Pedestrian Crash Risk Research Articles

A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics

Vehicle-pedestrian crashes are generally severe due to the vulnerability of pedestrians compared to the occupants of vehicles. However, the estimation of pedestrian crash risk by severity has not been given adequate attention in the field of proactive safety assessments applying traffic conflict techniques. This study proposes a novel analytical framework to estimate real-time pedestrian crash risk by severity at the signal cycle level while incorporating the effect of time-varying exogenous variables. Specifically, the study applies a non-stationary bivariate extreme value model to jointly model the post encroachment time and Delta-V for estimating real-time pedestrian crash risk by severity at individual signal cycles. The proposed framework is tested on 144 h of video data collected from three signalized intersections in Queensland, Australia. The developed bivariate extreme value model has been found to reliably predict severe and non-severe pedestrian crash frequencies compared to the historical crash records of severe and non-severe pedestrian crashes at those signalized intersections. Results suggest that the frequency of pedestrian conflicts per signal cycle and average pedestrian speed in a signal cycle are associated with real-time pedestrian crash risks. In addition, pedestrian conflicts per signal cycle and average vehicle speed per cycle were associated with the interaction severity component of the non-stationary bivariate extreme value model. The proposed proactive estimation of pedestrian crash risk by severity levels can help design time-sensitive countermeasures for vulnerable road users.

A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse—a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users’ information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle–pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network.

Evasive actions to prevent pedestrian collisions in varying space/time contexts in diverse urban and non-urban areas

This study aims to identify driver-safe evasive actions associated with pedestrian crash risk in diverse urban and non-urban areas. The research focuses on the integration of quantitative methods and granular naturalistic data to examine the impacts of different driving contexts on transportation system performance, safety, and reliability. The data is derived from real-life driving encounters between pedestrians and drivers in various settings, including urban areas (UAs), suburban areas (SUAs), marked crossing areas (MCAs), and unmarked crossing areas (UMCAs). By determining critical thresholds of spatial/temporal proximity-based safety surrogate techniques, vehicle–pedestrian conflicts are clustered through a K-means algorithm into different risk levels based on drivers' evasive actions in different areas. The results of the data analysis indicate that changing lanes is the key evasive action employed by drivers to avoid pedestrian crashes in SUAs and UMCAs, while in UAs and MCAs, drivers rely on soft evasive actions, such as deceleration. Moreover, critical thresholds for several Safety Surrogate Measures (SSMs) reveal similar conflict patterns between SUAs and UMCAs, as well as between UAs and MCAs. Furthermore, this study develops and delivers a pseudo-code algorithm that utilizes the critical thresholds of SSMs to provide tangible guidance on the appropriate evasive actions for drivers in different space/time contexts, aiming to prevent collisions with pedestrians. The developed research methodology as well as the outputs of this study could be potentially useful for the development of a driver support and assistance system in the future.

Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm

Introduction: Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash. Additionally, the pattern of nighttime pedestrian crashes differs significantly according to the intersection and segment location, which requires further exploration. Data: This study applied Association Rules Mining (ARM), a rule-based machine learning method, to reveal the association of nighttime pedestrian crash risk factors according to the intersection and segment locations using 2,505 nighttime pedestrian fatal and injury crashes in Louisiana (2015–2019). Results and conclusions: Based on the generated rules, the results show that nighttime pedestrian crashes at the intersection are associated with right-turn vehicle movement, older drivers (>64 years) at the high-speed intersection, senior pedestrians (>64 years) in rainy weather conditions, violation by pedestrian age group ‘<15 years’, and alcohol-intoxicated pedestrian violation in business/industrial areas. Additionally, ‘careless operation’ at the intersection is associated with alcohol-involved drivers. Most of the nighttime pedestrian crashes at segments are associated with roadways with no physical separation and the absence of streetlights. Driver alcohol involvement and their physical condition (inattentive/distracted) are also associated with pedestrian crashes associated at the segment location at night. Other segment pedestrian crashes are linked to the interstate in dark conditions, open country locations, and high-speed roadways. Additionally, the crash site investigation identified several critical pedestrian safety concerns including the lack of crosswalk facilities, high driveway density, and pedestrian behavioral patterns (e.g., crossing at roadway segments close to the intersection location). Practical Applications: The findings of this study can be used for selecting the appropriate countermeasures based on a case-by-case basis. The exposure patterns can be used in educational campaigns to strategically reduce nighttime pedestrian crashes.

The Most Vulnerable User: Examining the Role of Income, Race, and the Built Environment on Pedestrian Injuries and Deaths

This study examined the incidence of injurious and fatal pedestrian crashes for lower-income and affluent communities in Broward and Palm Beach counties, FL, finding notable differences in the environmental risk factors for these populations. In lower-income areas, pedestrian deaths and injuries increased with traffic volumes, multilane streets, and restaurants and shopping centers. They decreased with the presence of raised medians, which can serve as a refuge island for crossing pedestrians. These variables all suggest that, at least in lower-income areas, pedestrian death and injury was associated with difficulties safely accessing household-supporting destinations. For affluent areas, the factors associated with increased pedestrian death and injury were those relating to recreation and nightlife—specifically, bars and clubs, hotels, and restaurants. Neither traffic volumes nor multilane roads proved to be meaningfully related to increased pedestrian death or injury in affluent areas. Perhaps most notably, higher concentrations of Black populations were strongly related to increased pedestrian death and injury, even after accounting for differences in income. Considered as a whole, these results suggest that pedestrian crash risk, like much else in U.S. society, is strongly intertwined with broader issues of racial and income inequality. Attempts to address the safety of the transportation system’s most vulnerable users need to move beyond asserting that any pedestrian project constitutes a safety enhancement, and to begin to more meaningfully account for social vulnerabilities associated with race and income.

Investigating pedestrian crash patterns at high-speed intersection and road segments: Findings from the unsupervised learning algorithm

Pedestrian crashes at high-speed locations are a persistent road safety concern. Driving at high speeds means that the driver has less time to react and make evasive maneuvers to avoid a pedestrian crash. On top of this, other crash-contributing factors such as humans (pedestrians or drivers), vehicles, roadways, and surrounding environmental factors actively interact together to cause a crash at high-speed locations. The pattern of pedestrian crashes also differs significantly according to the high-speed intersection and segment locations which require further investigation. This study applied association rules mining (ARM), an unsupervised learning algorithm, to reveal the hidden association of pedestrian crash risk factors according to the high-speed intersection and segments separately. The study used Louisiana pedestrian fatal and injury crash data (2010 to 2019). Any crash location with a posted speed limit of 45 mph or above is classified as a high-speed location. Based on the generated association rules, the results show that pedestrian crashes at a high-speed intersection are associated with the intersection geometry (3-leg) and control (1 stop, no traffic control device), driver characteristics (careless operation, failure to yield, inattentive-distracted, older, and younger driver), pedestrian-related factors (violations, alcohol/drug involvement), settings (open country, residential, business, industrial), dark lighting conditions and so on. Most pedestrian crashes at high-speed segments are associated with roadways with no physical separation, dark-no-streetlight conditions, open country locations, interstates and so on. The findings of the study may help to select appropriate countermeasures to reduce pedestrian crashes at high-speed locations.

Effects on crash risk of automatic emergency braking systems for pedestrians and bicyclists

Objective The first automatic emergency braking (AEB) system was presented in 2003 and aimed to mitigate or reduce rear-end crashes. Since then, several AEB systems aimed to reduce other collision types have been introduced and studies have shown that they reduce crash risks. The aim with this study was to evaluate crash reductions of cars fitted with AEB systems with pedestrian detection and those with bicyclist detection. Methods The study is based on the Swedish Traffic Accident Data Acquisition that includes road traffic accidents reported by the police and by emergency hospitals. Crashes occurring between 2015 and 2020 and with cars from model years 2015 to 2020 were included. The statistical analysis used odds ratio calculations with an induced exposure approach where the outcomes of sensitive and nonsensitive crashes were studied. The sensitive crashes were hit pedestrians and bicyclists, respectively. The nonsensitive crash type in both comparisons was struck vehicles in rear-end crashes. Evaluations were also made for different light and weather conditions and for high and low speed roads. Results Seven hundred and twelve hit pedestrians and 1,105 hit bicyclists were included, and the nonsensitive crashes consisted of 1,978 vehicles. The overall reduction on crash risk for AEB with pedestrian detection was 8% (±15%; ns) and for AEB with bicyclist detection it was 21% (±17%). When separating for light conditions, no reduction in crash risk for AEB with pedestrian detection nor for AEB with bicyclist detection could be seen in darkness. However, in daylight and twilight conditions, AEB with pedestrian detection reduced pedestrian crash risk by 18% (±19%; ns) and AEB with bicyclist detection reduced bicyclist crash risk by 23% (±19%). No significant reductions could be seen when separating for weather conditions except for a 53% (±31%) reduction for bicyclists in rain, fog, and snowfall. A larger reduction on high-speed roads (50–120 km/h) compared with low-speed roads (10–40 km/h) was also found. Conclusions AEB systems with bicyclist detection were found to reduce the numbers of hit bicyclists, especially in daylight and twilight conditions. In darkness, no reduction for hit pedestrians or bicyclists was found.

Leading pedestrian intervals – Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis

A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the “element of surprise” for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle–pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft’s proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group’s cloud-based SMART SafetyTM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle–pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.

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.

Effects of automatic emergency braking systems on pedestrian crash risk

ObjectiveAutomatic emergency braking (AEB) that detects pedestrians has great potential to reduce pedestrian crashes. The objective of this study was to examine its effects on real-world police-reported crashes. MethodsTwo methods were used to assess the effects of pedestrian-detecting AEB on pedestrian crash risk. Vehicles with and without the system were examined on models where it was an optional feature. Poisson regression was used to estimate the effects of AEB on pedestrian crash rates per insured vehicle year, and quasi-induced exposure using logistic regression compared involvement in pedestrian crashes to a system-irrelevant crash type. ResultsAEB with pedestrian detection was associated with significant reductions of 25%–27% in pedestrian crash risk and 29%–30% in pedestrian injury crash risk. However, there was not evidence that that the system was effective in dark conditions without street lighting, at speed limits of 50 mph or greater, or while the AEB-equipped vehicle was turning. ConclusionsPedestrian-detecting AEB is reducing pedestrian crashes, but its effectiveness could be even greater. For the system to make meaningful reductions in pedestrian fatalities, it is crucial for it to work well in dark and high-speed conditions. Other proven interventions to reduce pedestrian crashes under challenging circumstances, such as improved headlights and roadway-based countermeasures, should continue to be implemented in conjunction with use of AEB to prevent pedestrian crashes most effectively.

Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need to examine pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010–2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (&lt;15 years), senior pedestrians (&gt;64 years), older drivers (&gt;64 years), and other driving behaviors such as “failure to yield,”“inattentive/distracted,”“illness/fatigue/asleep.” Additionally, young drivers (15–24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55–64 years), speed limit (30–35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high speed limits (&gt;50 mph) during the dark without streetlight condition. Some other risk factors linked with “high speed limit” related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, and pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study

Introduction: Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics. Method: A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared. Results: The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed. Conclusions: The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes. Practical Applications: Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

Higher risk of auto versus pedestrian crashes in school-age children on school days.

This study examines the rates of pediatric auto versus pedestrian collision (APCs) and determined ages and periods of greatest risk. We hypothesized that the rate of APC in children would be higher on school days and in the timeframes correlating with travel to and from school. Retrospective case-control study of APC on school and nonschool days for patients younger than 18 years at an urban Level II pediatric trauma center from January 2011 to November 2019. Frequency of APC by hour of the day was plotted overall, for school versus nonschool days and for age groups: 0 year to 4 years, 5 years to 9 years, 10 years to 13 years, and 14 years to 17 years. t Test was used with a p value less than 0.05, which was considered significant. There were 441 pediatric APC in the study period. Frequency of all APC was greater on school days (0.174 vs. 0.101; relative risk [RR], 1.72, p < 0.001), and APC with Injury Severity Score greater than 15 (0.039 vs. 0.024; p = 0.014; RR, 1.67; 95% confidence interval, 1.10-2.56). Comparing school day with nonschool day, the 0-year to 4-year group had no significant difference in APC frequency (0.021 vs. 0.014; p = 0.129), APC frequency was higher on school days in all other age groups: 5 years to 9 years (0.036 vs. 0.019; RR, 1.89; p = 0.0134), 10 years to 13 years (0.055 vs. 0.024; RR, 2.29; p < 0.001), and 14 years to 17 years (0.061 vs. 0.044; RR, 1.39; p = 0.045). The greatest increase in APC on school days was in the 10-year to 13-year age group. All school age children are at higher risk of APC on school days. The data support our hypothesis that children are at higher risk of APC during transit to and from school. The age 10-year to 13-year group had a 129% increase in APC frequency on school days. This age group should be a focus of injury prevention efforts. Prognostic and Epidemiologic; Level IV.

Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes

This study examined the impact of density, diversity, design, distance to transit, and destination accessibility, five measures, known as the 5Ds, that characterize the built environment, on pedestrian–vehicle crashes in Seoul, Korea. Using spatial analysis based on 500-m grid cells, this study employed negative binomial regression models on the frequencies of three specific types of pedestrian–vehicle crashes: crashes causing death, major injury, and minor injury to pedestrians. Analysis shows that compact and mixed-use urban environments represented by 5D measures have mixed effects on pedestrian safety. Trade-off effects are found between a higher risk for all types of pedestrian crashes, and a lower risk for fatal pedestrian crashes in 5D urban environments. As a design variable, a higher number of intersections is more likely to increase some types of pedestrian crashes, including fatal crashes, a finding which warrants policy attention to promote pedestrian safety near intersection areas. This study also confirms an urgent need to secure the travel safety of pedestrians near public transit stations due to the higher risk of pedestrian crashes near such facilities. Various destinations, such as retail stores, traditional markets, and hospitals, are associated with pedestrian crashes. Pedestrian safety measures should be implemented to reduce the likelihood of pedestrian crashes near major destination facilities.

Spatial Equity Analysis of Nighttime Pedestrian Safety: Role of Land Use and Alcohol Establishments in Albuquerque, NM

The United States experienced a 53% increase in pedestrian fatalities between 2009 and 2018, with 2018 having a 3.4% increase from 2017. Of the 2018 pedestrian fatalities with known lighting conditions, 76% occurred in dark/nighttime conditions, with 50% occurring between 6:00 and 11:59 p.m. Despite past research exploring several contributing characteristics for nighttime pedestrian crashes, there is limited research that investigates the spatial aspects of land use attributes and sociodemographic factors. Have these nighttime pedestrian collisions been concentrated in certain land uses? Could an establishment with the capacity to serve alcohol invoke a greater risk of pedestrian crashes? Does sociodemographic status correlate with clustering for fatal crashes, severe crashes, or both? To better understand the spatial characteristics of the recent increase in pedestrian collisions, we analyzed crash data from Albuquerque, New Mexico for pedestrian fatalities and severe injuries from 2013 to 2018 relative to lighting condition, land use (with a focus on alcohol establishments), and race/ethnicity on the block group level. We used confidence intervals and Getis-Ord Gi* statistics to verify the statistical integrity of the trends. Findings suggested that pedestrian fatality and severe injury rates were higher within a quarter mile of bars at night and in areas with elevated concentrations of minority populations. Pedestrian fatality and severe injury hot spots appeared to have higher percentages of non-white residents, coupled with lower sidewalk coverage and more arterials or collectors.

Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects

Vulnerable road users (VRUs)-related crashes are recognized as an important public safety problem. However, few macro-level studies of VRUs-involved crashes have considered the long-term spatial, temporal, or spatiotemporal effects in the crash risk. This study analyzes the bicycle and pedestrian crash risk in different injury severities by using three multivariate Bayesian space–time models. These models address different spatiotemporal effects to account for possible correlations across injury severities over space and time. Various explanatory variables are used to examine the contributory risk factors, including socio-demographic features, roadway structures, and weather characteristics. Spatio-temporal conditional autoregression with an ANOVA style (ST-CARanova) models outperform other two space–time models in most circumstances. The long-term spatiotemporal effects, such as relatively high temporal autocorrelations, significant spatial heterogeneity, and weak spatiotemporal interactions, are found in this study. The increase of female ratios, young people ratios, unemployment rates, and annual average high temperatures could increase the county-level crash risk of cyclists and pedestrians. The findings provide useful insights for policy makers to improve the safety of cyclists and pedestrians.

Pedestrian safety analysis of urban intersections in Kolkata, India using a combined proactive and reactive approach

The assessment of pedestrian safety is often conducted in a reactive way by analyzing pedestrian crash data. However, in a developing country, the availability of reliable crash data is a major challenge. Therefore, without relying solely on reactive approach, it is essential to combine some methods that can proactively assess pedestrian safety. In this background, the present study proposed a methodology combining both reactive and proactive approaches to assess pedestrian safety at urban intersections in Kolkata, India. The method developed in the present study utilizes a combination of the historical crash data analysis, the analysis of pedestrian-vehicular conflict (i.e., pedestrian-vehicular post-encroachment time), along with pedestrians’ risk perception toward the built environment and traffic parameters, to identify the key factors influencing pedestrian safety and to recognize potential risk-prone intersections in Kolkata. Based on the combined reactive and proactive assessments, there is evidence that high vehicle volume, pedestrian-vehicular volume ratio, absence of traffic signal, inaccessibility of the pedestrian crosswalk, absence of police personnel, higher approaching speed, commercial area, inadequate sight distance, presence of slum population, and higher population density near the intersection significantly increase the risk of pedestrian crashes. Finally, using this combined proactive-reactive approach, the present study also identifies and ranks twenty-five high risk-prone intersections for pedestrians. This is a significant step toward scientific decision making and allowing the use of information beyond historical crash records.

Pedestrian safety models for urban environments with high roadside activities

Virtually every traveller starts and ends as a pedestrian. Therefore walking, an essential part of a non-motorised transport mode, is very important especially in urban settings. To achieve safe walkable environments, practical tools are needed by transport professionals to assess and mitigate the influence of development on pedestrian safety, including pedestrian-vehicle accidents. Therefore, a better assessment of the risks faced by pedestrians is important to achieve a safer walking environment and to reduce the risks.In spite of the importance of roadside activities in urban areas, it appears that there are no sufficient studies which have given attention to them and investigated their influence on pedestrians’ safety. As a consequence, this study developed models to assess pedestrian safety and capture the effect of pedestrian and roadside activities’ intensity. The study found that the number of bus stoppings per unit of time, parking, pedestrian crossing and violations’ volume, the traffic speed variation, the number of intersecting side roads, in addition to through and intersecting traffic volume, were among the significant risk factors related to the pedestrian crash risk. These factors were then linked with the risk of a pedestrian crash using generalized regression models of which the Poisson model seem to be the most satisfactory.

Does elderly safety matter? Associations between built environments and pedestrian crashes in Seoul, Korea

Korea’s elderly population is growing rapidly, as is attention to elderly pedestrian safety. Despite a consensus that the elderly are vulnerable to pedestrian safety issues, our understanding of the determinants of elderly pedestrian crashes is limited. This study explores which attributes of the built environment affect the risk of pedestrian accidents among the elderly, particularly with respect to injury severity, in Seoul, Korea. We compare the impacts of various determinants on pedestrian crashes to specify how the associations between various built environments and pedestrian accidents differ by pedestrian age. We also examine how the associations vary by neighborhood economic attributes. Our findings provide policy implications for identifying various attributes of the built environment that increase the risk of elderly pedestrian crashes and improving the safety of elderly pedestrian by neighborhood economic status.