Pedestrian Injury Severity Research Articles

Modeling Pedestrian Injury Severity in Pedestrian-Vehicle Crashes in Rural and Urban Areas: Mixed Logit Model Approach

Pedestrian-vehicle crashes are more likely to result in severe pedestrian incapacitating injuries and fatalities than other types of crashes. In this study, mixed logit models are developed to investigate and identify significant contributing factors to the pedestrian injury severity in pedestrian-vehicle crashes in both rural and urban areas in North Carolina, United States. Pedestrian-vehicle crash data from the Highway Safety Information System database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: fatality; injury class 1 (disabling injury); injury class 2 (evident injury); injury class 3 (possible injury); and no injury (property damage only). The estimation results show that factors such as a bad driver’s physical condition, heavy trucks, dark light condition, speed limit between 35 and 50 mph and speed limit above 50 mph will significantly increase pedestrian injury severities in both rural and urban areas. The developed model and analysis results provide insights on developing effective countermeasures to reduce pedestrian injury severities in pedestrian-vehicle crashes and improve traffic system safety performance.

Evaluation of the effectiveness of traffic calming measures on vehicle speeds and pedestrian injury severity in Ghana

Objectives: Each year, pedestrian injuries constitute over 40% of all road casualty deaths and up to 60% of all urban road casualty deaths in Ghana. This is as a result of the overwhelming dependence on walking as a mode of transport in an environment where there are high vehicular speeds and inadequate pedestrian facilities. The objectives of this research were to establish the (1) impact of traffic calming measures on vehicle speeds and (2) association between traffic calming measures and pedestrian injury severity in built-up areas in Ghana.Method: Vehicle speeds were unobtrusively measured in 38 selected settlements, including 19 with traffic calming schemes and 19 without. The study design used in this research was a matched case–control. A regression analysis compared case and control casualties using a conditional logistic regression.Results: Generally, the mean vehicle speeds and the proportion of vehicles exceeding the 50 km/h speed limit were significantly lower in settlements that have traffic calming measures compared to towns without any traffic calming measures. Additionally, the proportion of motorists who exceeded the speed limit was 30% or less in settlements that have traffic calming devices and the proportion who exceeded the speed limit was 60% or more in towns without any traffic calming measures. The odds of pedestrian fatality was significantly higher in settlements that have no traffic calming devices compared to those that have (odds ratio [OR] = 1.98; 95% confidence interval, 1.09–4.43). The protective effects of a traffic calming scheme that has a speed table was notably higher than those where there were no speed tables.Conclusion: It was clearly evident that traffic calming devices reduce vehicular speeds and, thus, the incidence and severity of pedestrian injuries in built-up areas in Ghana. However, the fact that they are deployed on arterial roads is increasingly becoming a road safety concern. Given the emerging safety challenges associated with speed calming measures, we recommend that their use be restricted to residential streets but not on arterial roads. Long-term solutions for improving pedestrian safety proposed herein include bypassing settlements along the highways to reduce pedestrians’ exposure to traffic collisions and adopting a modern way of enforcement such as evidence-based laser monitoring in conjunction with a punishment regime that utilizes the demerit points system.

Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings

Pedestrian red-light violations at signalized crossings are an important traffic safety concern, particularly in densely populated cities. In this study, the authors quantitatively investigated the factors associated with pedestrian red-light violations and injury severity resulting from pedestrian–motor vehicle crashes at signalized crossings. Random parameter probit models are used to account for individual-specific heterogeneity that arises from a set of unmeasured factors related to traffic conditions and the pedestrians’ physical and mental status. Data for the analysis are based on the historical crash record maintained by the Hong Kong Transport Department. Children younger than age 11 years are not only associated with a higher likelihood of pedestrian red-light violations but also tend to have a higher probability of severe injuries. Factors including summer, dual carriageways with a central traffic island, and pedestrian age of 12 to 25 years are solely related to a higher likelihood of pedestrian red-light violations; meanwhile, variables solely associated with a higher probability of severe injuries include crashes that occur between 22:00 and 06:59, crashes occurring in rainy weather, crashes involving pedestrians older than age 46 years, and bus crashes. Appropriate countermeasures are recommended to curb pedestrian red-light violations and to reduce the injury severity of pedestrian crashes.

Modelling the severity of pedestrian injury in pedestrian—vehicle crashes in North Carolina: A partial proportional odds logit model approach

More than 2,000 pedestrians are involved in reported traffic crashes with vehicles in North Carolina every year. Of these, 10% to 20% of these pedestrians were killed or severely injured. Studies are needed to explore the reasons under such situation for improvement. Traditional methods for modeling crash injury severities use multinomial logit (MNL) models, mixed logit (ML) models, or ordered logit/probit models. However, traditional MNL and ML models treat injury severity levels as nonordered, ignoring the inherent hierarchical nature of crash injury severities, whereas the data used in ordered logit models should be strictly subjected to the proportional odds (PO) assumption. A partial proportional odds (PPO) logit model approach is applied here to address these concerns. The predictors in the PPO model can have different effects on different levels of the dependent variable who violates the PO assumption. This study uses police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, roadway characteristics are examined. Comparisons of different models are also made and results show that PPO outperforms others. Parameter estimates and associated marginal effects are calculated and used to interpret the model and evaluate the significance of each dependent variables, followed by recommendations in the Conclusion.

Walking under the influence of the alcohol: A case study of pedestrian crashes in Tennessee

Alcohol adversely affects human behavior and judgment, and it is one of the contributing factors in traffic crashes. Although a large body of research has investigated driving behavior under the influence of the alcohol, to the best of our knowledge, no study has investigated the crash characteristics of the pedestrians under the influences of the alcohol. Tennessee Police Crash Data from 2011 to 2016 was used in this study to identify crashes between motor vehicles and pedestrians who were walking under the influence of alcohol (WUI). Results indicate that the number of fatally injured pedestrians for WUI cases has increased since 2011. Alcohol was present in 7% of the pedestrian crashes. Tested pedestrians averaged BAC levels of 0.17 g/dL. As pedestrian injury severity increased, the share of the WUI crashes increased. WUI contributed in 22% of the fatally injured pedestrian and only in 2% of the pedestrian crashes with no-injury. Comparisons indicate that the WUI crashes had their characteristics, which distinguished them from non-WUI crashes. Analysis indicates that 83% of the WUI crashes occurred in the nights; moreover, 54%, 69%, and 85% of WUI crashes respectively occurred on weekends, mid-block section of the road, and areas with no traffic control device. Results of a binary logit regression indicate that pedestrian’s age, males, posted speed limit, and nighttime crashes had a positive association with the WUI crashes. On the other hand, urban context, intersection crashes, driver maneuvers (i.e., parking-related, turning, and straight), and daylight had a negative association with the WUI crashes. Findings are discussed in line with road safety countermeasures.

Pedestrian Injury Severity Analysis in Motor Vehicle Crashes in Ohio

According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio in order to explore the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. The model results indicate that older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), struck by truck, dark-unlighted roadways, six-lane roadways, and speed limits of 40 mph and 50 mph were all factors associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 a.m. to 3:59 p.m.), weekdays, and daylight condition were all factors associated with less severe injuries. This study provides specific safety recommendations so that effective countermeasures can be developed and implemented by policy makers, which in turn will improve overall highway safety.

A clustering approach to injury severity in pedestrian-train crashes at highway-rail grade crossings

ABSTRACTThis research studied potential factors associated with pedestrian injury severity levels sustained in train-pedestrian crashes at highway-rail grade crossings (HRGCs) using the Federal Railroad Administration's ten-year data. The analysis focused on nonsuicide pedestrian crashes and took into consideration the unobserved heterogeneity. Latent class clustering (LCC) addressed unobserved heterogeneity by creating distinct subgroups with relatively homogeneous attributes within each subgroup. HRGC inventory variables were the basis for the LCC; the process split the dataset into five distinguished clusters. Binary logit models for each cluster and the complete data set were estimated. A generalized linear mixed model, based on the complete data set, allowed examination of the clustering and comparison of the modeling results. Findings provided justification for the use of LCC as the first step in accounting for unobserved heterogeneity. Different HRGC, pedestrian, and crash characteristics were associated with pedestrian injury severity across different clusters. Higher train speed was associated with more severe injury propensity, regardless of the conditions of the HRGCs. Other variables including freight train involvement, train hitting pedestrian, HRGCs with the absence of flashing lights, advance warnings, rural areas, lower visibility conditions, and older pedestrians increased pedestrian injury severity levels with varying effects in different clusters.

Pedestrian injury severity in the event of a collision with a truck: are energy absorbing adaptive deformable fronts suitable

It is well known that adaptive deformable fronts attached to trucks reduce pedestrian injury. However, the suitability of these frontal attachment devices from a holistic point of view, considering both primary and secondary impacts, is questioned in this paper. Using head-injury-criteria index, the effects of pedestrian gait and different closing speeds are considered for evaluation of numerous truck-pedestrian side and rear impact situations. Numerical simulations, considering coupled rigid and deformable-body dynamics, have been carried out in this paper to arrive at conclusions. For primary side impact scenario DEA frontal attachment provides a performance improvement specifically for 0% gait; however, the performance improvement is non-uniform for other gait cycles. For primary rear impact scenarios, it is observed that DEA is not a useful alternative at all. For secondary collision scenarios (involving both side and rear impacts) DEA is not at all an effective means for performance improvement.

Pedestrian injury severity in the event of a collision with a truck: are energy absorbing adaptive deformable fronts suitable

It is well known that adaptive deformable fronts attached to trucks reduce pedestrian injury. However, the suitability of these frontal attachment devices from a holistic point of view, considering both primary and secondary impacts, is questioned in this paper. Using head-injury-criteria index, the effects of pedestrian gait and different closing speeds are considered for evaluation of numerous truck-pedestrian side and rear impact situations. Numerical simulations, considering coupled rigid and deformable-body dynamics, have been carried out in this paper to arrive at conclusions. For primary side impact scenario DEA frontal attachment provides a performance improvement specifically for 0% gait; however, the performance improvement is non-uniform for other gait cycles. For primary rear impact scenarios, it is observed that DEA is not a useful alternative at all. For secondary collision scenarios (involving both side and rear impacts) DEA is not at all an effective means for performance improvement.

The effects of neighborhood characteristics and the built environment on pedestrian injury severity: A random parameters generalized ordered probability model with heterogeneity in means and variances

Transportation infrastructure facilities and pedestrian/driver behaviors are associated with neighborhood characteristics and the built environment. However, the effects of neighborhood characteristics and the built environment on pedestrian injury severity are not well documented. To investigate and quantify the effects of neighborhood characteristics and built environment on pedestrian injury severity, a random parameters generalized ordered probit model with heterogeneity in means and variances was proposed to consider the ordinal nature of injury data and the issue of threshold and unobserved heterogeneity. A total of 3867 pedestrian-vehicle crashes that occurred in Florida Department of Transportation District 7 from 2011 to 2014 were analyzed. Based on the estimation results, three factors (African American community, school zone, and bus stop area) related to neighborhood characteristics and the built environment were identified to have significant influence on pedestrian injury severity. Pedestrian-vehicle crashes that occurred in African American community or bus stop area are less likely to involve incapacitating and fatal injuries. The presence of a school within a 0.5-km buffer from a crash leads to a decreased probability of fatal injury and an increased probability of incapacitating injury. In addition, the estimated parameter for elderly pedestrian indicator (50<age≤65) was found to be random with significant heterogeneity in both mean and variance (where the mean and variance are associated with intersection-related crash indicator). Compared to younger pedestrians (age≤30), 74.3% of elderly pedestrians who are involved in intersection-related crashes are more likely to suffer severe injury (fatal or incapacitating injury), while 52.4% of elderly pedestrians who are not involved in intersection-related crashes are more likely to suffer severe injury.

Investigating factors influencing pedestrian injury severity at intersections

ABSTRACTObjective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.

Application of partial proportional odds model for analyzing pedestrian crash injury severities in Switzerland

ABSTRACTAccording to the Swiss Microcensus, Switzerland is a place where people choose to walk more than 40% of their daily trip time, resulting in a higher pedestrian crash to total crash ratio when compared to many other developed countries. Furthermore, the high number of old and young pedestrians as well as the large number of pedestrian crashes with higher levels of severity makes the pedestrian crash analysis for Switzerland very important. This study aims to identify the important factors that influence the injury severity levels of pedestrian–vehicle crashes in Switzerland. To address this we employ a partial proportional odds model to analyze pedestrian crash injury severities in Switzerland, based on the 5-year (2008–2012) pedestrian safety data. The study also focuses on a separate analysis for two vulnerable groups of pedestrians (young and old pedestrians) to examine the change in the influence of factors over age. The analysis involves an extensive evaluation based on pedestrian and driver characteristics; main reason for the crash; traffic, temporal, vehicle, roadway geometric and environmental characteristics. The study shows that irrespective of age, dark unlighted areas, sight obstructions, midblock crossings and heavy vehicles adversely affect the injury severity levels of pedestrians.

Considering built environment and spatial correlation in modeling pedestrian injury severity

ABSTRACTObjective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.

Detection of the toughest: Pedestrian injury risk as a smooth function of age

ABSTRACTObjective: Though it is common to refer to age-specific groups (e.g., children, adults, elderly), smooth trends conditional on age are mainly ignored in the literature. The present study examines the pedestrian injury risk in full-frontal pedestrian-to–passenger car accidents and incorporates age—in addition to collision speed and injury severity—as a plug-in parameter.Methods: Recent work introduced a model for pedestrian injury risk functions using explicit formulae with easily interpretable model parameters. This model is expanded by pedestrian age as another model parameter. Using the German In-Depth Accident Study (GIDAS) to obtain age-specific risk proportions, the model parameters are fitted to the raw data and then smoothed by broken-line regression.Results: The approach supplies explicit probabilities for pedestrian injury risk conditional on pedestrian age, collision speed, and injury severity under investigation. All results yield consistency to each other in the sense that risks for more severe injuries are less probable than those for less severe injuries. As a side product, the approach indicates specific ages at which the risk behavior fundamentally changes. These threshold values can be interpreted as the most robust ages for pedestrians.Conclusions: The obtained age-wise risk functions can be aggregated and adapted to any population. The presented approach is formulated in such general terms that in can be directly used for other data sets or additional parameters; for example, the pedestrian's sex. Thus far, no other study using age as a plug-in parameter can be found.

Hierarchical ordered model for injury severity of pedestrian crashes in South Korea

Introduction: The high percentage of fatalities in pedestrian-involved crashes is a critical social problem. The purpose of this study is to investigate factors influencing injury severity in pedestrian crashes by examining the demographic and socioeconomic characteristics of the regions where crashes occurred. Method: To understand the correlation between the unobserved characteristics of pedestrian crashes in a defined region, we apply a hierarchical ordered model, in which we set crash characteristics as lower-level variables and municipality characteristics as upper-level. Pedestrian crash data were collected and analyzed for a three-year period from 2011 to 2013. The estimation results show the statistically significant factors that increase injury severity of pedestrian crashes. Results: At the crash level, the factors associated with increased severity of pedestrian injury include intoxicated drivers, road-crossing pedestrians, elderly pedestrians, heavy vehicles, wide roads, darkness, and fog. At the municipality level, municipalities with low population density, lower level of financial independence, fewer doctors, and a higher percentage of elderly residents experience more severe pedestrian crashes. Municipalities ranked as having the top 10% pedestrian fatality rate (fatalities per 100,000 residents) have rates 7.4 times higher than municipalities with the lowest 10% rate of fatalities. Their demographic and socioeconomic characteristics also have significant differences. The proposed model accounts for a 7% unexplained variation in injury severity outcomes between the municipalities where crashes occurred. Conclusion: To enhance the safety of vulnerable pedestrians, considerable investments of time and effort in pedestrian safety facilities and zones should be made. More certain and severe punishments should be also given for the traffic violations that increase injury severity of pedestrian crashes. Furthermore, central and local governments should play a cooperative role to reduce pedestrian fatalities. Practical applications: Based on our study results, we suggest policy directions to enhance pedestrian safety.

Injury severity of pedestrians involved in road traffic crashes in Addis Ababa, Ethiopia

ABSTRACTAddis Ababa, with a population of four million, is the largest urban center in Ethiopia. Recently, road traffic crashes, and particularly pedestrian crashes, have become a challenging problem within the city. Walking is the principal mode of transportation in Addis Ababa, accounting for about 60% of daily trips, whereas pedestrian injuries account for about 85% of total injury crashes. Noting this disparity, the objective of this study was to examine the influence of roadway geometric, traffic features, spatial characteristics, and driver/pedestrian demographics on injury severities of pedestrians in Addis Ababa. Police reported pedestrian crashes in Addis Ababa from 2009 to 2012 have been modelled using a random-parameters logistic regression model to account for unobserved heterogeneity potentially resulting from crash under-reporting, operational and geometric features, and human behavioral factors. Factors associated with increasing probability of fatal pedestrian injury include crashes that occur on high-speed roads, at intersections, during darkness, and that involve heavy vehicle-pedestrian collisions. Drivers who are less educated were more likely to be involved in fatal crashes involving pedestrians. Interestingly, pedestrian injuries are more severe when a car is driven by family, friends, or relatives as compared to a vehicle's owner. The detailed findings of this research are contrasted with findings from developed countries, and their implications are discussed in relation to infrastructure and policy interventions.

Mixed Effects Logistic Model to Address Demographics and Neighborhood Environment on Pedestrian Injury Severity

This paper examines the effects of demographics and neighborhood environment on pedestrian injury severity to inform proactive countermeasures for improving pedestrian safety. A mixed effects logistic model addressing unobserved heterogeneity was developed from 3,948 pedestrian-involved crashes that occurred in Florida from 2011 to 2014. Six normally distributed random parameters were identified to reflect random effects on the pedestrian injury severity. The heterogeneity of two demographic factors (older and male pedestrians) suggested the need for more customized education programs to improve pedestrian safety awareness and knowledge, especially for older pedestrians. Relative to low-income areas, 67.7% of pedestrians involved in crashes in higher-income areas were less likely to sustain severe injury. Analysis of sample data also indicated that low-income areas tended to have had more unsafe behaviors by pedestrians related to higher injury severity (e.g., crossing at dark in unlighted areas). Higher-income areas tended to have had more unsafe behaviors by drivers related to higher injury severity (e.g., distracted driving). Other significant factors included lighting conditions (daylight, darkness without lighting), speed limit, alcohol or drug impairment, dart or dash behavior, crossing indicator, and traffic control device indicator. Regarding neighborhood land use types, two indicators about the presence of bus stops and department stores or supermarkets nearby were significant, and their effects were also random. Further investigations are needed to identify systematically the need for effective countermeasures in severe injury crash clusters in the future.

Evaluating Aging Pedestrian Crash Severity with Bayesian Complementary Log–Log Model for Improved Prediction Accuracy

Reliable prediction accuracy is an essential attribute for crash prediction models. Generally, more severe injury outcomes, such as fatalities, are rarer than less severe crashes, such as property damage only or minor injury crashes. The complementary log–log (cloglog) model, commonly used in epidemiological research, is known for its accuracy in predicting rare events. This study implemented the cloglog model in analyzing pedestrian injury severity and compared its performance with the two conventional models used in injury severity research: the probit and logit models. The three models were developed with data from 1,397 crashes involving aging pedestrians that occurred in Florida from 2009 through 2013. The response variable, injury severity level, was binary and categorized as either fatal or severe injury or minor or no injury. The study used three accuracy metrics (deviance information criteria, prediction accuracy, and receiver operating characteristics curves) to compare the performance of the models. The cloglog model outperformed the probit and logit models in overall goodness of fit and prediction accuracy. More important, the cloglog model outperformed the other two models considerably for predicting fatal and severe crashes according to the recall metric (72% accuracy versus 43% and 41% for probit and logit models, respectively). However, the other two models outperformed the cloglog model in predicting crashes with no or minor injuries. Of predictor variables included in the model, six were found to significantly influence fatal or severe injuries for aging pedestrians at 95% Bayesian credible interval. These variables included pedestrian age, alcohol involvement, first harmful event, vehicle movement, shoulder type, and posted speed.

Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

SummaryThe present study intended to (1) investigate the injury risk of pedestrian casualties involved in traffic crashes at signalized intersections in Hong Kong; (2) determine the effect of pedestrian volumes on the severity levels of pedestrian injuries; and (3) explore the role of spatial correlation in econometric crash‐severity models. The data from 1889 pedestrian‐related crashes at 318 signalized intersections between 2008 and 2012 were elaborately collected from the Traffic Accident Database System maintained by the Hong Kong Transport Department. To account for the cross‐intersection heterogeneity, a Bayesian hierarchical logit model with uncorrelated and spatially correlated random effects was developed. An intrinsic conditional autoregressive prior was specified for the spatial correlation term. Results revealed that (1) signalized intersections with greater pedestrian volumes generally exhibited a lower injury risk; (2) ignoring the spatial correlation potentially results in reduced model goodness‐of‐fit, an underestimation of variability and standard error of parameter estimates, as well as inconsistent, biased, and erroneous inference; (3) special attention should be paid to the following factors, which led to a significantly higher probability of pedestrians being killed or sustaining severe injury: pedestrian age greater than 65 years, casualties with head injuries, crashes that occurred on footpaths that were not obstructed/overcrowded, heedless or inattentive crossing, crashes on the two‐way carriageway, and those that occurred near tram or light‐rail transit stops. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Analyzing pedestrian crash injury severity under different weather conditions

ABSTRACTObjective: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain.Method: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach.Results: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit.Conclusions: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.