Pedestrian-vehicle Crashes Research Articles

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian–vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features’ local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle–pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

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.

Environmental measures to improve pedestrian safety in low- and middle-income countries: a scoping review.

This scoping study aims to identify environmental road safety measures implemented in low- and middle-income countries (LMICs) to reduce pedestrian injuries from collisions with motor vehicles. This review followed Arksey and O'Malley's approach and reported results using the PRISMA-SCR 2018 checklist. A literature review was conducted in Medline, Google Scholar, and the Transport Research International Documentation database using keyword-derived medical subject heading terms. A total of 14 articles met the pre-established inclusion criteria and were analyzed using a data extraction matrix. The findings were categorized methodically into three prominent themes: (1) methods for reducing pedestrian exposure, (2) traffic calming strategies, and (3) measures for enhancing pedestrian visibility. Traffic calming strategies, including vehicular speed reduction, roadway contraction, and vertical and horizontal diversionary tactics, emerged as the most effective interventions for reducing pedestrian injuries within LMICs. Conversely, interventions geared towards minimizing pedestrian exposure, such as zebra crossings, crosswalks controlled by traffic signals, underpasses, or overpasses, often produced minimal effects, and occasionally exacerbated the risk of pedestrian accidents. Lack of pedestrian visibility due to density of street vendors and parked vehicles was associated with a higher risk of injuries, while billboards impaired drivers' attention and increased the likelihood of collisions with pedestrians. In LMICs, the effectiveness of environmental measures in reducing vehicle-pedestrian crashes varies widely. In the face of resource constraints, implementing interventions for pedestrian safety in LMICs necessitates careful prioritization and consideration of the local context.

Investigating pedestrian-vehicle crashes on interstate highways: Applying random parameter binary logit model with heterogeneity in means

In the U.S., the interstate highway system is categorized as a controlled-access or limited-access route, and it is unlawful for pedestrians to enter or cross this type of highway. However, pedestrian-vehicle crashes on the interstate highway system pose a distinctive safety concern. Most of these crashes involve ‘unintended pedestrians’, drivers who come out of their disabled vehicles, or due to the involvement in previous crashes on the interstate. Because these are not ‘typical pedestrians’, a separate investigation is required to better understand the pedestrian crash problem on interstate highways and identify the high-risk scenarios. This study explored 531 KABC (K = Fatal, A = Severe, B = Moderate, C = Complaint) pedestrian injury crashes on Louisiana interstate highways during the 2014–2018 period. Pedestrian injury severity was categorized into two levels: FS (fatal/severe) and IN (moderate/complaint). The random parameter binary logit with heterogeneity in means (RPBL-HM) model was utilized to address the unobserved heterogeneity (i.e., variations in the effect of crash contributing factors across the sample population) in the crash data. Some of the factors were found to increase the likelihood of pedestrian’s FS injury in crashes on interstate highways, including pedestrian impairment, pedestrian action, weekend, driver aged 35–44 years, and spring season. The interaction of ‘pedestrian impairment’ and ‘weekend’ was found significant, suggesting that alcohol-involved pedestrians were more likely to be involved in FS crashes during weekends on the interstate. The obtained results can help the ‘unintended pedestrians’ about the crash scenarios on the interstate and reduce these unexpected incidents.

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers’ own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle–pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

Advances in estimating pedestrian measures through artificial intelligence: From data sources, computer vision, video analytics to the prediction of crash frequency

Data are essential for planning walkable cities that are comfortable, convenient and safe to pedestrians. Yet, in contrast to massive vehicular traffic data, data on pedestrian traffic have not been systematically collected by municipal governments. Nowadays, geospatial big data provide rich information related to human activities and, hence, can capture street scenes in an innovative way. Using bus dashcam videos (on 244.36 km of roads covered by 33 bus routes in Hong Kong) and deep learning methods (Fast R-CNN and Deepsort), this study proposes a new method for estimating pedestrian volume from this data source. In comparison, we generate two alternative measures from household travel surveys and Google Street View images. The estimates are validated by manual counts at selected locations on a main road. Using five different modelling approaches (including three variants of negative binomial and two variants of random forest models), the pedestrian volume estimates are used for predicting pedestrian-vehicle crashes. The results show that pedestrian volumes calculated from bus dashcam videos consistently show comparable, if not better, performance in explaining crash frequency. In the future, different data sources should be used to supplement each other so that a more complete picture of pedestrian flows at the city level can be obtained.

Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.

Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach

There has been a concerning rise in pedestrian fatalities resulting from traffic accidents. This study conducts an in-depth analysis of the factors that contribute to the injury severity of pedestrians considering human, vehicle, roadway, and environmental characteristics of the crashes. A novel approach is proposed by combing a data mining technique and mixed logit models. First, the Apriori algorithm is employed to uncover patterns between fatal and incapacitating injury outcomes and their influencing factors. Then, mixed logit models are developed to investigate heterogeneity across all observations under two different lighting conditions. It is found that eight variables show heterogeneity affecting the injury severity of crash outcomes. Results also indicate that drivers older than 65 years old will increase the probability of pedestrian injury severity at dark-unlighted roads. Additionally, the present of signal and double yellow line has could increase the injury severity. Creating optimal lighting conditions at pedestrian crossings during nighttime hours and enhancing safety education initiatives for pedestrians are critical factors to improve pedestrian safety. The finding of this study will benefit policymakers and road safety professionals develop more effective strategies for preventing pedestrian injuries in vehicle crashes.

Temporal assessment of injury severities of two types of pedestrian-vehicle crashes using unobserved-heterogeneity models

This study explores the temporal instability and non-transferability of the determinants affecting injury severities of pedestrians struck by motorcycles and non-motorcycles. Using the pedestrian-vehicle crash data in Rawalpindi, Pakistan, over three years (2017–2019), three possible crash injury severity categories (minor injury, severe injury, and fatal injury) are estimated using alternative models to account for unobserved heterogeneity. These are a random-parameters multinomial logit (RP-ML) model with heterogeneity in means and variances and a latent-class multinomial logit (LC-ML) model with class probability functions. Temporal instability and non-transferability in the effects of explanatory variables are confirmed using a series of likelihood ratio tests based on the two alternative models. Various variables are observed to determine pedestrian-injury severities, and the estimation results show significant temporal instability and non-transferability in both RP-ML and LC-ML models. However, several explanatory variables produce relatively temporally stable and transferable effects, providing valuable insights to implement effective countermeasures from a long-term perspective. Moreover, out-of-sample predictions are simulated to confirm the temporal instability and non-transferability. At the same time, the LC-ML models produce higher differences for temporal instability and lower differences for non-transferability compared to the RP-ML model. Understanding and depth comparing the estimation results, likelihood ratio tests, and out-of-sample predictions using alternative models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated in terms of temporal instability and non-transferability.

Pedestrian safety in an automated driving environment: Calibrating and evaluating the responsibility-sensitive safety model

The severity of vehicle–pedestrian crashes has prompted authorities worldwide to concentrate on improving pedestrian safety. The situation has only become more urgent with the approach of automated driving scenarios. The Responsibility-Sensitive Safety (RSS) model, introduced by Mobileye®, is a rigorous mathematical model developed to facilitate the safe operation of automated vehicles. The RSS model has been calibrated for several vehicle conflict scenarios; however, it has not yet been tested for pedestrian safety. Therefore, this study calibrates and evaluates the RSS model for pedestrian safety using data from the Shanghai Naturalistic Driving Study. Nearly 400 vehicle–pedestrian conflicts were extracted from 8,000 trips by the threshold and manual check method, and then divided into 16 basic scenarios in three categories. Because crossing conflicts were the most serious and frequent, they were reproduced in MATLAB’s Simulink with each vehicle replaced with a virtual automated vehicle loaded with the RSS controller module. With the objectives of maximizing safety and minimizing conservativeness, the non-dominated sorting genetic algorithm II was applied to calibrate the RSS model for vehicle–pedestrian conflicts. The safety performance of the RSS model was then compared with that of the commonly used active safety function, autonomous emergency braking (AEB), and with human driving. Findings verified that the RSS model was safer in vehicle–pedestrian conflicts than both the AEB model and human driving. Its performance also yielded the best test results in producing smooth and stable driving. This study provides a reliable reference for the safe control of automated vehicles with respect to pedestrians.

Comparative analysis of pedestrian crash severity at United Kingdom rural road intersections and Non-Intersections using latent class clustering and ordered probit model

Pedestrian safety is a critical issue in the United Kingdom (UK) as pedestrians are the most vulnerable road users. Despite numerous studies on pedestrian-vehicle crashes globally, limited research has been conducted to explore the factors contributing to such incidents in the UK, especially on rural roads. Therefore, this study aimed to investigate the severity of pedestrian injuries sustained on rural roads in the UK, including crashes at intersections and non-intersections. We utilized the STATS19 dataset, which provided comprehensive road safety data from 2015 to 2019. To overcome the challenges posed by heterogeneity in the data, we employed a Latent Class Analysis to identify homogeneous clusters of crashes. Additionally, we utilized the Ordered Probit model to identify contributing factors within each cluster. Our findings revealed that various factors had distinct effects on the severity of pedestrian injuries at intersections and non-intersections. Several parameters like the pedestrian location in footway and one-way roads are only statistically significant in the intersection section. Certain factors such as the day of the week, the pedestrian's location in a refuge, and minor roads (class B roads) were found to be significant only in the non-intersection section.Parameters includingpedestrians aged over 65 years and under 15 years, drivers under 25 years, male drivers and pedestrians, darkness, heavy vehicles, speed limits exceeding 96 km/h (60 mph), major roads (class A roads), and single carriageway roadsare significant in both sections. The study proposes various measures to mitigate the severity of pedestrian-vehicle crashes, such as improving lighting conditions, enhancing pedestrian infrastructure, reducing speed limits in crash-prone areas, and promoting education and awareness among pedestrians and drivers. The findings and suggested measures could help policymakers and practitioners develop effective strategies and interventions to reduce the severity of these incidents and enhance pedestrian safety.

‘Safety in Numbers’ for Walkers: Effects of Pedestrian Volume on Per-Pedestrian Crash Rate and Severe Injury Probability

The ‘safety in numbers’ phenomenon is very intriguing to many walking advocates. Some studies have examined the phenomenon for walkers, but they mostly focused on the relationship between pedestrian volume and the likelihood of vehicle–pedestrian crash occurrences. This study evaluated the effects of the ‘safety in numbers’ phenomenon not only on the risk of crash occurrences, but also on the probability of severe or fatal injuries. Pedestrian volume and crash data obtained from the six districts in Seoul Metropolitan City and Jeju Island were jointly analyzed to examine the expected pedestrian crash rate and the probability of severe or fatal injury as a function of pedestrian volume. The analysis confirmed that the expected pedestrian crash rate and the probability of severe or fatal injuries decline as pedestrian volume increases, although the absolute number of crashes and injuries increases. The increase in the number of pedestrian crashes is less than the increase in pedestrian volume. In addition, the probability of severe or fatal injuries tends to diminish with a larger pedestrian volume. These findings can be used as logical evidence to support future policies promoting walking trips, and they suggest that policy measures encouraging walking trips can deliver additional benefits beyond the well-known economic, health, and environmental benefits.

Analysis of the injury severity of motor vehicle–pedestrian crashes at urban intersections using spatiotemporal logistic regression models

This paper conducted a comprehensive study on the injury severity of motor vehicle–pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.

Exploring the Effect of Visibility Factors on Vehicle–Pedestrian Crash Injury Severity

The objective of this work was to determine the effect of visibility-related factors and some environmental and human factors on the severity of pedestrian-vehicle crashes. It was hypothesized that decreasing visibility, contributed to by factors such as lighting, number of lanes, speed limit, and weather, are associated with an increase in injury severity. Some of the key results of the final model indicate that higher speed limits, less light conditions, and no traffic controls were significantly correlated with increased pedestrian injury severity when roadway visibility factors were under consideration. Dusk and dark with or without lighting were found to be factors correlated with increased pedestrian injury severity, while inclement weather was found to be correlated with lower pedestrian injury severity when environmental visibility-related factors were considered. Furthermore, a spatial autocorrelation revealed a high concentration of pedestrian–vehicle crashes in the Nashville and Memphis areas. This work is similar to prior works in their goal to study factors that affect pedestrian injury severity. While other models have looked at a large range of possible factors that may affect pedestrian injury severity, the model developed in this work focuses on visibility factors, environmental factors, and human-related factors. Another contribution is the data and modeling of the data. This study utilizes a dataset from Tennessee with more categories recorded for the visibility-related factors and applies a multinomial logistic regression model to the data.

Impact analysis of road infrastructure and traffic control on severity of pedestrian–vehicle crashes at intersections and non-intersections using bias-reduced logistic regression

Pedestrian safety is essential to realize a walkable city. This study analyzed the impact of road infrastructure and traffic control on severity of pedestrian–vehicle crashes at intersections and non-intersections. We applied a bias-reduced logistic regression analysis using 86,406 pedestrian–vehicle crashes data including 2636 fatal accidents in Japan. The results of the study indicate that medians increased the severity of crashes at intersections; on the other hand, they decreased the severity at non-intersections. In addition, flashing traffic signals increase the severity while stop signs decrease the severity at intersections.

Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians

Objective Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between factors in specific exposure areas is the first step to improving safety. Therefore, examining the causal factors’ impact on pedestrian-vehicle crash severity in a given area is vital for formulating effective measures to reduce the risk of pedestrian fatalities and injuries. Methods This study implements the Thiessen polygon algorithm deployed to define older pedestrians’ exposure influence area. Enabling trip characteristics and built environment information as exposure index settings for the background of the pedestrian severity causal analysis. Then, structural equation modeling (SEM) was applied to conduct a factor analysis of the crash severity in high- and low-exposure areas. The SEM evaluates latent factors such as driver risk attitude, risky driving behavior, lack of risk perception among older pedestrians, natural environment, adverse road conditions for driving or walking, and vehicle conditions. The SEM crash model also establishes the relationship between each latent factor. Results In total, drivers’ risky driving behavior (0.270, p < 0.05) in low-exposure areas significantly impacts older pedestrian crash severity more than in high-exposure areas. Lack of risk perception among older pedestrians (0.232, p < 0.05) is the most critical factor promoting crash severity in high-exposure areas. The natural environment (0.634, p < 0.05) in high-exposure areas positively influences older pedestrians’ lack of risk perception more than in low-exposure areas. Conclusions Significant group differences (p-values ∼ 0.001-0.049) existed between the causal factors of the high-exposure risk areas and the low-exposure risk factors. Different exposure intervals require detailed scenarios based on the critical risks identified. The crash severity promotion measures in different exposure areas can be focused on according to the critical causes analyzed. Those clues, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs toward improving the safety and mobility of older pedestrians.

Behavioral pathway analysis of pedestrian injury severity in pedestrian-motor vehicle crashes

Despite the health and environmental benefits of walking as an active and sustainable mode of transport, the increasing number of road crashes involving pedestrians has become a public health concern. This study investigates the direct and indirect effects of various contributing factors on pedestrian injury severity in pedestrian-motor vehicle crashes. Data on pedestrian-vehicle crashes from North Carolina between 2015 and 2019, including the pedestrian pre-crash actions and injury severity, were used. Path analysis was applied to uncover the inter-relationship between contributing factors, pedestrian pre-crash behaviors, and injury severity using random parameter ordered logit and mixed logit models. The results indicate that pedestrian intoxication, lighting condition, number of lanes, speed limit, and vehicle type contribute directly and indirectly (through pre-crash behaviors) to increasing injury severity. It was also observed that pedestrian position and traffic control directly decreased injury severity but indirectly increased injury severity through pre-crash behaviors. This study provides valuable information about the relationship between various crash factors and pedestrian injury severity. For researchers and agencies interested in pedestrian safety improvements, this study gives valuable insight by uncovering the roles of pedestrians’ pre-crash action on injury severity outcomes. For countermeasures, the study recommends better street lighting and re-evaluation of posted speed limits on roads with high pedestrian traffic. A regular educational campaign should inform pedestrians of the importance of yielding to motorists at locations without crosswalks. Also, drivers’ compliance with existing pedestrian-vehicle interaction laws should be monitored, especially at intersections.

Combining emerging hotspots analysis with XGBoost for modeling pedestrian injuries in pedestrian-vehicle crashes: a case study of North Carolina

Pedestrians might face more dangers and sustain severer injuries in crashes than others. Also, the crash data has inherent patterns related to both space and time. Crashes that happened in locations with highly aggregated uptrend patterns should be worth exploring to examine the most recently deteriorative factors affecting pedestrian-injury severities in crashes. Therefore, applying proper modeling approaches is needed to identify the causes of pedestrian-vehicle crashes to improve pedestrian safety. In this study, an emerging hotspot analysis is firstly utilized to identify the most targeted hotspots, followed by a proposed XGBoost model that analyzes the most recently deteriorative factors affecting pedestrian injury severities. The overall accuracy of the best model on the hotspot dataset is 94.49%, which shows a relatively high performance compared to conventional models. Seven factors are identified to increase the likelihood of fatal injury, including “land development: farm, wood and pasture” (FWP), “interstate”, “US route”, “hit and run”, “alcohol-impaired driver” (AID), “urban”, and “alcohol-impaired-pedestrian”. While for incapacitating injury, there are five significant factors including “work zone”, “interstate”, “US route”, “curved roadway” and “alcohol-impaired-pedestrian”. The results of this research could give a solid reference for the identification of contributing factors affecting pedestrian-injury severities to policymakers and researchers.

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.

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.