Pedestrian Crashes Research Articles

Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver’s age, victim’s age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.

Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities

In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind.

Factors affecting injury severity at pedestrian crossing locations with Rectangular RAPID Flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models

This paper evaluates the effectiveness of Rectangular Rapid Flashing Beacons (RRFB) on crash severity. The study used and compared XGBoost and Random Parameters Discrete Outcome Models (RPDOM) respectively. The dataset comprises of 312 pedestrian crossing locations, among which 154 treatment locations were provided with the Rectangular Rapid Flashing Beacons (RRFB) and 158 control locations without RRFB. These control locations have similar roadway, traffic, and land use characteristics of that of the treatment locations but are not treated with RRFB or other pedestrian crossing countermeasures. This study shows the impact of RRFB and other factors on severity of nighttime, pedestrian, total and rear-end crashes. Crash severity data was compiled from driver, vehicle, and event level data of each crash. Due to availability of larger number of observations for total (35,553), rear-end (15,675) and nighttime crashes (8,144) XGBoost was used, and due to less observations for pedestrian crashes (369), it was modeled using RPDOM. The results showed positive impact of RRFB for the reduction of nighttime crashes. It was noted that RRFB reduces the K and A nighttime crashes according to the SHAP values from the XGBoost model but does not have the desired significance for rear end and overall total crashes in the study area. From the RPDOM, it was seen that RRFB showed statistically significant reduction in injury severity of pedestrian crashes and nighttime crashes. To compare the two models, nighttime crashes were modeled using both the techniques, the prediction accuracy of XGBoost Model was 97% which was much greater than that of the RPDOM at 73.8% prediction accuracy. Thus, both XGBoost and the RPDOM model for showed positive impact of installing RRFB in reducing the severity of nighttime crashes.

An exploratory analysis of the effects of speed limits on pedestrian injury severities in vehicle-pedestrian crashes

IntroductionPedestrian safety is gaining nationwide attention in the recent years with increasing fatalities and serious injuries relative to other modes of transportation. The percentage of pedestrian fatalities in all traffic fatalities in Florida clearly highlights the importance of the vulnerability. Considering the importance of ‘Safe System’ approach, this study aims to investigate the contributing factors for different speed limits in vehicle-pedestrian crashes and resulting injury severities. MethodThis study explored the pedestrian crashes over three speed limit zones, namely, low- (≤25 mph), medium- (30–40 mph) and high-speed (45–70 mph) roadways in Florida. Utilizing 2017 (inclusive) crash data in Florida, random parameters multinomial logit models with unobserved heterogeneity in means and variances were estimated. The crash data include a wide variety of factors related to the spatial, temporal, vehicle and traffic characteristics, environmental characteristics, roadway attributes, and human factors. ResultsThe model estimates indicated that there were significant differences in pedestrian-injury severities in these speed limit zones. It is noteworthy that crashes involving pedestrian had many explanatory variables that produced marginal effects varying across low-, medium-, and high-speed limit zones. More importantly, the likelihood of pedestrian injury severity at high-speed limit zones is about 3.1 times higher for roadways with 40,000 vehicles per day, 3.2 times higher for two-way roads with positive median barrier, and 1.7 times higher for urban principal roadways relative than those at medium speed limit zones. The model results also indicate that the likelihood of severe pedestrian injury is higher for District 1, afternoon peak (4–6 p.m.), turn-lane, and dark condition on high-speed limit zones. ConclusionFormulation of countermeasures to mitigate pedestrian injury severity should be considered separately for three distinct speed limit zones. These countermeasures include the vehicle technology, roadway-infrastructure design, drivers' awareness, and pedestrians’ education interventions to minimize the system kinematic energy.

Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity

Traffic crashes involving pedestrians have a high frequency in developing countries. Among road users, pedestrians are the most vulnerable, as their involvement in traffic crashes is usually followed by severe and fatal injuries. This study aims to identify pedestrian crash patterns and reveal the random parameters in the dataset. A three-year (2015–2017) pedestrian crash dataset in Mashhad, Iran, was employed to investigate the influence of a rich set of factors on pedestrian injury severity, some of which have been less accounted for in previous studies (e.g., the vicinity to overpasses, the existence of vegetated buffers, and park lanes). A two-step method integrating latent class cluster analysis (LCA) and the mixed logit model was utilized to consider unobserved heterogeneity. The results demonstrated that various factors related to the pedestrian, vehicle, temporal, environmental, roadway, and built-environment characteristics are associated with pedestrian injuries. Furthermore, it was found that integrated use of LCA and mixed logit models can considerably reduce the unobserved heterogeneity and uncover the hidden effects influencing severity outcomes, leading to a more profound perception of pedestrian crash causation. The findings of this research can act as a helpful resource for implementing effective strategies by policymakers to reduce pedestrian casualties.

One-year impact of a multicomponent, street-level design intervention in Mexico City on pedestrian crashes: a quasi-experimental study

BackgroundMexico City implemented the Pasos Seguros programme to prevent pedestrian injuries and deaths at dangerous road intersections, which included street-level design changes, such as visible pedestrian crossings, sidewalk widening, refuge...

Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

A Bayesian generalised extreme value model to estimate real-time pedestrian crash risks at signalised intersections using artificial intelligence-based video analytics

Pedestrians represent a vulnerable road user group at signalised intersections. As such, properly estimating pedestrian crash risk at discrete short intervals is important for real-time safety management. This study proposes a novel real-time vehicle-pedestrian crash risk modelling framework for signalised intersections. At the core of this framework, a Bayesian Generalised Extreme Value modelling approach is employed to estimate crash risk in real-time from traffic conflicts captured by post encroachment time. A Block Maxima sampling approach, corresponding to a Generalised Extreme Value distribution, is used to identify pedestrian conflicts at the traffic signal cycle level. Several signal-level covariates are used to capture the time-varying heterogeneity of traffic extremes, and the crash risk of different signal cycles is also addressed within the Bayesian framework. The proposed framework is operationalised using a total of 144 hours of traffic movement video data from three signalised intersections in Queensland, Australia. To obtain signal cycle-level covariates, an automated covariate extraction algorithm is used that fuses three data sources (trajectory database from the video feed, traffic conflict database, and signal timing database) to obtain various covariates to explain time-varying crash risk across different cycles. Results show that the model provides a reasonable estimate of historical crash records at the study sites. Utilising the fitted generalised extreme value distribution, the proposed model provides real-time crash estimates at a signal cycle level and can differentiate between safe and risky signal cycles. The real-time crash risk model also helps understand the differential crash risk of pedestrians at a signalised intersection across different periods of the day. The findings of this study demonstrate the potential for the proposed real-time framework in estimating the vehicle-pedestrian crash risk at the signal cycle level, allowing proactive safety management and the development of real-time risk mitigation strategies for pedestrians.

The effect of age on injury patterns in pedestrian deaths.

Although it is known that elderly pedestrians are at increased risk of injury and death from vehicle crashes the specific pattern of lethal injuries related to age has not been extensively studied. Data on the numbers of pedestrian fatalities and ages were obtained from 1990 to 2020 from the Traffic Accident Reporting System, The University of Adelaide, Adelaide, South Australia and detailed autopsy data on fatal pedestrian crashes from the pathology database at Forensic Science SA, Adelaide, South Australia from 2000 to 2020. Fatal injuries were separated into the following regions: head/face, spine, chest, abdomen and limbs/skeleton. Analysis of 634 cases of pedestrian fatalities (1990-2020) showed a significant decline in numbers over the years (p < 0.001). Analysis of fatal injuries in 219 cases (2000-2020) showed a significant reduction in the proportion of fatal head injuries with increasing age (p < 0.05), a significant increase in the proportion of fatal chest injuries with increasing age (p < 0.01) and a significant increase in the proportion of fatal limb/skeletal injuries with increasing age (p < 0.05). Older pedestrians are, therefore, more likely to sustain lethal chest and limb/skeletal injuries than head injuries compared to those who are younger, presumably due to greater physical fragility that occurs with age, with loss of protective muscle bulk and bone density.

Partial proportional odds model for analyzing pedestrian crashes, threshold heterogeneity by scale and proportional odds factor

Despite low traffic in Wyoming, pedestrian crash severity accounts for a high number of fatalities in the state. Thus this study was conducted to highlights factors contributing to those crashes. The results highlighted that drivers under influence, type of vehicle, location of crashes, estimated speed of vehicles, driving over the recommended speed are some of factors contributing to the severity of crashes. In this study, we used proportional odds model which assumes that the impact of each attribute is consistent or proportional across various threshold values. However, it has been argued that this assumption might be unrealistic, especially at the presence of extreme values. Thus, the assumption was relaxed in this study by shifting the thresholds based on some explanatory attributes, or proportional odds effects. In addition, we accounted for the spread rate, or scale, of the model's latent distribution of pedestrian crashes. The results highlighted that the partial proportional odds model through proportional odds factor and scale effects result in a significant improvement in model fit compared with the standard proportional odds model. Comparisons were also made across standard normal, simple partial ordinal model, and partial ordinal accounting for scale heterogeneity. In addition, various potential threshold structures such as symmetric and flexible were considered, but similar goodness of fits were observed across all those models. Extensive discussion has been made regarding the formulation of the implemented methodology, and its implications. • More flexibility is given to the model by relaxing the assumption of proportional odds and scale, or spread rate. • The results highlight a significant improvement in the model fit, compared with the standard model. • Some of factors contributing to the severity of pedestrian crashes are discussed. • Almost all identified results linked to the lack of attention and caution of the drivers.

Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns

Walking plays an important role in overcoming many challenges nowadays, and governments and local authorities are encouraging healthy and environmentally sustainable lifestyles. Nevertheless, pedestrians are the most vulnerable road users and crashes with pedestrian involvement are a serious concern. Thus, the identification of pedestrian crash patterns is crucial to identify appropriate safety countermeasures. The aims of the study are (1) to identify the road infrastructure, environmental, vehicle, and driver-related patterns that are associated with an overrepresentation of pedestrian crashes, and (2) to identify safety countermeasures to mitigate the detected pedestrian crash patterns. The analysis carried out an econometric model, namely the mixed logit model, and the association rules and the classification tree algorithm, as machine learning tools, to analyse the patterns contributing to the overrepresentation of pedestrian crashes in Italy. The dataset consists of 874,847 crashes—including 101,032 pedestrian crashes—that occurred in Italy from 2014 to 2018. The methodological approach adopted in the study was effective in uncovering relations among road infrastructure, environmental, vehicle, and driver-related patterns, and the overrepresentation of pedestrian crashes. The mixed logit provided a clue on the impact of each pattern on the pedestrian crash occurrence, whereas the association rules and the classification tree detected the associations among the patterns with insights on how the co-occurrence of more factors could be detrimental to pedestrian safety. Drivers’ behaviour and psychophysical state turned out to be crucial patterns related to pedestrian crashes’ overrepresentation. Based on the identified crash patterns, safety countermeasures have been proposed.

Examining the impact of exposure, built environment and socio-demographics on pedestrian safety: A case study of Greater London

Walking as a mode of travel alleviates congestion, pollution and concerns over physical fitness. However, safety concerns might be a barrier for some people to walking more often, so finding ways to improve pedestrian safety is important. This paper conducts a ward-level study of Greater London such that factors which are associated with pedestrian safety can be identified and interventions to improve safety can be more appropriately targeted. A wide range of factors relating to exposure, land use, built and natural environment, and socio-demographics, including markers of deprivation were considered in our analysis. We employed a multilevel random parameters negative binomial regression model with a hierarchical structure which encompassed the boroughs and wards of Greater London allowing dependency in the data and unobserved heterogeneity to be addressed more fully. The results show that BAME population, number of schools, job density and alcohol expenditure are associated with an increase in pedestrian crashes as are crime rate and children in child benefit households, which are both markers of deprivation. Also, the average number of cars per household, the proportion of green space and the percentage of adults who commute by walking have a decreasing effect on pedestrian crash frequency. This study identifies important determinates of ward-level pedestrian safety and estimates the magnitude of their association with pedestrian safety and in doing so reveals important between borough (local authority) differences in the Greater London area in terms of pedestrian safety which were not previously known or well-understood.

Trauma Patients at Risk of In-Hospital Mortality

The trauma incidence in South Africa is significantly high and forms one of the top-five causes of unnatural death. Thus, one of the higher incidences of trauma across the world. Subsequently the in-hospital mortality forms a high burden of disease on the health systems in South African health care facilities. Therefore, analysing the incidence of trauma and the various mechanisms of trauma allows for in-hospital trauma mortalities to assessed. The aim of this study was to analyse the in-hospital mortality distribution factors at a Johannesburg Trauma unit over an eight-year retrospective review from 01 January 2005 –31 December 2012 and Analysis of Trauma Bank data forms at Charlotte Maxeke Johannesburg Academic Hospital (CMJAH). Looking at demographics, physiological parameters, mechanism of Injury, Injury Severity Score (ISS) and New Injury Severity Score (NISS). Ethics through the Wits Ethics Committee was obtained. The Statistica V8 application was used to analyse the data that was collected and a P value of &lt;0.05 was reported as significant. A total of 18, 087 Priority 1 patients were reviewed in this study. This sample demonstrated the 21-45 years of age group was the most affected trauma group (87.3%). Penetrating injuries (Stab and gunshot wounds) had the highest incidence yet lower mortalities. However, blunt injuries (ranging from Pedestrian- and Motor- vehicle crashes, blunt assaults, falls and burns) had lower incidences and higher mortalities. The highest in-hospital mortality was due to burns patients (22.56%) followed by pedestrian vehicle crashes patients affecting the predominantly young male population. Poor probability of survival was demonstrated amongst Motor Vehicle Crashes (Ps 44%) and Motorbike Crashes (34%), and PVCs.

Factors affecting severe pedestrian crash percentages at intersections in Colorado 2006–2018

This article investigates factors associated with fatal and severe injury pedestrian crash percentages at intersections in Colorado. Many published studies associate road and traveler characteristics with the frequency or severity of pedestrian crashes without reference to specific locations. The objective of this study is to determine whether road and traveler characteristics, aggregated by intersection, partly explain differences in severe crash percentages at intersections. From 2006 to 2018, there were a total of 17,047 reported crashes involving pedestrians and motor vehicles in all of Colorado. This study analyzes 3,015 of these crashes that had the GPS coordinates needed to identify their locations at intersections and included the information needed to identify the pedestrian outcomes of the crash. The results of logistic and linear regressions found that lighting condition, vehicle speed, turning movement of vehicle, vehicle type, pedestrian age, and driver or pedestrian impairment by drugs or alcohol were most associated with severe crash percentages at intersections. These findings identify crash characteristics at intersections with higher severe crash proportions that can potentially be addressed to improve safety.

Investigation of pedestrian and bicyclist safety in public transportation systems

IntroductionUnderstanding the crash risk factors associated with pedestrians and bicyclists is vital to promoting pedestrian- and bicycle-friendly communities and ensuring the sustainability of the transportation system. Transit stations perform as important mode transfer locations for bicyclists and pedestrians. However, past studies on contributing factors to pedestrian and bicycle crash in transit station service areas are limited. This study develops a model to guide the prioritization of areas for bicyclist and pedestrian safety improvement. MethodThis study analyzes pedestrian and bicyclist safety in subway station service areas using publicly available crash data from New York City (NYC) area. For the model development, various subway station and built-environment characteristics related variables were aggregated to the subway station service area level. Spatial regression and random parameter models were developed to account for spatial heterogeneity and spatial dependency in the crash data. Performance measures of the developed models showed that the Semi-Parametric Geographically Weighted Poisson Regression (S-GWPR) model better explained the variation in the crash frequency. ResultsThe results showed that subway stations in NYC with high ridership were positively associated with pedestrian and bicycle crashes. The number of bus stops in a subway station service area was also positively associated with pedestrian and bicycle crashes. This study also found that additional bike lanes can reduce bicycle-involved crashes. ConclusionAreas surrounding transit stations, where pedestrian and bicyclist activities are usually high, require special attention to deploy appropriate safety countermeasures. The findings of this study suggest that cities can install more bike lanes to promote the safety of bicyclists. Using the results of this study, traffic safety planners can prioritize areas for the implementation of safety countermeasures in NYC.

The incidence burden of unreported pedestrian crashes in Illinois

Objective Communities with high rates of pedestrians struck by motor vehicles may miss out on mitigation resources and suffer worse medical outcomes if crashes there go unreported to police. This study investigates the places, people, and communities in Illinois where struck pedestrians are most likely to go unreported. A better understanding of the true burden and distribution of struck pedestrians will help guide policy and direct investments and interventions where they are most needed. Methods Hospital records of pedestrians treated for injuries sustained by a motor vehicle that were not able to be linked with a corresponding crash report across three consecutive years are investigated. Discordance rates of struck pedestrians are calculated and disaggregated by region. A presentation of summary statistics is accompanied by an ordinary least squares predictive model to estimate the relationship between discordant struck pedestrians and sociodemographic factors. Results The incidence of unreported struck pedestrians was not randomly distributed. Blacks struck by a motor vehicle were disproportionately likely to go unreported to police. Zip codes with the most unreported crashes per capita on average had double the poverty rate and 2.6 times the carless household rate as the rest of Illinois. Struck pedestrians diagnosed at the hospital with an intoxicating substance went unreported to police nearly 70% of the time. Generally, more severe head and thorax injuries were more likely to be reported. Struck pedestrians outside of Cook County averaged a 60% discordance rate, those within Cook County averaged a discordance rate of about 50%. Struck pedestrian cases reported to police averaged emergency department charges of about $2,500 more than unreported cases. Conclusions Underlying and contributing factors influential of a struck pedestrian's decision of whether to report to police is complex and layered by social constructs mixed with difficult economic decisions, often further complicated by the fog of impairment. Recommendations are made for community outreach to stress the importance of reporting incidents to police, along with adjusting case count numbers in police records using hospital data and discordance rates.

What affects pedestrian crossing difficulty at urban intersections in a developing country?

In developing countries, road traffic crashes involving pedestrians have become a foremost concern. At present, road safety assessment plans and selection of interventions are primarily restricted to traditional approaches that depend on the investigations of historical crash data. However, in developing countries such as India, the availability, consistency, and accuracy of crash data are major concerns. In contrast, proactive approaches such as studying road users' risk perception have emerged as a substitute method of examining potential risk factors. An individual's risk perception offers vital information on probable crash risk, which may be beneficial in detecting high-risk locations and major causes of crashes. Since the pedestrian fatality risk is not uniform across the urban road network level, it may be expected that pedestrians' perceived risk measured in terms of “crossing difficulty” would also vary across the sites. In this perspective, the present paper establishes a mathematical association between the pedestrians' perceived “crossing difficulty” and actual crashes. The model outcome confirms that pedestrians' perceived crossing difficulty is a good surrogate of fatal pedestrian crashes at the intersection level in Kolkata City, India. Subsequently, to examine the impact of traffic exposures, road infrastructure, land use, spatial factors, and pedestrian-level attributes on pedestrians' “crossing difficulty”; a set of Ordered Logit models are developed. The model outcomes show that high vehicle and pedestrian volume, vehicular speed, absence of designated bus stop, the presence of inaccessible pedestrian crosswalk, on-street parking, lack of signalized control (for both vehicle and pedestrian), inadequate sight distance, land use pattern, slum population, pedestrian-vehicular post encroachment time, waiting time before crossing, road width, and absence of police enforcement at an intersection significantly and positively increase pedestrian's crossing difficulty at urban intersections. To end, the model findings are advantageously utilized to develop a set of countermeasures across 3E's of road safety.

Measuring Neighbourhood Walking Access for Older Adults

Older adults are an important part of the world’s population. Many researchers have worked on walking as a mode of transport and measuring walking access. However, considering older adults (aged 65 and over) walk time, older population, and older pedestrians’ safety to measure walking accessibility has not been widely discussed. This study proposes two Walking Accessibility Index (OWAI1 and OWAI2) to measure walking access levels for older adults around the neighbourhoods. The index considers the older travelers’ walk time to reach various destinations (e.g., shopping, healthcare, education, and recreation services), land use mix, pedestrian crash datasets, street connectivity and the older population. Among these two proposed indices, OWAI1 statistically performs better. The transport and urban planners can use the newly developed OWAI1 for future planning and policy implementations. The index may be applied to measure disabled commuters’ walking access levels as considerable walking speed is lower. Besides, the proposed index is also appropriate for other adults by using the corresponding variables for that particular age group. Metropolitan Melbourne is used in this paper as the case study to measure older adults’ walking accessibility. This paper outlines that the older adults’ walking access level is very low for most Melbourne areas, negatively impacting their travel behavior.

A Critical Review of Grade-Separated Pedestrian Crossing Facilities

Vehicle–pedestrian crashes are one of the common road accident types. Pedestrian crossing warrants are guidelines that recommend a particular type of pedestrian crossing facility to be provided at a crossing location. Various countries around the world have their crossing warrant guidelines, but most of them are focused toward at-grade facilities. Even with existing warrant criteria, the number of pedestrian–vehicle crashes is alarming. This indicates that the warrant criteria and their threshold values may need reexamination. Several of the existing warrant guidelines do not provide the required clarity for the provision of grade-separated pedestrian crossing facilities (GSPCFs). GSPCFs include overpasses (or foot overbridges), underpasses (or subways), and partial underpasses (or hump subways). The existing warrant guidelines consider GSPCFs as a single unit and do not identify which type of GSPCF should be provided at a crossing location. Appropriate GSPCFs not only minimize the accident rate but also eliminate delays for both vehicles and pedestrians. This paper conducts an in-depth analysis of the existing literature on urban midblock GSPCF warrants around the world. The discussion highlights the limitations associated with existing warrant guidelines and the need to formulate improved GSPCF warrants.

Examining Pedestrian Crash Frequency, Severity, and Safety in Numbers Using Pedestrian Exposure from Utah Traffic Signal Data

Examining Pedestrian Crash Frequency, Severity, and Safety in Numbers Using Pedestrian Exposure from Utah Traffic Signal Data