Pedestrian-vehicle Crashes Research Articles

Do high visibility crosswalks improve pedestrian safety? A correlated grouped random parameters approach using naturalistic driving study data

In this study, the effectiveness of High-Visibility Crosswalks (HVCs) in improving pedestrian safety at urban settings is assessed using SHRP2 (Second Strategic Highway Research Program) Naturalistic Driving Study (NDS) data. Various HVCs located at different positions on the roadway segment (mid-block vs end-of-block) and featuring different HVC marking designs (continental, bar-pair, and ladder) were selected for the assessment. As no pedestrian-vehicle crashes or conflicts were identified from the forward-facing videos and time series information of the SHRP2 Naturalistic Driving Study data, crash surrogate measures (i.e., speed; acceleration; throttle pedal actuation; and brake application) were employed to identify and analyze modifications in driving behavior at or near the HVCs.The surrogate measures were statistically modeled using a correlated grouped random parameters estimation framework. This can account for panel effects arising from multiple traversals undertaken by each participant, for the effect of unobserved characteristics, as well as for their unobserved correlations, which constitute possible misspecification issues of statistical modeling. The results of the analysis showed that the presence of HVC modifies driving behavior, thus reducing the risk of motor vehicle – pedestrian conflicts. Apart from the presence of HVC, the HVC type (ladder, continental or bar-pair), the HVC location (mid-block or end-of-block) and various driver, roadway and trip characteristics were found to affect the vehicle speed, acceleration, throttle pedal actuation, and brake application.

Exploring pedestrian injury severities at pedestrian-vehicle crash hotspots with an annual upward trend: A spatiotemporal analysis with latent class random parameter approach

Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend.

Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances

Using pedestrian-vehicle crash data in North Carolina from 2007 to 2018, this study explores the potential variation in the influence of factors affecting pedestrian injury severity in different time periods (weekday/weekend and three-year period). To capture unobserved heterogeneity, random parameters logit models with heterogeneity in means and variances are employed. In developing the model, several categories of factors are considered, including characteristics of the pedestrian, driver, crash, locality and roadway, time and environment, traffic control, and work zone. Transferability tests are conducted to examine the possible temporal instability of the estimation results between different time periods. According to the results, factors such as “ambulance rescue” and “curved roadway” produce temporally stable effects on pedestrian injury severity. However, strong temporal instabilities in effects on pedestrian injury severity are found for most factors across the three-year period and the weekday/weekend. In regard to structure, the model offers more insights by accounting for possible heterogeneity in the means and variances of the random parameters. Detailed policy-related recommendations are provided based on the analysis results. The findings of this work should be helpful to policymakers in future planning on safety improvements for pedestrians within the transportation system.

Roles of personal and environmental factors in the red light running propensity of pedestrian: Case study at the urban crosswalks

Pedestrian is vulnerable to mortality and severe injury in road crashes. Red light running violation of pedestrians is one of the leading causes to the crashes at signalized intersections, at which the crash involvement rates of pedestrians are high. Therefore, it is important to identify the factors that affect the propensity of red light running of pedestrian. In this study, effects of both personal factors (pedestrians’ demographics and behavior) and environmental factors (presence and behavior of other pedestrians, signal time, and traffic condition) on the individual decision of red light running violation are examined, using the video observation surveys at the signalized crossings that are prone to pedestrian-vehicle crashes and have moderate pedestrian and vehicular traffic volumes in the urban area. Crossing behaviors of 6320 pedestrians are captured. Results of a random parameter logit model indicate that pedestrian gender, age, number of lanes, presence of a companion, number of pedestrians around, presence of other violators in the same cycle, time to green, red time, traffic volume, and percentage of heavy vehicles all affect the propensity of red light running violation of pedestrians. Also, there are significant interaction effects by pedestrian’s gender and age, presence of other violators, with a companion, and traffic volume on the propensity. Findings are indicative to the development of effective engineering, enforcement and educational initiatives combating the red light running violation behavior of pedestrians. Therefore, pedestrian safety level at the signalized intersections can be enhanced.

Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: Insights from different hierarchical Bayesian random-effects models

To systematically account for the spatiotemporal features and unobserved heterogeneity within pedestrian-vehicle crashes, this paper employs the spatiotemporal analysis and hierarchical Bayesian random-effects models to explore the factors contributing to pedestrian-injury severities of pedestrian-vehicle crashes involving single vehicle in North Carolina from 2007 to 2018. Ten spatiotemporal patterns of the crashes are identified by applying an improved spatiotemporal analysis. Significant temporal instability and the spatiotemporal instability of the factors to the pedestrian-injury crashes are identified by the likelihood ratio tests. A hierarchical Bayesian random intercept logit model with random-effects across the spatiotemporal groups is firstly employed for the whole dataset. The comparison between different hierarchical models indicates that addressing random-effects across observations and increasing the number of random parameters could both improve the model performance. Then a hierarchical Bayesian random-effects-only logit model, which allows all parameters to be randomly distributed across observations, is developed to further investigate the unobserved heterogeneity in spatiotemporal segmented datasets. The significant improvements in terms of model fit and the hit accuracy underscore the superiority of the random-effects-only model. The marginal effects of the human, vehicle, crash, locality, roadway, environment, time, and traffic control factors for each spatiotemporal dataset also provide insights into possible inherent reasons for the spatiotemporal instability/tendency of the crash and correlated factors. Meanwhile, specific countermeasures are given to locations especially in which the spatially aggregated patterns of the crashes have new, consecutive, and intensifying temporal tendencies. This study provides a framework for engineers and researchers to identify spatiotemporal patterns of the crashes and explore the factors affecting pedestrian-injury severities especially in those existing crash-prone areas.

Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006–2016

This paper investigates factors associated with the severity of pedestrian outcomes from motor vehicle crashes by analyzing a database of all 13,856 reported pedestrian crashes in Colorado over an 11-year period from 2006 to 2016. A total of 14,391 pedestrians were involved in these crashes, resulting in 612 (4.3%) pedestrian fatalities, 11,576 (80.4%) pedestrian injuries, and 2203 (15.3%) property damage only outcomes. The objective is to analyze crash records, as similarly compiled by other states, to show how lives potentially saved by improved factor levels can be estimated as needed for benefit-cost comparisons of alternative countermeasures.Odds ratios of fatal versus non-fatal pedestrian outcomes are computed both independently (unadjusted) and from logistic regression (adjusted) for each factor level accounting for possible correlations between factors. Also computed are odds ratios for fatal plus incapacitating injuries and odds ratios for just 2011−2016 versus all years. This study found that intersection proximity, lighting condition, vehicle type and speed, pedestrian age, pedestrian impairment, and driver impairment by drugs or alcohol were all significant factors associated with the severity of pedestrian outcomes from motor vehicle crashes. Risk ratios from these odds ratios are used to estimate lives potentially saved by having better factor levels present at the time of these crashes. These estimates reflect the relative magnitudes of benefits that might be achieved by potential countermeasures taking into account the number of cases affected.

Mixed logit approach to modeling the severity of pedestrian-injury in pedestrian-vehicle crashes in North Carolina: Accounting for unobserved heterogeneity

In transportation, pedestrians are among the most vulnerable entities. Each year, a total of about 2,000 pedestrians are reported to be involved in traffic crashes with vehicles in North Carolina. Research efforts are needed to identify influencing factors and develop safety improvement measures for pedestrians. This study applies mixed logit (ML) model approach to exploring the potential unobserved heterogeneities across individual injury observations. Factors that significantly contribute to pedestrian injury severities resulting from pedestrian-vehicle crashes are examined under a variety of categories, including motorist, pedestrian, environmental, and roadway (etc.) characteristics. Police reported pedestrian-vehicle crash data collected from 2007 to 2014 in North Carolina are utilized. Parameter estimates and associated elasticities are used to interpret the results.

Examining Injury Severity of Pedestrians in Vehicle-Pedestrian Crashes at Mid-Blocks Using Path Analysis.

Walking is a sustainable mode of transport which has well established health and environmental benefits. Unfortunately, hundreds of thousands of pedestrians lose their lives each year over the world due to involvement in road traffic crashes, and mid-blocks witness a significant portion of pedestrian fatalities. This study examined the direct and indirect effects of various contributing factors on the pedestrian injury severity in vehicle–pedestrian crashes at mid-blocks. Data of vehicle–pedestrian crashes during 2002–2009 were extracted from the NASS-GES, with pre-crash behaviors and injury severity included. The SEM path analysis method was applied to uncover the inter-relationships between the pedestrian injury severity and various explanatory variables. Both the direct and indirect effects of these explanatory variables on the pedestrian injury severity were calculated based on the marginal effects in the multinomial and ordered logit models. The results indicate some variables including number of road lanes and the age of pedestrian have indirect impacts on the injury severity through influencing the pre-crash behaviors. Although most indirect effects are relatively small compared with the direct effects, the results in this study still provide some valuable information to improve the overall understanding of pedestrian injury severity at mid-blocks.

Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka, Bangladesh

Although the rate of road crashes and their severity is relatively higher in developing countries, there is still a lack of research on pedestrian-vehicle crash severity in these contexts, particularly in Bangladesh. Therefore, this study aimed to identify the contributing environmental, road, and vehicular factors that influenced pedestrian—single-vehicle crash severity in Dhaka, a megacity and the capital of Bangladesh. A binary logistic regression model was developed in this study by analyzing a data set of pedestrian—single-vehicle crashes involving casualties in Dhaka from 2010 to 2015. The model identified seven significant factors influencing pedestrian-vehicle crash severity. Significant factors increasing the likelihood of fatal crashes included crashes during adverse weather, dawn/dusk period, night period (where street light was absent), off-peak period, crashes where road divider was unavailable, road geometry was straight and flat, and crashes those were occurred by heavier vehicles. Besides, crashes at three-legged intersections were less likely to be fatal. Both similarities and differences were found among the significant factors influencing pedestrian-vehicle crash severity in Dhaka from the findings of the developed countries. The findings of this study would help transport engineers and planners to design safer roadways for both pedestrians and vehicles.

Investigating the Factors Influencing Pedestrian–Vehicle Crashes by Age Group in Seoul, South Korea: A Hierarchical Model

Walking is the most natural form of moving in everyday life. However, the urban environment is not always safe for walking. Hence, it is appropriate to create pedestrian safety-oriented neighborhood environments to promote walkability. This study identifies factors that can affect the severity of pedestrian injuries by considering and comparing the individual characteristics of pedestrian crashes and the built environment of neighborhoods using a hierarchical model. The main results of this study are as follows. Those under 18 and over 65 years of age, which are recognized as vulnerable road users, are more affected by the neighborhood environments than other age groups. Parks, convenience stores, and restaurants are likely to play a significant role in reducing the severity of pedestrian injuries. However, for pedestrians under 18 years of age, convenience stores that students often visit after school are likely to increase the severity of pedestrian injuries. The severity of pedestrian injuries is likely to be lower at non-signalized crosswalks and intersections than at signalized crosswalks and intersections. The study is expected to contribute to existing literature on the topic and guide policy makers and planners to arrive at consensus approaches when making decisions on how to build pedestrian safety-oriented neighborhoods.

Effects Influencing Pedestrian–Vehicle Crash Frequency by Severity Level: A Case Study of Seoul Metropolitan City, South Korea

This study aimed to determine how built environments affect pedestrian–vehicle collisions. The study examined pedestrian–vehicular crashes that occurred between 2013 and 2015 in Seoul, Korea, by comparing and analyzing different effects of the built environment on pedestrian–vehicle crashes. Specifically, the study analyzed built environment attributes, land use environment, housing types, road environment, and traffic characteristics to determine how these factors affect the severity of pedestrian injury. The results of the statistical analysis appear to infer that the built environment attributes had dissimilar impacts on pedestrian collisions, depending on the injury severity. In general, both incapacitating and non-incapacitating injuries appear to be more likely to be caused by the built environment than fatal and possible injuries. These results highlight the need to consider injury severity when implementing more effective interventions and strategies for ensuring pedestrian safety. However, because of the small sample size, an expanded research project regarding this issue should be considered, as it would contribute to the development and implementation of effective policies and interventions for pedestrian safety in Korea. This study therefore offers practical information regarding the development of such an expanded study to inform future traffic safety policies in Seoul to establish a “safe walking city.”

Effects of Road Networks on Vehicle-Pedestrian Crashes in Seoul

Effects of Road Networks on Vehicle-Pedestrian Crashes in Seoul

A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes.

Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel model to examine the differences between the lower-level individual characteristics of pedestrian crashes and the upper-level neighborhood environmental characteristics in Seoul, South Korea. The main results of this study are as follows. The individual characteristics of pedestrian-vehicle crashes are better at explaining pedestrian injury severity than built environment characteristics at the neighborhood level. Older pedestrians and drivers suffer more severe pedestrian injuries. Larger vehicles such as trucks and vans are more likely to result in a high severity of pedestrian injuries. Pedestrian injuries increase during inclement weather and at night. The severity of pedestrian injuries is lower at intersections and crosswalks without traffic signals than at crosswalks and intersections with traffic signals. Finally, school zones and silver zones, which are representative policies for pedestrian safety in South Korea, fail to play a significant role in reducing the severity of pedestrian injuries. The results of this study can guide policymakers and planners when making decisions on how to build neighborhoods that are safer for pedestrians.

Who is to blame for crashes involving autonomous vehicles? Exploring blame attribution across the road transport system

The introduction of fully autonomous vehicles is approaching. This warrants a re-consideration of road crash liability, given drivers will have diminished control. This study, underpinned by attribution theory, investigated blame attribution to different road transport system actors following crashes involving manually driven, semi-autonomous and fully autonomous vehicles. It also examined whether outcome severity alters blame ratings. 396 participants attributed blame to five actors (vehicle driver/user, pedestrian, vehicle, manufacturer, government) in vehicle–pedestrian crash scenarios. Different and unique patterns of blame were found across actors, according to the three vehicle types. In crashes involving fully autonomous vehicles, vehicle users received low blame, while vehicle manufacturers and government were highly blamed. There was no difference in the level of blame attributed between high and low severity crashes regarding vehicle type. However, the government received more blame in high severity crashes. The findings have implications for policy and legislation surrounding crash liability. Practitioner summary: Public views relating to blame and liability in transport accidents is a vital consideration for the introduction of new technologies such as autonomous vehicles. This study demonstrates how a systems ergonomics framework can assist to identify the implications of changing public opinion on blame for future road transport systems. Abbreviation: ANOVA: analysis of variance; DAT: defensive attribution theory; IV: independent variable

Exploring the Determinants of the Severity of Pedestrian Injuries by Pedestrian Age: A Case Study of Daegu Metropolitan City, South Korea.

Pedestrian-vehicle crashes can result in serious injury to pedestrians, who are exposed to danger when in close proximity to moving vehicles. Furthermore, these injuries can be considerably serious and even lead to death in a manner that varies depending on the pedestrian’s age. This is because the pedestrian’s physical characteristics and behaviors, particularly in relation to roads with moving vehicles, differ depending on the pedestrian’s age. This study examines the determinants of pedestrian injury severity by pedestrian age using binary logistic regression. Factors in the built environment, such as road characteristics and land use of the places where pedestrian crashes occurred, were considered, as were the accident characteristics of the pedestrians and drivers. The analysis determined that the accident characteristics of drivers and pedestrians are more influential in pedestrian-vehicle crashes than the factors of the built environmental characteristics. However, there are substantial differences in injury severity relative to the pedestrian’s age. Young pedestrians (aged under 20 years old) are more likely to suffer serious injury in school zones; however, no association between silver zones and injury severity is found for elderly pedestrians. For people in the age range of 20–39 years old, the severity of pedestrian injuries is lower in areas with more crosswalks and speed cameras. People in the age range of 40–64 years old are more likely to be injured in areas with more neighborhood streets and industrial land use. Elderly pedestrians are likely to suffer fatal injuries in areas with more traffic signals. This study finds that there are differences in the factors of pedestrian injury severity according to the age of pedestrians. Therefore, it is suggested that concrete and efficient policies related to pedestrian age are required to improve pedestrian safety and reduce pedestrian-vehicle crashes.

EFFECT OF ALIROCUMAB ON FIRST AND TOTAL REVASCULARIZATIONS IN PATIENTS WITH RECENT ACUTE CORONARY SYNDROME IN THE ODYSSEY OUTCOMES TRIAL

Background On urban roads, road users interact in a highly complex environment. Few previous multivariable studies investigated the association between the built urban environment (including the road, roadside and human activity) and crashes. Roadside design and amenities and facilities that impact road user movements were rarely considered. This research aimed to identify characteristics of the built urban environment associated with the frequency of pedestrian-vehicle casualty crashes (PVC) on shopping strips in metropolitan Melbourne, Australia. Methods The study had a cross-sectional design. Strip shopping arterial road segments in Melbourne, Australia, were identified (n = 142). Data were collected for a broad range of potential risk factors relating to the road, roadside and human activity. Analysis using Poisson regression identified factors associated with PVC frequency. Diagnostic tests were conducted to assess model fit. Results From 2005 to 2009 there were 519 (median = 5.4/km) PVC on the 142 midblock road segments. Aspects of the built environment associated with increased PVC frequency were: road width, unsignalised intersections, medians or traffic islands, midblock pedestrian crossings, roadside development height, off-street parking facilities, establishments with a late night liquor license, railway stations and tertiary education institutions. Lane width and parking clearways were associated with significant reductions in PVC. Speed limits were associated with PVC in a non-linear fashion. There was no association between traffic volume or estimated pedestrian activity and PVC. Conclusions A range of risk factors were related to PVC on strip shopping road segments including traditionally studied characteristics like road design and less commonly studied aspects such as the roadside and facilities and amenities on the road segment. This study can contribute to the design of PVC countermeasures and better risk assessment tools for urban road segments.

Factors affecting injury severity in vehicle-pedestrian crashes: A day-of-week analysis using random parameter ordered response models and Artificial Neural Networks

Abstract The high number of vehicle–pedestrian crashes in the United State has gained increased attention among transportation safety analysts in recent years. Being directly exposed to the collision force makes pedestrians more prone to becoming severely injured when in crash than other road users. Considering the fact that pedestrian-involved crashes is a serious public health problem, the current study’s aim is to investigate the contributing factors associated with injury severity of pedestrian crashes by time-of-week. Separate injury severity models for weekday and weekend crashes were developed, and the overall stability of the model estimates was examined through likelihood ratio tests. For this purpose, random parameter ordered-response models were employed to specify the ordinal nature of injury severity levels and capture the potential unobserved heterogeneity. In addition, Artificial Neural Network (ANN) was used to explore the nonlinear relationship between explanatory variables and severity outcomes. Comparison of the prediction performance demonstrated that optimized ANN provides superior results compared to conventional statistical approaches. A variable impact analysis was then conducted on the optimized ANN to investigate the effects of the explanatory variables on injury severity. The results revealed the factors that are significantly associated with pedestrian fatalities. These findings further provide insights for a better understanding of pedestrian injury severity in weekday vs. weekend crashes through the impact analysis of various explanatory variables.

Identifying Patterns of Pedestrian Crashes in Urban Metropolitan Roads in India using Association Rule Mining

Pedestrian safety is an important component of efforts to prevent road traffic injuries. Pedestrians constitute to around 22% of the total deaths occurring on the world roads. According to the recent report by the Ministry of Road Transport and Highways (MoRTH), Government of India, the number of pedestrian-related deaths was 15,746 (10.5%) of total persons killed in the country during the year 2016. This high proportion of mortality and severity injury among pedestrians necessitates more investigation to identify determinants to reduce crashes in the future. The present research used the Apriori algorithm of supervised association rule mining to identify the patterns of pedestrian severity injury in urban Indian metropolitan city, Chennai. Using the RADMS database of the government of Tamilnadu, vehicle-pedestrian crashes were analyzed for recent two years between 2015 and 2016. The results highlight the fact that middle-aged pedestrians are more vulnerable to road traffic crashes. Exceeding speed limits than the posted speed, especially in the highways, results in fatal crashes among pedestrians. Vehicle-pedestrian crashes are frequent at sites where there are no median separators when drivers do not respect the right of way rules. The findings of the present study will help the traffic safety professionals to understand patterns of crashes and take necessary countermeasures to decrease pedestrian injury-related crashes potentially.

Identification of Contributing Factors in Vehicle Pedestrian Crashes in Chennai using Multiple Correspondence Analysis

In India, 10.5% of total accident death and injury of 2016 are related to pedestrians. Identification of the vehicle, roadways, environment or human factors involved in vehicle-pedestrian crashes has become an essential factor in implementing countermeasures. Multiple Correspondence Analysis (MCA), a categorical data analysis technique was used in this study on 2016 vehicle-pedestrian accidents from the Road Accident Data Management System (RADMS) database of Chennai city to detect patterns and associations that lead to accidents. This study identifies, two key clusters and six distant clusters of variables to have factors contributing to vehicle-pedestrian crashes. The associated variables and its categories found in the key clouds were collision type, cause of accidents, junction control, and pedestrian age. The association suggests that pedestrians in the age group of 25 to 34 are mostly injured at traffic signals where the cause of the accident is usually due to non-respect of the right way of rules. Also, driving against the flow of traffic, changing lane without due care and dangerous overtaking were associated with hitting an object. Other non-trivial variables identified were the time of day, season, availability of central divider, injury severity and speed limit. This technique provides data on the associated pattern and the significance of variables that most likely resulted in a pedestrian-vehicle crash. Based on the findings, appropriate countermeasures are also suggested that could potentially help transportation safety researches and policymakers towards developing strategies that prevent pedestrian accidents.

Walking While Black: Racial Gaps in Hit-and-Run Cases

Is there a racial gap in the treatment of crime victims, and when does it first occur? We provide a causal test for racial gaps in victimization and clearance rates, using the unintentional nature of vehicle-pedestrian crashes: the victim’s race should not depend on the driver’s characteristics, conditional on location and time. We find that American drivers flee 13% more often if they hit a Black pedestrian, and clearance rates of hit-and-run cases are 11% lower for Black victims. The evidence points to both statistical discrimination and out-group bias as mechanisms behind the racial gap in hit-and-run rates.