Investigating the risk factors associated with pedestrian injury severity in Illinois

Nighttime safety of pedestrians: The role of pedestrian automatic emergency braking systems

The rise in vulnerable road user fatalities, e.g., pedestrians, is alarming. Nighttime poses unique challenges among the risk factors in pedestrian-involved crashes, accounting for approximately 74% of fatalities in the United States. Safe vehicles, a key element of the Safe System Approach, are a promising solution to improve pedestrian safety. Vehicle automation, particularly Pedestrian Automatic Emergency Braking (P-AEB) systems, can mitigate pedestrian-involved crashes. However, the P-AEB systems' effectiveness, especially in darkness, has remained uncertain. Through analyzing the recent Insurance Institute for Highway Safety nighttime dataset from 2021 to 2023, integrated with vehicle data to create a unique database (1973 field tests), this study aims to understand the P-AEB systems' effectiveness at night and explore correlates of their performance, especially the role of headlight technologies, employed sensors, vehicle propulsion type (Electric Vehicles (EVs) vs. non-EVs), and vehicle weight and size within the scope of controlled experimental data. Using a random-effects Heckman sample selection model with panel data, the study estimates the crash probability and impact speed in case of a crash, addressing the inherent panel structure of the data and unobserved heterogeneity among different vehicles and scenarios. Statistics indicate that P-AEBs stopped vehicles in 63.6% of the cases and, on average, reduced speed by 33.12% for the crashes. Results reveal that P-AEBs perform better in vehicles with light-emitting diode headlights than those with halogen headlights and may be relatively less effective in larger, heavier cars and EVs. Additionally, integrating camera and radar sensors improves P-AEBs' reliability instead of relying solely on cameras. Future efforts can enhance nighttime pedestrian safety by focusing on advanced headlight technologies and sensor integration, improving P-AEBs' prediction algorithms for various scenarios, and addressing systems' limitations in larger, heavier vehicles and EVs.

Do sports utility vehicles (SUVs) and light truck vehicles (LTVs) cause more severe injuries to pedestrians and cyclists than passenger cars in the case of a crash? A systematic review and meta-analysis

QUESTIONS

In the case of a road traffic crash, do sports utility vehicles (SUVs) and light truck vehicles (LTVs) cause more severe injuries to pedestrians and cyclists than passenger cars? Does any effect differ between adults and children?

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, TRID and Global Index Medicus were searched up to September 2024, with no restrictions by setting or language.

INCLUSION CRITERIA

Eligible studies had to compare injury severity between pedestrians and/or cyclists hit by an SUV or LTV versus a passenger car. Only sources using real-world crash data were included.

MAIN OUTCOME MEASURE

Injury severity, defined either as 'fatal versus non-fatal injury' or as 'killed or seriously injured (KSI) versus slight injury'.

RESULTS

24 studies were included in the meta-analysis. The results were similar between pedestrians and cyclists. When combining pedestrians and cyclists, the pooled odds of KSI versus slight injury if hit by an SUV/LTV versus a passenger car were higher among adults/all-age samples by 1.24 (95% CI 1.15, 1.34) and higher among children by 1.28 (95% CI 1.19, 1.37). The odds of fatal versus non-fatal injury if hit by an SUV/LTV versus a passenger car increased among adults/all-age samples by 1.44 (95% CI 1.33, 1.56) and among children by 1.82 (95% CI 1.57, 2.11; p=0.006 for heterogeneity by age).

CONCLUSION

In the case of a crash, SUVs and LTVs cause more severe injuries to pedestrians and cyclists than passenger cars. This effect is larger for fatalities than for KSIs, and the fatality effect is particularly large for children.PROSPERO registration number CRD42024597283.

Investigation of injury severity level in truck-related crashes at school zones using mixed generalized ordered models

The number of truck registrations is steadily increasing in Korea. The proportion of truck deaths compared to the total number of traffic crashes was 23.9%, which is significantly higher than that of other vehicles. In the field of traffic safety, the Children's Safety Measures Policy by government aims to enhance the safety of children's commuting routes by expanding school zones. Nonetheless, truck crashes continue to occur in school zones. Therefore, this study analyzed the factors that affect the severity of truck traffic crashes in school zones in order to contribute to safety improvements. In the study, a distinction is made between various levels of severity to determine the factors that contribute to each level. The generalized ordered models were applied to investigate injury severity levels. Moreover, in order to account for heterogeneity issue, the mixed-effects models with random parameters were used for the analysis. These models were constructed using data collected from school zones over a period of recent ten years. The results showed that crashes occurred at night and on the weekend, as well as human factors such as the age of the victim and the gender of the offender, the types of vehicles involved, and the road type, have been identified as important factors contributing to crash severity. By considering the factors that contribute to truck crashes in school zones, it is anticipated that road safety can be enhanced.

Investigating temporal trends in risk factors related to injury severity of crashes with pedestrians in urban areas

OBJECTIVE

The implementation of road safety policy in urban areas can potentially change the severity profile of crashes, as well as how risk factors influence crash severity. In this sense, this study aims to empirically evaluate possible changes in the severity profile of crashes with pedestrians and in the influence of risk factors for pedestrian injuries after the efforts of the Decade of Action for Road Safety in the city of Fortaleza, Brazil.

METHODS

This was done using data from crashes with pedestrians between 2009 and 2019; divided into three periods. Two categorical modeling analyses were performed using the mixed logit modeling approach, including sociodemographic, environmental, vehicle, road type, and traffic control device factors. In the first analysis, a single model was estimated, and time (period) was included as an explanatory variable; in the second one, models were estimated for each period.

RESULTS

According to temporal analysis, a reduction was evident in the severity profile of crashes with pedestrians over the decade of action. In general, the safety interventions seemed to have little or no impact on pedestrian gender, young pedestrians (up to 15 years old), crashes at night, crashes during weekends and crossings near traffic lights. Regarding crashes on arterial roads, the results suggest an increase in the marginal effects for fatal crashes after the decade of action, while other variables, such as heavy vehicles and expressways, showed positive marginal effects in all periods, indicating that the direction of their effect did not change. This is a potential indication that the overall safety impact of policies during the decade were not effective for these types of crashes. It was possible to identify considerable reduction in the marginal effects for older pedestrians (60+) for both severe and fatal crashes.

CONCLUSION

Although it is not possible to claim that this change comes from specific actions or controlled factors, the results presented here indicate an improvement in road safety for these users, in line with the goals of the Safe Systems Approach and the Decade of Action for Road Safety to reduce severe and fatal traffic injuries.

Factors influencing pedestrian injury severity in Chile: A hierarchical probit ordered model approach

INTRODUCTION

Traffic crashes remain a leading cause of fatalities worldwide, with higher fatality and injury rates in non-developed countries. Understanding the relationship among variables influencing traffic crashes and its outcome, measured as crash severity, is crucial for developing effective and targeted countermeasures to mitigate this problem.

METHOD

In this study, we analyze traffic crashes involving pedestrians in Chile from 2022 to 2023. This allowed us to consider the entire country rather than a specific urban area, which is the first of its kind for a Latin American country. A Hierarchical Ordered Probit (HOPIT) model was estimated to model both risk propensity and severity of pedestrian and vehicle crashes while maintaining an ordered threshold structure. Findings reveal that pedestrian and driver characteristics significantly influence crash severity.

RESULTS

Male drivers have a higher probability of being involved in more severe crashes. Meanwhile, older pedestrians present a higher risk of severe and fatal injuries. Crash severity is significantly influenced by variables related to vehicle type and environmental factors. Pedestrians hit by heavy-duty vehicles have a 60% and 30% higher chance of suffering fatal or severe injuries, respectively. Highways exhibit a 421% higher chance of fatal injuries, followed by crashes at night and crashes in rural areas with 380% and 267%, respectively.

PRACTICAL APPLICATIONS

This research indicates the need for targeted safety measures addressing pedestrian and driver demographics and behavior, vehicle types, and environmental factors to effectively reduce pedestrian injury severity.

Exploring patterns in older pedestrian involved crashes during nighttime

Nighttime crashes involving older pedestrians pose a significant safety concern due to their age-related vulnerabilities such as reduced vision and slower reaction times. This study analyzes crash data from Texas for six years (2017-2022) using Association Rules Mining (ARM) to identify patterns and associations affecting crash severity for older pedestrians aged 65-74 years and those over 74 years under varying lighting conditions. The findings reveal that high-speed limits and complex road environments significantly increase the risk of fatal or severe injuries for both age groups, particularly under inadequate lighting. Additionally, demographic factors, adverse weather conditions, and specific road features further influence crash outcomes. These insights highlight the need for interventions, including lower speed limits, enhanced street lighting, and the implementation of advanced technologies such as modern pedestrian detection systems, sensor technology, pedestrian bags, accessible pedestrian signals, to improve the safety of older pedestrians. Policymakers should leverage these insights to formulate strategies that improve road safety for older pedestrians, addressing their unique vulnerabilities in various nighttime conditions.

The role of human errors and violations in pedestrian-related crashes: Harnessing a unique database and accounting for heterogeneity

INTRODUCTION

Human factors are often major contributors to pedestrian crashes. However, police-reported pedestrian-involved crash data often have gaps in crash details. Overcoming this limitation, the Pedestrian and Bicycle Crash Data Tool (PBCAT) provides a more comprehensive high-quality database capturing the sequence of events.

METHODS

In addition to human and roadway environmental factors, there could be unobserved factors (e.g., pedestrian conspicuity, impact speed, or riskiness by a driver) that could be either unavailable or not used in the analysis; however, these unobserved factors could have a significant influence on pedestrian injuries. This study applies finite mixture models to address unobserved heterogeneity in pedestrian injuries which is usually overlooked. As a result, the associations of one or more of the observed factors with pedestrian injuries across different latent (unobserved) classes can be different.

RESULTS

Harnessing this unique database for North Carolina reveals that in most (95%) of the crashes (N=24,886) occurring between 2009 and 2019, pedestrians were either killed or injured. Risky behaviors by drivers and pedestrians contributed to 7.91% and 50.59% of these crashes, respectively. Recognition errors (e.g., dash or dart-out) and violations (e.g., failure to yield) by pedestrians contributed to 22.08% and 28.58% of crashes, respectively. Recognition errors and violations by drivers contributed to only 2.12% and 3.11% of crashes respectively each of which is significantly lower than those by pedestrians. Results of the ordered Probit model indicate that the chance of pedestrian fatality is significantly higher if a pedestrian makes recognition errors and violations, a driver makes performance errors, and either the pedestrian or driver is impaired.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

The finite mixture model shows that pedestrians belong to two latent groups across which there is significant heterogeneity in pedestrian injuries and variations in the associations of observed factors with pedestrian injuries. The practical implications are discussed in the paper.

Distracted Walking: Does it impact pedestrian-vehicle interaction behavior?

Several studies have developed pedestrian-vehicle interaction models. However, these studies failed to consider pedestrian distraction, which considerably influences the safety of these interactions. Utilizing data from two intersections in Vancouver, Canada, this research uses the Multi-agent Adversarial Inverse Reinforcement Learning (MA-AIRL) framework to make inferences about the behavioral dynamics of distracted and non-distracted pedestrians while interacting with vehicles. Results showed that distracted pedestrians maintained closer proximity to vehicles, moved at reduced speeds, and rarely yielded to oncoming vehicles. In addition, they rarely changed their interaction angles regardless of lateral proximity to vehicles, indicating that they mostly remain unaware of the surrounding environment and have decreased navigational efficiency. Conversely, non-distracted pedestrians executed safer maneuvers, kept greater distances from vehicles, yielded more frequently, and adjusted their speeds accordingly. For example, non-distracted pedestrian-vehicle interactions showed a 46.5% decrease in traffic conflicts severity (as measured by the average Time-to-Collision (TTC) values) and an average 30.2% increase in minimum distances when compared to distracted pedestrian-vehicle interactions. Vehicle drivers also demonstrated different behaviors in response to distracted pedestrians. They often opted to decelerate around distracted pedestrians, indicating recognition of potential risks. Furthermore, the MA-AIRL framework provided different results depending on the type of interactions. The performance of the distracted vehicle-pedestrian model was lower than the non-distracted model, suggesting that predicting non-distracted behavior might be relatively easier. These findings emphasize the importance of refining pedestrian simulation models to include the unique behavioral patterns from pedestrian distractions. This should assist in further examining the safety impacts of pedestrian distraction on the road environment.

Comparing road traffic injuries by types of road users among children and adolescents in South Korea, 2011-2021

INTRODUCTION

Road traffic injuries (RTIs) are the leading cause of mortality among children and adolescents. This study aimed to compare clinical characteristics and identify factors associated with severe RTIs based on types of road users among children and adolescents with RTIs.

METHODS

A retrospective multicentre observational study was conducted using the Emergency Department-based Injury In-depth Surveillance registry in South Korea. A total of 78 021 participants younger than 19 years who presented with RTIs to the participating emergency departments from 2011 to 2021 were classified into four groups: passengers, pedestrians, motorcyclists and bicyclists. Demographic and injury-related factors were analysed using a multivariate logistic regression model to determine associations with severe RTIs, as indicated by the Excess Mortality Ratio-based Injury Severity Score of ≥16. The prevalence of traumatic brain injury (TBI), hospitalisations, intensive care unit (ICU) admissions and severe RTIs among road users was compared.

RESULTS

Head injuries were most prevalent in passengers (55.3%), motorcyclists (46.7%) and bicyclists (50.1%). Motorcyclists exhibited the highest proportion of TBI (8.3%), total admissions (28.8%), ICU admissions (8.2%), severe RTIs (41.0%) and mortality (2.0%). Safety devices significantly reduced severe RTIs in passengers and motorcyclists (adjusted OR (95% CI) 0.77 (0.70 to 0.85) and 0.69 (0.62 to 0.76), respectively.

CONCLUSION

The distinct clinical characteristics and factors associated with severe RTIs among different road user types in children and adolescents highlight the need for targeted interventions. Tailoring strategies to the specific requirements of each group is essential for effectively mitigating the occurrence of severe RTIs in this vulnerable demographic.

Identifying factors related to pedestrian and cyclist crashes in ACT, Australia with an extended crash dataset

As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.

Temporal instability of the determinants of fatal/severe elderly pedestrian injury outcomes in intersections and non-intersections before, during, and after the COVID-19 pandemic

This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections.

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.

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

A matched case-control approach to identify the risk factors of fatal pedestrian crashes on a six-lane rural highway in India

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case-control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions

Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored. This study examines the variations in the impact of risk factors on walking-along-the-road pedestrian-involved crash injury severity across various lighting conditions. Preliminary stability tests on the walking-along-the-road pedestrian-involved crash data obtained from Ghana revealed that the effect of most risk factors on injury severity outcomes is likely to differ under each lighting condition, warranting the estimation of separate models for each lighting condition. Thus, the data were grouped based on the lighting conditions, and different models were estimated employing the random parameter logit model with heterogeneity in the means approach to capture different levels of unobserved heterogeneity in the crash data. From the results, heavy vehicles, shoulder presence, and aged drivers were found to cause fatal pedestrian walking-along-the-road severity outcomes during daylight conditions, indicators for male pedestrians and speeding were identified to have stronger associations with fatalities on roads with no light at night, and crashes occurring on Tuesdays and Wednesdays were likely to be severe on lit roads at night. From the marginal effect estimates, although some explanatory variables showed consistent effects across various lighting conditions in pedestrian walking-along-the-road crashes, such as pedestrians aged < 25 years and between 25 and 44 years exhibited significant variations in their impact across different lighting conditions, supporting the finding that the effect of risk factors are unstable. Further, the out-of-sample simulations underscored the shifts in factor effects between different lighting conditions, highlighting that enhancing visibility could play a pivotal role in significantly reducing fatalities associated with pedestrians walking along the road. Targeted engineering, education, and enforcement countermeasures are proposed from the interesting insights drawn to improve pedestrian safety locally and internationally.

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.

Risk perceptions of pedestrians for traffic and road features

Traffic fatalities from 2015 to 2019 in Uttar Pradesh (UP), India show that pedestrians and cyclists have the largest share of total road fatalities. This study analyzed the pedestrian's perceptions of risk in the medium-sized city-Bulandshahr-UP, India regarding the traffic and road features. Perception of risk provides important information in identifying potential risks and explaining travel choices by pedestrians. The study locations were selected based on identified blackspots i.e. clustering of actual fatal crashes during 2015-2019 in UP. The types of locations at the blackspots were intersections below flyover, four-way signalized intersections, midblocks and foot of flyovers. An empirical analysis is presented in the study by taking pedestrians' ranking of the selected risk factors like traffic speed, free left turn at intersections, unmarked crosswalks, median width, traffic volume and the number of lanes and using the Rank-ordered logit model. Traffic speed and median width were ranked as the two highest risk factors by pedestrians. The results also indicated that increased numbers of lanes are more likely to be perceived riskier by older age groups of pedestrians and females at intersections below flyovers and midblocks. A comparison of different locations shows that all the factors were significant at four-way signalized intersections, indicating more perceived risk by pedestrians at intersections. These significant results can be used by practitioners to design safer intersections and midblocks at selected locations for pedestrians in UP, India.

A temporal analysis of crash injury severities in multivehicle crashes involving distracted and non-distracted driving on tollways

Distracted driving is a prominent cause of traffic crashes and may increase the severity of collisions. Due to the larger speeds on toll ways, distracted driving crashes are more severe than on other types of roads, making it worthwhile to investigate. This study examined the variation in the influence of factors affecting injury severity in crashes involving distracted and non-distracted driving, as well as the change over time, using crash data from Florida toll ways from the 2017 to 2019. A series of random parameters logit models with heterogeneity in the means and variances were developed to analyze different driver-injury severities (no injury, minor injury, and severe injury) in crashes involving distracted and non-distracted driving. In addition, likelihood ratio tests were conducted to determine whether model parameters differed between different driver behaviors (distracted/non-distracted driving) and among years. Several factors potentially impacting injury severities were studied, including driver, crash, vehicle, roadway, environment, temporal, and others. Significant disparities were observed between the contributing factors of the severity of crashes involving distracted and non-distracted driving. Results showed that considerable differences were also observed in the severity of injuries caused by two types of crashes and distracted driving resulted in more serious crashes than non-distracted driving. Despite model results indicated that factors influencing injury severity altered over time, several factors, such as motorcycle involvement and commercial car involvement, still exhibited relative temporal stability in non-distracted driving crashes over the three years. Temporal instability and non-transferability were also captured by out-of-sample predictions to verify the temporal shifts of contributing variables from year to year. This study is useful for distinguishing the influence mechanisms between the two types of crashes involving distracted and non-distracted driving, and the results can be applied for safety countermeasures development.

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

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

The Impact of Aging Drivers and Vehicles on the Injury Severity of Crash Victims

Against a general trend of increasing driver longevity, the injuries suffered by vehicle occupants in Spanish road traffic crashes are analyzed by the level of severity of their bodily injuries (BI). Generalized linear mixed models are applied to model the proportion of non-serious, serious, and fatal victims. The dependence between vehicles involved in the same crash is captured by including random effects. The effect of driver age and vehicle age and their interaction on the proportion of injured victims is analyzed. We find a nonlinear relationship between driver age and BI severity, with young and older drivers constituting the riskiest groups. In contrast, the expected severity of the crash increases linearly up to a vehicle age of 18 and remains constant thereafter at the highest level of BI severity. No interaction between the two variables is found. These results are especially relevant for countries such as Spain with increasing driver longevity and an aging car fleet.

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Macro-level literature analysis on pedestrian safety: Bibliometric overview, conceptual frames, and trends

Due to the high volume of documents in the pedestrian safety field, the current study conducts a systematic bibliometric analysis on the researches published before October 3, 2021, based on the science-mapping approach. Science mapping enables us to present a broad picture and comprehensive review of a significant number of documents using co-citation, bibliographic coupling, collaboration, and co-word analysis. To this end, a dataset of 6311 pedestrian safety papers was collected from the Web of Science Core Collection database. First, a descriptive analysis was carried out, covering whole yearly publications, most-cited papers, and most-productive authors, as well as sources, affiliations, and countries. In the next steps, science mapping was implemented to clarify the social, intellectual, and conceptual structures of pedestrian-safety research using the VOSviewer and Bibliometrix R-package tools. Remarkably, based on intellectual structure, pedestrian safety demonstrated an association with seven research areas: "Pedestrian crash frequency models", "Pedestrian injury severity crash models", "Traffic engineering measures in pedestrians' safety", "Global reports around pedestrian accident epidemiology", "Effect of age and gender on pedestrians' behavior", "Distraction of pedestrians", and "Pedestrian crowd dynamics and evacuation". Moreover, according to conceptual structure, five major research fronts were found to be relevant, namely "Collision avoidance and intelligent transportation systems (ITS)", "Epidemiological studies of pedestrian injury and prevention", "Pedestrian road crossing and behavioral factors", "Pedestrian flow simulation", and "Walkable environment and pedestrian safety". Finally, "autonomous vehicle", "pedestrian detection", and "collision avoidance" themes were identified as having the greatest centrality and development degrees in recent years.

An integrated text mining, literature review, and meta-analysis approach to investigate pedestrian violation behaviours

The goal of this study is to provide an overview of previous research that investigated pedestrian violation behaviour, with a focus on identifying the contributing factors of such behaviour, its impact on pedestrian safety, the mitigation strategies, the limitations of current studies, and the future research directions. To that end, the Latent Dirichlet Allocation (LDA) text mining method was applied to extract a comprehensive list of studies that were conducted during the past 21 years related to pedestrian violation behaviours. Using the extracted studies, a multi-sectional literature review was developed to provide a comprehensive understanding of the different aspects related to pedestrian violations. Afterward, a meta-analysis was undertaken, using the studies that reported quantitative results, in order to obtain the average impact of the different contributing factors on the frequency of pedestrian violations. The study found that pedestrian violations are one of the hazardous behaviours that contribute to both the frequency and severity of pedestrian-vehicle collisions. According to the literature, the waiting time at the curbside, traffic volume, walking speed, pedestrian distraction, the presence of bus stops and schools, and the presence of on-street parking are among the key factors that increase the likelihood of pedestrian violations. The study has also reviewed a wide range of strategies that can be used to mitigate violations and reduce the safety consequences of such behaviour, including simple engineering-based countermeasures, enforcement, solutions that rely on advanced in-vehicle technologies, and infrastructure connectivity features, educational programs, and public campaigns.

Shedding light on the pedestrian safety crisis: An analysis across the injury severity spectrum by lighting condition

OBJECTIVE

Pedestrian fatalities in the United States increased 51% from 2009 to 2019. During that time, pedestrian fatalities occurring at night increased by 63.7%, compared to a 17.6% increase for pedestrian fatalities occurring during daylight conditions. Have there also been increases in serious, minor, and possible pedestrian injuries (i.e., have all pedestrian collisions been occurring more frequently)? Have pedestrian collisions been getting more severe (i.e., are there now higher proportions of more severe injuries)? Have trends differed between night and day? What role does street lighting play in the nighttime trends?

METHODS

We analyzed pedestrian fatalities, serious injuries, minor injuries, and possible injuries that occurred in California, North Carolina, and Texas from 2010 to 2019 using linear regressions to explore the strength and statistical significance of trends. We then parsed these trends by lighting condition, exploring outcomes during the day and night and with and without street lighting.

RESULTS

Findings suggest that increases in daytime minor (7.9%) and possible (7.5%) injuries closely mirrored increases in population (9.8%). Increases in daytime fatal/serious injuries were significantly higher (43.1% and 35.1%, respectively), suggesting worsening severities during the day. Increases in nighttime minor/possible injuries (31.9% and 27.6%, respectively) were significantly larger than those during the day, suggesting that pedestrian collisions are occurring more frequently at night. Substantial increases in nighttime fatal/serious injuries (78.0% and 74.7%, respectively) likely reflect a combination of worsening severity (seen throughout the day) and increasing frequency (seen particularly at night). A pedestrian injured in the dark was found to be 5.0 times more likely to be killed than a pedestrian injured during the day. While a lack of street lighting does not seem to be the cause of the disproportionate increase in pedestrian injuries at night, pedestrians struck without a street light were 2.4 times more likely to be killed than those struck in the presence of a street light.

CONCLUSIONS

As we find ourselves in the midst of a pedestrian safety crisis, understanding that severities have increased throughout the entire day and frequencies have increased particularly at night helps illuminate a path forward.

Using ordered and unordered logistic regressions to investigate risk factors associated with pedestrian crash injury severity in Victoria, Australia

INTRODUCTION

The safety of pedestrians is a major concern in Victoria, Australia. Despite the considerable number of pedestrian fatalities and injuries in traffic crashes, a limited number of studies focused on pedestrian crash severity in Victoria.

METHODS

This study investigates and identifies the influential factors determining the severity of pedestrian injuries in traffic crashes in Victoria by using crash data from 2010 to 2019. An unordered multinomial logit model and an ordered logit model are developed for this purpose.

RESULTS

The results indicate that pedestrian crashes on weekends, in the period of 10 a.m. to 10 p.m., on dark streets, at intersections, in areas with a speed limit above 50 km/h, and on medians or footpaths are associated with a higher probability of severe and fatal injuries. Male pedestrians, children, and older adults (>59) were more likely to sustain a higher level of injury in crashes. Concerning the driver characteristics, no significant relationship was found between pedestrian injury severity and driver gender and license status, but older drivers were more likely to cause severe and fatal injuries. Pedestrian collisions with motorcycles, heavy vehicles, light commercial vehicles, bus/minibus/coach, and trams increase the probability of more severe injuries compared to cars. Moreover, older vehicles are associated with a higher probability of severe pedestrian injuries. Comparison of the model results illustrated that the MNL model was slightly better fitted on the data than the ordered logit model, but the conclusions inferred from these two models were generally similar.

PRACTICAL APPLICATION

To reduce the injuries of pedestrian crashes, we recommend improving lighting conditions and sidewalk design, implementing speed reduction strategies at high pedestrian activity areas, introducing more pedestrian crossings at midblock, installing warning signs to drivers, and discouraging the use of vehicles that are more than 20 years old.

Investigating risk factors associated with pedestrian crash occurrence and injury severity in Texas

OBJECTIVE

This study investigates various risk factors associated with pedestrian crash occurrence and injury severity based on 78,497 reported pedestrian-involved crashes across Texas from 2010 through 2019.

METHODS

Crashes are mapped to over 708,738 road segments, along with road design, land use, transit, hospital, rainfall, and other location features. Negative binomial models examine the association between pedestrian crash frequency and various contributing factors, and a heteroskedastic ordered probit model investigates the severity of injuries at the individual crash level.

RESULTS

Results from this study show the practical significance of microlevel variables in predicting pedestrian crashes. Proximity to schools and hospitals and presence of transit are all associated with higher pedestrian crash frequencies yet are rarely included in other models. Total pedestrian crash and fatal crash counts rise with the number of lanes, population, and job densities, though greater median and shoulder widths provide some protection. Higher speed limits are associated with lower crash frequencies but more deaths. Pedestrian crashes are more likely to be severe and fatal at night (8 p.m. to 5 a.m.), without overhead lighting, and when involved pedestrians and/or drivers are intoxicated. Use of light-duty trucks also significantly increases risk of severe or fatal pedestrian injury. Though newer vehicle safety features may be argued to lower crash severity or protect vehicle occupants, newer crash-involved vehicles in Texas are not found to deliver less severe pedestrian injury. Pedestrian and driver characteristics-both age and gender-are practically (and statistically) significant. Injury severity rises with pedestrian age, yet younger and/or female pedestrians on straight roadways, off the state (and interstate) highway system, and in the presence of a traffic control device (stop sign or signal) are less likely to be seriously injured, on average.

CONCLUSIONS

Findings underscore the benefit of enhanced vehicle safety features for pedestrians, campaigns against driving and walking while intoxicated, improved roadway design, enforcement of safety countermeasures near schools and bus stops, and installment of additional traffic controls and streetlights wherever more pedestrians exist.

Predicting effects of built environment on fatal pedestrian accidents at location-specific level: Application of XGBoost and SHAP

Understanding locally heterogeneous physical contexts in built environment is of great importance in developing preemptive countermeasures to mitigate pedestrian fatality risks. In this study, we aim to investigate the non-linear relationship between physical factors and pedestrian fatality at a location-specific level using a machine learning approach. The state-of-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), is employed for a binary classification problem, in which nationwide locations where fatal pedestrian accidents occurred for the years from 2012 to 2019 in Korea serve as positive samples (n_p = 13,366). For negative samples, locations with no pedestrian accidents are selected randomly to the size that is 10 times larger (n_n = 133,660) than positive samples. Fifteen features under the categories of road conditions, road facilities, road networks, and land uses are assigned to both the positive and negative sample locations using Geographic Information System (GIS). A method is proposed to avoid the class imbalance problem, and a final unbiased model is utilized to predict fatal pedestrian risks at the negative sample locations. In addition, Shapley Additive Explanations (SHAP) is introduced to provide a robust interpretation of the XGBoos prediction results. It is shown that 21.6% of the negative sample locations have a probability of fatal pedestrian accidents greater than 0.5 (or 78.4% accuracy). Generally, a road segment that lies in many of the shortest routes in a dense residential area with many lively activities from aligned buildings is a potential spot for fatal pedestrian accidents. However, based on the SHAP interpretation, the relationships between the features and pedestrian fatality are found nonlinear and locally heterogeneous. We discuss the implications of this result has for drafting policy recommendations to reduce pedestrian fatalities.

What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model

INTRODUCTION

Identifying factors contributing to the risk of older pedestrian fatal/severe injuries, along with their possible interdependency, is the first step towards improving safety. Several previous studies focused on identifying the influence of individual factors while ignoring their interdependencies. This study investigated the leading risk factors associated with older pedestrian fatalities/severe injuries by identifying the interdependency relationship among variables.

METHOD

A Bayesian Logistic Regression (BLR) model was developed to identify significant factors influencing pedestrian fatalities and severe injuries, followed by a Bayesian Network (BN) model to reveal the interdependency relationship among the statistically significant variables and crash severity. Furthermore, the probabilistic inference was conducted to identify the leading cause of fatal and severe injuries involving older pedestrians. The models were developed with data from 913 pedestrian crashes involving older pedestrians at signalized intersections in Florida from 2016 through 2018.

RESULTS

Vehicle maneuver, lighting condition, road type, and shoulder type were directly associated with older pedestrian fatality/severe injury. Vehicle maneuver (going straight ahead) was the most significant factor in influencing the severity of crashes involving older pedestrians. The interdependency of vehicle moving straight, nighttime condition, and two-way divided roadway with curbed shoulders was associated with the highest likelihood of fatal and severe-injury crashes involving older pedestrians.

CONCLUSIONS

The Bayesian Network revealed the interdependency between variables associated with fatal and severe injury-crashes involving older pedestrians. The interdependency relationship with the highest likelihood to cause fatalities/severe-injuries comprised factors with the significant individual contribution to the severity of crashes involving older pedestrians. Practical applications: The interdependencies among variables identified in this research could help devise targeted engineering, education, and enforcement strategies that could potentially have a greater effect on improving the safety of older pedestrians.

Exploring the impacts of built environment on pedestrian injury severity involving distracted driving

INTRODUCTION

This study develops an injury severity model that demonstrates level of pedestrians' injury severity during pedestrian-vehicle collisions, specifically those involving distracted driving.

METHOD

It uses data from a police-reported collision database that contains pedestrian-vehicle collision information in Nova Scotia, Canada. A latent segmentation-based ordered logit (LSOL) model is developed in this paper that comprehensively examines the influence of built environment characteristics on pedestrian injury severity. It estimates a latent segment allocation model within LSOL modeling framework to capture unobserved heterogeneity across pedestrians. Two latent segments, high- and low-risk segments, are identified probabilistically based on pedestrian characteristics and action, driver action, and collision attributes.

RESULTS

Results suggest that higher mixed land-use, transit stop density, length of sidewalk in the collision locations, and collisions occurring near schools yield lower pedestrian injury severity. In contrast, pedestrian-vehicle collisions in arterial roads, curved roads, sloped roads, and roundabouts tend to result in severe injuries. Interactions between distracted driving type and built environment characteristics are examined in this study. For example, using a communication device while driving on straight roads increases likelihood of higher pedestrian injury severity. This study also confirms the existence of heterogeneity across latent segments. For instance, higher percentage of people commuting by walking in the collision areas yield severe pedestrian injury in high-risk segments and lower injury severity in low-risk segments. Practical applications: The findings of this study will assist transportation planners and road safety stakeholders in developing effective and prioritized policies to reduce pedestrian injury severity involving distracted driving incidents.

An integrated clustering and copula-based model to assess the impact of intersection characteristics on violation-related collisions

The main goal of this study is to investigate the impact of a variety of factors on the frequency and the severity of pedestrian-vehicle collisions that involve pedestrian violations. To that end, the collision dataset of the City of Hamilton between 2010 and 2017 was reviewed to filter out pedestrian collisions that involved pedestrian violations. A Latent Class Analysis (LCA) method was applied to divide the dataset into a set of homogeneous clusters, based on traffic and intersection characteristics. A copula-based multivariate model was then developed for each cluster in order to study the impact of the different factors on collisions under the prevailing conditions of each cluster. The results showed that the number of bus stops within the intersection area is directly associated with the frequency and the severity of collisions involving pedestrian violations. A reduction in collisions was observed with the increase in the frequency of buses at intersections that are located along main transit routes. Moreover, the presence of schools near the intersection tends to increase the frequency of collisions involving pedestrian violations, especially at large intersections. The results also revealed that the presence of central refuge islands, despite their overall safety benefits, increases the likelihood of collisions involving pedestrian violations in large intersections. The results of this study provide valuable insights for a better understanding of the safety consequences of pedestrian violations. Such understanding assists engineers and planners to design intersections that reduce the frequency of pedestrian violations and mitigate their negative safety consequences.

Hidden patterns among the fatally injured pedestrians in an Iranian population: application of categorical principal component analysis (CATPCA)

Background

Identifying hidden patterns and relationships among the features of the Fatal Pedestrian Road Traffic Injuries (FPRTI) can be effective in reducing pedestrian fatalities. This study is thus aimed to detect the patterns among the fatally injured pedestrians due to FPRTI in East Azerbaijan province, Iran.

Methods

This descriptive-analytic research was carried out based on the data of all 1782 FPRTI that occurred in East Azerbaijan, Iran from 2010 to 2019 collected by the forensic organization. Categorical Principal Component Analysis (CATPCA) was performed to recognize hidden patterns in the data by extracting principal components from the set of 13 features of FPRTI. The importance of each component was assessed by using the variance accounted for (VAF) index.

Results

The optimum number of components to fit the CATPCA model was six which explained 71.09% of the total variation. The first and most important component with VAF = 22.04% contained the demographic and socioeconomic characteristics of the killed pedestrians. The second-ranked component with VAF = 12.96% was related to the injury type. The third component with VAF = 10.56% was the severity of the injury. The fourth component with VAF = 9.07% was somehow related to the knowledge and observance of the traffic rules. The fifth component with VAF = 8.63% was about the quality of medical relief and finally, the sixth component with VAF = 7.82% dealt with environmental conditions.

Conclusion

CATPCA revealed hidden patterns among the fatally injured pedestrians in the form of six components. The revealed patterns showed that some interactions between correlated features led to a higher mortality rate.

Modelling bus-pedestrian crash severity in the state of Victoria, Australia

Understanding the relationship between bus-pedestrian crash severity and factors contributing to such crashes is important. However, there exists a dearth of research on the factors affecting bus-pedestrian crash severity. This study aims to fulfil this gap by investigating the factors affecting the severity of pedestrian injuries. A data set of bus-pedestrian crashes in the State of Victoria, Australia was analysed over the period of 2006 - 2019. Through the results of association rule discovery method, the factors that increase the risk of pedestrian fatality are darkness, pedestrian walking on carriageway with traffic, intersections, high speed zone, old pedestrian, young bus driver and weekend holidays. Furthermore, co-occurrence of factors that increase the risk of a pedestrian fatality were extracted. To reduce the injuries of bus-pedestrian crashes, we recommend improving the light conditions, reducing the jaywalking behaviour of pedestrians, implementing speed bumps in high speed zones and installing pedestrian detection systems on buses.13 years of bus-pedestrian crashes in Victoria, Australia was analyzed.Association rules discovery was used for modeling pedestrian fatality.Darkness, pedestrian movement, zone speed and age effect the rate of fatality.Pattern of pedestrian fatality in collision with bus was extracted.

Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida

Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.

Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks

Reducing traffic fatal crashes has been an important mission of transportation. With the rapid development of sensor and Artificial Intelligence (AI) technologies, the computer vision (CV)-based crash anticipation in the near-crash phase is receiving growing attention. The ability to perceive fatal crash risks in an early stage is of paramount importance as well because it can improve the reliability of crash anticipation. Yet this task is challenging because it requires establishing a relationship between the driving scene information that CV can recognize and the fatal crash features that CV will not get until the crash occurrence. Image data with the annotation for directly training a reliable AI model for the early visual perception of fatal crash risks are not abundant. The Fatality Analysis Reporting System (FARS) contains big data on fatal crashes, which is a reliable data source for finding fatal crash clusters and discovering their distribution patterns to tell the association between driving scene characteristics and fatal crash features. To enhance CV's ability to perceive fatal crash risks earlier, this paper develops a data analytics model from fatal crash report data, which is named scenario-wise, spatio-temporal attention guidance. First, the paper identifies five descriptive variables that are sparse and thus allow for decomposing the 5-year (2013-2017) fatal crash dataset to develop scenario-wise attention guidance. Then, an exploratory analysis of location- and time-related descriptive variables suggests dividing fatal crashes into spatially defined groups. A group's temporal distribution pattern is an indicator of the similarity of fatal crashes in the group. Hierarchical clustering and K-means clustering further merge the spatially defined groups into six clusters according to the similarity of their temporal patterns. After that, association rule mining discovers the statistical relationship between the temporal information of driving scenes with fatal crash features, such as the first harmful event and the manner of collisions, for each cluster. The paper illustrates how the developed attention guidance supports the design and implementation of a preliminary CV model that can identify agents of a possibility to involve in fatal crashes from their environmental and context information.

Modeling duration of overtaking between non-motorized vehicles: A nonparametric survival analysis based approach

The use of non-motorized vehicles in urban city has improved the convenience of short-distance travel and reduced traffic pollution. However, the overtaking behaviour of non-motorized vehicles impacts traffic safety and efficiency significantly. The objective of this study is to model the durations of overtaking behaviour in the non-motorized vehicle exclusive lane. A total of 3010 overtaking events of non-motorized vehicles were extracted from two locations in Chengdu, China. The nonparametric survival analysis was conducted to model the overtaking duration of non-motorized vehicles. The categorical variables that significantly influence the overtaking duration were examined by the Log-rank test. The results show that the overtaking durations of female riders is longer than that of male riders. It takes longer for electrical bikes to complete overtaking than conventional bikes. When the non-motorized vehicle is under the load state (i.e. passengers or goods on the non-motorized vehicle), the overtaking behaviour takes more time than the un-load state. Moreover, it takes less time to overtake the non-motorized vehicle with load than to overtake the one without load. When there is a wrong-way driving phenomenon or under higher traffic volume, the duration is longer compared to the normal traffic and lower traffic volume conditions. The findings of this study attempt to provide a more profound understanding of non-motorized vehicles overtaking behaviour under different traffic conditions and give insights to the safety research of non-motorized vehicles.

A new approach in analyzing the accident severity of pedestrian crashes using structural equation modeling

Background:

According to official statistics in Iran, there were 17000 fatalities in road traffic crashes in 2018 that 25% of all crash fatalities belong to pedestrians. In most of the researches related to pedestrians’ safety, one aspect of the traffic crash (e.g. the injury or crash severity) is almost considered for the investigation. In order to perform a complete study of the crash, accident size can be utilized which involves different aspects of the crash. Accident size is described in terms of the number of fatalities and injured individuals and the number of dam-aged and involved vehicles in a crash.

Methods:

According to the fact that accident size has multiple indicators and it is not measured directly, traditional methodologies cannot be applied. So, in the present study the effective factors on the accident size of pedestrian crashes are investigated through structural equation modeling. For the purpose of this study, 3718 pedestrian-involved crash data occurred in Isfahan province is used for the modeling. The independent variables are weather conditions, road surface conditions, time, horizontal and vertical alignments, road type and location, driver’s gender and age, vehicle type, pedestrian’s age, gender and clothing color.

Results:

The results indicated that highways, the pedestrians’ invisibility, female and old-aged pedestrians, heavy vehicles, old-aged and female drivers are related to the increase of the accident size in pedestrian crashes. These results denote that the mentioned variables are associated with the higher number of injuries, fatalities, the higher number of involved and damaged vehicles in a crash.

Conclusions:

Present study shows the importance of considering safety improvement measures in highways, educating the people in the society about the traffic safety, the separation of pedestrian and motor vehicle traffic flow and considering the old people in policies and programs for mitigating the accident size.

An integrated spatio-temporal approach to examine the consequences of driving under the influence (DUI) in crashes

Driving under the influence (DUI) is illegal in the United States because a driver's mental and motor skills can be seriously impaired by alcohol or drugs. Consequently, DUI violators' involvement in severe crashes is high. Motivated by the spatial and temporal nature of traffic crashes, this study introduces an integrated spatio-temporal approach to analyzing highway safety data. Specifically, this study estimates Geographically and Temporally Weighted Regression (GTWR) models to understand the consequences of DUI in crashes. GTWR can theoretically outperform traditional regression methods by accounting for unobserved heterogeneity that may be related to the location and time of a crash. Using Southeast Michigan crash data, this study finds that DUI is associated with a 25% higher likelihood of injury in a crash. The association between injury severity and DUI varies significantly across space and time. From the spatial aspect, DUI crashes in rural or small-town areas are more likely to cause injuries than urban crashes. From the temporal aspect, different times are associated with varying relationships between injury severity and DUI. If focusing on DUI crashes in late nights and early mornings, on Fridays, the entire northeast part from Clinton Charter Township to Port Huron is associated with severer injuries than other regions including Detroit's urban area and its south. On Mondays, the DUI crashes in the northwest are also more likely to cause severe injuries. The methodology introduced in this study takes advantage of modern computational tools and localized crash/inventory data. This method offers researchers and practitioners an opportunity to understand highway safety outcomes in great spatial and temporal details and customize safety countermeasures for specific locations and times such as saturation patrols.

The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.

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.

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.

Motor vehicle towing: An analysis of injuries in a high-risk yet understudied industry

OBJECTIVES

National fatality rates for commercial tow truck operators exceed those of other first responders who also perform traffic incident management services. The objectives of the current study are to (1) characterize causal factors associated with injuries among commercial tow truck operators engaged in roadside assistance through analysis of coded and free text data obtained from U.S. Occupational Safety and Health Administration (OSHA) investigation files, and (2) utilize supplemental data sources to analyze environmental factors for injuries in which commercial tow truck operators were struck by roadway traffic.

METHODS

Searches of OSHA's online IMIS database were performed to identify investigations of incidents in which tow truck operators were injured while performing roadside assistance duties. Freedom of Information Act (FOIA) requests were submitted to obtain full investigation files for each case. Coded and narrative text analyses were performed to identify causal themes across the identified cases.

RESULTS

One-hundred and six cases of tow truck operators being killed or severely injured were identified in IMIS; 41 FOIA requests for related investigation documents were fulfilled. Two major event type themes were identified which accounted for 9 in 10 of the cases identified. These were (1) 'struck-by' incidents, which were primarily injuries resulting from contact with roadway traffic, rolling vehicles and equipment or other non-motorized objects; and (2) 'caught-in or -between' incidents, which were primarily injuries resulting from being pinned beneath and between vehicles and being caught in moving parts.

CONCLUSIONS

The towing industry should provide initial and refresher safety training on vehicle loading and unloading, defensive techniques when exposed to traffic on roadways, and proper wheel chocking and braking procedures. States should include tow trucks as a first responder vehicle type in their "Move Over" laws and implement public awareness campaigns to protect all first responders, including tow truck operators.

Investigation of pedestrian crashes using multiple correspondence analysis in India

Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009-2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.

Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

The relationship between impact speed and the probability of pedestrian fatality during a vehicle-pedestrian crash: A systematic review and meta-analysis

BACKGROUND

Pedestrians struck in motorised vehicle crashes constitute the largest group of traffic fatalities worldwide. Excessive speed is the primary contributory factor in such crashes. The relationship between estimated impact speed and the risk of a pedestrian fatality has generated much debate concerning what should be a safe maximum speed limit for vehicles in high pedestrian active areas.

METHODS

Four electronic databases (MEDLINE, EMBASE, COMPENDEX, and SCOPUS) were searched to identify relevant studies. Records were assessed, and data retrieved independently by two authors in adherence with the PRISMA statement. The included studies reported data on pedestrian fatalities from motorised vehicle crashes with known estimated impact speed. Summary odds ratios (OR) were obtained using meta-regression models. Time trends and publication bias were assessed.

RESULTS

Fifty-five studies were identified for a full-text assessment, 27 met inclusion criteria, and 20 were included in a meta-analysis. The analyses found that when the estimated impact speed increases by 1 km/h, the odds of a pedestrian fatality increases on average by 11% (OR = 1.11, 95% CI: 1.10-1.12). The risk of a fatality reaches 5% at an estimated impact speed of 30 km/h, 10% at 37 km/h, 50% at 59 km/h, 75% at 69 km/h and 90% at 80 km/h. Evidence of publication bias and time trend bias among included studies were found.

CONCLUSIONS

The results of the meta-analysis support setting speed limits of 30-40 km/h for high pedestrian active areas. These speed limits are commonly used by best practice countries that have the lowest road fatality rates and that practice a Safe System Approach to road safety.

A pedestrian serious injury risk prediction method based on posted speed limit

The purpose of this study was to develop a serious injury risk prediction algorithm for pedestrians, using data from the South Australian Traffic Accident Reporting System. Two algorithms were developed to estimate serious injury risk, using a logistic regression analysis of 6,868 vehicle-pedestrian crashes extracted from TARS data. In this study, an optimal model based on the best combination of risk factors according to the Akaike information criterion (AIC) was developed. Additionally, a secondary GPS model using only crash site characteristics that can be derived from GPS coordinates from the crash scene was also developed. The optimal model is based on site and environmental conditions that could be derived from GPS data (posted speed limit, distance from crash site, natural lighting conditions, road geometry, road horizontal alignment and road vertical alignment) as well as pedestrian age/gender, driver age/gender and vehicle model year. The second model only included features that could be derived from GPS data. The optimal model was reasonable in accuracy and gave an under-triage rate of 10% when the injury threshold was set to 15%, with a corresponding over-triage rate of around 60%. The GPS model, despite not being as accurate as the optimal model may be adequate in the absence of all the risk factors required for the optimal model, requiring an injury threshold of 20% to give an under-triage rate of 10%, with the corresponding over-triage rate being around 70%. Both models can potentially be used for serious injury risk prediction (SIRP) for pedestrians involved in a collision with a vehicle.

Injury Severity of Bus–Pedestrian Crashes in South Korea Considering the Effects of Regional and Company Factors

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.