Correlation Between Street Design Features and Bicyclist Safety in Kings County, New York

Correlation Between Street Design Features and Bicyclist Safety in Kings County, New York

AP Research May 19, 2020 Word Count: 4735

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Introduction About 48,800 New Yorkers rely on bicycles and the city’s bike lanes every day to commute throughout the city according to reports from the New York City Department of Transportation.​1 To accommodate for this, the New York City Department of Transportation has built and maintained a system of bike lanes throughout the city’s five boroughs. The implementation of these bike lanes has shown to increase bicyclists’ safety. A study published in the American Journal of Public Health on the change in car-bicycle accidents showed that the implementation of bike lanes in New York City contributed to a significant decrease in bicyclist-related accidents along streets that featured newly created bike lanes.​2 However, this has not stopped traffic incidents involving cyclists to occur, as in 2016, 2017, and 2018 alone, 4,597, 4,411, and 4,305 injuries involving cyclists occurred in each year, respectively.​3 Although New York City's extensive bike lane system has clearly made an effort to make bicycling safer, more could be done to further reduce incidents involving bicyclists within New York City.

In addition to the thousands that rely on bicycling as a means of transportation, prior research depicts bicycling to have medical benefits, providing bicyclists with an outlet for exercise. According to a study published by the journal Environmental Health Perspectives, greater use of modes of transportation such as bicycles has been associated with environmental, health, and equity benefits, as they provide for an affordable means of transportation for 1. Macmillan, Alexandra, Jennie Connor, Karen Witten, Robin Kearns, David Rees, and Alistair Woodward, ​The Societal Costs and Benefits of Commuter Bicycling: Simulating the Effects of Specific Policies Using System Dynamics Modeling​, (Auckland, NZ: Environmental Health Perspectives, 2014), 335.

2. Macmillian, 335.

3. Lesser, Chris, ​NYC: Media Hypes Bike Lane Backlash, ​(New York, NY: Bicycle Retailer & Industry News, 2011) 1.

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commuters that also has an extremely small environmental footprint while also reducing congestion in cities.​4 The aforementioned study also found that the presence of vehicular commuters within cities has been associated with greater greenhouse gas emissions, noise pollution, and stress among residents, as when compared to those who arrived at work via a vehicle, those who commuted to work bicycling had experienced fewer work-family conflicts and fatigue.​5 In addition to these benefits as motivators, the residents of New York City have expressed their support as a poll conducted by Quinnipiac University in New York City found that more than half of all New Yorkers supported the creation and existence of bike lanes.​6 With this information, one may find it is apparent that the institution of bicyclist-friendly policies is beneficial to cities and their residents and numerous ways.

4. New York City Department of Transportation, ​Cycling in the City,​ (New York, NY: the Official Website of the City of New York, 2019), 4.

5. Chen, Li, Cynthia Chen, Raghavan Srinivasan, Claire E. McKnight, Reid Ewing, and Matthew Roe, ​Evaluating the Safety Effects of Bicycle Lanes in New York City, (​ New York, NY: American Journal of Public Health, 2012), 1120.

​6. ​Vision Zero View​, (New York, NY: New York City Department of Transportation, 2020).

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Literature Review History of Bike Lanes in North America Bicycling has been a large part of American life throughout history. Going back to the late 19th century, the popularity of bicycling among Americans could be seen with a surge in support for better roads (including side paths, or dedicated bicycle trails) by elite organizations that were fueled by upper-class Americans’ desire to fight against rural opposition to road improvements.​7 Support can also be seen more recently with “the bicycle boom of the 1970s [which] had revived unresolved conflicts over sharing the public road.”​8 The popularity of biking among Americans today was illustrated in a study conducted by the Green Lane Project that found that “60% of Americans say they would bike more often if they had a safe place, such as a green lane, to ride.”​9 Thus, seeing the popularity of bicycling both throughout history and in modern times, it is clear that bicycles play an extremely large role in moving people throughout cities on a daily basis. Additionally, support for bicyclist friendly infrastructure from cities was a result of the positive changes in neighborhoods brought by expansion in bike lane system with the intention of attracting younger residents as bike lanes are often seen as a symbol of urban renewal by property developers.​10 The combination of the support from city governments, expressed in developers’ desire to attract the creative class, and civilians, shown in their previous

7. Longhurst, James, ​Bike Battles: A History of Sharing the American Road​ (United Kingdom: University of Washington Press, 2015), 78.

8. Longhurst, 188.

9. ​Green Bike Lanes Double in 2012​, (Illinois: Professional Safety, 2013), 14.

10. de Chardon, Cyrille Médard, ​Bike Lanes Are White Lanes / Bicycle Justice and Urban Transformation, (​Nebraska: Local Environment, 2017), 1039.

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waves of support for bicycling, presents a coherent argument for the relevance of bicycle safety along city bike paths.

Importance of Topic of Discussion Considering that this study analyzes the safety of bike lanes within Kings County, one of the boroughs of New York City, it would be appropriate to first analyze the effectiveness of bike line programs in other North American cities to first obtain an idea of how New York’s bicycle lane system compares in effectiveness. In the assessment of other cities’ bike lane systems, the different methods of ensuring their effectiveness and usage can be seen in cities’ policies. This study demonstrated the effectiveness and safety on Valencia Street in San Francisco before and after bike lanes were implemented by evaluating traffic patterns.​11 ​It was concluded that automotive traffic “dropped by 10 percent on Valencia Street and redistributed to parallel arterials,” otherwise known as nearby streets under the same classification, after bike lanes were implemented.​12 The redistribution of traffic onto other streets allows bicyclists to continue to move throughout bike lanes with less traffic, leading to a decrease in the possibility of being involved in an accident involving an automobile. Efforts to increase usage of bike lanes can be seen where San Francisco’s bike lanes coincide with the city’s Bay Area Rapid Transit (BART) rail system. In an effort to increase BART ridership, bike racks were installed at BART stations.​13 As a result, it was observed that “the presence of bike stations and increases in the

11. Cervero, Robert, Benjamin Caldwell, and Jesus Cuellar, ​Bike-and-ride: build it and they will come​ (San Francisco, CA: Journal of Public Transportation, 2013), 2.

12. Cervero, 2.

13. Cervero, 2.

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bike rack and electronic locker spaces were statistically associated with increased bicycle access trips to BART.”​14 Such an idea clearly draws a connection between bike lane usage and their built environment, which is defined as “population and employment density, mixed land use, street network design, and destination accessibility.”​15 Through a meta-analysis of bicycle traffic counts, sociodemographic factors, and available transportation from the 1996 to 2016 from 20 different American urban centers, it was found that an increase in proximity to workplaces, universities to dedicated bike lanes in streets and intersections showed increased bicycle traffic.​16 Similar reports regarding an increase in bicycle traffic were seen in the attention given to bicyclist-friendly infrastructure in Washington D.C., where “bicycling increased 200% on Pennsylvania Avenue after green [bike] lanes were installed.”​17 Furthermore, changes made to bicycle-related infrastructure in Minneapolis also resulted in increased bicyclist traffic after their Department of Transportation worked to “narrow certain busy streets from four to three lanes in order to add bike lanes and pedestrian amenities.”​18 Clearly, efforts for bike lane building and maintenance can be seen throughout many major cities within the United States.

14. Sallaberry, Michael, ​Valencia street bicycle lanes: A one year evaluation​ (San Francisco, CA: City of San Francisco Department of Parking and Traffic, 2000) 102.

15. Le, Huyen T. K., Ralph Buehler, and Steve Hankey, ​Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas ​(Virginia, ​Environmental Health Perspectives​, 2018) 1.

16. Le, 1.

​17. ​Green Bike Lanes Double in 2012,​ (Illinois: Professional Safety, 2013), 14.

18. Walljasper, Jay, ​Brave New Nonmotorized World, ​(Illinois: ​Planning,​ 2008), 23.

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When looking into New York, bike lane usage is evidently encouraged through a number of programs employed by the New York City Department of Transportation. For example, New York City’s public libraries "are also supporting bike-sharing by being a station in citywide systems or setting up their own bike lending programs.”​19 These changes come in conjunction with the expansion of New York’s bike lane system, as Transportation Alternatives spokesman Joseph Cutrufo states: “A huge segment of the population is interested in biking, but they’re not comfortable with the streets right now. If you build [more bike lanes], they will come.”​20 The safety of New York City’s bike lanes is also being actively monitored by New York City’s Department of Transportation. Such efforts are shown in their ​Vision Zero ​initiative, which monitors reported injuries and deaths along New York City streets on a yearly basis, as the Vision Zero program’s initiative is stated as being to reclaim New York City public spaces for bicyclist, pedestrian, and public transportation use.​21

Area of Focus The idea to analyze the street design in New York originated from an article published in the American Journal of Public Health on the change in car-bicycle accidents throughout New York City before and after bike lanes were built in each of the five boroughs. Another study that was significant in the development of my research is a study regarding the built environment (businesses, offices, schools, etc.) and bike lane use. It discovered that features of the built environment were predictors of increases in commuter volumes, especially with areas that had

19. Miller, Rebecca T, ​Life in the Bike Lane,​ (New York, NY: Library Journal, 2016), 8.

20. Guse, Clayton, ​NYC Would Get 100 New Miles of Protected Bike Lanes Each Year under Proposed Bill,​ (New York, NY: New York Daily 2019) 1.

​ 21. ​Vision Zero View,​ (New York, NY: New York City Department of Transportation, 2020).

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accessible parks.​22 After having understood these findings, I wondered if other aspects of the built environment such as street design features could impact bicyclist traffic and bicyclist safety.

From there, key points of both of these studies were combined and further background research was conducted specifically regarding the features of the streets hosting these lanes and the possible relationship between the presence of those features in New York City streets and safety.

However, due to the vastness of New York City’s bike lane system, I chose to only focus on Kings County as it contains a variety of high-density and lower-density residential and commercial areas. This adds to the value of using Kings County as my location of interest as these areas are comparable to areas of Manhattan and the Bronx, which are mostly high-density, and Queens and Staten Island, which are mostly low-density, and as Kings County has the median population density compared to the other New York City, being 35,219.1 people per square mile according to the U.S. Census Bureau.​23 This does not constitute that my findings are generalizable to these areas, however, it does make my findings an acceptable base for the judgment of the other boroughs of New York City.

The refinement of the focus of my research in terms of geographical area and targeted subjects allowed me to question the extent to which the features of bike lanes along Kings County principal arterial, minor arterial, and collector streets impact the frequency of automobile-bike accidents. After having defined a topic of interest for my research, I began to conduct preliminary research on possible data sources to be able to determine a course of action 22. Le, Huyen T. K., Ralph Buehler, and Steve Hankey, ​Correlates of the Built Environment and Active Borough), New York (​ New ​Travel: Evidence from 20 US Metropolitan Areas (Virginia: ​Environmental Health Perspectives​, 2018) 1.

2​ 3. ​ U.S. Census Bureau QuickFacts: New York City, New York; Bronx County (Bronx Borough), New York; Kings County (Brooklyn Borough), New York; New York County (Manhattan Borough), New York; Queens County (Queens Borough), New York; Richmond County (Staten Island​York, NY:​ U.S. Census Bureau​, 2019).

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for the research that I set out for myself. I was able to learn that the New York City Department of Transportation has a plethora of information regarding the frequency of bicycle-related accidents on each street as well as features present on New York City streets. I looked further into and familiarized myself with the New York City Department of Transportation’s Vision Zero website, which reports the location of car, bicycle, and pedestrian-related accidents that are reported to the New York Police Department from 2009 to 2018. Taking the information provided by the Vision Zero website, I furthered my interest in the possible connection between street design features and bicyclist safety.

Using the information provided by the Vision Zero initiative, I was able to assess the aforementioned relationship between street design and bicycle safety by combining numerical data from the past 3 years regarding injuries that have occurred on bike lanes in Kings county and data regarding the features of these bike lanes.

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Existing Gap Through extensive research, it was found that present data available on the safety of New York City’s bike lane system is limited. While the implementation of bike lanes has been shown to be extensively researched, investigation associating street design or features of streets hosting bike lanes with bike safety is not widely examined. Current research dictates that the implementation of bike lanes on New York streets has resulted in “reduced vehicular speeds and fewer conflicts between vehicles and bicyclists after installation of these lanes.”​24 The research fails to take other features present on streets into account such as stops per mile along streets that host bike lanes or speed limit signage along the aforementioned streets, even though the previous research has connected the built environment to the effectiveness and popularity of bike lanes.

Methodology This research took a quantitative approach to analyze the relationship between street design and the safety of bike lanes. Through this, it was hoped that a trend or correlation could be made between a number of street design features and the safety of bike lanes. The data collected was mostly numerical to make interpretation and graphing simpler. Only one of the data sets was not numerical and is instead a simple categorical data set.

In order to collect my data, I utilized a number of publicly available sources containing data regarding street design and accident locations and frequencies, which were mostly provided by the New York State Department of Transportation. I studied 50 conventional bike lanes, which do not contain special features such as barriers, signs indicating that a bike lane is present

24. Chen, Li, Cynthia Chen, Raghavan Srinivasan, Claire E. McKnight, Reid Ewing, and Matthew Roe, ​Evaluating the Safety Effects of Bicycle Lanes in New York City, ​(New York, NY: American Journal of Public Health, 2012), 1120.

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in an area, or lanes shared by cars and bicyclists. I chose to study 50 streets in order to narrow down my data without compromising the accuracy of my study. In order to do so, I collected the names of all streets in Kings County and used Google’s Random Number Generator to select my streets to be studied. Research began by collecting the number of injuries in 2016, 2017, and 2018 on specific bike lanes from the Vision Zero website. I took these numbers and found the average number of injuries for the past three years. Then, to analyze the features that make up the ‘street design’ of a street hosting a bike lane, the speed limit, signage condition, number of lanes, and the number of stops were recorded. To standardize my data regarding stops (a stop sign, speed bump, or traffic signal), the previously established distances were used to find the stops per mile. I was able to record the number of lanes, the number of stops, and the distance using Google Maps, while data for the speed limit and signage condition were obtained from the Vision Zero website.

Before being able to conclude my collection, the purpose that each street was built with was taken into account. My research analyzed Principal Arterial, Minor Arterial, and Collector streets, which all serve different purposes in moving traffic. Non-highway Principal Arterial streets are described by the New York City Department of Transportation as streets running through urban centers with the largest amount of traffic (e.g. Avenues, Turnpikes, Boulevards).​25 Meanwhile, Minor Arterial streets are intended to connect smaller streets to principal arterial streets and aim to provide service for moderately sized trips and don’t divide any identifiable communities or neighborhoods.​26 Finally, collector streets provide service to commuters moving within residential neighborhoods and bring in traffic from smaller residential streets to arterial 25. New York State Department of Transportation, ​Functional Class Map NYSDOT Region 11: Kings County​, (New York, NY: Department of Transportation), 1.

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streets.​27 Classifying streets allowed me to better assess the bike lanes of Kings County in question. This aided me in my secondary goal of identifying an ideal number of stops per mile, the number of lanes, and the speed limit for each street classification.

Results In order to assess the correlations between my collected data, Google Sheets was utilized to create graphs and to perform basic calculations. I first attempted to analyze the relationship between the number of injuries per mile and the number of stops per mile of the street. It was found that there was a linear relationship that can be represented by the function y = .0553x + 4.75 where x represents the number of injuries per mile and y represents the number of stops per mile. This also means that with every increase of 1 stop per mile, there are .0553 more injuries per mile. Thus, the general relationship that an increase in stops results in an increase in injuries was established. This went against my original hypothesis that an increase of stops would be associated with a decrease of injuries per mile. However, when my data was analyzed by street classification different trends could be seen. When the relationship between the number of injuries per mile and the number of stops per mile of collector streets was analyzed, an indirect relationship was displayed where my hypothesis was supported. On collector streets, as stops per mile increased, injuries per mile decreased, represented by the formula y = -0.144x+377, which also means that with every increase of 1 stop per mile, there are 0.144 fewer injuries per mile,

26. New York State Department of Transportation, ​Functional Class Map NYSDOT Region 11: Kings County​, (New York, NY: Department of Transportation), 1.

27. New York State Department of Transportation, 1.

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which can be seen on this graph:

Figure 1 However, for minor arterial streets, a relationship similar to that of my general findings was seen where a direct relationship can be seen between the number of stops per mile and the number of injuries per mile, shown in the graph below:

Figure 2

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Finally, in an analysis of the relationship between the number of injuries per mile and the number of stops per mile of principal arterial streets, no clear strong relationship can be seen, as the relationship was represented by the line y = -9.58 x 10​.3​x+ 7.28. The slope of the line, -9.58 x 10​-3​, is extremely close to 0 (-0.00935 when not expressed in scientific notation) and can be seen in the following graph:

Figure 3 I then furthered my analysis by examining the relationship between the number of injuries per mile and the signage condition of the street by calculating the average number of injuries per mile of streets with or without signage. For this section, I judged results by categorizing differences of 0.5 injuries per mile or more as significant, and differences of less than 0.5 injuries per mile as not significant. I found that there was not a significant difference between the average number of injuries of streets with signage and streets without signage, the results being 5.458685212 and 5.087645481, respectively. However, once again, when results were analyzed by street classification, a different comparison could be seen. A significant difference in the averages was seen in minor arterial streets, where the average number of

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injuries for minor arterial streets with signage was 5.792417869 injuries per mile and the average number of injuries for minor arterial streets without signage being 6.710538132 injuries per mile.

A larger difference was seen in principal arterial streets, as the average number of injuries for principal arterial streets with signage was 8.78622222 injuries per mile and the average number of injuries for principal arterial streets without signage being 6.898838842 injuries per mile. In contrast to the findings for minor and principal arterial streets, collector streets saw little difference with the average number of injuries for collector streets with signage being 2.533564723 injuries per mile and the average number of injuries for collector streets without signage being 2.349394354 injuries per mile.

Data set

Difference (Not signed - Signed)

Conclusion

Collector

- 0.184190369

Not significant

Minor Arterial

0.918120263

Signed is better

Principal Arterial

- 1.887383358

Not signed is better

All

- 0.371039731

Not significant

I then furthered my analysis by conducting a simple categorical comparison similar to that of the signage condition with the speed limit of streets. I found that for streets with a speed limit of 20 miles per hour, the average number of injuries per mile was 1.862962963, while for streets with a speed limit of 25 miles per hour, the average number of injuries per mile was 5.609254774. As the difference between streets with a speed limit of 20 miles per hour and 25 miles per hour was clearly significant (being about 3.746291811 injuries per mile), it can be seen that lower speed limits have an effect on lowering the frequencies of injuries. I was only able to conduct a street classification-specific comparison with collector streets as all of the minor

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arterial and principal arterial streets that I analyzed had speed limits of 25 miles per hour. For collector streets, those with a speed limit of 20 miles per hour had an average of 1.862962963 injuries per mile (collector streets were the only streets with speed limits of 20 miles per hour) while those with a speed limit of 25 miles per hour had an average of 2.518321712 injuries per mile. These values agree with the previously mentioned findings comparing all streets studied with a speed limit of 25 miles per hour to streets with a speed limit of 20 miles per hour, as the difference in average injuries per mile of collector streets with different speed limits was significant as well, being about 0.655388749. These differences can be depicted graphically here:

Finally, to end my analysis of simple relationships, I analyzed the relationship between the number of injuries per mile and the number of lanes of streets. However, due to the lack of variability in my findings regarding the number of lanes of streets, my findings were inconclusive.

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Validity In order to test the validity of my data in a mathematical sense, Chi-squared tests were conducted. Chi-squared tests can be used for a number of functions, such as testing independence, association, and homogeneity. For the purpose of my calculations, Chi-squared tests for an association were conducted, as I was searching for an association between street design features and injuries per mile. It should be noted that Chi-squared tests for association simply test to see if an association is present, and do not indicate what type of association exists (e.g. positive, negative, exponential, logarithmic). However, Chi-squared tests require categorical data; thus. my numerical data first needed to be categorized. To do so, my numerical data regarding stops per mile and injuries per mile were categorized by frequency of the nearest integer. I then placed this data into a two-way table comparing injuries per mile and stops per mile. I then calculated the expected counts for each possible combination of injuries per mile and stops per mile within the range using the following formula: E xpected value = (Σcolumn)(Σrow) total These values were then compared to the observed values by inserting them into the TI-84 calculator’s list command 𝜲​2​-Test function in order to find the Chi-squared test statistic and then p-value, which represents the probability that my findings could occur again given that each test is independent. 𝜲​2

P-value

All Streets

123.631

3.735 x 10​-19

Collector

35.675

1.103 x 10​-6

Minor Arterial

121.272

3.544 x 10​-19

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Principal Arterial

34.488

0.00291

Figure 4 In statistics, a number of definitions and aims must first be identified before interpreting a chi-squared statistic. Firstly, a null hypothesis and an alternative hypothesis must be established.

In the case of a Chi-squared test for association, the null hypothesis must be to assume that there is no association present, making the alternative hypothesis that there is an association present.

Then, an alpha value must be established. A commonly used and widely accepted alpha value is 𝛂 = 0.10, so for the purpose of my calculations, I selected 0.10 to be my alpha value. Holding findings to an alpha value of 0.10 indicates that there is a 10% risk of a type I error occurring, which means that we would falsely conclude that an association exists when in reality, an association does not exist.

This identified alpha value can then be applied to my findings by comparing it to the found p-values. As the p-values identified for all streets, collector streets, minor arterial streets, and principal arterial streets were all less than 0.10 (being 3.735 x 10​-19​, 1.103 x 10​-6​, 3.544 x 10​-19​, and 0.00291, respectively), we reject the null hypothesis, which states that no association exists for all four tests. These findings further reinforce the validity of the relationships previously found regarding all streets, minor arterial streets, and collector streets. However, they contradict my findings regarding principal arterial streets.

In an analysis of the relationship between the speed limit and injuries per mile, I conducted a chi-squared test to analyze the relationship between the speed limit and injuries per

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mile. My null hypothesis was that there was no association between the speed limit and injuries per mile, and my alternative hypothesis was that there was an association between the speed limit and injuries per mile. Unfortunately, due to a lack of variation, I was only able to conduct calculations for the collector streets data set. The 𝜲​2 was 2.87179 and the p-value was 0.0901.

When 𝛂 = 0.10, we reject the null hypothesis as the p-value is smaller than the chosen alpha.

These findings are also consistent with my original results.

Discussion After having observed the apparent relationships between street design features and safety on bike lanes, a number of conclusions can be drawn. Regarding stops on streets, it is suggested that the New York City Department of Transportation: 1. Reduces the number of stops on minor arterial streets in Kings county, as it was observed that a direct relationship exists between the number of stops per mile and the number of injuries per mile on minor arterial streets.

2. Increases the number of stops on collector streets in Kings county, as it was observed that an indirect relationship exists between the number of stops per mile and the number of injuries per mile on collector streets.

3. Does not change the number of stops on principal arterial streets in Kings county, as no strong relationship was observed between the number of stops per mile and the number of injuries per mile on principal arterial streets.

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These three relationships can be seen in Figures 1, 2, and 3, as they graphically demonstrate the relationship between stops per mile and injuries per mile on the three street classifications analyzed.

In an analysis of speed limits, due to the lack of variation of speed limits in minor arterial and principal arterial streets, conclusions can only be drawn by collector streets. This conclusion would be to push for streets’ speed limits to be 20 miles per hour, as streets with speed limits of 20 miles per hour had significantly fewer injuries per mile when compared to streets with speed limits of 25 miles per hour. This claim is further supported by the sample of collector streets, as they saw a similar drop in the number of injuries per mile when comparing streets with speed limits of 20 miles per hour to streets with speed limits of 25 miles per hour. Finally, in an analysis of signage conditions, a number of conclusions can be drawn. As significant differences in the average number of injuries per mile could only be seen in minor arterial streets and principal arterial streets, it is suggested that the New York City Department of Transportation extends speed limit signage to all minor arterial streets and removes speed limit signage on all principal arterial streets in Kings County. This conclusion stems from the finding that on minor arterial streets, signed streets had a higher number of average injuries per mile, while on principal arterial streets, signed streets had a lower number of average injuries per mile.

Additionally, in an assessment of the reliability of my results, all datasets except for the principal arterial data set passed a chi-squared test. This was assessed by holding my findings to a significance level of 𝛂 =.10, indicating that there would be a 10% chance of a Type I error and

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finding that for all data sets the p-value found was less than .10. These tests show consistency in my methods and my results.

Limitations Unfortunately, due to school closures from the COVID-19 virus, I was unable to access my school’s copy of IBM’s SPSS program. This decision was made initially in order to complete calculations related to reliability and validity, however, due to the program’s cost and my inability to access the school’s files from home, SPSS was not and still is not available for my use. This was accommodated for by conducting the Chi-squared tests for association.

Additionally, due to a lack of public information, my data collection was also limited.

Any accidents that were not reported to the New York Police Department could not be included in my data set. Although I attempted to reach out to local New York bicycle safety coalitions to utilize their data, their data was not location-specific. I also worried that the entries of bicycle coalitions would overlap with that of the Vision Zero Website.

Future Research With the aforementioned conclusions being taken into account, future research can go a number of ways. Firstly, to assess the lack of coverage of other boroughs of New York City, future researchers could repeat this analysis in the other boroughs of New York Cities to assess the strength of the findings discussed in this paper. Furthermore, a repetition of this analysis throughout the rest of the city of New York could shed light on how population density may

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have an impact on the effectiveness of bike lanes, as all five of the boroughs have varying levels of population density. Moreover, if this analysis were to be repeated in other cities, future findings could support or discredit the aforementioned findings, which also brings up the possibility that these relationships are only present in New York City (which could possibly be due to local culture, population distribution, etc.). It is encouraged that future researchers performing similar studies take the limitations of this analysis into account when conducting their studies. Thus, future researchers should exercise any possible access to SPSS software to further calculations related to reliability and validity.

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