Conference of the International Journal of
Arts & Sciences
Boston (June 2009) Volume 1, Number 20 CD-ROM ISSN: 1943-6114 CopyrightÂŠ 2009 IJAS http://www.InternationalJournal.org
Conference of the International Journal of Arts & Sciences Vol. 1 (20) - Boston (June 2009) ISSN: 1943-6114 CD-ROM. Pages: 1-17
Gridlock: Turning Solo Drivers into Public Transit Users Alexander Heil, Merrimack College, United States Stuart Cole, University of Glamorgan, Wales
Abstract As a result of a shift towards solo car commuting in the United States, congestion on major urban highways has become an increasing problem leading to considerable private, social and environmental costs. Nationwide, total annual congestion costs in the U.S., measured by travel time delays and wasted gasoline, increased from $14 to $78 billion over the last 20 years (Schrank and Lomax 2007). In addition, significant costs are imposed by environmental damage including noise and air pollution. A comprehensive policy response needs to address this unsustainable situation, but in order to design and develop effective transport policies, the root causes of this problem need to be understood. An analysis of decision-making factors and their relative importance for peak period commuters was conducted by using Boston, Massachusetts as the case study. The results of the calibrated multinomial logit model identified the availability and cost of parking as the most statistically significant variable in affecting mode choice behavior. Keywords: gridlock, traffic, congestion, highways
Introduction Congestion on major highways is a growing problem with considerable social costs in air pollution, noise, lost time, and traffic crashes. Over the last 20 years, vehicle miles traveled for passenger cars have increased 50 percent, while route miles constructed have increased only 3 percent. In 2003, a peak-period trip took 40 percent longer than the same trip during nonpeak hours. Almost 60 percent of major roads in urban areas were congested during peak periods. The average length of the peak period in heavily congested urban areas was 7.1 hours in 2003; it was just 4.5 hours in 1982 (Cambridge Systematics 2004). Drivers were delayed 4.2 billion hours due to congestion in 2005, with a combined cost (fuel and time) of $78.2 billion (Schrank & Lomax 2007). Gaining a more complete understanding of the root causes of the problem will help transportation decision-makers develop a comprehensive policy response to address these trends. For example, although recreational driving and errands are key contributors to the growing congestion problem, another major factor is the prevailing use of automobiles to travel to and from work. Based on U.S. census data, the number of solo car commutersâ€” people driving aloneâ€”increased from 73.2 percent of the total commuting population in 1990 to 75.7 percent in 2000, as illustrated in Figure 1.
Figure 1: Modal Split Comparison (2000 US Census Data) 80.00%
30.00% 20.00% 12.20% 9%
0.00% Drive alone
Work from home
Even though large metropolitan areas such as Boston and New York City have a greater share of public transit users than the national average (Downs 2004), data on Boston commuter patterns reveal that the city still follows the national trend of increased solo car commuting. In fact, the modal share of commuting via single-occupant vehicle in the Boston metropolitan area has increased by about 10 percent over the last 20 years. The trend of different transportation modes is shown in Table 1. Table 1: Greater Boston Modal Shares (Percentage) Year Driving Alone Carpool Public Transit Working at Home Other Total
1980 54.5 16.8 16.9 1.4 10.4 100.0
*Total for 2000 is rounded off. Source: Goodman & Narkosteen 2004
1990 63.4 9.8 15.9 2.6 8.3 100.0
2000 63.9 8.4 16.9 3.4 7.5 100.0*
Boston was selected as the case study for this research because of transportation characteristics such as a functioning and extensive public transit system, increasing highway congestion, and a limited ability to expand the existing highway network. Congestion and appropriate policy responses are a growing concern and the researchers regarded Boston as a valuable case study of commuter modal choices and the underlying variables in that decisionmaking. The purpose of the study was to understand the decision-making variables that peakperiod commuters consider and the relative importance of those variables. The researchers found that radial commuters, who travel from the periphery of the city to its center, are more likely and amenable to using public transportation than are orbital commuters, who travel from suburb to suburb outside the city center. Orbital commuters are more resistant to using transit, largely because of the easy availability of parking for them and lack of feasible alternatives to driving. It is likely that similar patterns exist in other U.S. cities that have radial and orbital commuter flows comparable to the Boston area. With suburbanization prevalent in many metropolitan areas, the researchers expect this trend to increase and therefore further contribute to congestion on U.S. highways. Cities that actively attempt to intervene through policy approaches need to be aware of the important variables involved in modal choices and how those can differ depending on the selected route.
Literature Review An extensive body of research exists that addresses commuter behavior and modal choice situations. A limited selection of these studies has been reviewed in order to understand the variables or decision-making factors that have been shown to be significant. Curtis and Headicar (1997) pointed out that age is one of the main determinants in the choice of commuters to use cars rather than public transportation. In particular, their study looked at the behavior that leads to car dependency and how people can be influenced to switch from car usage to other modes of transportation. Based on data collected in Oxford, they found that the individual most likely to switch is a male in his 30s, who (most importantly,) undertakes a short commute of 5 miles or less. Part-time employees seemed to be less likely to consider changing from the use of the car to the use of public transportation. The authors identified convenience and doubts about public transportation as the main deterrents for a switch. Additionally, it was found that 92 percent of the solo car commuters had free parking available to them, mostly provided by the employer. Trip scheduling was the focus of Small (1982) and his study. The study identified factors such as family status, occupation, and employersâ€™ policies towards work-hour flexibility as the main determinants for commuter decision-making. Small concluded in his research that different trade-off behavior will be found within different commuter groups. Travel modal choice has also been analysed in light of human habit-forming behavior. Aarts (1996) pointed out that mode decisions are made by weighing pros and cons of an alternative,
while not failing to consider peopleâ€™s habits. Since commuting is a repetitive activity, the research concluded that individuals are led by prior decisions and consequently invest less and less time and complex mental strategies, in making commuting decisions. Gaerling (1998) expanded on the concept of commuting as an example of repetitive behavior. He described commuting as an example of automated behavior, making preceding deliberate decisions about mode or route choice virtually irrelevant. Wilson and Shoup (1990) reviewed the relevant literature on parking and its effect on car commuting. Their review demonstrated the strong positive relationship between employer provided parking and solo commuting by car. The effect was measured by looking at (1) the share of commuters who drive to work alone; (2) the number of cars driven per 100 employees; and (3) parking price elasticity of demand. The authors conducted comparisons of programs that included the introduction or elimination of parking charges or employer subsidies and the provision of parking benefits and subsidies by private employers. They found that an elimination of subsidies caused a 19-81 percent reduction in the number of solo drivers. Parking and its subsidised or free provision to employees by employers has consistently been identified as one of the key issues affecting commuter modal choice decision. Shoup and Pickrell (1980), estimated that 93 percent of all auto commuters did not pay for parking themselves. Shoup (1995) pointed out that based on data in the 1990 Nationwide Personal Transportation Survey, more than 90 percent of car commuters park free of charge at their work location. It was estimated that approximately two-thirds of all peak period commuters receive a parking subsidy which could cover the entire cost of parking. Eliminating these parking subsidies shifts the cost from the employer to the individual. Shoup estimated that, based on data collected in Los Angeles, Washington D.C. and Ottawa, the modal share of individual car commuters would decline by 25 percent when employer contributions cease to exist;in the study, the average share of individual solo driving fell from 67 percent to 42 percent. When considering all vehicles and not just solo driving, the impact was slightly less than 20 percent. Dasgupta et al. (1994) analyzed data from five British cities. In all five cases, parking charges in the Central Business District (CBD) were doubled. On average, trips by car decreased by 17 percent while bus and rail or walking increased by 10 percent. Again, parking appeared to have a very strong impact on the modal choice behavior of commuters. In a study for the Montgomery County Department of Public Works and Transportation (Potomac Survey Research for Montgomery County 2000) the behavior of single occupancy vehicle commuters was investigated. Essentially, the study attempted to find out which travel demand measures might persuade car commuters to choose alternative transportation means. The study found that even though congestion was perceived as a major problem, car commuters were still unlikely to shift mode because of the convenience, freedom and flexibility that the car provides. In addition, the cost of this mode of transportation (at least the sum of all out of pocket expenses) was regarded as significantly lower than any other
modal choice. The investigation also pointed out that one of the main reasons why car travel is so attractive is the free or subsidised parking options that are provided by employers in the area. Hess (2001) investigated the effect that free parking had on commuter modal choice decisions in Portland, Oregon. The study estimated a multinomial logit model using three overall categories of variables: price variables, land use variables, and household resource and taste variables. The model strongly predicted that by raising the parking cost paid by the employee, as well as improving service quality of the public transit system, the share of solo car commuters could be significantly reduced. Cervero (1990) analyzed in detail the responsiveness of transit users to changes in fares, service, quality and cross-price factors. Riders were relatively insensitive to changes in fares, the structure of fares, e.g.differentiated pricing in different zones, or pricing by length of trip or time of day. However, it seemed that public transport users were sensitive to a change in service quality (which might include time or frequency of trains, depending on the definition). The study also found that cross-price elasticities of fares on car ridership were negligible. In general, responsiveness among public transport users was disparate between different groups, differentiated by socio-economic characteristics or type of service, but similar within their own group. Goodwin (1993) stated that transit policies such as frequency changes, quality of service or, to a certain extent, even price, affect transit use and related modal choice. In this sense, transport pricing policies such as fares, associated parking along commuter rail systems or transit densities and frequencies affect commuters. Goodwin (1993) studied six surveys undertaken in Yorkshire since 1972 and pointed out that public transport had an influence on car ownership. This was especially the case in households with multiple vehicles. The quality of public transport service was a direct factor explaining these variations and impacted the usage of transit service. It is likely that different groups of commuters separated by their incomes will show different behavioral decision-making patterns. Vickrey (1969) described this issue by distinguishing between the high value and low value user of transport infrastructure. On the other hand, different income groups also showed different price and cross-price elasticities of demand. Bhattachearjee et al. (1997) undertook a study dealing with commutersâ€™ responses to travel demand measures in Bangkok. The author calibrated an ordered probit model based on data that was collected using survey responses to Likert scales from Bangkok commuters. The study showed that based on separate income groups, reactions differed considerably across the range of individuals commuting to work. Huang et al. (2000) investigated the economics of carpools and which factors and variables might primarily affect commuters who chose this transport mode. Based on logit-based models, the study found that fuel costs, value of time, preferences, and traffic congestion affected this modal choice most strongly.
Gomez-Ibanez and Fauth (1980) studied the impact of auto restraint policies in downtown Boston and developed a set of proposed congestion reducing transport-pricing policies. The authors identified road pricing as one of the more effective traffic demand management tools available to planners. Pricing was identified as a way to alter decision-making. The authors identified the problem of increasing congestion and stated the following three types of policies to curb congestion: (1) a total access ban (for cars) to the CBD; (2) a reduction of the supply or an increase in the price of parking; and (3) congestion tolls and area licenses. The aim of their study was to predict the impact of each of these policy tools on Boston commuters. Based on the study, area licenses, or charging drivers for access to a section of the urban area, were seen as having the greatest impact on commuter behavior.
Boston Commuter Choices In 2005, the researchers collected data from Boston-area commuters in regard to their modal choice decisions. After an initial pilot survey, the data collection relied on a Web-based questionnaire distributed to commuters in the area. The questionnaire queried travelers on their socioeconomic characteristics, their modal choices, and several aspects of travel choices such as travel time and cost, plus preference statements regarding the attractiveness of various travel options. The modal choice set consisted of theseoptions: • Travel by car • Combined travel by car and Massachusetts Bay Transportation Authority (MBTA) • Travel by MBTA only • Carpooling or vanpooling • Walking • Travel by bicycle In addition to general modal choices, the researchers observed four distinct travel patterns in the commuters’ responses based on the commuters’ place of work and residential location: • Radial commuting into the central business district (“Radial”) • Reverse radial commuting out of the city (“Reverse”) • Orbital commuting from suburb to suburb (“Orbital”) • Central commuting within the central business district (“Central”) The modal choice data broken down by route or travel pattern is shown in Table 2. Approximately 50 percent of all commuters travelled alone to work. The share of solo drivers exceeded 90 percent for orbital commuters but fell below 30 percent for radial travelers.
Table 2: Modal Choice Summary by Route Count
Radial Car (drive alone) MBTA Car & MBTA Other (carpool, vanpool, etc.) Subtotal
57 42 72 33 204
28 21 35 16 100
Orbital Car (drive alone) MBTA Car & MBTA Other (carpool, vanpool, etc.) Subtotal
155 2 1 12 170
91 1 1 7 100
Central Car (drive alone) MBTA Car & MBTA Other (carpool, vanpool, etc.) Subtotal
24 43 8 26 101
24 43 8 26 100*
Reverse Car (drive alone) MBTA Car & MBTA Other (carpool, vanpool, etc.) Subtotal
18 7 1 -26
69 27 4 0 100
Total Group Car (drive alone) MBTA Car & MBTA Other (carpool, vanpool, etc.) TOTAL
254 94 82 71 501
51 19 16 14 100
*Subtotal is rounded off. Note: Bicycling, walking, and all vehicle pooling alternatives are combined in “Other.”
The data was collapsed to account for Central Business District (CBD) and non-CBD commuters. A CBD commuter was defined as an individual who begins or ends his or her journey in the CBD. Consistent with Table 2 and repeated in collapsed form in Table 3,
roughly 50 percent of the sampled commuters drove solo when traveling to and from work. Another 16 percent used a combination of cars and MBTA, while approximately 19 percent used public transportation for the entire journey. The percentage shares for carpooling, vanpooling, bicycling, and walking were all less than 10 percent each for all modes and destinations except pooling within the central business district. Table 3: Destination of Commute and Modal Choice (By Commuters) Modal Choice Bicycle Car (drive alone) Car & MBTA MBTA Pooling Walking to work Total
Non-CBD 2 173 2 9 8 2 196
CBD 5 81 81 85 36 17 305
Total 7 254 83 94 44 19 501
% of Total 1.4 50.7 16.6 18.8 8.8 3.8 100.0*
*Total is rounded off. CBD = Bostonâ€™s central business district
Commuters with higher incomes traveled longer distances and allocated larger budgets to their travel as can be seen in Table 4. Regardless of income, all licensed drivers reported having access to a car for their commutes to and from work. Table 4: Commuter Profile by Modal Choice Modal Share By %
Bicycle Car (drive alone) Car & MBTA MBTA Pooling Walking to work Total
1 51 17 19 9 4 100%*
Average Travel Time (min) 20.4 33.5 51.8 35.2 50.0 15.4 37.4
Average Annual Household Income $90,000 $87,195 $95,380 $66,017 $99,226 $93,529 $86,059
Monthly Budget spent on Commute $37.93 $136.67 $160.39 $49.46 $180.40 $5.00 $121.70
Average Budget As % of Monthly Income 0.5 1.9 2.0 0.9 2.2 0.1 1.7%
Average One-Way Distance (miles) 4.34 15.90 22.34 7.07 31.92 1.88 16.02
Average # of Cars
Average Household Size
2.43 2.98 2.84 2.29 2.84 1.84 2.77
3.86 3.52 3.51 3.13 3.50 2.74 3.42
*Total percentage is rounded off. Note: Total average income of $86,059 is the result of computation based on the total data set and cannot be recalculated with data in the table because of missing income information in each modal choice category.
The researchers disaggregated the data into commuter subsets. This information is provided in Table 5. Radial commuters, those who drive back and forth between Boston proper and the suburbs, were divided relatively evenly within the various transportation alternatives. On the other hand, orbital commuters, those who live and work in the suburbs and travel in arcs
between them, were predominantly car users, and solo drivers at that. Their extensive car use required parking. Sixty-eight percent of all commuters in the sample (orbital and radial) stated that they needed to park their cars at some point during their travel. Approximately 72 percent of this group had employer-provided parking available to them. For 96 percent of those individuals, parking was supplied at no direct cost to the commuter. Overall, approximately 70 percent of all commuters who parked their cars during their commute did so free of charge. Table 5: Commuter Summary by Route Choice
Route Type By %
Average Travel Time (min)
Average Annual Household Income
Monthly Budget For Commute
Average % of Monthly Income
Average One-Way Distance (Miles)
Average # of Cars
Average Household Size
Radial Orbital Central Reverse Total
41 34 20 5 100%
49.5 31.8 23.2 34.6 37.4
$89,885 $88,665 $76,763 $74,600 $86,059
$161.14 $113.31 $64.32 $90.18 $121.70
2.15 1.53 1.01 1.45 1.70%
22.0 16.7 3.5 12.9 16.0
2.9 3.04 2.15 2.38 2.77
3.5 3.7 2.9 3.1 3.4
Note: Total average income of $86,059 is the result of computation based on the total data set and cannot be re-calculated with data in the table because of missing income information in each route choice category.
The collected data also contained information on the relative importance of different transportation mode characateristics such as travel cost, the ability to engage in activities while traveling, or congestion. Figure 2 shows this commuter preference data. Overall, 90 percent of the respondents ranked travel time as important or very important in deciding which travel mode to use. This percentage overshadowed all other decision-making factors, especially any service quality issues associated with different modes. More than 70 percent of commuters cited monetary cost as important or very important. Figure 2: Factors and their relative importance in modal choice Use cell phone Exercise Read Newspaper Privacy Environmental concern Running errands Bad weather Personal Safety Total travel cost C ongestion Predictability of travel time Travel time 0 20 40 60 80 100 Percent (Important & Rather Important)
Modal Choice Analysis The researchers used the sample data to calibrate a multinomial logit modal choice model. The model was set up to include the six travel choices as outlined on page nine above. Independent variables included a variety of quantitative factors such as travel time and travel cost and qualitative factors such as environmental concerns, the ability to read while traveling, car availability, and destination of the commute. The model was structured to break out the four different route distinctions without setting up separate logit model calibrations. In the statistical analysis, the significant variables were found to be travel time, car availability, destination of the commute, and parking availability at a significance level of 0.05. For solo car commuters, job location and parking carried the most significance. Indeed, car commuters were six to nine times more likely to drive alone to work than use public transit when they had an orbital commute and parking was available. For car- or vanpooling commuters, employer-provided parking made them six times more likely to use pooling arrangements than to use public transit exclusively. Overall, parking availability was by far the most significant variable in the analysis. The model indicated that changes in this variable could affect commuter choices significantly. As a result, when forecasting the effects of eliminating subsidized or free parking, the model predicts a nearly 16-percent reduction in solo car commuting, with drivers shifting to a combination of public transportation and cars, as shown in Table 6. Table 6: Model-Predicted Travel Mode Shares Modal Choice
Predicted Modal Shares (Base Case)
Predicted Modal Shares (No Free Parking)
Change In # of Commuters
Change In % of Commuters
Change In % of Modal Split
Car Car/MBTA Pooling MBTA TOTAL
258 80 6 96 440
217 119 6 98 440
−41 39 0 2
−15.9 48.8 0.0 2.1
−9.3 8.8 0.0 0.5
58.6% 18.2% 1.4% 21.8% 100.0%
49.3% 27.0% 1.4% 22.3% 100.0%
Further breaking down the results, for orbital commuters parking was the most significant issue in modal choice, but its availability rather than its cost may be paramount. Table 7 shows the effects of eliminating all employer-provided free parking. Indeed, based on the calibrated choice model, and assuming that all free or subsidized parking were eliminated and replaced with parking at a cost, only a small share of orbital commuters actually are predicted to change their modal choice. This unresponsiveness to cost might be the result of a lack of public transit alternatives, the length of a person’s commute, the relatively high household income, or the overall perceived convenience of solo automobile driving.
For radial commuters, the statistical relationships were not as strong, as their choices were based on a variety of factors, all of relatively equal statistical significance. However, based on the calibrated choice model, radial commuters also showed some sensitivity to parking as a main policy variable. Eliminating parking availability with subsidized rates caused nearly 20 percent of them to shift to MBTA/car use as their modal choice. Table 7: Effect of Eliminating Employer-Provided Free or subsidized Parking Mode Choice Car Car/MBTA Pooling MBTA
Change In # of Commuters −41 39 0 2
−4 1 0 3
−38 38 0 0
In summary, the calibrated mode choice model based on the collected data suggests parking as the main policy variable if the intent is to alter solo automobile use. Other variables carry some significance but are not as effective in changing commuter mode choices. Policy recommendations, as outlined in Sections 3.1 and 3.2, therefore need to be based on the relative importance of parking cost and availability. However, significant differences have been documented for both orbital and radial commuters, which necessitates the differentiation of policy recommendations for these two groups of commuters.
Policy Recommendations for Orbital Commutes The researchers identified two main problems complicating the objective of improving orbital congestion around Boston’s central business district. First, MBTA services do not appear to work as well for commuters who need to travel circumferentially. Even though it may be possible to complete the trip by first entering the district, transferring, and then traveling back out to the suburbs on a different line, the process may prove cost prohibitive, not to mention inconvenient, especially in a market where time is valued highly. Second, employers along the periphery generally have space at their disposal and therefore make parking available to their employees at highly subsidized rates. These findings suggest a three-phased policy approach. Modification of current parking policies. Setting higher prices for all parking spaces appears to be a first step toward causing a change in behavior, at least for radial commuters. The predominant system of receiving free or subsidized parking is a form of compensation to an employee and the competition for employees strengthens the use of such non-salary compensation tools. An alternative might be to allow employees to cash out their parking benefits in order to reallocate their funds by receiving other employee benefits or higher compensation.
However, from a political perspective, the adoption of higher parking charges could be regarded as inequitable because it creates an undue financial burden on orbital commuters who have no alternative means of traveling to work and who settled in the suburbs not realizing the additional cost burden. The analyzed data suggest that orbital commuters are unlikely to be influenced by changes in parking costs and availability. Any modified parking policy should include other strategies that account for the lack of orbital public transit options. Creation of orbital park-and-ride systems. When parking at the workplace is discouraged, an incentive is created for commuters to select an alternative mode of travel. With no access to public transport, a favored mode for orbital commuters is a car- or vanpool. To facilitate growth in the use of car- and vanpools, orbital park-and-ride systems could be set up that enable commuters to park their vehicles in large garages and then share vehicles from there. Parking at these facilities could be free of charge as a further incentive. Several large parking garages could be placed along the Iâ€“95/128 and Iâ€“495 ring roads, preferably at their connections with major highways in the area, such as the Massachusetts Turnpike. The funding and operation of the park-and-ride system would have to fall within the responsibility of the Commonwealth of Massachusetts or the local metropolitan planning organization. Congestion is a regional problem and therefore should be addressed by a similarly regional planning and policy approach. Orbital public transit. The introduction of a parking fees or taxes, with funds to be collected by the local governments, might generate enough cash flow, even after deducting administrative costs, to fund planning and construction of a public transit alternative. Expanding the public transportation system with an express bus service could cover the orbital ring roads and use the current subway and commuter rail stations as start and end points. Because of the use of existing infrastructure, the most efficient option would likely be the MBTA running and operating the orbital bus system. An advantage to using existing infrastructure is the avoidance of potentially prohibitive startup and investment costs. Still, transferring to local transportation at the end points or stops of such a bus system would remain a problem because only a few communities have independent local bus networks. At the end points of the express lines, buses could fan out into the immediate area around the station to increase accessibility. For stations along the orbital bus line, making parking and bicycle stands available potentially could provide incentives for travelers to use the newly created public transit option. In addition, economic incentives in the form of subsidies or tax advantages potentially could provide employers with a motive to create their own shuttle service between the orbital bus stops and office locations. Potential pitfalls of the overall approach might not be as great as they appear on first blush. For instance, public acceptance of parking charges would be increased by showing that these funds are used to improve other transportation modes, and in turn pointing to a reduction in congestion, instead of merely becoming a general revenue source for local governments. The current economic recession and financial problems faced by many households is nevertheless a reason for households to be skeptical of any newly introduced charges.
Also, capitalizing on existing infrastructure would lessen the impact on MBTA of the investment in orbital options. Hence, fares for commuters traveling radially could be left unaffected by this expansion, minimizing perceived fairness or equity issues. All three strategies would need to be started and realized at about the same time, as commuters and employers would be less willing to pay parking fees if progress is slow on establishing feasible transportation alternatives.
Policy Recommendations for Radial Commutes Decision-making by radial commuters is driven by different factors than circumferentially traveling individuals. Understanding those differences is important for developing a set of recommendations for radial commutes. Improvement of MBTA service frequency and quality. Travel time was identified as one of the key factors in decision-making by radial commuters, whereas service quality was considered less important. The frequency of MBTA service, especially during rush hours, is one of the variables that can be altered through policy. Increasing the frequency would cut travel times by reducing the waiting times. Consequently, such a change in operations also would decrease the total generalized cost, or the combination of monetary cost and travel time, of this modal choice for commuters. The increased frequency could be combined with added train capacity, which provides greater comfort and ability to engage in other activities during the commute. Whereas both of these changes are valid, they might be unrealistic in the climate of budget shortfalls and deficits faced by public transit agencies such as the MBTA. The frequency of trains might be more problematic and expensive for the MBTA than increased train capacity, assuming that additions in capacity could be achieved by using existing rolling stock and not require the acquisition of additional rail cars. Current policy for commuter rail operations calls for two trains per hour during peak travel times, dropping to one per hour for most of the day. However, altering this would mean a significant increase in capital and operating expenses. In fact, doubling the frequency of commuter rail operations would add to budgetary pressures, and a fare increase to mitigate the costs clearly would be counterproductive to making the train system more attractive. Expansion of MBTA parking and pricing. Inadequate parking facilities at many MBTA stations create a bottleneck in expanding public transit as an alternative to solo car commuting. An immediate investment in added parking capacity, at least at the large feeder stations at the end of the red, green, blue, and orange lines, should be considered in concert with increased quality of service and trip frequency. Reducing parking fees at most stations also might be worthwhile. The data show that commuters consider monetary expense an important factor in their selection of travel mode.
Low parking fees could enable MBTA to remain competitive with car use, at least in financial terms. Allow solo drivers to use HOV lanes for a fee. Existing high occupancy vehicle (HOV) lanes could be made accessible to solo drivers for a fee, at least relieving congestion if not actually promoting public transit or reducing the environmental impacts from solo driving. Tolls would have to be collected electronically so that traffic flow would not be disrupted, by using, for example, FASTLANE toll transponders currently used on the Massachusetts Turnpike. New high occupancy toll lanes, with access for solo drivers, could be added to the current toll system on the Tobin Memorial Bridge, the harbor tunnels, and the Massachusetts Turnpike. The revenue generated by this approach could be used to improve the road infrastructure and HOV lane design. In addition, it could contribute to some of the public transit improvements outlined above; however, cross-subsidization is subject to limitation by federal and local legislation. Important to note is that current HOV lanes do not have much excess capacity and simply may be unable to take additional vehicles, paying or not. Elimination of parking spaces and introduction of parking fees or taxes. Similar to proposed orbital policy, a radial strategy that includes parking management should address both the availability and price of parking at existing infrastructure. Eliminating some parking spaces and hiking the costs of others would squeeze some commuters from private car use to public transportation to minimize their expenses. The study found that, faced with less inviting parking prospects, more commuters will opt to use MBTA in conjunction with their cars. Similar to the orbital approach, the revenue collected from radial commuters could add to investment in public transit alternatives or park-and-ride and vehicle-pooling strategies. Again, diverting the revenue to such improvements would mitigate some of the financial impacts.
Conclusions The analysis found that eliminating free or subsidized parking would shift solo car commuters toward public transportation. In particular, such a change was found to reduce the number of solo car commuters overall by 16 percent, with reductions of approximately 3 percent and 20 percent for orbital and radial commuters, respectively. As a result, the solo car modal share was estimated to be reduced by approximately 9 percent. The changes are almost exclusively caused by radial commuters adjusting their behavior, suggesting that the issue of congestion among orbital commuting paths may be tougher to resolve. Regarding orbital commuters, modifying existing parking management approaches, providing park-and-ride schemes, and creating an MBTA public transport option offer promise. For radial commuters, improving MBTA service quality and frequency, expanding
MBTA parking facilities, allowing paid access to HOV lanes for solo drivers, and actively managing parking inventories in downtown Boston may be most influential policy changes. Congestion as a policy issue is receiving more and more attention worldwide. Cities have begun to experiment with strategies that induce commuters to switch from cars to more sustainable forms of transportation. The data suggest that policies directed at parking options, travel time, and specifically orbital commuting alternatives might be appropriate in addressing local congestion issues. As a final thought, the current economic recession may alter economic decision-making and cause commuters to select their transportation mode more based on cost and other financial aspects. Additionally, changes in employment, real estate prices, and other economic variables could structurally alter the decision-making described in this study. Future research will be required to assess the impact of the recession on commuter mode choices.
References Aarts, H. (1996). Habit and Decision Making: The Case of travel mode choice, Published Dissertation, Katolieke Universiteit Nijmegen, University Press. Bhattacharjee, D., Haider, S. W., Tanaboriboon, Y. and Sinha, K. C. (1997). Commuters' attitudes towards travel demand management in Bangkok, Transport Policy, vol. 4, no. 3, 161-170 Cambridge Systematics (2004). Traffic Congestion and Reliability: Linking Solutions to Problems, Prepared with Texas Transportation Institute for the Federal Highway Department. Cervero, R. (1990). Transit pricing research: A review and synthesis, Transportation, vol. 17, 117-139. Curtis, C. and Headicar, P. (1997). Targeting travel awareness campaigns: Which individuals are more likely to switch from car to other transport for the journey to work?, Transport Policy, vol. 4, no. 1, 57-65 Dasgupta, M., Oldfield, R., Sharman, K., and Webster, V. (1994). Impact of Transport Policies in Five Cities, Project Report 107, Transport Research Laboratory, Crowthorne, Berkshire. Downs, A. (2004). Still Stuck in Traffic: Coping with peak hour traffic congestion, The Brooking Institution, Washington DC and The Lincoln Institute of Land Policy, Cambridge. Gaerling, T. (1998). Behavioural Assumptions Overlooked in Travel-Choice Modelling, in: de Dios Ortuzar, J., Hensher, D. and Jara-Diaz, S. (eds.) Travel Behaviour Research: Updating the State of Play, Oxford: Elsevier Science Ltd.
Gomez-Ibanez, J. and Fauth, G. R. (1998). Downtown Auto Restraint Policies: The costs and benefits for Boston, in Button, K. J. and Stough, R., (eds.), Transport Policy, The International Library of Comparative Politics, Edward Elgar Publishing Ltd. Goodman, M., Dana, A. and Nakosteen, R. (2004). MASS.commuting, MassInc. Boston: The Massachusetts Institute for a New Commonwealth. Goodwin, P.B. (1993). Car ownership and public transport use: Revisiting the interaction, Transportation, vol. 27, 21-33. Hess, D. B. (2002). The effects of free parking on commuter mode choice: Evidence from Travel Diary Data, Working Paper #34, The Ralph and Goldy Lewis Center for Regional Policy Studies, UCLA, Los Angeles, CA, Extracted on January 23, 2004 from: URL: http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1015andcontext=lewis. Huang, H.-J., Yang, H. and Bell, M. G. H. (2000). The models and economics of carpools, The Annals of Regional Science, vol. 34, 55-68. Potomac Survey Research for Montgomery County (2000). Commuting Behaviour Research in Bethesda and Friendship Heights, MD, Department of Public Works and Transportation, Division of Transit Services, Extracted on 15th July 2005 from URL: http://www.wageworks.com. Schrank, D. and Lomax, T. (2007). The 2007 Urban Mobility Report, Texas Transportation Institute. Shoup D and Pickrell, D. (1980). Free Parking as a Transportation Problem, Washington, DC: US Department of Transportation. Shoup, D. (1995). An Opportunity to Reduce Minimum Parking Requirements, Journal of the American Planning Association, vol. 61, no. 1, 14-28. Small, K. A. (1994). The Scheduling of Consumer Activities: Work Trips, American Economic Review, 72(3) June, 467-79, in Mohring, H. (1982) (ed.) The Economics of Transport Vol.I, Edward Elgar Publishing Ltd., 363-378. US Census (1999). United States Department of the Census, Geographic Changes for Census 2000 and Glossary, extracted from the website: http://www.census.gov. US Census (2000). United States Department of Census, United States Census Data, extracted from the website: http://www.census.gov.
Vickrey, W. S. (1960). Congestion Theory and Transport Investment, American Economic Review LIX(2) May 251-60, in Mohring, Herbert (ed.) (1994) The Economics of Transport, vol. I, 353-362. Wilson, R. and Shoup, D. C. (1990). Parking subsidies and travel choices: Assessing the evidence, Transportation, vol. 17, 141-157.
Boston (June 2009) Volume 1, Number 20 CD-ROM ISSN: 1943-6114 CopyrightÂŠ 2009 IJAS http://www.InternationalJournal.org
Sample of paper published in the conference proceedings' CD-ROM. Each IJAS formatted submission is also individually posted on the internet,...
Published on Nov 24, 2009
Sample of paper published in the conference proceedings' CD-ROM. Each IJAS formatted submission is also individually posted on the internet,...