Measuring Unsafe Pedestrian Behavior Using Observational Data *Achilleas Kourtellis, Ph.D Pei-Sung Lin, Ph.D., P.E., PTOE Makarand Gawade Center for Urban Transportation Research University of South Florida 4202 East Fowler Avenue, CUT100 Tampa, FL 33620-5375 Tel:(813) 974-8073 Fax:(813) 974-5168 kourtellis@cutr.usf.edu Word Count: 4593 words + 5 figures + 4 tables = 6843 words November 14, 2012 * corresponding author
ABSTRACT Florida has a severe problem with pedestrian and bicyclist fatalities. Recent trends show that Florida s pedestrian fatality rate is almost double the national average. Traditional safety programs rely on crash data to develop safety campaigns or countermeasures to increase safety. Since crash data are not readily available and a long time has to pass before meaningful data is collected, a risk score was developed to measure the behavior of road users at selected sites in Hillsborough and Miami-Dade counties. Surveys were conducted in June-July 2012 in two of the highest pedestrian crash and fatality counties in Florida to collect data and establish baseline conditions. The surveys included opinion surveys of pedestrians and observations of pedestrians and bicyclists, and their interaction with drivers. The locations where the surveys were conducted were selected based on site characteristics including pedestrian treatments or features, crash history, and land use. The two surveys offered insight on the difference between what people know about the law or correct behavior, and what they actually do in reality. Results pinpoint the problems and aid in deciding the focus of safety campaigns and target audience. The risk score showed that the majority of sites exhibited unsafe behavior from pedestrians, bicyclists, and drivers. The risk score has the potential to aid in measuring the effectiveness of a safety campaign launched by FDOT focused on increasing the awareness on traffic laws. This way, appropriate countermeasures or funds can be selected for the higher ranking sites first.
Kourtellis et al. 1 INTRODUCTION Based on NHTSA Traffic Safety Facts, Florida had the highest pedestrian fatality rate, 2.51 pedestrian fatalities per 100,000 population, among all states in 2009 (1). The Florida pedestrian crash rate is double the national average. According to the Dangerous by Design report, a national study in 2011, Florida had an especially high percentage of pedestrian and bicyclist fatalities relative to its population. In addition, based on the same report, the following statements can be summarized: Florida has six percent of the population in the U.S., but 11 percent of all U.S. pedestrian fatalities occurred in Florida, 17.4 percent of all U.S. bicyclist fatalities occurred in Florida, and Florida has been in the top three most dangerous states for bicyclists and pedestrians every year since 2001. According to the report, Florida also has four metropolitan areas ranked in the top ten of the most dangerous cities for walking in the U.S. including: 1) Orlando-Kissimmee, 2) Tampa-St. Petersburg-Clearwater, 3) Jacksonville, and 4) Miami-Fort Lauderdale-Pompano Beach (2). It is very difficult to pinpoint the root cause for the Sunshine State s roadways being so dangerous for pedestrians and bicyclists. The debate centers on a behavior versus infrastructure argument. Although there are many factors to consider, common contributing issues may include the following: Population growth contributes to congested highways and traffic-related issues, particularly in urban centers such as the Tampa Bay and Miami areas. Florida s large immigrant population may be unfamiliar with traffic laws. Florida s warm and humid climate allows for year-round outdoor travel and tourism. A research team at the the University of South Florida has been coordinating with FDOT to conduct surveys and establish the level of law awareness of users, as well as the magnitude of the problem based on non-traditional data. The developed site risk score aims to assist program managers in evaluating campaigns, and establish countermeasures without having to wait on crash data which are rare events and one has to wait for a long period for meaningful data. The team hopes to change pedestrian law awareness in both user groups (pedestrians and drivers) so that a behavior is established where all users share the road with fewer incidences, injuries and fatalities. The target areas were Miami-Dade and Hillsborough counties. This paper investigates the level of knowledge of people or their perception regarding pedestrian laws, and what they actually do in reality. Ideally these two should match. The investigation continues on the potential relationship and comparison between these two data sets. The paper describes the method of collecting data on these, and through analysis understand better the reasons why pedestrian crash rates are at such high levels compared to the rest of the country. Section two presents a literature review on similar studies, section three describes the study methodology, section four presents the data collection effort and section five presents the analysis and results. Finally, a discussion on conclusions and future work concludes the paper. LITERATURE REVIEW A detailed review was performed to better understand the previous research conducted in the field of pedestrian safety focusing on studies similar to the current study. Martin et al. (3; 4) conducted a comprehensive review of pedestrian safety and summarized studies evaluating the effect of different engineering countermeasures on pedestrian safety. The demographic characteristics of pedestrian crashes reviewed showed that 60 percent of pedestrian crashes occurred at an intersec-
Kourtellis et al. 2 tion. Pedestrians alone or both pedestrians and vehicles were at fault for 59.1 percent of pedestrian related crashes. These statistics further emphasize that the behavior of pedestrians while crossing on or near intersections is at risk and warrants more insightful research. Different characteristics which influence these behavioral fallacies are studied to understand their relation (if any) to pedestrian crash risks. It has been proven in recent research that distracted pedestrians/drivers show higher unsafe crossing behavior (5). The distraction was attributed to the use of mobile phones while crossing the intersection. Epidemiological research has attributed gender as a risk factor for pedestrian injury with males being more likely to be in a pedestrian crash (6; 7). A simulation technique called Pretend Road Method was used to understand the roles of age in children s pedestrian safety (8). The study concluded that an older aged pedestrians were more aware while crossing. Several before-and-after studies have been conducted to understand the effect of engineering countermeasures (e.g. adding crosswalks, pedestrian signals, signs, stop signs, etc) on pedestrian safety. The measure of pedestrian safety is the risk factor which can be measured by crash rates. Crash rates have been used as risk measures in previous studies evaluating the effect of crosswalks at uncontrolled locations (9) and the pedestrian safety program for children (10). Crash rates, however, are an inefficient risk measure due to the very low frequency rate and pedestrian exposure. Hence, several studies have used surrogate measures to measure a pedestrian risk factor. Behavioral changes like speed of vehicles, yielding behavior of drivers, percentage of pedestrians crossing on crosswalk, numbers of violations at pedestrian red signal, and cautiousness shown by pedestrians before crossing are some of the surrogate measures used in different studies (11; 12; 13). There have been several techniques to collect data: observation by manual counting, video recordings and interviews (14). Interviews are generally done to understand the public s perceptions (15) or their travel experiences (16). STUDY METHODOLOGY The study was designed to collect data on two main aspects: 1)What road users (pedestrians and drivers) know regarding pedestrian traffic laws and 2)What they do in reality. As mentioned earlier, part of the problem is that crash data are not a timely measure because it takes time (years) for trends to be established and become known to administrators. A new method has to be employed, where data collected within a short time period can be analyzed to identify pedestrian pedestrian safety issues, as well as specific hot spot locations. First, the sites to perform surveys were selected. Then, mapping of the crashes for the last five available years (2006-2010) was utilized to create hot spots of pedestrian crashes. Following the data collection, an analysis of the data resulted in the calculation of a pedestrian risk score for the sites surveyed. The methodology is described in more detail in the following subsections. For the purposes of this study, both pedestrians and bicyclists were included together in the data collection and analysis since they are both considered vulnerable road users. Site Selection The study involved collecting data of pedestrian crossing behavior and driver behavior towards crossing pedestrians in Hillsborough and Miami-Dade counties. The sites were selected by a three stage process:
Kourtellis et al. 3 1. Pedestrian Crash Hot Spot: Pedestrian crash data for years 2006-2010 were obtained from FDOT s Crash Analysis Reporting System (CARS) Database. This data provided the details of all the pedestrian crash locations, as well as other variables such as injury severity, number of pedestrians and drivers, type of crash, etc. These crash locations were geocoded in ArcGIS c and overlayed on a street map as one point for each crash on the exact coordinates the crash was reported. The primary reason for this mapping was to select the sites with historically large numbers of pedestrian crashes (trend for last 5 years of available data). Figure 1 shows the method used to create the hot spots which simply represent the locations with the highest concentration of crashes. The method is described in Figure 1. A circle (buffer) of 100ft radius was drawn for each crash, this making its effective diameter 200ft. If two crashes were 200ft apart they created one cluster of size 2 and so on. The clusters kept growing based on the number of neighboring crashes. The radius of the circle was changed in trials from 50ft to 250ft, resulting in distances of 100ft to 500ft between two neighboring crashes. What changed with this range was the cluster size, which is how many crashes are included in the highest clusters. The location of the highest cluster size remained primarily the same for every trial. Since the purpose of this exercise was to identify the locations or sites for the surveys with the highest crash frequency (hot spots), this method was deemed adequate. The clusters therefore had a different size for each of the two counties, depending on how many total crashes occurred in each county. Figures 2 and 3 represent the hot FIGURE 1 Method used to identify hot spots. spot maps for the two counties. The highest 10 cluster size locations are shown. Based on funding and resources, site selection criteria for Hillsborough and Miami-Dade counties were location hot spots of more than four and nine crash-buffers respectively because this allowed for a total of 20 sites for Hillsborough and 34 for Miami-Dade. Hot spots with smaller cluster size (less than four for Hillsborough and nine for Miami-Dade) were omitted from the data collection for this study because not all sites could be surveyed. 2. Intersection Characteristics: Annual Average Daily Traffic (AADT) obtained from FDOT online data, the number of lanes along the route, the presence of bus stops in the vicinity of the site and the presence of sidewalks and pedestrian features such as pedestrian signals, crosswalks, etc. The intersection characteristics helped identify the sites with the highest combination of all desirable features: high traffic volume and as many pedestrian features as possible. The land use surrounding the sites was also coded in general terms so that the expected pedestrian traffic was included in the selection. Land use was divided into residential, commercial and mixed use. 3. Random Selection: To make the study less biased, the final sites were randomly selected from the second stage list, using a random number operator. Eventually, 34 sites were selected in
Kourtellis et al. 4 FIGURE 2 Examples of Miami-Dade County pedestrian crash hot spots.
Kourtellis et al. 5 FIGURE 3 Examples of Hillsborough County pedestrian hot spots.
Kourtellis et al. 6 Miami-Dade and 20 sites were selected in Hillsborough counties. A visual check was performed by mapping the selected sites on street maps to observe if the sites were uniformly distributed as per the hot spot buffers throughout the county areas. DATA COLLECTION Data collection for this study involved two different surveys as explained in the methodology section: 1. Opinion Surveys 2. Observational Surveys Every site had two surveyors. One focused on the observational survey and the other surveyor performed the opinion survey. The surveys were performed for three hours at every site, and the time of day/day of week was designed to match the crash history. The surveys are described in detail in the following sections. Opinion Surveys The first survey was the public opinion survey which was designed to collect the understanding or knowledge of the users as it applies in pedestrian safety and pedestrian traffic laws. The survey also included demographics, crash experience, road-user experience and safety perception questions. It was found to be extremely difficult to collect full surveys as pedestrians either did not have time or did not want to take the survey. Several surveys were therefore incomplete. As an incentive, a tote bag, t-shirt, and reflectors were given at the site, and the opportunity to sign up for a bicycle drawing online after the survey completion was also given as an option. Major importance was given to the pedestrian law survey section, hence, surveyors were asked to give those questions first priority. Observational Surveys Observational surveys offer an insight into the passing pedestrians behavior and walking/crossing patterns. As explained in the literature review section, a list of surrogate measures were to be used in order to calculate the pedestrian risk score. These surrogates included: User (pedestrian or bicyclist) crossing on crosswalk on user signal green time User crossing on crosswalk on user signal red time User not crossing on the crosswalk in perpendicular direction to street User not crossing on the crosswalk in diagonal direction to street Driver yielding/not yielding for the waiting or crossing user (vehicular movement was noted and classified in three types: through movement, left turn, right turn) User did/did not use the sidewalks (if available) before and after crossing the intersection. User risk increases if proper precautions are not taken while crossing the streets. Hence, data for the following measures were also collected: Was the alertness of the user hindered while he was crossing the intersection? (the alertness of the user was considered to be hindered if he/she were crossing while using a cell phone or electronic device, or talking to another crossing user). Were the bicyclists wearing safety gear like a helmet and using bicycle lights? Were the pedestrians or bicyclists moving in the direction of vehicular traffic or in the direction against vehicular traffic?
Kourtellis et al. 7 Some demographic characteristics of the crossing users were collected as well. As the study required the observers to collect data of the actual behavior of the users, the demographic characteristics were not asked of the users as it would distract them. These characteristics were estimates and depended solely on discretion of the observer. The approximate age group, race, and gender of the users was collected. Surveyors also observed the behavior of drivers when they were in the potential conflict with the user. A traffic conflict is defined as An observable situation in which two or more road users approach each other in space and time to such an extent that there is risk of collision if their movements remain unchanged. Hence, it was observed whether or not the drivers yielded for a crossing/waiting user. The vehicle s movement on the intersection was observed as well. ANALYSIS AND RESULTS Three separate data sets were created; one for the user s (pedestrian or bicyclist) behavior, one for vehicle interaction with users, and one for the opinion survey questionnaire. Several analyses were conducted with a principal goal of estimating the current risk score of the different sites in the two counties. The following are some analyses conducted and presented in this paper: Aggregate analysis Risk score analysis Relationship analysis Part 1: Aggregate Analysis As mentioned earlier, the law awareness regarding pedestrians was a major subject in the surveys. The following questions were asked by the surveyors to collect data on the law awareness of users. Again here, users refers to both pedestrians and bicyclists. Table 1 summarizes the answers given by the respondents for the questions in the two counties. The following were some questions asked in the survey: Q1: Which user has the right of way in the following situations? Q1a: At signalized intersection with marked or unmarked crosswalk, user is on the crosswalk. Q1b: At midblock location with marked crosswalk, user is on crosswalk. Q1c: At midblock location with unmarked crosswalk, user is crossing. Q2: A vehicle can proceed when a user is crossing a crosswalk and clears their half of the road. Q3: Users can cross anywhere they want (not necessarily on a crosswalk or at intersection). Q4: When there are sidewalks, users are prohibited from walking on the roadway. Q5: What is Jaywalking? Q6: When you are walking along a road without sidewalks, which side should you be? Q7: When you are biking along a road without sidewalks, which side should you be? Q8: At a crosswalk with signals, you can start crossing when? As seen in Table 1, there are some variations in user law awareness levels in the two counties: Hillsborough county users have a better law awareness than Miami-Dade county users concerning right-of-way while crossing streets in different situations. Approximately, 61 percent of pedestrians and 58 percent of bicyclists are aware of the direction in which they should travel (walk against traffic or bike with traffic).
Kourtellis et al. 8 TABLE 1 Responses to Law Awareness Survey Questions Q1a Q1b Q1c Survey Answers Hillsborough Miami-Dade Hillsborough Miami-Dade Hillsborough Miami-Dade Vehicle 11.48% 22.97% 19.67% 24.52% 65.00% 56.25% User (ped/bicyclist) 88.52% 77.03% 80.33% 75.48% 35.00% 43.75% Q2 Q3 Q4 Survey Answers Hillsborough Miami-Dade Hillsborough Miami-Dade Hillsborough Miami-Dade Strongly agree 4.10% 13.27% 3.23% 3.88% 16.94% 36.00% Agree 42.62% 34.60% 12.90% 18.93% 58.06% 39.50% Disagree 38.52% 38.86% 54.84% 47.57% 20.16% 19.50% Strongly disagree 14.75% 13.27% 29.03% 29.61% 4.84% 5.00% Q5 Survey Answers Hillsborough Miami-Dade Walking for fun 0.88% 3.37% Crossing the road in a diagonal line 1.77% 6.18% Crossing the road at any place other than at a marked crosswalk 85.84% 71.91% Not crossing perpendicular with the road 4.42% 4.49% Crossing the road midblock between two adjacent signalized intersections 7.08% 14.04% Q6(Walk) Q7(Bike) Survey Answers Hillsborough Miami-Dade Hillsborough Miami-Dade Didn t answer 0.00% 7.31% 0.00% 7.76% Walk(Q6)/bike(Q7) with traffic 34.68% 29.22% 60.48% 55.71% Both directions 4.84% 0.91% 1.61% 0.91% Walk(Q6)/bike(Q7) against traffic 60.48% 62.56% 37.90% 35.62% Q8 Survey Answers Hillsborough Miami-Dade Steady red signal 2.52% 5.29% Flashing red signal 2.52% 6.73% Steady green signal 79.83% 74.04% Numeric countdown 5.88% 9.13% Any time 9.24% 4.81% * Shaded cells represent correct possible answers based on Florida law. Approximately 80 percent of users are aware of the correct time to cross at a signalized intersection. Higher percentages of Hillsborough county users have some understanding of jaywalking. (Based on Florida statutes: Jaywalking is crossing the street midblock and between two signalized intersections with crosswalks and signals. Also, pedestrians have to cross the road on right angles to the edge and not diagonally). Overall the majority of pedestrians/bicyclists that responded to the survey knew the pedestrian laws and how to be safe on the road. Table 2 illustrates a summary of all the variables collected in the observational survey for the user and vehicle data sets. The following are inferences based on the descriptive statistics presented in Table 2: Usage of sidewalk: It was observed that in both counties, most of the users used the side-
Kourtellis et al. 9 TABLE 2 Descriptive Statistics for Observations Attributes Hillsborough Miami-Dade Number of sites 20 34 Number of users (pedestrians & bicyclists) 1575 4084 Number of bicyclists 422 (26.79%) 612 (14.98%) Percent of non-alert users 15.97% 15.84% Percent of users who didn t walk on sidewalk 4.07% 4.20% Percent of bicyclist who didn t use helmet 96.78% 86.45% Percent of bicyclists riding against traffic 29.74% 22.47% Percent of users not crossing on crosswalk 27.79% 20.42% Percent of crosswalk users crossing on red 24.40% 34.59% Percent of vehicles not yielding to users 44.12% 44.75% Percent of users crossing diagonally 49.15% 45.24% walks whenever available. There was no significant difference in the usage of sidewalks by users or bicyclists. Alertness of user: Similar results were observed for both counties with the bicyclists being more alert while crossing than pedestrians. A total of 18.22 percent and 16.67 percent non-alert pedestrians were observed in Hillsborough and Miami-Dade, whereas 7.60 percent and 10.43 percent non-alert bicyclists were observed in the two counties respectively. Helmet usage and bicycle lights usage by bicyclists: Helmet usage in Miami-Dade was higher than in Hillsborough, but the usage is still very low. Crossing behavior of users: There was a higher percentage of users crossing on the crosswalk in Miami-Dade County than in Hillsborough County, but ironically red signal violators were higher in Miami-Dade. Vehicles yielding behavior for users: Aggregate drivers yielding behavior in both counties did not show significant statistical difference. Demographics: Figure 4 shows the variation in gender distribution across users. Race: Caucasian, Hispanic and African American users were observed predominantly in both counties with a higher share of Hispanic users in Miami-Dade County. Asian users were not very frequent. Age: Age distribution was similar for pedestrians and bicyclists as well across the two counties. Figure 5 shows that the majority of users were from the young or middle-aged population (81.64% of all users). The categories were what the surveyors observed (not the actual age group) since they did not survey all pedestrians. The age group categories were: 1)Child (<18 yrs old), 2) Young (19-30 yrs old), 3) Middle (31-55 yrs old) and 4) Older (>55 yrs old) approximately. Part 2: Risk Score Analysis The risk score is an empirical score calculated to measure the unsafe behavior observed at each site based on the data collected. It is a weighted score of all the risk measures determined in the observational survey. A single risk score was calculated for every site. The risk score was calculated based on the users crossing behavior (user risk score) and vehicle-user interaction (driver
Kourtellis et al. 10 $!!"#,!"#!"#$"%&'(")*+),-"#-) +!"# *!"# )!"# (!"# '!"# &!"# %!"# B.>47.# =47.# $!"#!"# -./.012345# 637708929:;<# -./.012345# =34>3#?3@A@7301# 637708929:;<#?3@A@7301# =34>3#.-"#)/01")1"#)2*,%&0) FIGURE 4 Gender demographics for the two counties. '!"# &$"# =7.2*#>?%@#<-+#42*A# B4506#>%CD'$#<-+#42*A# 8.**2)#>'$D$$#<-+#42*A# E2*)-#>F$$#<-+#42*A# &!"#!"#$"%&'(")*+),-"#-) %$"# %!"# $"#!"# ()*)+,-./0#1.22+34-4567# ()*)+,-./0#8./9.# :.;<;2.+,#1.22+34-4567# :.;<;2.+,#8./9.#.-"#)/01")20)3*,%&0) FIGURE 5 Age distribution for the two counties.
Kourtellis et al. 11 risk score). The following formula was used: User Risk Score = P i W i (1) Where, i represents the different surrogates, P is the proportion of the users and W is the weight of different surrogates used in the study. The different risk measures included in the calculation above were the following (weight, W in parenthesis): User crossing on crosswalk (0), User not crossing on crosswalk (-2), User crossing on user green signal (1), User crossing on user red signal (-1), Alert user (0), Non-alert user (-2), Bicyclist riding in direction of traffic (1), Bicyclist riding against direction of traffic (0), User approaching intersections with no sidewalks (-2). The baseline or control condition of a user doing everything safely (crossing on the crosswalk, being alert and using sidewalks or walking/riding on the correct side) had a weight of zero, so unsafe behavior was penalized with a negative value. The magnitude of the weight is arbitrary but empirical based on previous work performed by the authors (17). Engineering judgment and common sense were used to establish the weights. For example, if the users crossed on green, then the weight was positive one, but if they crossed on red, then it was negative one. If the users did not cross on the crosswalk then they were given a weight of negative two. The idea being that this behavior is more dangerous than crossing on the crosswalk even on red signal time. Similarly, the driver risk score was calculated with the same formula as above, but with the measure P being the number of vehicles not yielding to a crossing pedestrian. The weight for this behavior was a negative two. The two risk scores (user, driver) are shown in Table 3. In both counties, the average risk scores are negative. Hence, on average, sites are not safe, which corresponds with the fact that the surveyed sites had the highest crash frequency in the last 5 years. The t-values indicate that the difference in the risk score for the sites is statistically not significant at 95 percent confidence interval. We also observed that there are four sites in Miami- Dade county and three sites in Hillsborough county with positive user risk scores. These numbers are greater than the base value of zero, which indicate safer sites. These values are good indicators of how safe/unsafe the sites are and will be helpful to evaluate the improvement in safety after the awareness campaign. Part 3: Relationship Analysis The variables computed in this study were categorical. Hence, simple Pearson s correlation or regression models could not be used to understand the relationship between different variables. Chi- square or Fisher s exact test were instead used to check if there is any relationship between two variables. The investigation was to determine if there was a relationship between the variables as described: i
Kourtellis et al. 12 TABLE 3 User and Driver Risk Scores for Miami-Dade and Hillsborough Counties Miami-Dade User Driver Hillborough User Driver Sites Risk Score Risk Score Sites Risk Score Risk Score 1-0.67-1.09 1 0.18-1.11 2-1.18-0.50 2-1.30-0.50 3-0.97 0.00 3-0.39 0.00 4-0.61-2.00 4-0.92-0.80 5-0.23 0.00 5-0.50-0.67 6-1.43 0.00 6-2.36-1.66 7-0.47 0.00 7-0.07-0.33 8-0.46 0.00 8-0.20-1.60 9-0.38-1.36 9-0.72 0.00 10 0.67-1.18 10-1.10-1.56 11-0.92-0.73 11-0.13-0.67 12-0.12-0.69 12-0.42-1.00 13 0.04-1.17 13 0.19-0.34 14-0.44 0.00 14-0.07 0.00 15-1.33-0.80 15-1.34 0.00 16-0.81-0.78 16 0.24-0.86 17-0.60-1.40 17 0.09 0.00 18-1.16-1.05 18-0.21-1.60 19-0.94-0.29 19-0.52-1.23 20-0.49-0.89 20-1.37-0.86 21-0.85-0.55 Mean -0.55-0.74 22 0.51-0.60 Std dev 0.67 0.59 23-0.68 0.00 t value -0.81-1.25 24-0.69-1.08 25-0.67 0.00 26-0.25-1.00 27 0.35-0.10 28-0.05-1.32 29-0.72-1.33 30-0.45-1.14 31-1.14-0.50 32-1.13-1.60 33-1.44 0.00 34-0.22-0.71 Mean -0.59-0.70 Std dev 0.52 0.56 t value -1.13-1.25 Hypothesis: H o = There is no relationship between the two variables H 1 = There is a relationship between the two variables The chi-square value was computed. If it exceeded the critical value, then the null hypothesis is rejected and it can be stated that there is a relationship between the two variables. To understand if there is any strength in the relationship between the categorical variables, contingency coefficient or Cramer s V measures were used. A value closer to one suggests a strong relationship between the variables.
Kourtellis et al. 13 Table 4 gives the test result values for crossing locations (on crosswalk/not on crosswalk) and other variables. Chi-square values and Cramer s V values (in parenthesis) can be seen for any two variables. TABLE 4 Relationship Analysis for Crossing Location Variable 2 County Variable 1 Gender Race Age Alertness Riding Use of Type of Direction Sidewalk User Hillsborough 0.23 72.31* 15.1* 0.6 2.9 90.2** 0.11 Crossing (0.011) (0.19) (0.02) (0.09) (0.23) (0.23) (0.007) Miami-Dade Location 28.32* 158.13* 21.9* 9.13* 3.3 97.7** 0.04 (0.078) (0.19) (0.07) (0.05) (0.08) (0.15) (0.03) * indicates that there is some relation between Variables 1 & 2 but Cramer s V value indicates that the relationship is weak. ** indicates that there is a reasonably good relationship between variable 1 and 2. Table 4 shows that the relationships vary across the two counties with Miami-Dade county showing more of a relationship between the crossing locations and the variables. Similar test runs were performed for different variables and layer variables. The highlighted results are listed below: For males, there is no relationship between being alert and crossing behavior. However, females showed a good relationship between the two behaviors. Both males and females showed a weak relationship between walking on the sidewalk and crossing on the crosswalk. Similarly, a weak relationship was observed between the two behaviors for pedestrians and bicyclists. There is a weak relationship between race and crossing behavior for both pedestrians and bicyclists. The relationship is stronger among pedestrians. No relationship was observed between gender and crossing behavior for both pedestrians and bicyclists. CONCLUSION AND FUTURE WORK The team calculated a risk score based on observational data which is a measure of unsafe behavior at selected sites. The scores showed that based on their characteristics and observed pedestrian/bicyclist and driver behavior, the selected sites were primarily unsafe for pedestrians. Based on the calculated risk score, 12 percent of sites in Miami-Dade County and 15 percent of sites in Hillsborough County exhibited marginally safer behavior. For both counties, the driver risk score was lower (negative value) than the pedestrian risk score. Therefore, drivers were riskier and more dangerous towards pedestrians. A comparison of the two surveys showed some interesting results: Based on the survey responses, the majority of pedestrians and bicyclists (82%) knew that a pedestrian or bicyclist has the right-of-way on a crosswalk and a smaller percentage (60%) knew that a vehicle has the right-of-way when a pedestrian is crossing midblock without a marked crosswalk. It was observed that about 44 percent of drivers did not yield to a crossing pedestrian when on a crosswalk. About 47 percent of pedestrians asked, did not know that a vehicle can actually proceed when its half of the road is cleared by a crossing pedestrian.
Kourtellis et al. 14 Regarding crossing location, 80 percent of users know that they cannot cross anywhere they want, but 24 percent were observed crossing somewhere other than a crosswalk. Approximately 75 percent of users said that when sidewalks are present, pedestrians are prohibited from using the road, and the observational data corroborates that only four percent of users were observed not using the sidewalks (sidewalks were presents at all sites). About 98 percent of users asked, knew some definition of jaywalking, but 47 percent were observed to cross the road diagonally, and obviously not on a crosswalk. (some start on the crosswalk but did not follow the crosswalk path, rather changed direction halfway). Fifty eight percent of bicyclists knew that they have to ride with traffic, but 52 percent were observed riding against traffic. A total of 84 percent of users knew when they are allowed to start crossing at a crosswalk with pedestrian signals, but about 30 percent were observed crossing on red pedestrian signal. These conclusions show that the method of using opinion surveys and observational surveys to establish the difference between what people know to be the law or the correct behavior, and what is their actual behavior, varies significantly. This method can help pinpoint the problematic areas and group of users with the largest benefit from a safety campaign. The risk score calculated can be used in many sites, and can aid in evaluation of the change in risk or safety of specific sites based on individual characteristics and pedestrian/driver behavior at those sites. Future work includes more analysis of the data and inclusion of a driver opinion survey to make the data set more complete. In certain areas, there is a clear distinction between the driver population and the pedestrian population, so separate surveys have to be used to collect more accurate results relevant to each population. Such an analysis will be a guide to government agencies and can steer educational campaigns or law enforcement efforts in improving pedestrian safety. In this study, an univariate analysis of the different variables was conducted. In the future, there is a scope of undertaking for a multivariate analysis like logit modeling, or cluster analysis, to understand the effects of pedestrian characteristics, other pedestrian behavior, and site characteristics on the final crossing behavior of pedestrians and interaction with drivers. REFERENCES [1] NHTSA, Fatality Analysis Reporting System, 2009. [2] Ernst, M. and L. Shoup, Dangerous by Design. Report, Surface Transportation Policy Partnership, 2011. [3] Campbell, B., C. V. Zegeer, H. H. Huang, and M. J. Cynecki, A Review of Pedestrian Safety Research in the United States and Abroad. Report FHWA-RD-03-042, Federal Highway Administration, 2004. [4] Martin, A., Factors Influencing Pedestrian Safety: A Literature Review, 2006. [5] Nasar, J., P. Hecht, and R. Wener, Mobile Telephones, Distracted Attention and Pedestrian Safety. Accident Prevention and Analysis, Vol. 40, No. 1, 2008, pp. 69 75. [6] Assailly, J., Characterization and Prevention of Child Pedestrian Accidents: An Overview, 1997.
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