Crash Risk Factors Research Articles

Risk factors for bicycle-related overtaking, rear-end, and door crashes in the United Kingdom

Abstract Background Prior studies have offered valuable insights into risk factors associated with bicycle crashes at intersections. Nevertheless, limited research focuses on investigating three types of bicycle crashes that commonly occur on road segments: overtaking, rear-end, and door crashes. Methods In the present research, risk factors for these three types of crashes on road segments were examined by analyzing British STATS19 reports of crashes from 1991 to 2020. We calculated adjusted odds ratios (AORs) with 95% confidence intervals (CIs) for multiple risk factors using multivariate logistic regression models. The analysis included 127,637 bicycle crashes, categorized into 18,350 overtaking, 44,962 rear-end, 6,363 door, and 57,962 other crashes. Results Speed limits of ≥ 40 miles per hour (mph) (AOR = 2.238, 95% CI = 2.159-2.320), heavy goods vehicles (HGVs) as crash partners (AOR = 2.867, 95% CI 2.473-3.323), and elderly crash partners (AOR = 2.013, 95% CI = 1.937-2.092) were significant risk factors for overtaking crashes. Notable risk factors for rear-end crashes included midnight hours (AOR = 1.269, 95% CI = 1.190-1.354) and unlit darkness (AOR = 1.486, 95% CI = 1.404-1.573). Speed limits 20-30 mph (AOR = 16.185, 95% CI = 13.514-19.382), as well as taxis and private rental automobiles (AOR = 2.695, 95% CI = 2.310-3.145), were factors associated with door crashes. Further intriguing findings from our joint-effect analysis included higher probabilities for overtaking crashes in rural regions when older drivers and HGVs were crash partners (AOR = 2.93, 95% CI = 2.79-3.08 and AOR = 2.62, 95% CI = 2.46-2.78). Conclusions The mentioned risk factors have remained mostly consistent since our previous study was conducted in 2011. However, current research elucidated that certain factors have become more significant under specific circumstances. For instance, in rural areas, cyclists were more likely to be involved in overtaking crashes with heavy goods vehicles as crash partners. Key messages • Bicycle crash; Road segment; Door crash; Overtaking crash; Rear-end crash

Road accident risk analysis of at- and near-port areas reflecting regional heterogeneous characteristics

ABSTRACT In port cities, road traffic safety management should consider various types of cargo that will shift in the trucks and containers. This applies not only in the case of ‘at port’ areas within a 2-km radius but also ‘near-port’ areas within a 4–8-km radius. This is important for traffic safety because the crash severity can be high as there are many trucks to the ports that can cause large-scale crashes. Therefore, to prepare traffic safety management strategies for port city, it is necessary to identify crash risk factors that affect the crash severity. This study developed a two-stage road crash severity model of ‘at-port’ and ‘near-port’ regions using a support vector machine and a Bayesian multinomial model. Additionally, a severity model suitable for Korean ports was proposed that considered spatial heterogeneity in the Bayesian multilevel multinomial model. Findings derived from this study can be used for policy development to increase road traffic safety in port cities. ‘This article is a revised and expanded version of a paper entitled [Accident Risk Analysis of At and Near-Port Areas Reflecting Regional Heterogeneous Characteristics] presented at [The 11th International Conference on Logistics and Maritime Systems, Korea, 4-7 September 2023]’.

Improving motorcyclist safety through hearing in all directions: survey results concerning a novel protective helmet with earpieces.

Every year over 200,000 motorcyclists are killed globally. One poorly understood risk factor for motorcycle crashes is the role of motorcyclists' ability to hear traffic and other sounds around them in all directions. Most motorcycle helmets protect the head and permit vision in the forward direction, but they impair the wearer's ability to hear. This study evaluated user perceptions of Protective Helmet with Earpieces Equipped, a novel system that affixes technology onto existing motorcycle helmets to allow motorcyclists to better hear the surrounding environment in all directions. 59 Colombian traffic police agents who rode motorcycles daily completed self-report surveys about their traditional motorcycle helmet, used a Protective Helmet with Earpieces Equipped helmet for two months, and then completed a follow-up survey. We tested two hypotheses: (a) at follow-up compared to baseline, motorcyclists would report decreases in perceived safety, comfort, and ability to detect sounds with their traditional helmet, as well as increased fatigue and stress from motorcycling with their traditional helmet, and (b) at follow-up, motorcyclists would report high perceived safety and perceived importance of hearing traffic sounds in all directions while motorcycling. Wilcoxon signed-rank test evaluated hypotheses. Participants rated perceived safety while using their traditional helmet to be significantly lower after using the Protective Helmet with Earpieces Equipped compared to before using it (Z=-3.5, p less than .001). There were no changes in other variables assessed. Following use of the new helmet, participants reported greatly improved safety (M=4.56, SD=0.54, where 4=safer and 5=substantially safer) and perceived the importance of hearing traffic sounds in all directions as high. Improved auditory perception could increase motorcyclist safety. Participating traffic agents felt the Protective Helmet with the Earpieces Equipped greatly improved their ability to hear and improved their motorcycling safety. Perceived safety while using their traditional helmet decreased following use of the alternative.

Schizophrenia, antipsychotic treatment adherence and driver responsibility for motor vehicle crash: a population-based retrospective study in British Columbia, Canada

ObjectiveTo examine the relationship between schizophrenia, antipsychotic medication adherence and driver responsibility for motor vehicle crash.DesignRetrospective observational cohort study using 20 years of population-based administrative health and driving data.SettingBritish Columbia,...

Identification of Risk Factors for Severe Younger and Older Driver Crashes in Massachusetts

Systemic safety analysis is a comprehensive and proactive approach to identifying and addressing high-risk roadway features and crash types that contribute to severe crashes. The Massachusetts Department of Transportation identified 14 emphasis areas for systemic improvement, including younger and older driver crashes. Research was undertaken to conduct separate comprehensive analyses of fatal and serious injury crashes involving younger and older drivers, using a systemic safety approach to develop risk factors and identify critical locations within the state. The study utilized crash, roadway, driver’s license, citation, healthy aging, and environmental justice data from various sources. Negative binomial regression models were developed to relate crash frequency with town-specific traffic, behavior, and site characteristics, considering exposure and spatial variance. The findings highlighted the significance of multiple risk factors contributing to severe crash frequency. For younger drivers, the presence of young drivers in the town, risk-taking behaviors such as unbelted and impaired driving, and infrastructure characteristics, particularly high-speed facilities and historically underinvested infrastructure, were key contributors to crash frequency. Older drivers’ crash frequency was influenced by the presence of older drivers, healthy aging indicators, risk-taking behaviors, and high-speed facilities. Based on the model results, risk scores were developed for each variable in the model and applied to each town. Towns were ranked using percentile ranking to classify them into primary and secondary risk categories. The towns identified in the primary risk category (18 towns in the top 5 percentiles) captured approximately 13.5% of severe younger driver crashes and 14.5% of severe older driver crashes.

Analyzing the Prevalence of and Factors Associated with Road Traffic Crashes (RTCs) among Motorcyclists in Bangladesh.

A cross-sectional survey was conducted using a structured questionnaire involving 402 motorcyclists from four major southeastern towns, comprising 350 (86.07%) males and 52 (12.93%) females. The chi-square test was applied in bivariate analysis, and binary multivariable logistic regression was performed to determine the risk factors of road traffic crashes. This study's findings revealed that the overall reported prevalence of road traffic crashes involving motorcycle drivers over one year was 68.66%. Multivariable logistic regression analysis revealed several factors that significantly impacted road traffic crashes. These factors included driving without a valid driving license, the young age (<20) of motorcyclists, driving in rainy weather, exceeding the speed limit, per-week working hours, smoking status, motorcycle ownership, the brand of motorcycle, and not wearing a helmet while driving. The study findings highlight the need for improving motorcycle safety by implementing measures such as imposing per-week work hour limits for riders, enforcing traffic regulations, and promoting helmet use among motorcycle drivers. The results of this study draw attention to the Bangladesh Road Transport Authority (BRTA) and motorcycle drivers in the country to decrease motorcycle crashes and the severity of injuries by implementing efficient guidelines and strategies for driving motorcycles. The findings of this study can assist policymakers and concerned authorities in taking the essential steps to lessen road traffic crashes among motorcyclists in Bangladesh.

Heart rate variability as an indicator of fatigue: A structural equation model approach

Driver fatigue significantly impairs performance and is a major risk factor for road crashes. However, fatigue is difficult to objectively measure and quantify. This study aimed to elucidate associations between heart rate variability (HRV) metrics and multidimensional fatigue construct encompassing objective and subjective measures in young drivers with self-reported short sleep. Eighty-four young adults underwent assessments during simulated driving. HRV indices RMSSD and LF/HF ratio were derived from an electrocardiogram, while relative Theta power, oculography drowsiness, lane position variability, and Karolinska Sleepiness Score measured fatigue. RMSSD negatively correlated with Theta power, and LF/HF ratio positively correlated with position variability. Exploratory factor analysis extracted three factors, with RMSSD and LF/HF loading onto one. Structural equation modelling tested the prediction of a latent fatigue construct from HRV. Two models demonstrated an acceptable fit. RMSSD negatively predicted fatigue, explaining 6.4 % of the variance, while the LF/HF ratio positively predicted fatigue, accounting for 3.7 % of the variance. In summary, HRV, particularly RMSSD, showed significant relationships with multidimensional fatigue. Lower RMSSD and higher LF/HF ratio are associated with greater fatigue levels. This study demonstrates that HRV indices exhibit significant relationships within a multidimensional model of fatigue incorporating objective performance, ocular, and subjective measures. The findings provide initial evidence towards using HRV for monitoring complex manifestations of fatigue, rather than single dimensions. Further applied research should investigate translating these findings to naturalistic settings and validating HRV thresholds predictive of on-road impairment.

Analysis of crash severity in rear-end and angle collisions on an urban roundabout in heterogeneous non-lane-based traffic conditions

ABSTRACT This study examines crash risk factors and develops a severity model for rear-end and angle collisions in roundabouts and its vicinity in non-lane-based heterogeneous traffic. The rear-end severity is primarily influenced by posted speed limits, violation type, collision partner, single-vehicle collision, and vehicle age. Whereas type of violation, collision partner, and presence of road surface condition are the critical influencing factors for determining the severity outcomes of angle collision. The marginal effects of angle collision results indicated that the probability of fatal injury increased by 28% due to heavy-vehicle-vulnerable road user collisions. In the rear-end case, risk factors such as collision partners (heavy vehicles-vulnerable road users) increased by 18% and single-vehicle collisions increased by 25%, highly influencing the severity of fatal injury at roundabouts. Notably, rear-end collisions exhibit higher severity compared to angle collisions. This study provides valuable insights for developing strategies to mitigate roundabout collision severity.

Qualitatively and quantitatively explore injury severity of light motor vehicle drivers involved in heavy goods vehicle crashes

ABSTRACT Crashes involving heavy goods trucks (HGVs) are of significant concern as it poses a higher risk of fatality to light motor vehicles (LMVs). The study constructs three Deep Forest models with different Cascade structures to explore the relationship between HGV-LMV crash severity and risk factors. Based on the HGV-LMV crash data in Shandong province, China, motorcycles, electric vehicles, and sedans are defined as the LMV. According to the comparison results, the Deep Forest with Cascade LightGBM is significantly better. Through model interpretability tools, the study found that motorcycle and electric vehicle drivers aged 58 to 86, and LMV drivers with 1 to 3 years of driving experience are more likely suffering severity and fatal injury (SFI) in HGV-LMV crashes. And, disobey traffic sign, illegal turning, overtaking, changing lane, and crashes happened on non-motorway, national and provincial roads have an positive effect on SFI.

Heat-induced risks of road crashes among older motorcyclists: Evidence from three motorcycle-dominant cities in Taiwan

IntroductionRoad crashes pose significant public health concerns worldwide, with vulnerable road users like motorcyclists accounting for 23% of global road deaths. Recently, heat exposure has been identified as a risk factor for road crashes by reducing driver performance. Given the rapid aging, many Asian cities face challenges regarding heat-induced road crashes among older motorcyclists, a significant segment of the road user population with lower motor abilities and thermal tolerance. In this study, I aim to investigate varying impacts of heat on road crashes across road users in different age groups using different transportation modes in three major Taiwanese cities characterized by motorcycle-dominant traffic cultures and (sub)tropical climates. MethodsTemperature data and records of road crashes involving motorcycles and car drivers from 2018 to 2021 were collected, merged based on the dates, and processed into a year-round dataset and a summer-time dataset. The generalized linear regression model with a Generalized Poisson link function was employed to estimate the association between temperature and road crash frequency, with the analysis stratified by two age groups (i.e., older and younger) and two transportation modes (i.e., car and motorcycle). Results and conclusionIn total, 24 models were built for four driver groups (i.e., older motorcycle, younger motorcycle, older car, and younger car drivers) across three cities using year-round and summer-time datasets. Findings reveal higher vulnerabilities to heat-induced road crashes among older road users, especially those driving motorcycles. Heat-induced road risks among motorcycle drivers significantly increased during the summer, while this pattern was not found for car drivers. Policymakers should develop targeted interventions aimed at mitigating heat-related road crashes among older motorcyclists. In the context of climate change and ageing, this research holds broader implications for the development of policies integrating heat mitigation strategies and traffic safety measures, particularly in Southeast Asian cities with similar demographics and tropical climates.

Factors affecting injury severity in motorcycle crashes: Different age groups analysis using Catboost and SHAP techniques

Objective Motorcycle crashes often result in severe injuries on roads that affect people’s lives physically, financially, and psychologically. These injuries could be notably harmful to drivers of all age groups. The main objective of this study is to investigate the risk factors contributing to the severity of crash injuries in different age groups. Methods This Objective is achieved by developing accurate machine learning (ML) based prediction models. This research examines the relationship between potential risk factors of motorcycle-associated crashes using (ML) and Shapley Additive explanations (SHAP) technique. The SHAP technique further helped interpreting ML methods for traffic injury severity prediction. It indicates the significant non-linear interactions between dependent and independent variables. The data for this study was collected from the Provincial Emergency Response Service RESCUE 1122 for the Rawalpindi region (Pakistan) over three years (from 2017 to 2020). The Synthetic Minority Oversampling Technique (SMOTE) is employed to balance injury severity classes in the pre-processing phase. Results The results demonstrate that age, gender, posted speed limit, the number of lanes, and month of the year are positively associated with severe and fatal injuries. This research also assesses how the modeling framework varies between the ML and classical statistical methods. The predictive performance of proposed ML models was assessed using several evaluation metrics, and it is found that Catboost outperformed the XGBoost, Random Forest (RF) and Multinomial Logit (MNL) model. Conclusion The findings of this study will assist road users, road safety authorities, stakeholders, policymakers, and decision-makers in obtaining substantial and essential guidance for reducing the severity of crash injuries in Pakistan and other countries with prevailing conditions.

Prevalence of fatigue while driving among two-wheeled vehicle drivers and associated factors: Exploratory approach from secondary analysis based on hospital data, Benin.

Fatigue while driving is one of the risk factors of road crashes. It's still poorly considered in interventions because of insufficient literature. In addition, the literature on this issue doesn't focus on two-wheelers, the most frequent users in the Benin context. The study examined the prevalence of fatigue while driving among two-wheeled vehicle drivers and the related factors. It's a secondary baseline data analysis from a cohort of road crash victims recruited from five hospitals in Benin. Data were collected from July 2019 to January 2020. Patients who identified themselves as drivers during the accident were included. Data on individual characteristics, including fatigue status in the moments preceding the collision, and other risk factors and environmental settings, were extracted. We used multivariate logistic regression. Among the respondents, 12.20% (95% CI=10.20-14.53) reported fatigue in the moments preceding the collision. The odds of fatigue while driving were significantly higher in male drivers (aOR=3.60; 95% CI=1.08-11.98), during professional trips (aOR=2.09; 95% CI=1.30-3.37), in non-helmet wearers (aOR=1.85; 95% CI=1.09-3.13), in users of stimulants (aOR=3.13; 95% CI=1.50-6.54), in those with a history of chronic diseases (aOR=1.95; 95% CI=1.16-3.27), at dusk (aOR=4.22; 95% CI=2.22-8.02), at night (aOR=6.90; 95% CI=3.95-12.05), and on Inter-State National Roads (aOR=2.01; 95% CI=1.18-3.43). Fatigue is a risk factor for road crashes in Benin, associated with other risk factors that highlight particularly vulnerable profiles and groups. Integrating prevention policies based on these cumulative risk factors will result in efficiency improvements.

Comparing fatal crash risk factors by age and crash type by using machine learning techniques.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Do MD&amp;A Risk Disclosures Reduce Stock Price Crash Risk? Evidence from China

This study examines whether and how risk disclosures in Management Discussion and Analysis (MD&amp;A) affected the stock price crash risk of China’s publicly listed firms over the period of 2017–2021. The empirical results show that risk disclosures within the MD&amp;A section are significantly and negatively associated with the future stock price crash risk, even after controlling for a broad set of well-known factors of crash risk. Additional tests revealed that the impact of MD&amp;A risk disclosures on the stock price crash risk is accentuated when the MD&amp;A disclosure contains more incremental information. The negative association between MD&amp;A risk disclosures and stock price crash risk is also more pronounced for firms with poorer information environments, for firms with weaker external monitoring, and for firms with more investor attention. Our findings are robust to alternative measures of the stock price crash risk, controlling for firm-fixed effects and endogeneity issues, and excluding certain samples. The results indicate that MD&amp;A risk disclosures could help alleviate information asymmetry and mitigate stock price crash risk.

Geographically weighted random forests for macro-level crash frequency prediction

Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.

Robust spatiotemporal crash risk prediction with gated recurrent convolution network and interpretable insights from SHapley additive explanations

The vigorous progress in artificial intelligence and the widespread availability of computing power and big data have resulted in remarkable achievements in applying deep learning methods to traffic engineering. However, the complexity of deep learning models poses a significant challenge to their interpretability, despite their scalability to big data. As a result, academia has shown a keen interest in interpreting these intricate models. To address these limitations, this study introduces the Gated Recurrent Convolution Network (GRCN) for predicting spatiotemporal crash risks. Moreover, to transform the opaque nature of the GRCN into a transparent framework, SHapley Additive Explanations (SHAP) was employed, a game theory-based approach. The application of SHAP analysis not only enhances the explainability of the GRCN model but also reveals complex causal relationships regarding crash risks, using extensive datasets encompassing taxi trips data, law enforcement, and weather conditions. The study incorporates both local and global interpretability analyses at the grid level. Additionally, it indicates that intersection red light violations tend to elevate crash risks. The findings further demonstrate that adverse weather conditions, such as low visibility and strong crosswinds, negatively affect driving behavior and correlate with higher crash risks. However, the presence of cloudy skies is associated with a decrease in crash risks. By shedding light on the intricate dynamics of crash risk factors, this study contributes to the field of traffic engineering and emphasizes the importance of interpretability in deep learning models. The insights gained from this research can inform the development of effective interventions and policies aimed at reducing crash risks and improving overall transportation safety.

Road traffic mortality in Zunyi city, China: A 10 – year data analysis (2013–2022)

PurposeThe study aimed to examine the pattern of motorization and the mortality rate related to road traffic crashes in Zunyi (a city in northern Guizhou province of China) from 2013 to 2022, and to identify the epidemiological characteristics of these crashes with to provide insights that could help improve road safety. MethodsData were obtained from the Zunyi traffic management data platform, and the mortality rates were calculated. We deployed various analytical methods, including descriptive analysis, Chi-square test or Fisher's exact test of categorical variable, circular distribution map analysis, and Rayleigh test to characterize the traits of road traffic crashes in the region. ResultsDuring the 10-year study period, 7488 people died due to road traffic accidents, with males accounting for 70.4% and females 29.6% (χ2 = 101.97, p < 0.001). The mortality rate increased from 7.80 deaths per 100,000 people in 2013 to 10.70 deaths per 100,000 people in 2016, but then decreased to 9.54 deaths per 100,000 people in 2019. A notable finding was that the death rate per 10,000 vehicles declined from 16.09 deaths per 10,000 vehicles in 2013 to 5.48 deaths per 10,000 vehicles in 2022. The study also found that vulnerable road users represented nearly half (48.76%) of all accident fatalities, and unlicensed or inexperienced driving contributed significantly to the occurrence of road traffic accidents. ConclusionAlthough the number of road traffic accidents in Zunyi has decreased, there are still some critical issues that need to be addressed, particularly for vulnerable road users and unlicensed drivers. Our results highlight the need of targeted interventions to address the specific risk factors of road traffic crashes, particularly those affecting vulnerable road users and drivers without sufficient experience or license.

Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities

Cycling levels in cities keep increasing, which is accompanied with more cyclists being involved in serious road crashes. This paper aims to contribute to safer urban cycling by examining risk factors associated with cycling in the four largest Dutch cities, incorporating spatial and temporal variations in bicycle crash risk. For this purpose, the crashes and exposure metrics are analysed on an hourly temporal resolution. The results reveal that utilising an hourly temporal resolution in the exposure metrics and bicycle crash risk gives more detailed results compared to daily averages of these metrics. Moreover, the exposure to cyclists and motorised vehicles both have a significant impact on bicycle crash risk. The results also imply that separating cyclists from high-speed motorised vehicles might be more important than implementing a lower speed limit to curb the increasing severity of crashes. Despite some local differences, the overall results of the risk factors are remarkably similar across the cities, providing increased generalisability and transferability of the study. The findings indicate that concerns about the effects of increasing bicycle use and large flows of motorised vehicles on bicycle crash risk are valid, showing the importance of efforts towards improving bicycle safety in cities.

Exploring nighttime pedestrian crash patterns at intersection and segments: Findings from the machine learning algorithm

Introduction: Pedestrian safety at nighttime is an ongoing critical traffic safety concern. Although poor visibility is primarily associated with nighttime pedestrian crashes, other contributing factors such as humans, vehicles, roadways, and environmental factors interact with each other to cause a crash. Additionally, the pattern of nighttime pedestrian crashes differs significantly according to the intersection and segment location, which requires further exploration. Data: This study applied Association Rules Mining (ARM), a rule-based machine learning method, to reveal the association of nighttime pedestrian crash risk factors according to the intersection and segment locations using 2,505 nighttime pedestrian fatal and injury crashes in Louisiana (2015–2019). Results and conclusions: Based on the generated rules, the results show that nighttime pedestrian crashes at the intersection are associated with right-turn vehicle movement, older drivers (>64 years) at the high-speed intersection, senior pedestrians (>64 years) in rainy weather conditions, violation by pedestrian age group ‘<15 years’, and alcohol-intoxicated pedestrian violation in business/industrial areas. Additionally, ‘careless operation’ at the intersection is associated with alcohol-involved drivers. Most of the nighttime pedestrian crashes at segments are associated with roadways with no physical separation and the absence of streetlights. Driver alcohol involvement and their physical condition (inattentive/distracted) are also associated with pedestrian crashes associated at the segment location at night. Other segment pedestrian crashes are linked to the interstate in dark conditions, open country locations, and high-speed roadways. Additionally, the crash site investigation identified several critical pedestrian safety concerns including the lack of crosswalk facilities, high driveway density, and pedestrian behavioral patterns (e.g., crossing at roadway segments close to the intersection location). Practical Applications: The findings of this study can be used for selecting the appropriate countermeasures based on a case-by-case basis. The exposure patterns can be used in educational campaigns to strategically reduce nighttime pedestrian crashes.

Multivariate Approach to Peak-Period Models for Crash Types and Severities in Variable Speed Systems on Freeways

According to current research, safety performance functions (SPFs) based on traditional annual average daily traffic are less suitable for addressing crash risk in traffic-management strategies that typically operate for short time intervals (e.g., peak period). Variable speed limit (VSL)/variable advisory speed (VAS) is a traffic-management strategy which is deployed to achieve speed harmonization typically during the peak hours of weekdays. However, peak-period SPFs for VSL/VAS-implemented freeways considering different crash types and severity levels are still unexplored. Therefore, in this study the multivariate Poisson lognormal (MVPLN) method was applied to estimate the different crash-type and severity-level models. The results showed that by addressing correlations among different crash types and severity levels, MVPLN models outperformed individual univariate Poisson lognormal crash-type and -severity models. From the developed models, several traffic- and roadway-related attributes—such as volume, average speed, standard deviation of speed, segment type (merge, diverge, and weaving), higher number of lanes, and so forth—could be identified as the potential crash-risk factors. Also, the proposed models were successful in capturing the spatial effects of segments (differences between segments downstream and upstream of the location of interest) on crash frequency. Further, the implementation of VSL/VAS strategy was found to be effective in reducing rear-end crashes in the crash-type model as well as property-damage-only (PDO) and C (non-incapacitating) crash severity levels in the crash-severity model. Policymakers and practitioners could benefit from this study in incorporating safety aspects into developing VSL/VAS algorithms.