passes to commuters living on the peninsula, south of Quinpool Road and east of Oxford Road. However, after 2004 all parking policies were consolidated into one policy which effectively cancelled this regulation (Horne, 2010). Reintroducing a similar restrictive parking policy would help discourage SOV use within the near zone, and encourage walking, cycling, or transit usage in its place. Of course, this policy will need to take commuter needs into account by allowing users that have disabilities or employment characteristics which require SOV use to still be able to apply for a parking pass. Reducing parking subsidies and restricting parking will not solve transportation issues at Dalhousie exclusively. While these actions will help level the playing field by making the costs of SOV more realistic, the university will need to offer more proactive solutions for SOV users that may be looking to switch modes as parking costs increase. An actual program The first proactive move at Dalhousie must be to establish a tangible transportation department solely responsible for the implementation and operation of sustainable transportation programs. Currently, the few transportation programs that do exist at Dalhousie are run by different university departments, including Facilities Management, the Dalhousie Student Union, and the Office of Sustainability. Instead, all transportation programs should fall under the umbrella of a single unit. Other universi-

Recommendations

ties have recognizable transportation organizations responsible for implementing and managing travel options for university commuters. In 2008, Transport Canada released a publication summarizing the successes of such programs at Canadian universities (Transport Canada, 2008). No mention is made of any programs at Dalhousie University, while programs such as UBCâ&#x20AC;&#x2122;s TREK and McMasterâ&#x20AC;&#x2122;s ACT (AllModes Commuting & Transportation) received significant coverage. These universities have developed a recognizable identity for their transportation organizations, and have hired designated transportation coordinators who are responsible for program operations. A similar model at Dalhousie will help make transportation programs more organized, and most importantly, more effective. Figure 3.5 UBCâ&#x20AC;&#x2122;s TREK website

This new transportation department would be responsible for all things related to sustainable transportation. General awareness and outreach is a basic, yet crucial step in its beginning stages. As identified in the literature review, basic awareness and marketing campaigns are cost-effective techniques in reducing SOV use (see page 7),which can be accomplished through a number of strategies. To begin, a transportation website would provide commuters with information regarding alternative forms of

Recommendations

transportation and give the organization an online presence. A good example of what a website can offer is UBC’s TREK website (Figure 3.5). The site offers many useful resources for commuters, including a cycle-route and transit planner, commuting tips, infrastructure maps, event information, and other general program information. In addition to a website, promotional campaigns encourage sustainable transportation through posters, advertisements, and other publicity events. Finally, a transportation orientation program can educate new students and staff and faculty about potential commute options. These sessions would help new students become familiar with transportation options and also generate awareness regrading the negative effects of SOV use. All of these different types of support actions would significantly contribute to creating a ‘culture’ of sustainable commuting. On-site mobility Another valuable lesson learned from the literature review is the value of on-site mobility services (see page 6). Such services allow sustainable commuters to be able to conveniently carry out the tasks and errands they are able to do if they drive their own personal vehicle. Services such as on-site car-sharing, bicycle loans, and circulating shuttles improve mobility for students and employees and ultimately support and encouragessustainable travel habits. A partnership with a local car-sharing company called CarShare HFX (Figure 3.6) would be relatively cost-free for the university (aside from providing campus parking spots for fleet vehicles) and would significantly improve on-site mobility options for Dalhousie commuters. Currently, more research is being conducted by Dalhousie’s Faculty of Management that is looking into the feasibility of a car-sharing partnership. Figure 3.6 CarShare HFX vehicle (Source: CarShare HFX)

Recommendations

Alternative work arrangements Human resources policy at Dalhousie University permits staff and faculty to adopt a compressed work week schedule. It does not, however, include support for telecommuting. Furthermore, neither of these options are noticeably encouraged by Dalhousie. Alternative scheduling scenarios eliminate trip frequencies, reduce office energy consumption, and can improve employee retention and morale. Although not every job will be conducive to teleworking or compressed work weeks, the benefits of their possibilities make alternative scheduling worth supporting and promoting.

Far zone recommendations One of the key findings from the survey analysis is that commute options are limited to three modes in the far zone; SOV, carpooling and transit. As a result, far zone SOV users interested in switching to sustainable modes are limited to joining a carpool or using transit. No carpooling programs Every question of the survey associated with carpooling identifies a general disinterest in potential programs, particularly amongst SOV users. Carpooling program are the least preferred potential transportation program for students and the second least preferred for staff and faculty. Furthermore, most SOV users indicate they would not switch to carpooling, even if a program was offered. Findings in the literature review suggest that time issues are likely the main reasons for this general dissatisfaction (see page 5). Scheduling conflicts are also a significant barrier to the success of carpooling programs, particularly at a university. Differing start and end times for university commuters are quite common, which makes organizing trips difficult. Based on these findings, it would be unreasonable to expect enough far zone SOV users to switch to carpooling to justify investing in a carpooling program.

Recommendations

Employee transit pass Due to a general disinterest in carpooling, transit remains as the only other possible alternative for far zone users. Currently, students are far more likely to use transit in the far zone than staff and faculty because of the U-Pass. The survey analysis indicates that the vast majority of staff and faculty are in favour of a similar U-Pass for employees. As a result, a simple solution might indicate that an employee U-Pass will yield similar results to students; however, findings in this paper regarding the current state of local transit suggests that this may not be true. First of all, the majority of far zone respondents indicated that improvements to the current transit network were among the most important out of all potential transportation programs. Other findings indicate significant time and accessibility-related issues, particularly for SOV users. Finally, socioeconomic trends of the sample indicate that respondents who have stable jobs, are older, and are more wealthy are less likely to use the current transit system. All of these results indicate significant issues with the current transit system in the far zone. Therefore, to recommend an employee U-Pass would be counter intuitive to these findings. The transit network must be significantly improved in the far zone, with comfortable express buses, less transfers and dedicated bus lanes before this could be a viable solution. Indeed, a U-Pass type system for staff and faculty may not be not appropriate at this time. Despite this, an optional transit pass system would be a useful alternative. Survey results indicate that 44% of staff and faculty would be encouraged to take transit if lower fares were offered. Among these, respondents suggested they would be willing to pay an average of $37.32 for a monthly transit pass (which costs $60 regularly). Dalhousie could buy a large amount of monthly transit passes in bulk from MetroTransit at a discounted rate (and perhaps offer a small subsidy) to help reduce the costs for potential users. These optional, discounted transit passes will offer an incentive for staff and faculty commuters to use transit, while recognizing it is not feasible option for commuters who are not well served by the current transit network. Dalhousie must also consider commuters living in areas not well served by transit. The university can help fill the gaps in this system by working with MetroTransit and other large employers to create a commuter shuttle service. Such a shuttle must compete with the SOV in terms of overall utility, including time, convenience, and cost. In terms of time, the shuttle must move as fast as possible. One of the most concerning results from the survey analysis is poor transit speed. Shuttle routes with fewer stops in strategic areas will help increase this speed; however, these buses will still be prone to traffic

Recommendations

congestion, particularly during peak travel periods. The ultimate goal of this shuttle is to reduce the amount of cars on the road, and while it certainly will improve regional congestion, it will not eliminate it completely. As a result, these time constraints can be alleviated by improving the comfort and on-route convenience of the shuttle service. Seats that are comfortable and spacious will allow commuters to rest more easily or carry out productive activities without being inhibited. The shuttles could also offer unique on-route services, such as free wireless internet service, lap-desks, and plug outlets, so commuters can be even more productive while commuting. These services are inexpensive, yet effective strategies in attracting new riders, and will also give the shuttle at competitive edge in comfort over the SOV. In 2008, Microsoft launched the Connector, an employer-based shuttle service that included wireless internet, wide seats, lap-desks, and plug outlets. Since its launch, the shuttle experienced incredible demand by its employees, eventually becoming the largest employee-owned shuttle in the U.S. (Wilson, 2009).

Figure 3.7 Microsoft shuttle, with wide seats and WIFI services

The shuttle should also be cost-effective to offer another competitive advantage over SOV. There are several ways the university can keep the costs of a shuttle low. First, sustainable transportation is an emerging issue for many departments in municipal, provincial and federal jurisdictions. External grants

Recommendations

for overhead costs would help reduce startup costs. The university may also consider partnering with other large, nearby employers, such as Saint Mary’s University, the IWK, King’s College, NSCAD, and perhaps the Spring Garden Merchants Association. These employers could form a transportation management association (TMA) and work with MetroTransit to create a shuttle service catered for their employees.

Figure 3.8 Express shuttle service “mock-up”

Indeed, an express shuttle service may be able to compete with SOV in terms of overall utility; however, significantly more research must be conducted. The analysis offered in this research paper simply indicates a hole that needs to be filled. Future detailed research will need to investigate more specific solutions. Community interest will need to be determined, and potential partnerships with other large employers need to be explored. Dalhousie should also work with MetroTransit to explore strategies on how to fill in current gaps in the network more specifically, and overcome the barriers of traffic congestion and accessibility.

Recommendations

Near zone recommendations Currently, 25% of staff and faculty and 7% of students drive to the university within the near zone. The university must focus on eliminating SOV usage within this zone with the strategy of encouraging them to switch to transit or active modes of transportation. One of the most topical issues discovered from the survey results was the high demand for bicycle infrastructure, particularly amongst respondents living in the near zone. In fact, bicycle lanes were the highest ranked transportation initiative for both students and staff and faculty. Obviously, as an employer, Dalhousie does not have the authority nor resources to build region-wide bike lanes for its commuters. It can still encourage cycling by providing on-site infrastructure and building bicycle lanes between its own campuses.

SEPTEMBER 2010

urts

SOUTH PARK ST.

UNIVERSITY AVE.

Realm DALHOUSIE Patio/Private CAMPUS MASTER PL AN Forecourts FRAMEWORK PL AN Main Pedestrian Spine

Active Recreational Corridor

Dedicated on Street Parking

Active University Green

Vehicular Travelway

Figure 3.9 Active transportation campus plan (IBI group, 2010) 5m



DALHOUSIE CAMPUS MASTER PLAN FRAMEWORK PLAN

of five essential parts:

ROBIE ST.

LEMARCHANT ST

ts of five essential parts: niversity Green, urts and the private/ follow apply to all parts d be considered early

he five essential parts uidelines should be sign stage and should nalysis of the vehicular

Carleton Campus

Studley Campus

SUMMER ST

y Avenue is discussed eetscape design ke up the concept.









Currently, Dalhousie’s Campus Master Plan indicates the intention to build a 3 metre, two-way bike lane along University Avenue to help link the Carleton and Studley campuses (Figure 3.13), with hopes that the municipality will continue the lane along Morris Street to the Sexton campus. Dalhousie

IBI GROUP | 35

Recommendations

should not only ensure these lanes get built, but also ensure these new throughways are enhanced with safety precautions (such as adequate lighting and regular snow removal). As Dalhousie establishes itself as a community leader in sustainable transportation principles, it can become an important and credible advocate for more bicycle lanes throughout the region. The analysis also identified that more bike racks was important to many respondents. In order to promote cycling, Dalhousie must improve parking infrastructure throughout the campus. The university currently has approximately 550 bike racks throughout the campus, however, many of these are in poor conditions and poorly maintained. Therefore, the university should substantially increase the number of bike racks on campus, while also improving existing racks while maintaining them regularly. Furthermore, in order to encourage cycling year-round in Halifax, new bicycle racks should also be covered. Finally, more advanced bicycle services, such as bicycle lockers, guarded parking, and end-of-use facilities, should be introduced as ridership increases.

Figure 3.10 An example of a covered bicycle rack (Source: www.uvic.ca)

100

Conclusion

Conclusion What has been done? The main objective of this research project was to recommend transportation management programs that can most effectively reduce SOV travel at Dalhousie University. Based on evidence found in the literature and the analysis on university travel behaviour, this project has recommended several strategies that will accomplish this objective. Of these strategies, this project strongly recommends both creating a centralized transportation department and reducing parking subsidies. It also recommends encouraging active transportation to commuters living near Dalhousie by improving on-site bicycle and pedestrian infrastructure while advocating for municipal improvements, such as bicycle lanes. Furthermore, this project also recommends investigating the feasibility of shuttle service to help fill in areas neglected by the current transit system.

What needs to be done? This paper has laid the foundation for more research. It has offered a comprehensive analysis of Dalhousie commuting behaviour and offered an extensive literature review regarding employer-based trip reduction strategies. Now, as Dalhousie begins to improve its transportation program, more research will need to be done. Currently, another similar transportation survey is being conducted at Dalhousie. The survey is shorter than the 2009 survey, but allows respondents to indicate which campus they commute to (an improvement from the 2009 version). Since the 2009 survey, there have been no significant transportation-related improvements, and as a result, it is not likely that modal split will change that much. However, similar surveys conducted on a regular basis will significantly enhance future research and allow a better understanding of how travel habits change through time. Aside from data collection, more practical research should examine specific recommendations in this paper in more detail. Therefore, in the Winter 2011 semester, more research will investigate the feasibility of a shuttle service, including logistical details and case studies.

101

Conclusion

Why is this important? Dalhousie University is at a crossroads regarding transportation. As a large post-secondary institution and employer without an effective and coherent transportation program, the university has two options. First, it can continue to sporadically offer transportation programs that are run by different departments, and disjointed from one another. Or it can begin to take transportation more seriously by creating a new, consolidated program that will establish and oversee all transportation issues. Under this option, Dalhousie will significantly reduce trips made by SOV and begin to enjoy many benefits such as reduced greenhouse gas emissions, improved air quality, fewer parking issues, and much more. These benefits also extend beyond Dalhousie itself. The greater community will also benefit from reduced congestion, improved health, and less land consumed by parking lots. Also, an effective program at Dalhousie will help improve future travel behaviour, as incoming students realize the benefits and ease of sustainable commuting. Indeed, with a bit of effort, Dalhousie can become a leader in employer-based sustainable transportation practices, both as a large employer in Halifax and as an important post-secondary institution in Canada.

102

Conclusion

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References

References Abraham, J. A Survey of Carsharing Preferences. Retrieved at www.ucalgary.ca/~jabraham/WTPPCarshare.PDF on October 15, 2010. University of Calgary, 1999. Association for Commuter Transportation, Urbantrans Consultants, PB Parson Binckerhoff, and ESTC. Mitigating Traffic Congestion: The Role of Demand-Side Strategies. Federal Highway Administration, US Department of Transportation, 2004. Atherton, T., G. J. Scheuernstuhl, and D. Hawkins. Transportation-Related Impacts of Compressed Work Week: The Denver Experiment. Transportation Research Record, No. 845, 1982. Bambra, C., M. Whitehead, A. Sowden, J. Akers, and M. Petticrew. “A Hard Day’s Night?” The Effects of Compressed Work Week Interventions on Health and Work-life Balance of Shift Workers: A Systematic Review. Journal of Epidemiol Community Health, Vol. 62, 2008, pp. 764-777. Brown, J., D. Hess, and D. Shoup. Unlimited Access. Transportation, Vol. 28, No. 3, 2001, pp. 233-267. Brown, J., D. Hess, and D. Shoup. Fare-Free Public Transit at Universities: An Evaluation. Journal of Planning Education and Research, Vol. 23, 2003, pp. 69-82. Buliung, R., K. Soltys, C. Habel, and R. Lanyon. The “driving” factor behind successful carpool formation and use. Transportation Research Record, No. 2118, 2008, pp. 31-38. CUTR, Guaranteed Ride Home/Emergency Ride Home. Retrieved at www.nctr.usf.edu/clearinghouse/grh.htm on October 11, 2010. National TDM and Telework Clearinghouse, Center for Urban Transportation Research at the University of South Florida, 2006. Dalhousie University. Transportation Plan Presentation. Dalhousie University’s Office of Sustainability, Halifax, N.S., 2009.

104

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Staff / Faculty Socioeconomic Data: by modal choice SOV Sample Walk n (%) n (%) n Gender Male 412 35.4 133 23.6 111 Female 753 64.6 431 76.4 173 Age 15-24 89 7.5 7 1.6 50 25-34 191 16.1 52 12.0 70 35-44 345 29.2 144 33.3 54 45-55 351 29.7 146 33.7 63 55-64 196 16.6 78 18.0 52 64 and over 11 0.9 6 1.4 1 Employment Status Permanent full-time 873 73.7 354 81.9 176 Full-time contract 127 10.7 35 8.1 39 Permanent part-time 50 4.2 13 3.0 20 Part-time contract 85 7.2 19 4.4 38 Other 49 4.1 11 2.5 17 Household income Less than $20,000 73 6.3 4 0.9 41 $20,000-40,000 112 9.6 26 6.1 36 $40,000-60,000 141 12.1 52 12.2 33 $60,000-100,00 344 29.6 143 33.6 68 More than $100,000 329 28.3 137 32.2 76 No Resp 165 14.2 63 14.8 35

Staff and faculty socioeconomic data results

Figure 3.11

32 24 0 13 23 10 11 0 41 12 0 2 3 4 4 7 13 24 3

39.1 60.9 17.2 24.1 18.6 21.7 17.9 0.3 60.7 13.4 6.9 13.1 5.9 14.2 12.5 11.4 23.5 26.3 12.1

(%)

Bike n

7.3 7.3 12.7 23.6 43.6 5.5

70.7 20.7 0.0 3.4 5.2

0.0 22.8 40.4 17.5 19.3 0.0

57.1 42.9

(%)

23 20 38 45 24 23

109 19 11 20 17

29 27 46 46 24 3

59 116

Transit n

13.3 11.6 22.0 26.0 13.9 13.3

61.9 10.8 6.3 11.4 9.7

16.6 15.4 26.3 26.3 13.7 1.7

33.7 66.3

(%)

1 24 11 70 65 38

182 20 6 6 1

3 29 74 83 25 1

82 133

Carpool n

0.5 11.5 5.3 33.5 31.1 18.2

84.7 9.3 2.8 2.8 0.5

1.4 13.5 34.4 38.6 11.6 0.5

38.1 61.9

(%)

70 77 93 205 209 90

503 97 42 70 43

78 150 202 183 135 6

269 471

9.4 10.3 12.5 27.6 28.1 12.1

66.6 12.8 5.6 9.3 5.7

10.3 19.9 26.8 24.3 17.9 0.8

36.4 63.6

by distance Near Zone n (%)

3 35 48 139 120 75

370 30 8 15 6

11 41 143 168 61 5

143 282

Far Zone n

0.7 8.3 11.4 33.1 28.6 17.9

86.2 7.0 1.9 3.5 1.4

2.6 9.6 33.3 39.2 14.2 1.2

33.6 66.4

(%)

Appendix - Data tables

109

References

Distance from bus stop (m) Distance from Dal bus stop (m) Intra-campus travel required Yes No 396 792

2961.0 5604.9 33.3 66.7

158 277

4686.6 8753.5

36.3 63.7

Staff / Faculty Commute Details Results: by modal choice SOV Sample n (%) n (%) Modal Split SOV 436 36.6 436 100.0 Walk 290 24.4 0 Bike 58 4.9 0 Transit 176 14.8 0 Carpool 217 18.2 0 One-way distance Less than 2km 232 19.5 16 3.7 3-5 km 250 21.0 58 13.3 6-10 km 207 17.4 82 18.8 11-20 km 221 18.6 122 28.0 21-30 km 139 11.7 80 18.4 31-40 km 73 6.1 41 9.4 41-50 km 24 2.0 17 3.9 Over 51 km 44 3.7 20 4.6 Commute length in time Less than 10 min 104 8.7 22 5.0 11-20 min 285 23.9 86 19.7 21-30 min 287 24.1 116 26.6 31-40 min 219 18.4 107 24.5 41-50 min 143 12.0 64 14.7 51-60 min 95 8.0 22 5.0 Over 61 min 57 4.8 19 4.4 Reason for SOV use Convenience 413 35.1 307 38.2 Joy of driving 44 3.7 32 4.0 Need to run errands 403 34.24 264 32.84 Affordable parking 95 8.071 76 9.453 Childcare 133 11.3 89 11.07 Need to drop off others 89 7.562 36 4.478 Occasional SOV use Occasionally used 642 53.9 436 100.0 Never used 548 46.1 0 0.0 Within 500m of bus stop Yes 861 72.4 246 56.4 No 319 26.8 182 41.7 Don't know 10 0.8 8 1.8

Staff and faculty travel habits results

Figure 3.12

22.4 38.3 25.2 9.3 3.1 0.7 1.0 27.5 6.3 53.75 0 7.5 5 22.1 77.9 96.9 2.8 0.3

65 111 73 27 9 2 3 22 5 43 0 6 4 64 226 281 8 1

84 206

29.0 71.0

64.1 31.4 4.1 0.3 -

186 91 12 1 0 0 0 0

284.8 406.6

100.0 -

(%)

0 290 0 0 0

Walk

56 2 0

26 32

7 0 12 3 5 2

8 31 11 5 2 1 0

16 28 9 5 0 0 0 0

0 0 58 0 0

20 38

167.2 398.9

Bike

34.5 65.5

96.6 3.4 0.0

44.8 55.2

24.1 0.0 41.38 10.34 17.24 6.897

13.8 53.4 19.0 8.6 3.4 1.7 0.0

27.6 48.3 15.5 8.6 -

100.0 -

(%)

48 127

571.3 2442.8

154 22 0

42 134

21 2 30 4 5 4

4 17 32 25 31 43 24

8 47 54 38 20 6 1 2

0 0 0 176 0

27.4 72.6

87.5 12.5 0.0

23.9 76.1

31.8 3.0 45.45 6.061 7.576 6.061

2.3 9.7 18.2 14.2 17.6 24.4 13.6

4.6 26.7 30.7 21.6 11.4 3.4 0.6 1.1

100.0 -

Transit n (%)

80 137

5729.5 10050.5

118 98 1

74 143

52 5 51 11 25 39

5 37 52 54 32 26 11

6 25 46 53 36 24 5 22

0 0 0 0 217

36.9 63.1

54.4 45.2 0.5

34.1 65.9

28.4 2.7 27.87 6.011 13.66 21.31

2.3 17.1 24.0 24.9 14.7 12.0 5.1

2.8 11.5 21.2 24.4 16.6 11.1 2.3 10.1

100.0

Carpool n (%)

250 506

117.8 303.2

700 52 5

322 435

209 22 205 44 64 42

102 276 208 92 46 25 8

229 249 192 75 9 2 0 1

188 287 54 126 96

33.1 66.9

92.5 6.9 0.7

42.5 57.5

35.7 3.8 35.0 7.5 10.9 7.2

13.5 36.5 27.5 12.2 6.1 3.3 1.1

30.3 32.9 25.4 9.9 1.2 0.3 0.0 0.1

25.0 38.2 7.2 16.8 12.8

by distance Near Zone n (%)

146 286

7931 14874

161 267 5

320 113

204 22 198 51 69 47

2 9 79 127 97 70 49

3 1 15 146 130 71 24 43

248 3 4 50 121

33.8 66.2

37.2 61.7 1.2

73.9 26.1

34.5 3.7 33.5 8.6 11.7 8.0

0.5 2.1 18.2 29.3 22.4 16.2 11.3

0.7 0.2 3.5 33.7 30.0 16.4 5.5 9.9

58.2 0.7 0.9 11.7 28.4

Far Zone n (%)

110

References

Preference of ST programs Bike racks on buses 3.60 EOU Facilities 3.59 More bike lanes 3.79 More bike racks 3.70 Less expensive transit fare 3.30 More frequent bus 3.65 Less transfers 3.64 Closer transit stops 3.52 Shorter travel time 3.63 Tax deductions on transit pass 3.49 Carpool programs 3.36 Gaurunteed ride home 3.65 ST funding preference Parking fees - Agree 747 Parking fees - Disagree 443 External grant - Agree 462 External grant - Disagree 728 Base budget allocation - Agree 529 Base budget allocation - Disagree 661 No funding - Agree 28 No funding - Disagree 1162 Carpooling program Yes 159 No 230 Not sure 221 Not applicable 523 Employee transit pass support Yes 1009 No 79 Not sure 102 Reduced transit passes Yes 526 No 650 Transit pass price ($) $15.74 Use ST modes with transit pass Yes 207 No 329 Not sure 187 Not applicable 422 Job condusive to telecommuting Yes 532 No 527 Not sure 130 Interest in telecommuting Yes 712 No 346 Not sure 132 Interest in compressed work week Yes 733 No 327 Not sure 115 283 122 31 290 108 33

59.83 29.1 11.1 61.6 27.5 9.7

66.5 24.8 7.6

64.9 28.0 7.1

44.0 44.27 11.7

192 193 51

153 95 37

148 97 45

133 132 25

14 12 7 240

44.7 44.29 10.9

247 22 21

24.5 46.1 28.4 0.2

80.5 7.34 12.16

28 25 17 220

107 201 124 1

351 32 53

84.79 6.64 8.57

22.0 35.7 36.7 5.6

232 58 110 180 143 147 0 290

17.4 27.6 15.7 35.5

95 154 158 24

14.0 20.3 19.5 46.2

51.1 48.9 40.4 59.6 40.1 59.9 4.4 95.6

3.51 3.52 3.78 3.59 3.24 3.62 3.67 3.49 3.54 3.49 3.43 3.62

31.2 148 67.9 139 $18.24

223 213 176 260 175 261 19 417

62.8 37.2 38.8 61.2 44.5 55.5 2.4 97.6

Walk

44.2 136 54.6 296 $10.75

3.59 3.60 3.72 3.70 3.19 3.63 3.61 3.48 3.66 3.42 3.20 3.64

by modal choice SOV n (%)

Sample n (%)

Staff / Faculty Program Preference Data:

Staff and faculty program preferences results

Figure 3.13

51 5 2

6 6 5 41

48 10 24 34 32 26 0 58

3.57 3.35 3.95 3.54 3.26 3.67 3.60 3.62 3.53 3.46 3.35 3.53

52.8 32.8 12.8

51.0 33.4 15.5

45.9 45.52 8.6

4.8 4.1 2.4 82.8

27 26 2

35 18 5

26 26 6

5 2 3 42

51.0 24 47.9 32 $13.98

85.17 7.59 7.24

9.7 8.6 5.9 75.9

80.0 20.0 37.9 62.1 49.3 50.7 100.0

(%)

Bike

169 2 5

30 15 13 118

117 59 61 115 96 80 2 174

3.74 3.74 3.90 3.86 3.50 3.75 3.63 3.57 3.68 3.62 3.46 3.80

46.6 44.8 3.4

60.3 31.0 8.6

44.8 44.83 10.3

8.6 3.4 5.2 72.4

109 42 25

102 49 25

70 81 25

26 1 5 129

41.4 146 55.2 29 $31.28

87.93 8.62 3.45

10.3 10.3 8.6 70.7

82.8 17.2 41.4 58.6 55.2 44.8 100.0

(%)

180 18 19

49 26 26 116

120 97 88 129 79 138 6 211

3.66 3.67 3.86 3.76 3.46 3.68 3.70 3.57 3.68 3.52 3.51 3.61

61.9 23.9 14.2

58.0 27.8 14.2

39.8 46.02 14.2

14.8 0.6 2.8 73.3

144 54 17

136 56 25

105 90 21

54 108 43 9

660 50 47

74 126 98 418

525 232 298 459 346 411 11 746

3.58 3.60 3.80 3.70 3.27 3.64 3.64 3.43 3.56 3.50 3.35 3.64

66.4 24.9 7.8

62.7 25.8 11.5

48.4 41.47 9.7

24.9 49.8 19.8 4.1

432 231 81

421 243 93

341 346 70

137 121 81 380

349 29 55

85 104 123 105

222 211 164 269 183 250 17 416

3.65 3.59 3.78 3.70 3.37 3.68 3.65 3.66 3.76 3.47 3.37 3.67

57.1 30.5 10.7

55.6 32.1 12.3

45.0 45.7 9.2

18.1 16.0 10.7 50.2

301 96 34

291 103 39

191 181 60

70 208 106 42

69.5 22.2 7.9

67.2 23.8 9.0

44.2 41.9 13.9

16.2 48.0 24.5 9.7

29.6 69.5 -

80.6 6.7 12.7

20.4 24.9 29.5 25.2

51.3 48.7 37.9 62.1 42.3 57.7 3.9 96.1

Far Zone n (%)

52.6 128 46.1 301 $10.67

87.2 6.6 6.2

10.3 17.6 13.7 58.4

69.4 30.6 39.4 60.6 45.7 54.3 1.5 98.5

by distance Near Zone n (%)

31.3 398 66.8 349 $18.65

82.95 8.29 8.76

22.6 12.0 12.0 53.5

55.3 44.7 40.6 59.4 36.4 63.6 2.8 97.2

Carpool n (%)

83.0 68 16.5 145 $10.75

96.02 1.14 2.84

17.0 8.5 7.4 67.0

66.5 33.5 34.7 65.3 54.5 45.5 1.1 98.9

Transit n (%)

111

References

Student Socioeconomic Data: Sample n Gender Male 386 Female 833 Age 15-24 763 25-34 405 35-44 57 45-55 10 55-64 0 64 and over 0 Student Status Full-time undergrad 801 Part-time undergrad 32 Full-time graduate 336 Part-time graduate 27 Continuing education 4 Other 33 49 90 55 67 18 1 0 0 81 6 40 8 1 5

61.8 32.8 4.6 0.8 65.0 2.6 27.3 2.2 0.3 2.7

57.4 4.3 28.4 5.7 0.7 3.5

39.0 47.5 12.8 0.7 -

35.3 64.7

by modal choice SOV n (%)

31.7 68.3

(%)

Student socioeconomic data results

Figure 3.14

432 11 171 8 1 13

439 180 17 3 0 0

189 440

Walk n

67.9 1.7 26.9 1.3 0.2 2.0

68.7 28.2 2.7 0.5 0.0 0.0

30.0 70.0

(%)

Bike n

27 1 22 3 1 4

21 33 4 0 0 0

33 24

46.6 1.7 37.9 5.2 1.7 6.9

36.2 56.9 6.9 0.0 0 0

57.9 42.1

(%)

197 14 86 4 1 7

186 108 10 5 0 0

98 210

Transit n

63.8 4.5 27.8 1.3 0.3 2.3

60.2 35.0 3.2 1.6 0.0 0.0

31.8 68.2

(%)

35 0 15 4 0 4

35 15 6 1 0 0

14 44

Carpool n

60.3 0.0 25.9 6.9 0.0 6.9

61.4 26.3 10.5 1.8 -

24.1 75.9

(%)

678 25 297 19 3 26

657 352 35 7 0 0

326 708

64.7 2.4 28.3 1.8 0.3 2.5

62.5 33.5 3.3 0.7 -

31.5 68.5

by distance Near Zone n (%)

122 7 38 8 1 7

104 53 22 3 0 0

60 123

66.7 3.8 20.8 4.4 0.5 3.8

57.1 29.1 12.1 1.6 -

32.8 67.2

Far Zone n (%)

112

References

Distance from bus stop (m) Distance from Dal bus stop (m) Intra-campus travel required Yes No

Modal Split SOV Walk Bike Transit Carpool One-way distance Less than 2km 3-5 km 6-10 km 11-20 km 21-30 km 31-40 km 41-50 km Over 51 km Commute length in time Less than 10 min 11-20 min 21-30 min 31-40 min 41-50 min 51-60 min Over 61 min Reason for SOV use Convenience Joy of driving Need to run errands Affordable parking Childcare Need to drop off others Occasional SOV use Occasionally used Never used Within 500m of bus stop Yes No Don't know

Student Commute Details Results:

Student travel habits results

Figure 3.15

25.1 35.6 18.3 7.1 5.3 4.9 3.8 50.2 8.4 33.8 2.3 2.9 2.5 26.2 73.8 89.6 9.4 1.0

310 440 226 88 65 60 47 263 44 177 12 15 13 324 912 1108 116 12

302 934

24.4 75.6

49.8 20.1 9.3 10.4 6.1 2.3 0.9 1.2

615 248 115 128 76 28 11 15

1273.5 2274.8

11.4 51.8 4.7 25.0 4.7

141 640 58 309 58

Sample n (%)

35 106

6088.0 9735.7

85 53 3

141 0

115 25 63 9 8 7

9 30 36 28 18 10 10

2 14 27 45 26 9 7 11

141 0 0 0 0

24.8 75.2

60.3 37.6 2.1

100.0 -

50.7 11.0 27.8 4.0 3.5 3.1

6.4 21.3 25.5 19.9 12.8 7.1 7.1

1.4 9.9 19.1 31.9 18.4 6.4 5.0 7.8

100.0 -

by modal choice SOV n (%)

158 481

84.3 119.3

617 14 8

69 570

61 9 54 1 2 1

231 295 100 10 3 0 0

523 105 9 2 0 0 0 0

0 640 0 0 0

Walk

24.7 75.3

96.6 2.2 1.3

10.8 89.2

47.7 7.0 42.2 0.8 1.6 0.8

36.2 46.2 15.6 1.6 0.5 -

81.8 16.4 1.4 0.3 -

100.0 -

(%)

56 2 0

11 47

4 1 4 0 0 0

13 31 10 3 1 0 0

11 36 9 2 0 0 0 0

0 0 58 0 0

20 38

117.6 320.5

Bike

34.5 65.5

96.6 3.4 -

19.0 81.0

44.4 11.1 44.4 -

22.4 53.4 17.2 5.2 1.7 -

19.0 62.1 15.5 3.4 -

100.0 -

(%)

73 236

109.1 1467.2

280 29 0

81 227

61 7 42 1 3 3

31 70 61 35 36 41 35

51 88 58 58 34 15 3 2

0 0 0 309 0

23.6 76.4

90.6 9.4 -

26.3 73.7

52.1 6.0 35.9 0.9 2.6 2.6

10.0 22.7 19.7 11.3 11.7 13.3 11.3

16.5 28.5 18.8 18.8 11.0 4.9 1.0 0.6

100.0 -

Transit n (%)

12 45

1900.5 5263.5

41 16 1

22 36

16 2 10 1 1 2

2 12 18 11 5 8 2

2 4 11 19 16 4 1 1

0 0 0 0 58

21.1 78.9

70.7 27.6 1.7

37.9 62.1

50.0 6.3 31.3 3.1 3.1 6.3

3.4 20.7 31.0 19.0 8.6 13.8 3.4

3.4 6.9 19.0 32.8 27.6 6.9 1.7 1.7

100.0

Carpool n (%)

256 795

93.2 177.4

1002 37 12

210 841

172 26 127 8 9 6

310 435 199 49 31 22 5

615 244 109 67 11 2 3 2

70 638 56 238 24

24.4 75.6

95.3 3.5 1.1

20.0 80.0

49.4 7.5 36.5 2.3 2.6 1.7

29.5 41.4 18.9 4.7 2.9 2.1 0.5

58.4 23.1 10.4 6.4 1.0 0.2 0.3 0.2

6.8 62.1 5.5 23.2 2.3

by distance Near Zone n (%)

46 137

6813.4 13006.6

104 79 0

114 69

91 18 50 4 6 7

0 4 27 38 34 38 42

0 4 6 61 65 26 8 13

71 2 2 71 34

25.1 74.9

56.8 43.2 -

62.3 37.7

51.7 10.2 28.4 2.3 3.4 4.0

2.2 14.8 20.8 18.6 20.8 23.0

2.2 3.3 33.3 35.5 14.2 4.4 7.1

39.4 1.1 1.1 39.4 18.9

Far Zone n (%)

113

References

Preference of ST programs Bike racks on buses EOU Facilities More bike lanes More bike racks More frequent bus Closer transit stops Shorter travel time Tax deductions on transit pass Better transit service Gaurunteed ride home Carpool programs ST funding preference Parking fees - Agree Parking fees - Disagree External grant - Agree External grant - Disagree Base budget allocation - Agree Base budget allocation - Disagree No funding - Agree No funding - Disagree Carpooling program Yes No Not sure Not applicable

Student Program Preference Data:

72.3 27.7 53.6 46.4 47.4 52.6 3.7 96.3 9.5 12.4 11.7 66.4

3.57 3.54 3.78 3.63 3.69 3.66 3.67 3.53 3.73 3.55 3.49 894 342 662 574 586 650 46 1190 117 153 145 821

Sample n (%)

Student program preferences results

Figure 3.16

25 43 69 4

82 59 82 59 50 91 12 129

3.54 3.44 3.57 3.60 3.69 3.54 3.73 3.53 3.78 3.56 3.24

17.7 30.5 48.9 2.8

58.2 41.8 58.2 41.8 35.5 64.5 8.5 91.5

by modal choice SOV n (%)

37 64 27 511

470 169 327 312 288 351 19 620

3.62 3.63 3.93 3.79 3.71 3.74 3.63 3.61 3.82 3.73 3.66

Walk

5.8 10.0 4.2 80.0

73.6 26.4 51.2 48.8 45.1 54.9 3.0 97.0

(%)

5 6 1 46

51 7 31 27 44 14 1 57

3.65 3.48 4.00 3.75 3.65 3.57 3.63 3.24 3.76 3.52 3.42

Bike

8.6 10.3 1.7 79.3

87.9 12.1 53.4 46.6 75.9 24.1 1.7 98.3

(%)

32 29 38 210

228 81 172 137 167 142 9 300

3.72 3.53 3.86 3.79 3.83 3.93 3.77 3.66 3.71 3.67 3.48

10.4 9.4 12.3 68.0

73.8 26.2 55.7 44.3 54.0 46.0 2.9 97.1

Transit n (%)

14 8 6 30

42 16 36 22 23 35 4 54

3.60 3.64 3.80 3.61 3.72 3.83 3.84 3.50 3.82 3.76 3.36

24.1 13.8 10.3 51.7

72.4 27.6 62.1 37.9 39.7 60.3 6.9 93.1

Carpool n (%)

80 119 94 758

769 282 563 488 512 539 34 1017

3.58 3.53 3.80 3.64 3.69 3.66 3.65 3.53 3.71 3.53 3.51

7.6 11.3 8.9 72.1

73.2 26.8 53.6 46.4 48.7 51.3 3.2 96.8

by distance Near Zone n (%)

37 34 51 61

124 59 99 84 72 111 12 171

3.53 3.61 3.64 3.52 3.68 3.66 3.79 3.50 3.79 3.63 3.33

20.2 18.6 27.9 33.3

67.8 32.2 54.1 45.9 39.3 60.7 6.6 93.4

Far Zone n (%)

114

References