Crash Location Research Articles

Case–control study of fatal bicycle crashes in peri-urban areas of Delhi

ObjectivesThere is a lack of evidence on interventions to improve the safety of cycling use in low- and middle-income countries. We investigated the impact of road design and traffic characteristics...

Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities

The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017–2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. The implications of the findings are discussed in the paper.

Role of the Built Environment, Roadway Characteristics, and Socioeconomic Factors in Keeping Teen Drivers Safe: An Investigation of the Factors Most Related to Teen Driver Fatalities

The role of engineering, through infrastructure design, is known to influence safe driving behavior generally, yet the areas of possible intervention that could improve safe outcomes for teens are less understood. It is critical to identify interventions specific to teens, as they have different driving risk factors than more seasoned drivers, and motor vehicle crashes are the second leading cause of death among teens in the United States. We examined 5,210 fatal crashes from 2015 to 2019 in Ohio using Fatality Analysis Reporting System data. We employed multinomial logistic regression to associate the likelihood of fatal crashes involving younger teens (16 to 17 years old) or older teens (18 to 19 years old) relative to adults (20 years and older) with built environment, socioeconomic, and roadway characteristics at crash locations. Separating crashes into the two groups of teens allowed us to distinguish the possible impact of training, as younger teens are mandated to undergo driving education and behind-the-wheel training under Ohio’s graduated driver licensing laws. We found that compared with neighborhood socioeconomics, built environment factors were more significant predictors of the likelihood of fatal crashes involving younger teens compared with adults. Specifically, fatal crashes at locations with lower road density and more diverse land use were more likely to involve younger teens. Although these results could be related to a mix of younger teen driver behavior and exposure factors, the finding highlights that improving the safety of local roads in urban and rural settings could help stem teen driving fatalities.

Descriptive analysis of road traffic crashes encountered by Tanzanian motorcycle taxi drivers trained in first aid

IntroductionIn Dar es Salaam, Tanzania, death from road traffic crashes (RTC) occurs at roughly double the global rate. In this study, we sought to understand the locations and types of vehicles involved in RTC in Dar es Salaam encountered by a cohort of motorcycle taxi drivers previously trained in first aid. MethodsThis was a quasi-non-randomized interventional study, cohort subtype, with three-month follow-up. Some 186 motorcycle taxi drivers were selected by convenience sampling from 16 heavily populated, central wards and trained in a basic hemorrhage control course. Participants reported the location and types of vehicles involved in RTCs they encountered and intervened upon through performing bleeding control interventions. Surveys were designed on KoboToolbox and administered via phone call at monthly intervals over a three-month period. The main outcome measures were the location of crash encounters and types of vehicles involved. ResultsIn all 62 unique participants (33.3 %) encountered and provided bleeding control interventions to 83 injured individuals following 69 RTC in at least 31 distinct city wards, despite training only having occurred in 16 wards. Eight crash locations were not recorded. Crashes in distant wards typically contained major roads. Most commonly, crashes involved a motorcycle without the involvement of another vehicle (n=20), followed by motorcycle vs. car/three-wheeled vehicle (n=15), motorcycle vs. bus/van (n=10), motorcycle vs. motorcycle (n=9), motorcycle vs. pedestrian (n=7), pedestrian vs. bus/van (n=2), pedestrian vs. car/three-wheeled vehicle (n=1), motorcycle vs. bicycle (n=1), multi vehicle (n=1), and other (n=3). ConclusionsMotorcycle taxi drivers trained in hemorrhage control frequently encounter and intervene upon RTC in wards where they are based as well as in distant locations, commonly in wards containing major roads. Expanding first aid training for motorcycle taxi drivers could improve timely access to emergency care for RTC victims. Since most crashes involved motorcycles, road safety training should be integrated into future courses.

Quantifying the heterogeneity impact of risk factors on regional bicycle crash frequency: A hybrid approach of clustering and random parameter model

The existence of internal and external heterogeneity has been established by numerous studies across various fields, including transportation and safety analysis. The findings from these studies underscore the complexity of crash data and the multifaceted nature of risk factors involved in accidents. However, most studies consider the effects of unobserved heterogeneity from one perspective −- either within clusters (internal) or between clusters (external) −- and do not investigate the biases from both simultaneously on crash frequency analysis. To fill this gap, this study proposes a hybrid approach combining latent class cluster analysis with the random parameter negative binomial regression model (LCA-RPNB) to explore the association between risk factors and bicycle crash frequency. First, the bicycle crash data is categorized into three clusters using LCA based on crash features such as gender, trip purposes, weather, and light conditions. Then, the separated crash frequency models for different clusters and the overall model are developed based on RPNB using regional factors of crash locations as independent variables and the crash frequency of different clusters respectively as dependent variables. The hybrid approach enables a comprehensive examination of internal and external heterogeneities among bicycle crash frequency factors simultaneously. Results suggest that the proposed hybrid approach exhibits superior fitting and predictive performance compared to the model only considers the effects of unobserved heterogeneity from one perspective with the lower values of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). This approach can help policymakers and urban planners to design more effective safety interventions by understanding the distinct needs of different bicyclist clusters and the specific factors that contribute to crash risk in each group.

Interactive effects of elevation difference, slope variation, and terrain formation on road traffic crashes occurrences using triangular irregular network

AbstractNumerous factors contribute to traffic crashes, which result in fatalities and injuries. Road traffic crashes and their severity level are likely caused by variations in elevation, slope, and terrain formation. As far as our knowledge is concerned, the triangular irregular network (TIN) was not used to analyze dangerous zones of the road network with respect to traffic crashes' location. The main purpose of this study was to investigate a dangerous road network that was affected by traffic crashes using TIN. To address the goal, this study used elevation differences slope variation, and terrain between road traffic crash locations. The study used the intersection zone road network coordinates of traffic crashes in the city of Budapest that were recorded from 2017 to 2021. Using tools like ArcGIS and MS Excel, the study organized, modeled, and analyzed the data. Geometrical interval classification was used to classify the data in accordance with the characteristics of elevation deference and slope variation. Both inferential and descriptive statistics were engaged to explain, analyze, and summarize the findings. The results of this study demonstrated that the majority of traffic crashes occurred in locations with lower elevations and their differences, steeper/cliff slopes between points, and flat terrain. The finding was in contrast to what was observed in other studies. This study advocates that TIN is the optimal method and substitute for analyzing the risky zone of the road network at macroscopic level.

Developing Safety Performance Functions for Severe Distraction-Related Crashes along Kentucky’s Rural and Urban Two-Lane Roadways

This study develops safety performance functions (SPFs) for severe (“KA” or “fatal and suspected serious injury”) distraction-related crashes along Kentucky’s rural and urban two-lane undivided roadway segments using recent four-year (2018–2021) crash records. Additional efforts were made to meticulously scrutinize crash narratives categorized as non-distracted and correct those cases. To account for crash under-dispersion, the Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) models were fitted and compared. The ZI-HTCMP model outperformed the other models with respect to several goodness-of-fit measures (e.g., mean absolute deviance and mean square prediction error). From the developed SPFs for rural and urban two-lane roads, wider lanes and higher speed limits (55 mph) were associated with increased severe distraction-related crash frequencies. Furthermore, some variables were found to be significant in rural areas, but insignificant in urban areas, and vice versa. For example, major collector roads, minor collector/local roads, the presence of roadside guardrails, wider right-hand shoulders, the presence of horizontal curves, and the presence of vertical grades were associated with increased crash frequencies along rural two-lane roads. In addition, the proportion of heavy vehicles (>5%) and the existence of paved shoulders were associated with increased crashes along urban two-lane roads. The empirical Bayes method was then used to rank the top 10 distraction-related high crash locations (HCLs) for both rural and urban two-lane segments. In-depth investigation of HCLs highlighted single-vehicle distraction-related crashes as the most common collision type. Countermeasures were proposed to help reduce severe distraction-related crashes, for example, installing chevron signs along rural two-lane roads.

Evaluation of Run-off-Road Crashes Involving Battery-Electric Vehicles

Electric vehicles (EVs) are increasing in popularity and are among the fastest-growing segments in new vehicle sales. Battery-electric vehicles (BEVs) are the highest-selling class of EV. BEVs use a unique frame construction (i.e., a “skateboard” chassis) with a heavy battery pack located below the occupant’s floorpan, and are heavier and have lower centers-of-gravity than internal combustion engine vehicles (ICEVs)—factors that could affect their crashworthiness with roadside features. A crash study was performed to compare the crash conditions for BEVs to ICEVs and hybrid-electric vehicles (HEVs) covering the numbers of vehicles, posted speed limits, crash locations, time of day, weather conditions, crash severities, and roadside fixed objects struck. Eight state departments of transportation provided crash data spanning 5 years that included vehicle identification numbers or make, model, and year combinations, which were used to isolate BEVs from ICEVs. Crash results were reviewed and EV crash conditions were found to be similar to those of ICEV and HEV crash conditions. Moreover, the frequency of run-off-road crashes was similar for BEVs and ICEVs. Because no substantial differences were observed between the two datasets, it is recommended that impact conditions described in the Manual for Assessing Safety Hardware be utilized during preliminary evaluation of roadside hardware using electric vehicles.

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.

Young novice drivers’ road crash injuries and contributing factors: A crash data investigation

Objective Collisions are a significant cause of injury and fatality among young novice drivers. Using real crash data, this study further explores the multifaceted and complex nature of young novice drivers’ crash injury risk by synthesizing different driver attributes and crash scenarios in order to update and validate previous research findings and provide more feasible recommendations for preventive measures. Methods Detailed data on traffic crash of young novice drivers were extracted from the National Automobile Accident In-Depth Investigation System (NAIS) in China, and a mixed research methodology using a Random Forest and multinomial logit modeling framework was used in order to explore and study the important influences on traffic crash injuries of young novice drivers in Songjiang District, Shanghai, during the period from 2018 to 2022. Results The results of the study showed that human, vehicle, road and environmental characteristics contributed 36.83%, 22.65%, 17.07% and 23.45% respectively to the prediction of crash injury level of novice drivers. Among the various single factors, driver negligence was the most important factor affecting the crash injury level of novice drivers. Age of the vehicle, crash location, road signal condition and time of crash all had a significant effect on the crash injury level of young novice drivers (95% of the confidence level). Conclusions The study comprehensively analyzed young novice driver crash data to reveal the crash injury risk and its severity faced by young novice drivers in different contexts, and suggested targeted safety improvements. There are similarities and differences with the results of previous studies, in which there are new contributions to understanding the driving risks of young novice drivers in daytime and nighttime.

Smart Accident Detection and Emergency Response System for Enhanced Road Safety

In the realm of road safety, prompt and accurate accident response is paramount to mitigating fatal injuries and saving lives. The existing approach, reliant on manual reporting to authorities or the injured party's ability to communicate with emergency services, introduces delays that can prove fatal. The presented system uses a combination of IoT modules to detect the accident and alert the EMS. It uses an accelerometer to detect the accident, a sim module equipped with a 2G sim to send an alert message consisting of the drivers details, car details, and crash location, acquired by the GPS module to the authorities and few selected individuals. The goal is to drastically reduce emergency response time, improving the likelihood of survival and quick recovery of the injured. This innovative approach not only addresses the current gaps in accident reporting but also contributes to an overall improvement in road safety infrastructure. This work uses a collection of sensors to identify an accident and point out the accident location. This data is sent as a text message to emergency services through which immediate help can be received.

Underwater acoustic analysis reveals unique pressure signals associated with aircraft crashes in the sea: revisiting MH370

Data analysis from the hydroacoustic stations of the Comprehensive Nuclear-Test-Ban Treaty Organization has unveiled distinctive pressure signals linked to aircraft crashes of varying sizes in the ocean. Notably, these signals were detected at distances ranging from two to five thousand kilometres, highlighting the efficacy of underwater acoustic technology in event identification and classification in marine environments. In this study, we investigate the plausibility of an aircraft, such as Malaysian Airlines Flight 370 (MH370), crashing into the sea leaving a discernible pressure signal at distant hydrophones. Consequently, we focus on recordings obtained from the hydroacoustic monitoring stations located at Cape Leeuwin and Diego Garcia, within a few minutes of the last satellite ping on the 7th arc, associated with the assumed crash time and location. Among the available data, only one relevant signal has emerged as a potential candidate, albeit recorded at a single station out of the two stations available. To ensure a comprehensive analysis, we also examine the time frame and location of the airplane along its initial route. Though no corresponding signal was observed. Nevertheless, the findings in this study narrow down the range of possibilities and present a novel scientific approach to investigate such incidents. These findings contribute to our understanding of acoustic signals associated with aircraft crashes at sea. They emphasise the potential for hydrophones to detect events even when the signal travels long distances through land. Ultimately, this research offers recommendations for conducting on-site experiments involving controlled explosions with energy levels similar to the impact of MH370 along the 7th arc. The aim is to encourage pertinent authorities to implement actions that could reveal insights into the destiny of MH370 specifically. Additionally, this initiative seeks to establish a comprehensive framework for addressing comparable incidents in the broader ocean context.

Comprehensive investigation of crashes associated with citywide speed limit reduction in Seattle, Washington

Road traffic crashes are a leading cause of fatalities and injuries worldwide, prompting authorities to implement various strategies to enhance road safety. One common approach involves lowering speed limits in an effort to mitigate the severity of crashes and improve overall traffic safety. Seattle, Washington has been lowering its citywide default speed limits on municipal roads since 2016, among several cities in the US. This research paper presents a comprehensive investigation of the impact of these speed limit reductions on various safety indicators, such as the crash frequency, crash severity, crash characteristics, crash type, crash location, contributing factors, and overall traffic safety performance. The study employs a comprehensive dataset encompassing several years before and after the implementation of lower speed limits. Data analyses indicate varying changes in crash severity distribution between before and after periods in Seattle. Fatal and incapacitating crashes in Seattle decreased by 19% after the initial speed limit lowering in 2016 and further decreased by 3% after the second speed limit lowering in 2018. Pedestrian-involved crashes experienced a 26% significant decrease while speeding-related had a 16% decrease after the city implemented lowering speed limits. There was also a statistically significant 4% decrease in property damage only collisions and a 7% decrease in parked car crashes. Distracted driving / inattention-related crashes also decreased by 4%. The Mid-Block (but intersection related) crashes saw a significant decrease by 6%, and Driveway Junction related crashes decreased by 1%. The findings provide insights into the effectiveness of speed limit reductions in enhancing road safety in urban environments. The findings contribute to the existing body of knowledge on speed management strategies and their impact on traffic safety. The outcomes of this study can inform transportation policymakers and practitioners in Seattle and other urban areas as they make informed decisions regarding speed limit regulations and road safety measures.

Visual hazardous models: A hybrid approach to investigate road hazardous events

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009–2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 820 times more fatal/grievous injuries than simple injuries on expressways per 1000 crosswalk crashes (posted speed limit: 100 km per hour). Working pedestrian age group (25–64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

Assessing non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity

Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection). Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across non-motorists) in the dependency structure. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and socio-demographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity for the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by crash location type.

Modeling the influence of connected vehicles on driving behaviors and safety outcomes in highway crash scenarios across varied weather conditions: A multigroup structural equation modeling analysis using a driving simulator experiment

Equipped with advanced sensors and capable of relaying safety messages to drivers, connected vehicles (CVs) hold the potential to reduce crashes. The goal of this study is to assess the impacts of CV technologies on driving behaviors and safety outcomes in highway crash scenarios under diverse weather conditions, including clear and foggy weather. A driving simulator experiment was conducted and the multigroup structural equation modeling (SEM) was employed to explore the complex interrelationships between the propensity of traffic conflicts, utilization of CV alerts, weather, psychological factors, driving behaviors, and other relevant variables for two different crash locations, namely a straight section and a horizontal curve. Two latent psychological factors including aggressiveness and unawareness were constructed from driving behavior as vehicles passed by crash scenes such as brake, throttle, steering angle, lane offset, and yaw. The SEM can measure latent psychological factors and model interrelationships concurrently through a single statistical estimation procedure. Results of the multigroup SEM showed that CV alerts could significantly reduce the unawareness on a horizontal curve and thus lower the propensity of traffic conflicts. Additionally, the overall effect of foggy weather on conflicts was found to be positive on a horizontal curve, despite the potential benefit of improving situational awareness. In contrast, the single group SEM failed to reveal any significant interrelationships in its structural model by pooling data from both crash locations. The obtained insights can guide the development of driving assistance systems, highlighting the necessity of customization considering weather conditions and location-specific factors.

Identifying contributing factors and locations of pedestrian severe crashes using hazard-based duration model

Pedestrian safety remains a significant concern, with the growing number of severe pedestrian crashes resulting in substantial human and economic costs. Previous research into pedestrian crashes has extensively analyzed the influences of weather, lighting, and pedestrian demographics. However, these studies often overlook the critical spatial variables that contribute to pedestrian crashes. Our study aims to explore these overlooked spatial variables by examining the distance pedestrians travel before encountering a severe crash. This approach provides a supplementary perspective in safety analysis, emphasizing the importance of pedestrian movement patterns. The model considers various factors that may influence pedestrian traveled distance before being involved in a severe crash, such as weather conditions, lighting conditions, and pedestrian demographics. Ohio’s pedestrian-involved crashes were gathered and analyzed as a case study. The results indicated that 50 % of fatal pedestrian crashes occurred within 0.84 miles of the pedestrians’ residences. Moreover, it was shown that factors including lighting condition, pedestrian age, drug toxication, and the location at impact significantly influence the pedestrians traveled distance. These findings provide valuable insights into the spatial distribution of pedestrian crashes and shed light on the factors contributing to their severity. By understanding these relationships, policymakers and urban planners can design targeted interventions such as improving street lighting, implementing traffic calming measures, and developing safety awareness campaigns for specific age groups, to enhance pedestrian safety and reduce the incidence of severe crashes.

A scoping review of E-scooter safety: Delightful urban slalom or injury epidemic?

IntroductionE-scooters are a convenient, relatively inexpensive and fun new mode of transportation with rapidly increased use in cities throughout the world. Despite the popularity of e-scooters, increases in rider injuries and fatalities have become a public health concern. Objective. To characterize the types of safety issues associated with e-scooter use. Methods. Scoping review methods focused on any safety issues associated with the use of dockless, stand-up, motorized, electric kick scooters from companies offering short-term rentals or through private ownership, published in English between January 2017 and March 2022. Databases included Academic Search Complete, Business Source Complete, Google Scholar, PsychINFO, PubMed, Scopus, SocINDEX, TRID Database, and Web of Science databases. After abstract screening and full text review, a total of 81 studies were included in the scoping review. Results. Few e-scooter riders wear helmets except where legally required. Alcohol impairment is a primary contributor to e-scooter injuries. Younger males, underage riding, double riding, speeding and rider distraction were also identified as behavioural contributors to e-scooter injury crashes. Environmental contributors included crash locations (e.g., sidewalks, bike paths, roads) parking issues (e.g., tripping hazard), fixed object collisions (e.g., lampposts, parked cars), road surface and slope (e.g., uneven, curbs, downhills) and insufficient nighttime visibility. E-scooter reliability was affected by brake failures and design limitations (e.g., stability over rough terrain). Injury patterns included mechanism of injury (loss of balance and/or control, fall) and moving object collisions (e.g., vehicles, pedestrians, bicyclists). The head, face, upper and lower extremities were the most common anatomical locations injured. Fractures, contusions, concussions, lacerations, and sprains were the most frequent injury types. Discussion. Policies, regulations and interventions that address helmet requirements, alcohol and drug use, rider skill and knowledge acquisition, restrictive speed limits, education and enforcement are discussed with respect to evidence-based safety guidance for transportation policy and decision makers. Conclusion. Behavioural, environmental and device design safety issues associated with e-scooter use require effective interventions to reduce the frequency and severity of rider injuries.

Feature Engineering and Decision Trees for Predicting High Crash-Risk Locations Using Roadway Indicators

Road crashes are a prevalent public health issue across the globe. The objective of this research was to develop a methodology for accurately classifying high-risk crash locations. The hypothesis of this study was that readily obtained roadway indicators can be used along with machine learning techniques to categorize locations as high crash-risk. A database containing 5,383 locations was created during 2012 to 2015 as part of the Hellenic National Road Safety Project and used to develop three binary machine learning models to classify high crash-risk locations based on roadway indicators. The three models were random forest, gradient boosting, and extra trees. This research used features engineering to reduce the number of indicators in the model, and the synthetic minority oversampling technique to address imbalances in the dataset between the minority (high crash-risk locations identified using crash reports) and majority classes (medium to low crash-risk locations identified based on local police testimonies, site inspections, and geometry analysis). Although all three models performed similarly, the extra trees model outperformed the other two on a range of performance metrics, including the area under the precision–recall curve and the F1-score. The findings revealed that design speeds, pavement markings, signage presence, and pavement condition were the most influential factors affecting roadway safety. The contribution of this research is in the development of a transferable methodology for classifying high crash-risk locations in addition to revealing key indicators for crash-risk potential, which in turn can inform cost-effective data collection and maintenance activities.

Machine Learning Approach to Enhance Highway Railroad Grade Crossing Safety by Analyzing Crash Data and Identifying Hotspot Crash Locations

Safe railway operation is vital for public safety, the environment, and property. Concurrent with climbing amounts of rail traffic on the Canadian rail network are increases in the last decade in the annual crash counts for derailment, collision, and highway railroad grade crossings (HRGCs). HRGCs are important spatial areas of the rail network, and the development of community areas near railway tracks increases the risk of HRGC crashes between highway vehicles and moving trains, resulting in consequences varying from property damage to injuries and fatalities. This research aims to identify major factors that cause HRGC crashes and affect the severity of associated casualties. Using these causal factors and ensemble algorithms, machine learning models were developed to analyze HRGC crashes and the severity of associated casualties between 2001 and 2022 in Canada. Furthermore, spatial autocorrelation and optimized hotspot analysis tools from ArcGIS software were used to identify hotspot locations of HRGC crashes. The optimized hotspot analysis shows the clustering of HRGC crashes around major Canadian cities. The analysis of cluster characteristics supports the results obtained for causal factors of HRGC crashes. These research outcomes help one to better understand the major causal factors and hotspot locations of HRGC crashes and assist authorities in implementing countermeasures to improve the safety of HRGCs across the rail network.