North Jersey Transportation Planning Authority (NJTPA)

Street Smart New Jersey Observational Pedestrian Safety Study: Final Report

Authors Dr. Mohammad Jalayer, Rowan University Dr. Patrick Szary, Rutgers CAIT Mr. Deep Patel, Rowan University Mr. Nima Khaki, Rowan University

June 2019

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

DISCLAIMER

This report has been prepared under the direction of the North Jersey Transportation Planning Authority with financing by the Federal Highway Administration of the U.S. Department of Transportation. This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the North Jersey Transportation Planning

Authority or the Federal Highway Administration.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

ABSTRACT

This report provides the results of the observational study to compare the rates of unsafe pedestrian and driver behaviors before and after the North Jersey Transportation Planning Authority (NJTPA) pedestrian safety education and enforcement campaign (Street Smart NJ) in several communities across the state of New Jersey. The behaviors – including unsafe crossing and crossing against a signal, failing to stop for pedestrians when turning, failing to stop before turning at a red light or stop sign, and running the red light signal or stop sign – were compared and measured in eight communities — Asbury Park, Garfield, Morris Plains, Newark, Princeton, Rutherford, Teaneck, and Woodbridge — in 2018-2019.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

EXECUTIVE SUMMARY The North Jersey Transportation Planning Authority contracted with the Center for Advanced

Infrastructure and Transportation (CAIT) at Rutgers, the State University of New Jersey and Rowan University, to evaluate the effectiveness of its Street Smart NJ pedestrian safety campaign in eight communities. The observational analysis shows that Street Smart NJ continues to make a positive impact on communities that implement campaigns. Due to the abnormally high rate of pedestrian fatalities The NJTPA first developed and piloted Street Smart and injuries, the Federal Highway Administration in 2013. The goal of the grassroots public education (FHWA) designated New Jersey a pedestrian focus campaign is to enhance pedestrian safety by increasing state and Newark a pedestrian safety focus city. awareness of safety risks and improving compliance with pedestrian and motorist laws. In order to evaluate Street Smart is a collaborative program that uses the effectiveness of Street Smart NJ, the NJTPA enforcement and public outreach to reinforce the safe periodically measures public awareness of pedestrian interaction of drivers and pedestrians, with the goal of safety laws and behavioral change. preventing behaviors that contribute to crashes. With assistance from a growing network of partners including Motor vehicle crashes involving pedestrians are a NJ TRANSIT, the New Jersey Division of Highway major roadway safety concern across the United Traffic Safety (NJDHTS), the Transportation States, and particularly in New Jersey. While overall traffic fatality rates have declined over the last two Management Associations (TMAs) and countless local decades, the proportion of pedestrian fatalities has elected officials, law enforcement, and residents, the increased as pedestrians remain the most vulnerable Street Smart NJ campaign program built on initial roadway users.1 On a nationwide scale, according to successes in five pilot communities and expanded to more the National Highway Traffic Safety Administration than 90 municipalities throughout the state and now works (NHTSA), in 2017, there were more than 5,977 with more than 90 community partners. pedestrian deaths and over 85,000 pedestrian injuries This report examines behavioral change through resulting from traffic crashes. This translates to, on observational data at high-risk intersections in eight average, a pedestrian killed every two hours and one communities that implemented campaigns in 2018 and injured every eight minutes in traffic crashes 2019. Evaluation of public awareness of pedestrian safety nationally. In 2017, New Jersey ranked second in the laws and reported behavioral change was also conducted nation for percentage of pedestrian fatalities with in these communities and is detailed in a separate report, respect to all traffic fatalities with nearly 30 percent North Jersey Transportation Planning Authority Street of all fatalities being pedestrians.

1“Pedestrian Traffic Fatalities by State”, Governors Highway Safety Association (GHSA) (Sources: https://www.ghsa.org) Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Smart New Jersey Behavioral Pedestrian Safety Study: • City of Newark: Raymond Boulevard and Mulberry Final Report, which can be found on the NJTPA Street. Data collection dates for the pre- and post- website. campaign observations were September 20, 2018, and The observational study measured the rate of four November 29, 2018, respectively. non-compliant pedestrian or driver behaviors to • Borough of Morris Plains: Franklin Place and assess the level of pedestrian risk: Speedwell Avenue. Data collection dates for the pre- 1. Unsafe crossing and crossing against the signal and post-campaign observations were October 2, (pedestrian) 2018, and November 12, 2018, respectively. 2. Turning vehicle failing to stop for pedestrian • Municipality of Princeton: Nassau Street and (driver) Washington Road. Data collection dates for the pre-

3. Failure to stop before turning right at a red signal and post-campaign observations were October 8, or stop sign (driver) 2018, and November 26, 2018, respectively. • Borough of Rutherford: Park Avenue and Glen 4. Running a red light signal or stop sign (driver) Road. Data collection dates for the pre- and post- The study team collected data to measure the campaign observations were October 15, 2018, and effectiveness of the campaign in geographically and demographically diverse communities across the state. December 3, 2018, respectively. By observing pedestrian and driver behaviors at a • Township of Woodbridge: Main Street and Eleanor critical intersection in each community, the team Place. Data collection dates for the pre- and post- assessed the impact of the campaign with statistical campaign observations were March 7, 2019, and May measurements. Observations were conducted 9, 2019, respectively. immediately before and after each community conducted a Street Smart NJ campaign. The selected Overall, the observation results demonstrate the positive communities and intersections include: impact the Street Smart NJ campaign has on changing • Township of Teaneck: State Street and Queen pedestrian and driver behaviors. The analysis of Anne Road. Data collection dates for the pre- and aggregated observations from all eight study sites reveals statistically significant reductions in non-compliant post-campaign observations were May 1, 2018, and pedestrian and driver behaviors, including: June 26, 2018, respectively.  60 percent reduction in motorists’ failure to stop • City of Asbury Park: Memorial Drive and before right turn at red signal or stop sign; Springwood Avenue. Data collection dates for the  40 percent reduction in turning vehicle failing to pre- and post-campaign observations were August stop for pedestrians in the crosswalk; 14, 2018, and October 23, 2018, respectively.  45 percent reduction in motorists running a red • City of Garfield: Midland Avenue and Van Winkle light signal or stop sign; and Avenue. Data collection dates for the pre- and post-  21 percent reduction in pedestrians crossing campaign observations were August 21, 2018, and against the signal November 7, 2018, respectively. Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

It should be noted that, in some communities and for some measures, the results were much stronger. For example, there was a 54 percent reduction in pedestrians against the signal in Asbury Park; a 73 percent reduction in failing to stop for pedestrians in

Woodbridge; and in Garfield, a 63.5 percent reduction in red light running. The study team also observed improvements in non-compliant pedestrian or driver behaviors after separating the data by intersection type and traffic control. Overall, all intersection configurations (5-leg, 4-leg, and 3-leg) are associated with favorable outcomes from the Street Smart NJ program.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

INTRODUCTION Two examples of effective campaigns are Click it or Ticket and Drive Sober or Get Pulled Over, which Pedestrian safety at intersections, where motor have been successful in changing driver behaviors. A vehicles cross paths with people walking, is a serious goal in New Jersey is to reduce the rate of violations matter of concern for traffic and road safety among both drivers and pedestrians in order to engineers and professionals. The severity of crashes improve pedestrian safety at intersections. involving pedestrians is high since pedestrians are Accordingly, the goals of the Street Smart NJ not protected by any automobile safety features (such campaign are to: as the mass and frame of the vehicle, airbags, and  Change motorist and pedestrian non- seatbelts). According to the National Highway compliant behavior to reduce the incidence Traffic Safety Administration (NHTSA)2, in 2017, of crashes resulting in injury and/or death to there were more than 5,977 pedestrian deaths and pedestrians. over 85,000 pedestrian injuries resulting from traffic  Educate motorists and pedestrians about their crashes. Also, 18 percent of pedestrian fatalities in roles and responsibilities for safely sharing 2017 occurred at intersections, according to NHTSA. the road (i.e., driving and walking in Due to the severity of pedestrian-related crashes, compliance with laws). these crashes merit special attention and additional  Increase enforcement of pedestrian safety analysis. In recent years, a considerable number of laws and roadway users’ awareness of that studies have explored the factors that contribute to effort. pedestrian crashes and developed effective safety The campaign (prior to a spring 2019 rebranding) countermeasures. Although engineering focused on five core messages to educate people about countermeasures (e.g., traffic signs, traffic signal safe driving and walking practices: Obey Speed controls, pavement markings, and roadway Limits and Stop for Pedestrians for drivers; Use geometry) can enhance pedestrian safety, the Crosswalks and Wait for the Walk for pedestrians; behavior of people driving and walking plays an and Heads Up, Phones Down for everyone (Figure 1). essential role in reducing crash risk. Education The safety campaign uses paid advertising, earned programs and public outreach efforts remind people media, signage, and social media to raise awareness. to adhere to the laws and keep safety in mind when This report provides the finding of pre- and post- traveling. campaign observational studies in the municipalities A review of existing literature shows that providing selected for this study. education and training, and conducting outreach campaigns are important strategies in increasing motorist and pedestrian awareness and to start changing behaviors.

2“Traffic Safety Facts”, National Highway Traffic Safety Administration (NHTSA), 2015 (Source: https://www.nhtsa.gov/road-safety/pedestrian-safety) Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Figure 1. Graphical Messages Used in the Street Smart NJ Campaign to Change Driver and Pedestrian Behaviors

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

OBSERVATIONAL ANALYSIS Transportation Management Associations (TMA’s) are critical partners in selecting and leading local Site Selection campaigns, as well as conducting their own independent evaluation reports for campaigns they The goal of selecting sites for the Street Smart NJ lead. campaign and observational study is to identify locations that could benefit from an improvement in Methods of Data Collection driver and pedestrian behavior and may exhibit The primary objective of the observational study is to measurable changes as a result of the campaign. determine if the campaign is effective in mitigating Historical crash data is one of the major criteria for non-compliant behaviors performed by drivers and site selection since locations with a high number of pedestrians, resulting in enhanced safety for pedestrians previous crashes are likely to continue to have the at the study locations. Given the fact that crashes are not highest number of future pedestrian crashes in the frequent events, it is more effective to observe the absence of intervention. occurrence of risky non-compliant behaviors by Additional considerations for site selection may motorists and pedestrians that can serve as proxy include different community types (e.g., urban and measures for safety. suburban) and diverse geographic coverage of the Safety improvement, by proxy, happens when there is a region. It was also important for locations to have reduction in the occurrence of non-compliant behaviors. large enough traffic and pedestrian flows in order to Therefore, the data collection efforts include provide sufficient data for comparison, and the conducting observations at the study locations to communities had to express an interest in document the behaviors of pedestrians and drivers for participating in the Street Smart NJ campaign. Figure both pre- and post-campaign. This requires identifying 2 shows the location of the selected communities. the necessary data type, the field collection method, and The NJTPA has assisted with Street Smart NJ how to process the raw data to provide a useful dataset campaigns in more than 90 municipalities across the for analysis purposes. state, as of the release of this report. The NJTPA invites communities and organizations statewide to use the program’s strategies and materials, but campaign implementation relies heavily on local partners proactively addressing pedestrian safety concerns and engaging the public.

While campaigns are typically four-to-six weeks long, the planning begins months in advance. It starts with a committee of key stakeholders (i.e., police officers, elected officials, community groups and other municipal departments and agencies) who will decide where to focus the campaign and when and how it will be conducted. The state’s eight Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Figure 2: Eight Evaluation Sites on Map of Population density in New Jersey

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Data Required to Assess Pedestrian and Driver Behavior

Conducting observational evaluations for each proxy measure requires two types of data to be collected: 1) counts of the occurrences of non-compliant behavior, and 2) counts of a measure of exposure or the number of opportunities that pedestrians or drivers have a chance to comply with or violate the traffic rules. Using these two types of data, it is possible to measure a rate of non- compliance at each location for each proxy behavior of interest. This rate is very important for comparing the pre- and post-campaign datasets to identify if there is a statistically significant change in driver and pedestrian behavior. The NJTPA used four core proxy behaviors to measure the impact of its Street Smart NJ campaign messaging. These proxy behaviors allow the evaluators to  Proxy 2: Turning Vehicle Fails to Stop for observe the non-compliant behavior and determine the Pedestrian: A vehicle making a left or right turn at a relevant measure of exposure in each substantive area of green signal or an unsigned intersection approach fails focus for the Street Smart NJ campaign: to stop for a pedestrian crossing parallel to the approach. The measure of exposure is the total number  Proxy 1: Unsafe Crossing and Crossing against the of left or right turning vehicles when pedestrians are Signal: A pedestrian crosses more than half of the present so that turning vehicles have an opportunity to street outside of the crosswalk or begins crossing the properly stop for pedestrians. street while the signal indicates “Don’t Walk.” The measure of exposure is the total number of pedestrians crossing the street.

 Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign: A right turning vehicle fails to make a complete stop and stay stopped for pedestrians before making a right turn on red. The measure of exposure is the total number of right turning vehicles that approach the stop bar on a red signal because all cars should stop before proceeding, whether or not a

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

pedestrian is present. For unsignalized Data Collection Schedule intersections, this proxy is a vehicle fails to To evaluate the safety proxy behaviors of community make a complete stop for pedestrians before members before and after the Street Smart NJ campaign, making a right turn at a stop sign. The measure of the study team must observe and record each measure at exposure is the total number of right turning pre-determined study locations. Collecting high-quality vehicles that approach the stop sign. data in each Street Smart NJ community requires coordination of several activities, including pre-campaign data collection, campaign duration, and post-campaign data collection. The study team collected pre-campaign observations as close as possible to the launch of the campaign, within approximately two to four weeks before the campaign. Similarly, the post-observations were collected as close as possible to the campaign conclusion, • Proxy 4: Running Red Light Signal or Stop within a window of approximately two to four weeks after Sign: A vehicle passing an intersection when the the campaign. traffic signal is red or there is a stop sign. The To the extent possible, the study team collected pre- and measure of exposure is the total number of post-campaign data at the same location, during the vehicles that enter the intersection, regardless same days of the week, at the same time of day and with of traffic signal color. similar weather conditions. This helps to minimize the For unsignalized intersections, this proxy is a vehicle source of bias and number of external, non-campaign passing the intersection fails to make a complete stop factors that can influence the behavior of drivers and at the stop sign. The measure of exposure is the total pedestrians. number of vehicles that enter the intersection. Pedestrian and motor vehicle traffic volumes are also crucial external factors that play a central role in the analysis. The above factors heavily influence volumes, but there are additional influences, such as economic trends and random chance that also contribute significantly to these total counts. The team controlled for vehicle and pedestrian volumes by collecting vehicle and pedestrian counts during the observation and calculating the proxy behaviors based on an exposure rate (i.e., observed proxy behaviors as a percentage of the overall vehicle and pedestrian volumes). It should be noted that the team only collected data during an average day, a weekday when traffic and pedestrian volumes are typical or consistent (Table 1). Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Data Collection Method collect high-quality data, the study team used two to As previously stated, the research team observed four high-resolution video recorders on tripods at each and recorded four non-compliant behaviors and site to record three to four hours of high-definition four measures of exposure for multiple intersection (HD) video. The cameras were equipped with wide- approaches at each study site. To ensure accurate angle lenses in order to monitor at least one approach counts, student research employees recorded and one crosswalk at all times. footage of each intersection approach to capture The use of video cameras allowed the compilation of a the occurrence of proxy safety variables to quantify comprehensive record of all vehicle and pedestrian overall pedestrian exposure risk. The video data movements at the study locations during the data enabled the extraction of behaviors of interest and collection period. Each camera also had an extended- represented the information in a manner that can be life battery pack and a 64GB memory card to allow for used for further analysis. uninterrupted video collection for six-hour time blocks. The study team synchronized each recording with a The students who were collecting video data in the field also collected conventional traffic counts of the clock to capture an accurate time range. The video proxy safety behaviors by hand in 15-minute recordings established a time reference that allowed the aggregations. Conventional traffic count data was a confirmation of proxy behaviors and automatic counts helpful way to supplement and double-check during the data collection period at study locations. observations logged from the video observations. To

Table 1. Pre- and Post-Campaign Data Collection Dates and Times by Study Site

Community and Intersection Pre-Campaign Post-Campaign

Teaneck ‒ State Street and Queen Anne Tuesday, May 1, 2018 Tuesday, June 26, 2018 Road 10 a.m. to 2 p.m. 10 a.m. to 2 p.m. Asbury Park ‒ Memorial Drive and Tuesday, August 14, 2018 Tuesday, October 23, 2018 Springwood Avenue 10 a.m. to 1 p.m. 10 a.m. to 1 p.m. Garfield ‒ Midland Avenue and Van Tuesday, August 21, 2018 Wednesday, November 7, 2018 Winkle Avenue 9 a.m. to 1 p.m. 9 a.m. to 1 p.m. Newark ‒ Raymond Boulevard and Thursday, September 20, 2018 Thursday, November 29, 2018 Mulberry Street 9 a.m. to 1 p.m. 9 a.m. to 1 p.m. Morris Plains ‒ Speedwell Avenue and Tuesday, October 2, 2018 Monday, November 12, 2018 Franklin Road 7 a.m. to 11 a.m. 7 a.m. to 11 a.m. Princeton ‒ Nassau Street and Monday, October 8, 2018 Monday, November 26, 2018 Washington Road 10 a.m. to 1 p.m. 10 a.m. to 1 p.m. Rutherford ‒ Park Avenue and Glen Monday, October 15, 2018 Monday, December 3, 2018 Road 9 a.m. to 1 p.m. 9 a.m. to 1 p.m. Woodbridge – Main Street and Eleanor Thursday, March 7, 2019 Thursday, May 9, 2019 Place 9:30 a.m. to 1:30 p.m. 9:30 a.m. to 1:30 p.m.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Processing Data for Analysis This can be interpreted as the proportion of drivers or One of the benefits of collecting field data using video pedestrians who did not comply with the regulations for recorders compared to tallying proxy behavior safe use of the intersection, and this rate is a proxy manually in real time is that the recordings enable more measure or safety risk. accurate review in the lab and a far more detailed A lower rate correlates with safer pedestrian and analysis. Moreover, playing back the video multiple motorist behaviors and a higher rate of individuals times allows for the detection of simultaneous complying with traffic safety laws at that intersection. behaviors at the same location, increasing the efficiency The raw data shows that the rates of non-compliance and accuracy of collected data. For this project, the dropped for most proxy measures in the study locations. study team utilized Simple Player, a computer program Additional statistical analyses are necessary to developed at the University of California, Berkeley, determine which drops are statistically significant, and that facilitates the logging of each proxy behavior which observed increases in rates of non-compliance occurrence and overall pedestrian and vehicle counts as are attributable to random variations in counts. The a time-stamped observation. This tool uses the research team made efforts to ensure that the before and QuickTime video player and provides the analysts with after comparison for each site and proxy measure are as an opportunity to watch the video, change the speed of consistent as possible. Therefore, the team recorded video playback, and record relevant behaviors and data on the same day of the week when possible and volumes. These observations were recorded in a text always at the same time of day. There were some file log, which created a comprehensive list of the time- weather differences between pre- and post-campaign stamps on the video frame corresponding to when the data collection months, so it is possible that some analyst logged each proxy behavior or differences in behavior are attributable to the seasonal vehicle/pedestrian count. This provides information not weather conditions. only on total counts and proportions but also information about when the non-compliant behaviors occurred.

SUMMARY OF RAW DATA The raw data in this project includes the counts of the number of compliant and non-compliant behaviors observed at each site and for each proxy behavior. The study team logged these counts directly from the video recordings, as summarized in Table 2. The rate of non- compliant behaviors is calculated as the number of non- compliant observations divided by the total number of all relevant observations for each proxy (i.e., compliant plus non-compliant).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Table 2. Counts of Compliant and Non-Compliant Behaviors by Location and Proxy Measure During Pre- and Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign

Community Proxy Non- Rate of Non- Non- Rate of Non- Compliant Compliant Compliant Compliance Compliant Compliance

1 360 112 0.237 167 86 0.340 2 110 57 0.341 120 32 0.211 Teaneck 3* N/A N/A N/A N/A N/A N/A 4 5,296 47 0.009 4,905 26 0.005 1 16 126 0.887 96 65 0.404 2 32 26 0.448 38 13 0.255 Asbury Park 3 36 18 0.333 45 5 0.100 4 3,325 11 0.003 2,902 7 0.002 1 62 44 0.415 58 33 0.363 2 28 13 0.317 24 13 0.351 Garfield 3 31 129 0.806 170 71 0.295 4 3,334 59 0.017 3,626 20 0.005 1 1454 629 0.302 1523 239 0.136 2 446 398 0.472 604 222 0.269 Newark 3 279 97 0.258 324 48 0.129 4 6,005 61 0.010 6,119 28 0.005 1 84 50 0.373 80 31 0.279 2 17 10 0.370 27 15 0.357 Morris Plains 3 29 29 0.500 13 5 0.278 4 6,727 303 0.043 5,483 94 0.017 1 1348 410 0.233 653 312 0.323 2 236 51 0.178 162 17 0.095 Princeton 3* N/A N/A N/A N/A N/A N/A 4 2,541 129 0.048 2,638 54 0.020 1 308 56 0.154 400 66 0.142 2 88 24 0.214 130 23 0.150 Rutherford 3 25 159 0.864 53 154 0.744 4 25 159 0.864 53 154 0.744 1 81 57 0.413 89 61 0.407 2 63 63 0.500 84 24 0.222 Woodbridge 3 61 22 0.265 91 7 0.071 4 84 63 0.429 101 42 0.294 *Note: “No Turn on Red” signs are placed at the intersection, so Proxy 3 was not measured.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

DATA ANALYSIS AND RESULTS To test whether a change in the rate of non-compliant behavior is significant, statistical calculations verify To identify the effectiveness of the Street Smart NJ whether or not it is possible to reject the null hypothesis campaign in changing behavior, the study team that the behavior did not change. The fundamental compared behaviors of pedestrian and drivers before equation to conduct the test is as follows: and after the campaign (pre- and post-campaign). The assumption is that each individual who drives or walks 휌̂ − 휌̂ Ζ = 2 1 through the intersection makes a decision to obey or 1 1 √휌̂(1 − 휌̂)( + ) disobey traffic regulations with some probability that is 푛1 푛2 Χ1 − Χ2 independent of the behavior of other drivers and 휌̂ = 푛1 − 푛2 pedestrians. Given this assumption, each driver or Χ1 휌̂1 = pedestrian that has an opportunity to be involved in 푛1 Χ2 unsafe, non-compliant behavior will either decide to 휌̂2 = 푛2 comply with traffic regulations or not, following a Bernoulli (binary) process. In this project, when a driver where: or pedestrian does not comply with a specific traffic Χ1: number of non-compliant events in pre-campaign regulation captured in the proxy variables, it is data considered a Bernoulli success. Whereas a Bernoulli Χ2: number of non-compliant events in post-campaign failure occurs when a safe, compliant behavior is data observed. In this situation, the success rate specifies 푛1: measure of exposure to pre-campaign data how likely people are to partake in unsafe behaviors. In 푛2: measure of exposure to post-campaign data a total population of drivers and pedestrians, the number 휌̂1: probability that a person did not comply with the of successes follows a binomial distribution and the regulations in pre-campaign data proportion of successes out of the total population of 휌̂2: probability that a person did not comply with the motorists and pedestrians follows an approximately rules in post-campaign data normal distribution, which was used for hypothesis 휌̂: pooled sample proportion or combined average of testing and quantifying the magnitude of the effect. As probabilities discussed earlier, by counting non-compliant and compliant behaviors, it is possible to measure a percent The estimate of the change in the rate of non- or proportion of the total drivers or pedestrians who compliance is the difference (휌̂2 − 휌̂1). A negative have an opportunity to comply with regulations or not. value indicates a decrease in the proportion of the More specifically, for each proxy, the study calculates drivers and pedestrian engaging in the unsafe behaviors, two different proportions, including proportions of non- representing an improvement in the traffic safety. The compliant behavior in the pre-campaign data, and null hypothesis indicates that the rate of non- proportions of non-compliant behavior in the post- compliance in pre-campaign is equal or less than post- campaign data. campaign (H0: 휌̂1 ≤ 휌̂2) and the alternative hypothesis indicates that the rate of non-compliance in pre-

campaign is greater than post-campaign (H1: 휌̂1 > 휌̂2).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

In order to analyze the observational results, it is first required to determine the significance level, which varies between 0 and 1. Researchers most often use significance values of 0.01, 0.05, or 0.10, corresponding to 99 percent, 95 percent, and 90 percent confidence level, respectively. Notably, in statistics, the p-value is the level of marginal significance within a statistical hypothesis test, which represents the probability of the occurrence of a given event. If the p-value is less than the significance level, the hypothesis test is statistically significant. To be specific, for a 95 percent significance level, if the p-value is less than 0.05, we conclude that a significant difference between the rates of non- compliances in pre- and post-campaign does exist.

In this project, the team considered a 95 percent confidence level. Given this, to be 95 percent certain that an observed drop shows an actual change in behavior as opposed to random fluctuation, the data would have to indicate a rejection of the null hypothesis at the level α = 0.05. The magnitude of the effect was also calculated for each proxy measure.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

STUDY LOCATIONS Teaneck, NJ Bergen County, Township of Teaneck– State Street and Queen Anne Road

Overview

The Township of Teaneck encompasses an area of six square miles with an estimated population of 41,311. The intersection of State Street and Queen Anne Road is located approximately a half-mile from Benjamin Franklin Middle School and in the geographic center of the township. Three blocks to the south is Milton A. Votee Park, and Windsor Park is two blocks to the west of the intersection. Moreover, north of Queen Anne Road, one block from the intersection, is the Yeshivat Camera 1 He’Atid, a private middle school, which increases pedestrian activity in this area during its normal hours of operation. The intersection features small buildings that house businesses facing the sidewalk to the south and automotive service businesses to the north. Surrounding land uses are primarily residential. To the south, past Interstate 80, Queen Anne Road turns into Teaneck’s main business district.

Queen Anne Road is a two-way street running in the Camera 2 north-south direction. There are two lanes in each direction on the south side of the intersection, with arrow markings in the southbound direction. The north side has one lane in each direction, with the southbound lane splitting into two lanes to allow turns at the intersection. Unprotected bike lanes are present in both directions on the north side of the study intersection. State Street runs east-west. There Figure 3. Intersection of Queen Anne Road and State Street and are dedicated left-turn pockets in both directions. Camera Views in Teaneck, NJ There is one through lane in each direction, which splits into three lanes at the intersection in both The intersection is controlled by a traffic signal and directions. There is a left-turn pocket in both pedestrian crosswalks are present at all four approaches. directions, with right turns permitted from the right The study team positioned cameras on the east and south lane. It should be noted that “No Right Turns on corners of the intersection to record all pedestrian and Red” signs are placed at this intersection, so Proxy 3 vehicle movements (Figure 3). was not measured. Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

The pre- and post-campaign data show similar vehicle volumes; however, there was a reduction in pedestrian volume during the post-campaign observation. The study team conducted post-campaign observations during summer break, which may have contributed to the decrease in pedestrian activity. Table 2 contains detailed information on pedestrian and vehicle volumes.

The analysis found that there is an increase in pedestrian traffic from noon to 1 p.m. The north-bound approach generated major pedestrian flow, while the west-bound approach showed more vehicles entering the intersection. Furthermore, Figure 4 evaluates the campaign’s impact, showing the change in rates of non- compliant behaviors from the pre- and post-campaign observations.

Table 3. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 112 472 0.237 86 253 0.340 43.3% 0.103 0.034 0.173 0.998 Insignificant Increase 2 57 167 0.341 32 152 0.211 -38.3% -0.131 -0.225 -0.032 0.005 Significant Reduction Teaneck 3 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 4 47 5343 0.009 26 4931 0.005 -40.1% -0.004 -0.007 0.000 0.017 Significant Reduction

The results for the Township of Teaneck show There were statistically significant reductions in red improvements in rates of non-compliance for drivers light signal running and turning vehicles failing to but no statistically significant change in pedestrian stop for pedestrians. These proxies reduced by 40.1 behaviors (Table 3). It should be noted that the total and 38.3 percent, respectively. number of pedestrians in the post-campaign observations dropped by 46 percent, which may be attributable in large part to the post-campaign being conducted after the end of the school year. Right turns on red are prohibited at this intersection during the hours of observation, so Proxy 3 measurement was not applicable at this site.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Figure 4. Changes in Rates of Non-Compliance in Teaneck–State Street and Queen Anne Road (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

While there were reductions in these two driver behaviors following the campaign, there was an insignificant increase in the number of pedestrians who did not use crosswalks or crossed against the signal.

In terms of hours, the maximum reduction in the non- compliant behaviors for each proxy are as follows: Proxy 1 between 10 a.m. and 11 a.m. (-4 percent), Proxy 2 between 12 p.m. and 1 p.m. (-64.5 percent), and Proxy 4 between 10 a.m. and 11 a.m. (-76.6 percent). Whereas for Proxy 1, an increase in the non-compliant behaviors occurred between 1 p.m. and 2 p.m. (119.0 percent), which is the maximum increase observed during the entire period.

Based on the results (Appendix 1), it is reasonable to conclude that enforcement action at the intersection during those hours will aid in decreasing non-compliant behaviors.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Monmouth County, City of Asbury Park– Memorial Drive and Springwood Asbury Park, NJ Avenue

Overview

The City of Asbury Park has a land area of 1.42 square miles and a population of 15,767. The intersection of Memorial Drive and Springwood Avenue is located approximately a half-mile from Asbury Park Middle School in the southern part of the township. Wesley Lake is one block to the west. The intersection is located near the Asbury Park Train Station and there are train tracks parallel to Memorial Drive. The intersection is surrounded by several residential apartments and a Camera 1 shopping center. The intersection is approximately one mile west of the shoreline.

Memorial Drive is a two-way, north-south street with two lanes in each direction. There is an isolated right- turn lane on the south-west side of the intersection that merges into Memorial Drive. Both sides of the intersection have crosswalks for pedestrians.

Springwood Avenue runs in the east-west direction. There is a dedicated right-turn pocket in the south Camera 2 direction. Springwood Avenue ends after one block east. It should be noted that Springwood Avenue is a two-way single lane road with a “No Turn on Red” sign on south-bound approach. Therefore, Proxy 3 was not measured for this approach.

A traffic signal controls the intersection and crosswalks are present at three intersection approaches. The study team positioned cameras on Figure 5. Intersection of Memorial Drive and Springwood Avenue and Camera Views in Asbury Park, NJ the southwest and northeast corners of the intersection in order to record all pedestrian and vehicle movements (Figure 5).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

Traffic volume for this intersection was similar during both the pre- and post-campaign data collection.

However, there was an increase in pedestrian volume during the post-campaign observation, which took place during summer. Table 2 provides the total pedestrian and vehicle volumes.

This intersection saw an increase in the pedestrian flow from noon to 1 p.m. The north-bound approach generated major pedestrian flow, while the west-bound approach showed more vehicles entering the intersection. Figure 6 shows the change in rates of non- compliant behaviors during the pre- and post-campaign observations. Table 4. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 126 142 0.887 65 161 0.404 -54.5% -0.484 -0.568 -0.384 0.000 Significant Reduction 26 58 0.448 13 51 0.255 -43.1% -0.193 -0.355 -0.013 0.018 Significant Reduction Asbury 2 Park 3 18 54 0.333 5 50 0.100 -70.0% -0.233 -0.378 -0.075 0.002 Significant Reduction 4 11 3336 0.003 7 2909 0.002 -27.0% -0.001 -0.004 0.002 0.256 Insignificant Reduction

Figure 6. Changes in Rates of Non-Compliance in Asbury Park – Memorial Drive and Springwood Avenue Road Intersection. (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

The results for the City of Asbury Park demonstrate The Asbury Park campaigns had promising results, with statistically significant improvements in pedestrian and significant improvements to pedestrian behaviors at the driver behaviors (Table 4). There was a 54.5 percent target intersection and drivers being more respectful of reduction in unsafe crossing and crossing against the traffic lights, stop signs, and “No Turn on Red” signs. signal following the campaign. For Proxy 2 (turning vehicle fails to stop for pedestrians), there was a 43 percent decrease in the rate of non-compliant behavior following the campaign. There were 70 and 27 percent reductions in rates of non-compliance for Proxies 3 and 4, respectively.

Breaking it down by time of day, the maximum reduction in the non-compliant behaviors for each proxy was observed as follows: Proxy 1 between 12 p.m. and 1 p.m. (-66.3 percent), Proxy 2 between 11 a.m. and 12 p.m. (-78.1 percent), Proxy 3 between 12 p.m. and 1 p.m. (-76.9 percent), and for Proxy 4 between 11 a.m. and 12 p.m. (-100.0 percent).

Whereas for Proxies 2 and 4, increases in non- compliant behaviors were reported between 12 p.m. and 1 p.m. (18.1 percent) and 10 a.m. to 11 a.m. (2.3 percent), respectively. This data highlights potential to target enforcement to address non-compliant behaviors at this intersection. The full releases are available in Appendix 2.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Bergen County, City of Garfield – Garfield, NJ Midland Avenue and Van Winkle Avenue

Overview

The City of Garfield is 2.10 square miles with a population of 30,489. The T-intersection of Midland Avenue and Van Winkle Avenue is located a mile from Garfield High School in the southern part of the city. A rail track runs parallel to Midland Avenue to the west and intersects Van Winkle Avenue. The intersection has residential apartments on its east side, and there is a pharmacy and shopping center to the east. The intersection is a half-mile east of the Passaic River.

Midland Avenue is a two-way, north-south street with Camera 1 one lane in each direction. Each lane splits into two lanes/pockets at the intersection approach. Vehicles traveling north have a left-turn pocket lane at the intersection. Vehicles approaching southbound have a right-turn pocket lane and are permitted to turn right on red. There is a crosswalk on the southern part of Midland Avenue. The intersection is controlled by a traffic signal.

Van Winkle Avenue is a two-way, two-lane road Camera 2 with one lane for each direction that starts at the T- intersection, goes west from Midland Avenue and terminates after the River Drive intersection at the Passaic River. There is a dedicated right turn pocket lane for the southbound turn onto Midland Avenue. It should be noted that turning right on red is prohibited from Van Winkle Avenue to Midland Avenue.

The study team positioned cameras on the west and Figure 7. Intersection of Midland Avenue and Van Winkle Avenue and Camera Views in Garfield, NJ north corners of the intersection in order to record all pedestrian and vehicle movements (Figure 7).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

This intersection showed nearly identical vehicle and pedestrian volumes for both the pre- and post-campaign observations. Table 2 presents detailed pedestrian and vehicle volumes. Pedestrian volume at this intersection increased between 10 a.m. and 11 a.m., while vehicle volumes increased in the afternoon from 12 p.m. to 1 p.m. Most vehicles entered the intersection from the west. The pre- and post-campaign non-compliance data is presented in Figure 8.

Table 5. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 44 106 0.415 33 91 0.363 -12.6% -0.052 -0.184 0.083 0.226 Insignificant Reduction 2 13 41 0.317 13 37 0.351 10.8% 0.034 -0.168 0.236 0.626 Insignificant Increase Garfield 3 129 160 0.806 71 241 0.295 -63.5% -0.512 -0.588 -0.421 0.000 Significant Reduction 4 59 3393 0.017 20 3646 0.005 -68.5% -0.012 -0.017 -0.007 0.000 Significant Reduction

The results for the City of Garfield indicate significant improvements in driver behaviors but show no statistically significant change in pedestrian behaviors (Table 5). Following the campaign, there was a 63.5 percent reduction in turning vehicles stopping for pedestrians before turning right on red and a 68.5 percent reduction in vehicles stopping at red signals. In regards to proper pedestrian crossing behavior, the 12-percent reduction in non-compliant Figure 8. Changes in Rates of Non-Compliance in Garfield – behavior is notable, but not statistically significant. Midland Avenue and Van Winkle Avenue (Proxy 1: Unsafe The number of turning vehicles stopping for Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right pedestrians appears to have gotten worse following Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light the campaign. There was a 10.8 percent increase in Signal or Stop Sign) the rate of drivers failing to stop for pedestrians; however, the change is not statistically significant.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

The hourly data found that the maximum drops in the non-compliant behaviors for each proxy were as follows: Proxy 1 between 12 p.m. and 1 p.m. (-49.3 percent), Proxy 2 between 10 a.m. and 11 a.m. (-69.6 percent), Proxy 3 between 9 a.m. and 10 a.m. (-71.6 percent), and Proxy 4 between 12 p.m. and 1 p.m. (-83.4 percent). However, for Proxy 1 and Proxy 2 there was an increase in the non-compliant behaviors between 9 a.m. and 10 a.m. (54.5 percent), and between 11 a.m. and 12 p.m. (500 percent). Based on these findings (see

Appendix 3), the study team recommends an increase in police enforcement to enhance pedestrian safety. Overall, the evaluation found unsafe behavior reduced at the study intersection in the City of Garfield (Midland Avenue and Van Winkle Avenue) and motorist compliance with traffic lights, stop signs, and “No Turn on Red” signs improved.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Essex County, City of Newark – Raymond Boulevard and Mulberry Newark, NJ Street

Overview

The City of Newark is New Jersey’s largest city with 277,105 residents spread across 24.19 square miles. The intersection of Raymond Boulevard and Mulberry Street is located a 0.3 mile from Military Park in the geographic central part of the city. The Passaic River is to the east of the intersection. The U.S. Social Security Administration, PSE&G, One Newark Center and the Seton Hall Law School are all located at this intersection The intersection is located 0.4 miles from the Newark Penn Station. As a result, this intersection Camera 1 experiences a high volume of pedestrians.

Raymond Boulevard is a two-way street running in the east-west direction. It has three lanes on one side and two lanes on the other side, which further splits into three while approaching near the intersection. There are designated right and left turn lanes at all of the intersection approaches.

Mulberry Street is a two-way street running in a north-south direction. It has three lanes in each Camera 2 direction and a parallel parking lane. The intersection is controlled by a traffic signal and crosswalks are provided on all four intersection approaches. Cameras were positioned on two corners of the intersection to record the movements of pedestrians and drivers (Figure 9).

Figure 9. Intersection of Raymond Boulevard and Mulberry Street and Camera Views in Newark, NJ

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

There were similar vehicle traffic volumes during the pre- and post-campaign observations; however, the pedestrian volume dropped during the post-campaign, possibly due to colder weather. See Table 2 for detailed information regarding pedestrian and vehicle volumes. The data shows there was an increase in the pedestrian flow from 12 p.m. to 1 p.m., and the highest vehicle volume was between 9 a.m. and 10 a.m. The majority of pedestrians and vehicles entered the intersection from the east. Figure 10 shows the change in rates of non-compliant behaviors between the pre- and post- campaign observations.

Table 6. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 629 2083 0.302 239 1762 0.136 -55.1% -0.166 -0.191 -0.141 0.000 Significant Reduction 2 398 844 0.472 222 826 0.269 -43.0% -0.203 -0.247 -0.157 0.000 Significant Reduction Newark 3 97 376 0.258 48 372 0.129 -50.0% -0.129 -0.184 -0.073 0.000 Significant Reduction 4 61 6066 0.010 28 6147 0.005 -54.7% -0.006 -0.009 -0.002 0.000 Significant Reduction

There were significant improvements in drivers and pedestrians complying with the laws following the campaign (Table 6). There was at least a 50 percent drop in non-compliant behaviors for three of the proxies. The largest change was a 55 percent reduction in pedestrians crossing outside of a crosswalk or against the signal (Proxy 1). In addition, there was a nearly 55 percent reduction in vehicles failing to stop at red lights (Proxy 4). There was a 43 percent reduction in turning vehicles failing to stop for pedestrians (Proxy 2) and a 50 percent reduction in turning vehicles failing to stop for pedestrians before turning right on red (Proxy 3).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

The largest drops in non-compliance were between 12 p.m. and 1 p.m. for proxies 1, 2 and 3 (-65.4 percent, -53.5 percent and -70.3 percent, respectively) and from 11 a.m. to 12 p.m. for proxy 4 (-79.7 percent). There was no increase in non- compliant behaviors noted for any proxy following the Street Smart NJ campaign.

Of all eight municipalities that participated in this study, the City of Newark saw the most significant decreases in non-compliant behavior following the campaign. It should be noted that the City of Newark observation site was the most heavily traveled intersection among the eight locations in this study.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Morris County, Borough of Morris Plains – Speedwell Avenue and Franklin Road Morris Plains, NJ

Overview

The Borough of Morris Plains is 2.56 square miles with a population of 5,528. The intersection of Speedwell Avenue and Littleton Road is located approximately a quarter mile from the Morris Plains 9/11 Memorial Park and Alfred Vail Elementary School is half-mile south. Two blocks to the west is the Morris Plains library. Running to the north Speedwell Avenue turns into Granniss Avenue. The Morris Plains train station is at the intersection, which generates increased pedestrian and vehicles volume during early morning hours. There are also stores on Franklin Place. Camera 1 Speedwell Avenue runs from north to south. It is a two-way street with one lane in each direction. On the south side of the intersection, the northbound lane splits into two lanes to allow for left-hand turns. On the north side of the intersection, the southbound lane splits into two lanes with a left turn only lane.

Littleton Road runs from east to west and has one lane in each direction. Coming from east to west, the westbound direction splits into two lanes at the Camera 2 intersection for right turns. Turning on red is not permitted from this direction at any time. Franklin Road runs east to west and has one lane in each direction.

The study team positioned cameras on the northeast and southeast corners of the intersection in order to record all pedestrian and vehicle movements (Figure 11).

Figure 11. Intersection of Speedwell Avenue and Franklin Road and Camera Views in Morris Plains, NJ

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

There was similar pedestrian traffic during both the pre- and post-campaign observations; however, vehicle volume during the pre-campaign observation was higher than post-campaign count. The difference in vehicle volume might be due to a change in the day of the week that data was collected. This change was necessitated by inclement weather. Table 2 contains detailed information on the pedestrian and vehicle volumes. Maximum pedestrian and vehicle volume were recorded from 7 a.m. to 8 a.m. Figure 12 shows the change in rates of non-compliant behaviors from the pre-campaign to the post-campaign observations.

Table 7. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 50 134 0.373 31 111 0.279 -25.2% -0.094 -0.207 0.025 0.060 Insignificant Reduction 10 27 0.370 15 42 0.357 -3.6% -0.013 -0.240 0.203 0.456 Insignificant Reduction Morris 2 Plains 3 29 58 0.500 5 18 0.278 -44.4% -0.222 -0.419 0.040 0.049 Significant Reduction 4 303 7030 0.043 94 5577 0.017 -60.9% -0.026 -0.032 -0.020 0.000 Significant Reduction

There was a significant improvement in the number of drivers stopping for people crossing before turning right on red and stopping at a red signal (44.4 percent and nearly 61 percent, respectively). While pedestrian behaviors improved, the results were not statistically significant. There were statistically insignificant reductions in turning vehicle’s failure to stop for pedestrians. Proxy 2 was reduced by only 3.6 percent (Table 7). In terms of hours, the largest drops in non- compliance for Proxies 1 and 2 were between 10 a.m. and 11 a.m., -58.3 percent, and -100 percent respectively. The largest drop in non-compliant behavior for Proxy 3 and 4 was between 9 a.m. and 10 However, there were some increases in non-compliant a.m. (-100 percent, and -76.7 percent, respectively). behaviors following the campaign: Proxy 1 between 9

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

a.m. and 10 a.m. (60.0 percent), Proxy 2 between 7 a.m. and 8 a.m. (37.2 percent), Proxy 3 between 8 a.m. and 9 a.m. (100.0 percent) and Proxy 4 between 10 a.m. and 11 a.m. (3.8 percent).

Future campaigns in Morris Plains may consider altering the message delivery to pedestrians at this intersection, as the data shows that pedestrian behavior did not achieve a statistically significant reduction in non-compliance. These results indicate that the campaign in this community was effective at reducing unsafe driving behaviors, which resulted in increased safety for both pedestrians and drivers.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Princeton, NJ Mercer County, Municipality of Princeton – Washington Road/ Vandeventer Ave and Nassau Street

Overview

The Municipality of Princeton is 1.84 square miles and has 28,595 residents. The intersection of Washington Road/Vandeventer Avenue and Nassau Street is located at the heart of Princeton’s central business district and next to the Princeton Garden Theatre and the Princeton United Methodist Church. It is approximately 0.2 miles from Palmer Square, a popular plaza with a collection of shops, restaurants, offices, and residential spaces. Camera 1 The intersection connects Princeton University to the plaza on Nassau Street and surrounding neighborhoods on Vandeventer Avenue, which increases the pedestrian volume during the university’s working hours.

Nassau Street is a two-way road running in the north- south direction with bike lanes in both directions. Vandeventer Avenue intersects with Nassau Street and connects Princeton University with local Camera 2 communities.

There are left-turn pockets and arrows in both directions on Nassau Street. Both roads have one lane in each direction and right turns on red are prohibited at all four approaches. Because of this, Proxy 3 was not measured for this intersection. The intersection is controlled by a traffic signal and there are crosswalks at all four approaches. Figure 13. Intersection of Nassau Street and Vandeventer Avenue and Camera Views in Princeton NJ

The study team positioned cameras on the southwest and northeast corners of the intersection to capture all movements (Figure 13).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

This intersection showed comparable vehicle volume during both pre- and post-campaign data collection.

However, the observations showed a decrease in the pedestrian volume during the post-campaign data collection. The variation in pedestrian volume might have been due to Cyber Monday and weather conditions, as the intersection is mostly used by Princeton University students and staff. Table 2 provides detailed information about pedestrian and vehicle volumes.

The highest pedestrian traffic was between 12 p.m. and

1 p.m., while the highest vehicle volume was between 11 a.m. and 12 p.m. The majority of the pedestrian flow came from the north-bound approach, while the west- bound approach showed more vehicles entering the intersection. Figure 14 summarizes the effect of the campaign and shows the change in rates of non- compliant behaviors between the pre- and post- campaign observations.

Table 8. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 410 1758 0.233 312 965 0.323 38.6% 0.090 0.055 0.126 1.000 Insignificant Increase 2 51 287 0.178 17 179 0.095 -46.6% -0.083 -0.142 -0.017 0.007 Significant Reduction Princeton 3 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 4 129 2670 0.048 54 2692 0.020 -58.5% -0.028 -0.038 -0.019 0.000 Significant Reduction

There were significant decreases in the number of percent for Proxy 4). In Proxy 1, there was an increase turning vehicles that failed to stop for people crossing in the non-compliant behaviors compared to pre- and in the number of drivers who ran red lights, 46.6 campaign for the overall period, but the maximum percent, and 58.5 percent respectively. There was an increase was observed between 12 p.m. and 1 p.m. (56.7 insignificant increase in the number of pedestrians who percent). Based on the results (Appendix 6), the study crossed outside of a crosswalk or against a signal (Table team suggests increasing police enforcement at this 8). The largest reductions in the non-compliant intersection during those hours to decrease the non- behaviors for the two driver proxies were between 11 compliant behaviors. a.m. and 12 p.m. (-82.5 percent for Proxy 2 and -73.5 Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Figure 14. Changes in Rates of Non-Compliance in Princeton ‒ Nassau Avenue and Washington Road

These results indicate that the campaign was effective in reducing unsafe driving behaviors, and therefore, improved safety for both pedestrians and drivers. The data showed an insignificant increase in pedestrian non- compliance behaviors, possibly attributable to data collection on Cyber Monday and a drop in temperature. Right turns on red are prohibited at this intersection during the hours of observation, so Proxy 3 was not observed at this site.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Bergen County, Borough of Rutherford Rutherford, NJ – Glen Road and Park Avenue

Overview

The Borough of Rutherford is 2.81 square miles and has 18,061 residents. The intersection of Glen Road and Park Avenue is located next to a Dunkin Donuts, the Park Avenue Pet Center, Goffin’s Hallmark Shop, and many other locally owned businesses. Continuing in the north direction is a rotary connecting Erie Ave and Park Avenue. When traveling south on Park Avenue, there are various parks for people to enjoy.

Park Avenue is a two-way road running in the north- Camera 1 south direction with on-street parking spaces on both sides of the street. Park Avenue does not have bike lanes, but it does have sidewalks for pedestrians. Glen Road runs in the east-west direction and also provides on-street parking spaces on both sides of the street. The Glen Road approach is controlled by a stop sign. It should be noted that there is a “No Left Turn” sign when driving from Glen Road onto Park Avenue. Because of this, Proxy 3 and Proxy 4 are Camera 2 similar at this location.

The study team positioned cameras on the southwest corners of the T-intersection in order to record all pedestrian and vehicle movements (Figure 15).

Figure 15. Intersection of Glen Road onto Park Avenue and Camera Views in Rutherford, NJ ddddddddddddddddddddddddddddddddddddddddddd

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

There were similar vehicle and pedestrian volumes during the pre- and post- campaign observations. Table 2 contains detailed information on pedestrian and vehicle volumes.

During the overall data collection period, maximum pedestrian volume was observed between 11 a.m. and 12 p.m. In addition, Figure 16 shows the changes in observed behaviors between the pre-and post- campaign.

Table 9. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 56 364 0.154 66 466 0.142 -7.9% -0.012 -0.062 0.036 0.311 Insignificant Reduction 2 24 112 0.214 23 153 0.150 -29.8% -0.064 -0.161 0.029 0.089 Insignificant Reduction Rutherford 3 159 184 0.864 154 207 0.744 -13.9% -0.120 -0.196 -0.041 0.001 Significant Reduction 4 159 184 0.864 154 207 0.744 -13.9% -0.120 -0.196 -0.041 0.001 Significant Reduction

There was a significant reduction in the number of vehicles failing to stop at the stop sign following the campaign (13.9 percent), which here is similar to vehicles failing to stop before right turn at stop sign (Proxy 3). There was a nearly 8 percent decrease in the number of people who crossed outside of a crosswalk; however, this change is not statistically significant. There was an insignificant statistical reduction in Proxy Figure 16. Changes in Rates of Non-Compliance in Rutherford ‒ 2 (29.8 percent) (Table 9). In addition, relative to other Park Avenue and Glen Road. (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop communities, Proxy 4 showed a higher rate of non- for Pedestrian, Proxy 3 Failure to Stop before Right Turn at Red compliant behaviors during both pre- and post- Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) campaign. There is a “No Left Turn” sign when driving compliance reduction among pre- and post-campaign from Glen Road onto Park Ave. While there is a stop was identified as follows: Proxy 1 between 9 a.m. and sign to control traffic at the end of Glen Road, many 10 a.m. (-44.2 percent), Proxy 2 between 12 p.m. and 1 drivers either did not stop at the stop line or did not stop p.m. (-61.5percent), Proxy 3 between 10 a.m. and 11 at all, resulting in a failure to yield in Proxy 4. a.m. (-23.8 percent), and Proxy 4 between 10 a.m. and Nonetheless, the study team did find a statistically 11 a.m. (-23.8 percent). Whereas, increase in the non- significant reduction in Proxy 4 non-compliance. compliant behaviors compared to pre-campaign According to the hourly results, the maximum non- Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

occurred as follows: Proxy 1 between 11 a.m. and 12 p.m. (26.0 percent), Proxies 2, 3, and 4 between 9 a.m. and 10 a.m., 132.2 percent, 4.6 percent, and 4.6 percent respectively. The results (Appendix 7) suggest that increased police enforcement or traffic controllers at the intersection for a specific time period may help decrease the non-compliant behaviors. Overall, the Borough of Rutherford showed a reduction in non-compliant behaviors. But there was no statistically significant change observed for pedestrians.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Middlesex County, Township of Woodbridge, NJ Woodbridge – Main Street and Eleanor Place

Overview

The township of Woodbridge is 23.2 square miles and has 99,403 residents. The 3-way T-intersection of Main Street and Eleanor Place is located near the Woodbridge Municipal Court, which is in the epicenter of the commercial area of Woodbridge. It is 0.2 miles away from the Woodbridge railway station. It should be noted that this location was evaluated in previous Street Smart NJ programs. Camera 1 Main Street connects the community to Route 9 and the Garden State Parkway. The east-west street has one lane in each direction near the intersection. To the west of the intersection, the North Jersey Coast Line railway track passes on a bridge over Main Street. There are several restaurants and stores on both sides of Main Street. Notably, there are no marked crosswalks on Main Street at the intersection, but there is a signalized crosswalk nearby, under the rail bridge. Camera 2 Eleanor Place is a north-south road that connects a residential area to Main Street. There is a restaurant on one side of Eleanor Place and mini-mart adjacent to the intersection. This approach has a marked crosswalk for pedestrians.

The intersection has no traffic signal and is controlled by a stop sign on Eleanor Place. The study team positioned cameras on the south and east Figure 17. Intersection of Main Street and Eleanor Place and Camera corners of the intersection in order to record all Views in Woodbridge, NJ pedestrian and vehicle movements (Figure 17).

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Results and Discussions

There were similar vehicle and pedestrian volumes during the pre- and post-campaign observations. Table 2 shows detailed information on pedestrian and vehicle volumes.

During the overall data collection period, the maximum pedestrian volume was observed between 10:30 a.m. and 11:30 a.m., while the maximum vehicle volume was observed between 11:30 a.m. and 12:30 p.m.

Figure 18 shows the changes in observed behaviors between the pre-and post-campaign.

Table 10. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 1 57 138 0.413 61 150 0.407 -1.5% -0.006 -0.119 0.106 0.456 Insignificant Reduction 2 63 126 0.500 24 108 0.222 -55.6% -0.278 -0.387 -0.155 0.000 Significant Reduction Woodbridge 3 22 83 0.265 7 98 0.071 -73.1% -0.194 -0.304 -0.086 0.000 Significant Reduction 4 63 147 0.429 42 143 0.294 -31.5% -0.135 -0.241 -0.024 0.008 Significant Reduction

There was a significant reduction in the number of vehicles failing to stop at the stop sign following the campaign (31.5 percent). There was a 1.5 percent decrease in the number of people who crossed outside of a crosswalk; however, this change is not statistically significant. There were significant statistical reductions in Proxy 2 and 3 (55.6 percent, and 73.1 percent, respectively). The Figure 18. Changes in Rates of Non-Compliance in Woodbridge‒Main Street and Eleanor Place. (Proxy 1: Unsafe findings are detailed in Table 10. Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) The largest reduction in the pedestrian proxy (Proxy

1) was between 12:30 p.m. and 1:30 p.m. (-25.0 The largest increases in non-compliant behaviors percent). The largest differences in the driver proxies were as follows: Proxy 1 between 11:30 a.m. and were between 10:30 a.m. and 11:30 a.m. (-67.52 12:30 p.m. (44.8 percent), and Proxy 2 between percent) for Proxy 2, between 9:30 a.m. and 10:30 11:30 a.m. and 12:30 p.m. (42.11 percent). a.m. (-100.0 percent) for Proxy 3, and between 12:30 p.m. and 1:30 p.m. (-51.12 percent) for Proxy 4 Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Based on these results (Appendix 8), it is appropriate to conclude that enforcement actions near the intersection during the noon hours could help decrease these non-compliant behaviors.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

SUMMARY OF RESULTS In addition to the results of each individual community, Table 12 shows the number of intersection legs and the Based on the statistical methods described in previous intersection control method. Grouping all intersections sections, the study team measured the significance in together by their configuration (number of intersection the change of each proxy at each location. Table 11 legs) allows for a comparison of the average percent presents a summary of the results with the observed change in the rate of non-compliance. change in the rate of non-compliant behaviors, 휌̂ − 휌̂ , 1 2 In terms of intersection geometry, 4-leg intersections and the P-value associated with this change. For a exhibited more consistent improvements across all four change to be statistically significant at the 95 percent measures: total percent changes were -22 percent for level (α = 0.05), the P-value must be less than 0.05. Proxy 1, -40 percent for Proxy 2, -53 percent for Proxy 3, and -51 percent for Proxy 4 (Table 14). The changes The results demonstrate that there was an overall for the 5-leg intersection (Morris Plains) were -25 decrease in dangerous behaviors following the percent for Proxy 1, -3 percent for Proxy 2, -44 percent campaigns and many of these reductions were for Proxy 3, and -61 percent for Proxy 4. The statistically significant. It is worth mentioning that more corresponding total for the 3-leg intersections were -12 than three hours of video data was collected at each site, percent for Proxy 1, -44 percent for Proxy 2, -65 percent allowing the sample sizes to be large enough to prove for Proxy 3, and -28 percent for Proxy 4. There was a that the changes in behavior appear to be systematic reduction in all aspects of the behaviors (Table 14). rather than simple random variations, especially at the Grouping all the intersection types shows that there are urban intersections. significant reductions with respect to non-compliance

behavior in all proxies. To be sure of the magnitude of the changes in behavior, it is best to look at the upper and lower bounds of The insignificant reductions associated with Proxy 1 confidence intervals, because the true change may be and Proxy 2 at 5-leg intersections could be a result of more or less than the observed change due to random the confusion that 5-leg intersections, compared to 4- variation. As described in the preceding section, Data leg intersections, can create for people traveling Analysis and Results, the calculation of these through them. But, it is not clear exactly why the confidence intervals are more accurate than the analysis behaviors vary at different intersection designs. for the hypothesis testing, because the method explicitly However, it should be noted that in this study, there is recognizes that the study team evaluated a change in only one 5-leg intersection in the Morris Plains, which rates rather than a simple normally distributed variable. is controlled by a traffic signal. It is possible that a combination of factors such as weather condition and Negative values are favorable results, as they show holiday contribute in different ways to the effectiveness reductions in unsafe behaviors, which is the goal of the of the Street Smart NJ campaign in changing pedestrian Street Smart NJ campaign. Positive values indicate and driver behaviors. increases in unsafe behaviors following the campaign. These increases can be associated with other influential factors such as weather conditions and day of the week.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Table 11. Change in Counts and Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

Pre-Campaign Post-Campaign Change Rate Lower Upper Community Proxy Non- Sample Rate Non- Sample Rate P- Signification 1 2 % Difference 95.0% 95.0% Compliant n1 (pˆ ) Compliant n2 (pˆ ) Value Test (pˆ2-pˆ1) CI CI 112 472 0.237 86 253 0.340 43.3% 0.103 0.034 0.173 0.998 Insignificant Increase 1 2 57 167 0.341 32 152 0.211 -38.3% -0.131 -0.225 -0.032 0.005 Significant Reduction Teaneck N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 3 47 5343 0.009 26 4931 0.005 -40.1% -0.004 -0.007 0.000 0.017 Significant Reduction 4 126 142 0.887 65 161 0.404 -54.5% -0.484 -0.568 -0.384 0.000 Significant Reduction 1 26 58 0.448 13 51 0.255 -43.1% -0.193 -0.355 -0.013 0.018 Significant Reduction Asbury 2 Park 3 18 54 0.333 5 50 0.100 -70.0% -0.233 -0.378 -0.075 0.002 Significant Reduction

4 11 3336 0.003 7 2909 0.002 -27.0% -0.001 -0.004 0.002 0.256 Insignificant Reduction

1 44 106 0.415 33 91 0.363 -12.6% -0.052 -0.184 0.083 0.226 Insignificant Reduction

2 13 41 0.317 13 37 0.351 10.8% 0.034 -0.168 0.236 0.626 Insignificant Increase Garfield 3 129 160 0.806 71 241 0.295 -63.5% -0.512 -0.588 -0.421 0.000 Significant Reduction

4 59 3393 0.017 20 3646 0.005 -68.5% -0.012 -0.017 -0.007 0.000 Significant Reduction

1 629 2083 0.302 239 1762 0.136 -55.1% -0.166 -0.191 -0.141 0.000 Significant Reduction

2 398 844 0.472 222 826 0.269 -43.0% -0.203 -0.247 -0.157 0.000 Significant Reduction Newark 3 97 376 0.258 48 372 0.129 -50.0% -0.129 -0.184 -0.073 0.000 Significant Reduction

4 61 6066 0.010 28 6147 0.005 -54.7% -0.006 -0.009 -0.002 0.000 Significant Reduction

1 50 134 0.373 31 111 0.279 -25.2% -0.094 -0.207 0.025 0.060 Insignificant Reduction

2 10 27 0.370 15 42 0.357 -3.6% -0.013 -0.240 0.203 0.456 Insignificant Reduction Morris Plains 3 29 58 0.500 5 18 0.278 -44.4% -0.222 -0.419 0.040 0.049 Significant Reduction

4 303 7030 0.043 94 5577 0.017 -60.9% -0.026 -0.032 -0.020 0.000 Significant Reduction

1 410 1758 0.233 312 965 0.323 38.6% 0.090 0.055 0.126 1.000 Insignificant Increase

2 51 287 0.178 17 179 0.095 -46.6% -0.083 -0.142 -0.017 0.007 Significant Reduction Pri nceton 3 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

4 129 2670 0.048 54 2692 0.020 -58.5% -0.028 -0.038 -0.019 0.000 Significant Reduction

1 56 364 0.154 66 466 0.142 -7.9% -0.012 -0.062 0.036 0.311 Insignificant Reduction

2 24 112 0.214 23 153 0.150 -29.8% -0.064 -0.161 0.029 0.089 Insignificant Reduction Rutherford 3 159 184 0.864 154 207 0.744 -13.9% -0.120 -0.196 -0.041 0.001 Significant Reduction

4 159 184 0.864 154 207 0.744 -13.9% -0.120 -0.196 -0.041 0.001 Significant Reduction

1 57 138 0.413 61 150 0.407 -1.5% -0.006 -0.119 0.106 0.456 Insignificant Reduction

2 63 126 0.500 24 108 0.222 -55.6% -0.278 -0.387 -0.155 0.000 Significant Reduction Woodbri dge 3 22 83 0.265 7 98 0.071 -73.1% -0.194 -0.304 -0.086 0.000 Significant Reduction

4 63 147 0.429 42 143 0.294 -31.5% -0.135 -0.241 -0.024 0.008 Significant Reduction

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

All of the 4-leg intersections in the study (i.e., Teaneck, percent (Table 13). Whereas Proxy 2 behavior (turning Asbury Park, Newark, and Princeton) are controlled by vehicle fails to stop for pedestrians) significantly a traffic signal. The 3-leg intersection in Garfield is decreased by 37.8 percent in signalized intersections, controlled by a traffic signal, and the 3-leg intersections and there was a significant reduction of 50.7 percent in in Rutherford and Woodbridge are controlled by a stop intersections with a stop sign. Proxy 3 behaviors (failure sign (Table 12). Overall, the aggregated results from all to stop before right turn at red signal or stop sign) communities show that the majority of pedestrian and showed significant reductions of 55 percent and 22.1 driver unsafe behaviors were improved following the percent at intersections controlled by a traffic light and Steet Smart NJ campaign (Table 14). Intersections with a stop sign, respectively. Proxy 4 (running a red which are controlled with a traffic light have significant light or stop sign) showed significant decreases of 59.7 reductions in non-compliant behaviors. Unsafe crossing percent at traffic signals and 16.5 percent at stop signs. and crossing against the signal at the intersection with Overall, signalized intersections showed greater traffic lights significantly decreased by 21.5 percent in reductions in unsafe behaviors when compared to comparison with unsafe crossing at an intersection with unsignalized intersections. a stop sign, which had an insignificant decrease of 8.4

Table 12. Statistically Significant Change in Rate of Non-Compliant Behaviors Based on Intersection Geometry (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign) Intersection Intersection Community Proxy 1 Proxy 2 Proxy 3 Proxy 4 Geometry Control Teaneck 4-Leg Signal 43.3% -38.3%* N/A -40.1%* Asbury Park 4-Leg Signal -54.5%* -43.1%* -70.0%* -27.0% Garfield 3-Leg Signal -12.6% 10.8% -63.5%* -68.5%* Newark 4-Leg Signal -55.1%* -43.0%* -50.0%* -54.7%* Morris Plains 5-Leg Signal -25.2% -3.6% -44.4%* -60.9%* Princeton 4-Leg Signal 38.6% -46.6%* N/A -58.5%* Rutherford 3-Leg Unsignalized -7.9% -29.8% -13.9%* -13.9%* Woodbridge 3-Leg Unsignalized -1.5% -55.6%* -73.1%* -31.5% *Statistically significant reduction in rate of non-compliance

Table 13. Statistically Significant Change in Rate of Non-Compliant Behaviors Based on Intersection Traffic Control (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

Traffic Control Proxy 1 Significant Test Proxy 2 Significant Test Proxy 3 Significant Test Proxy 4 Significance Test

Signalized -21.5% Significant -37.8% Significant -55.0% Significant -59.7% Significant

Unsignalized -8.4% Insignificant -50.7% Significant -22.1% Significant -16.5% Significant

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Table 14. Change in Counts and Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign (Proxy 1: Unsafe Crossing and Crossing against the Signal, Proxy 2: Turning Vehicle Fails to Stop for Pedestrian, Proxy 3: Failure to Stop before Right Turn at Red Signal or Stop Sign, and Proxy 4: Running Red Light Signal or Stop Sign)

Pre-Campaign Post-Campaign Change

Grouping Measure Sample Non- Rate Sample Non- Rate Significance % 흆̂ − 흆̂ P-Value ퟐ ퟏ Test 풏ퟏ Compliant 흆̂ퟏ 풏ퟐ Compliant 흆̂ퟐ Proxy 1 5,197 1,484 29% 3,959 893 23% -21.0% -0.060 0.000 Significant All Proxy 2 1,662 642 39% 1,548 359 23% -40.0% -0.154 0.000 Significant Intersections Proxy 3 890 454 51% 933 189 20% -60.3% -0.308 0.000 Significant Proxy 4 28,169 832 3% 26,252 425 2% -45.2% -0.013 0.000 Significant Proxy 1 134 50 37% 111 31 28% -25.2% -0.094 0.060 Insignificant

5-Leg Proxy 2 27 10 37% 42 15 36% -3.6% -0.013 0.456 Insignificant Intersections Proxy 3 58 29 50% 18 5 28% -44.4% -0.222 0.049 Significant Proxy 4 7,030 303 4% 5,577 94 2% -60.9% -0.026 0.000 Significant Proxy 1 4,455 1,277 29% 3,141 702 22% -22.0% -0.063 0.000 Significant

4-Leg Proxy 2 1,356 532 39% 1,208 284 24% -40.1% -0.157 0.000 Significant Intersections Proxy 3 430 115 27% 422 53 13% -53.0% -0.142 0.000 Significant Proxy 4 17,415 248 1% 16,679 115 1% -51.6% -0.007 0.000 Significant Proxy 1 608 157 26% 707 160 23% -12.4% -0.032 0.089 Insignificant

3-Leg Proxy 2 279 100 36% 298 60 20% -43.8% -0.157 0.000 Significant Intersections Proxy 3 402 310 77% 493 131 27% -65.5% -0.505 0.000 Significant Proxy 4 3,724 281 8% 3,996 216 5% -28.0% -0.021 0.000 Significant

Table 15 shows the significant reductions in non- To be specific, pedestrians in cold weather may be more compliant behavior among both pedestrians and drivers likely to rush, causing an increase in the probability of following the campaign; however, the improvement in unsafe behavior. driver behavior was two times larger than pedestrian behavior. The weather could have played a role in these On the other hand, in adverse weather conditions, results. For example, people walking are less likely to drivers tend to be more careful, which result towards wait for a signal before crossing in cold or inclement increased driver caution and safety compliance. In weather. The changing of seasons could have played a addition, pedestrians take more risks in crossing the role in this result. unsignalized intersections that carry low traffic volumes.

Table 15. Change in Rates of Non-Compliant Behaviors from the Pre- to Post-Campaign for All Intersections

Road Users Change of Non-Compliant Behavior Significance Test

Pedestrian -21 % Significant

Driver -41 % Significant

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Overall, the observational study showed significant positive results for the Street Smart campaign. Analysis of aggregated observations from all eight locations combined reveals statistically significant reductions in non-compliant behaviors among drivers and pedestrians, including (Table 14 and 15):

 41 percent reduction in non-compliant behavior of drivers;  21 percent reduction in pedestrian non-complaint behavior;  40 percent reduction in turning vehicle failing to stop for a pedestrian;  60.3 percent reduction in drivers failing to stop before turning right on red or at a stop sign;  45.2 percent reduction in drivers running a red light or stop sign; and  21 percent reduction in pedestrians crossing against the signal or outside the crosswalk

Regarding intersection geometry, there were statistically significant improvements for all four proxies at 4-leg intersections and for Proxies 2, 3, and 4 at 3-leg intersections. There were also significant reductions in Proxy 3 and Proxy 4 at 5-leg intersections.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

CONCLUSIONS

While overall improvements in unsafe behavior were In the Township of Woodbridge there was an documented, they are not spread evenly among the eight insignificant reduction in non-compliant behavior of communities. For example, in the Township of Teaneck, pedestrians. But there were significant improvements in there were significant improvements in driver behaviors all three driver behaviors. and an insignificant increase in non-compliant The combined changes in counts and rates of non- pedestrian behavior. While it is statistically unlikely that compliant behaviors from the pre- to post-campaign for Street Smart NJ directly caused worse behavior, there are all intersections showed significant improvement in a number of potential reasons why pedestrians were less behavior of both pedestrians and drivers. The 4-leg compliant after the campaign. Pedestrian behaviors at intersections in this study showed the most significant the beginning of May might have differed from the end reductions among all of the intersections. Some of these of June because a notable number of pedestrians during results could be related to the signalizing of the the pre-campaign were students at a nearby school, intersection, as the greatest behavioral improvements which was not in session during the post-campaign were evident at signalized intersections. The study found observations. that the Street Smart NJ campaigns worked as expected In the township of Asbury Park, there was a significant in improving the behavior of pedestrians and drivers. reduction in non-compliant behavior for both pedestrians Upon further analysis, non-compliant behaviors for and drivers. In the City of Garfield, there was significant drivers improved twice as much as non-compliant reduction in non-compliant behavior of drivers, while behavior among pedestrians. there was an insignificant reduction in non-compliant Overall, the Street Smart NJ program was largely behavior among pedestrians. For the City of Newark, the successful, although not universally so. Notably, busy Street Smart NJ campaign showed a significant urban intersections (e.g., Newark) showed more reduction in all non-compliant behaviors among both consistent improvements in safety behavior as a result of pedestrians and drivers. In Morris Plains, there was education and enforcement compared to suburban significant decrease in non-compliant behavior of locations (e.g., Rutherford) with lower traffic volumes. drivers, but there were no significant changes in This is a promising result because busy urban pedestrian behavior. The observations from Princeton intersections have higher crash rates and the areas where yielded mixed results; pedestrian behavior did not the greatest safety benefits can be realized through change significantly while drivers showed improvement. education and enforcement activities. Furthermore, the The studies in the Borough of Rutherford showed changes to the three vehicle behavior proxies improved significant reductions in the rate of vehicles traveling across almost all sites when the observations are through the Park Avenue and Glen Road intersection considered collectively. These results are consistent with without making a complete stop at a stop sign and in the the results of the behavioral survey, which found rate of vehicles failing to stop before right turn at a stop statistically significant improvements in terms of sign. All other behaviors in Rutherford showed pedestrian behaviors (i.e., crossing against the signal or insignificant improvements. outside the crosswalk) and driver behaviors (e.g., drivers

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

not stopping for pedestrians in crosswalk) in most of the communities after the Street Smart NJ campaign. The data confirmed that the Street Smart NJ program methodology demonstrated success in reducing risky behaviors among drivers and pedestrians.

This study is limited by the fact that changes in observed behaviors were measured, and the ultimate effect on safety depends on the extent to which these behaviors are correlated with crash risk and pedestrian safety. There are also additional factors such as community pedestrian safety infrastructure, household income, disparity in sample size between the different communities, and community efforts which potentially may affect the changes. The results presented in this report support the principle that education and enforcement programs such as Street Smart NJ can be effective in supporting engineering safety improvements. Such success highlights the value of using high-level systemic solutions in conjunction with fine-tuned engineering solutions at specific locations.

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 1: Hourly Observed Non-Compliance: Township of Teaneck

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 2: Hourly Observed Non-Compliance: City of Asbury Park

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 3: Hourly Observed Non-Compliance: City of Garfield

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 4: Hourly Observed Non-Compliance: City of Newark

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 5: Hourly Observed Non-Compliance: Borough of Morris Plain

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 6: Hourly Observed Non-Compliance: Municipality of Princeton

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 7: Hourly Observed Non-Compliance: Borough of Rutherford

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report

Appendix 8: Hourly Observed Non-Compliance: Township of Woodbridge

Rutgers Center for Advanced Infrastructure and Transportation and Rowan University, 2019 North Jersey Transportation Planning Authority Street Smart Behavioral Pedestrian Safety Study: Final Report