Abstract Because of heavy traffic on urban expressways, the exits of expressway interchanges have become accident-prone sites. This study explores the impacts of various traffic control devices and road conditions on road safety at interchange exits based on driving behavior data from navigation software. The traffic order index (TOI) based on driving behavior and speed variation is used to evaluate road safety. The general safety characteristics and partitioned safety characteristics of interchange exit sections for different traffic control devices and under different road conditions were described, and a structural equation model (SEM) was constructed to observe the influences of the traffic control devices, road conditions, congestion degree, and time on road safety. The results show that traffic control devices (the number of warning signs, number of advance exit signs and complexity of diagrammatic guide signs) and road conditions (the number of lanes and merging conflicts within 500 m) have significant influences on the road safety of interchange exits. Road conditions have the greatest impact on the safety of interchange exits, followed by the congestion index, traffic control devices, and time. The results could help traffic management departments reconstruct or rehabilitate traffic control devices and enable reasonable road planning at interchange exits. The safety evaluation method for traffic control devices and road conditions based on driving behavior data collected from navigation software could be further used on other roads.
{"title":"Traffic safety analysis at interchange exits using the surrogate measure of aggressive driving behavior and speed variation","authors":"Ying Yao, Xiaohua Zhao, Jia Li, Jianming Ma, Yunlong Zhang","doi":"10.1080/19439962.2022.2098439","DOIUrl":"https://doi.org/10.1080/19439962.2022.2098439","url":null,"abstract":"Abstract Because of heavy traffic on urban expressways, the exits of expressway interchanges have become accident-prone sites. This study explores the impacts of various traffic control devices and road conditions on road safety at interchange exits based on driving behavior data from navigation software. The traffic order index (TOI) based on driving behavior and speed variation is used to evaluate road safety. The general safety characteristics and partitioned safety characteristics of interchange exit sections for different traffic control devices and under different road conditions were described, and a structural equation model (SEM) was constructed to observe the influences of the traffic control devices, road conditions, congestion degree, and time on road safety. The results show that traffic control devices (the number of warning signs, number of advance exit signs and complexity of diagrammatic guide signs) and road conditions (the number of lanes and merging conflicts within 500 m) have significant influences on the road safety of interchange exits. Road conditions have the greatest impact on the safety of interchange exits, followed by the congestion index, traffic control devices, and time. The results could help traffic management departments reconstruct or rehabilitate traffic control devices and enable reasonable road planning at interchange exits. The safety evaluation method for traffic control devices and road conditions based on driving behavior data collected from navigation software could be further used on other roads.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"63 1-3 1","pages":"515 - 540"},"PeriodicalIF":2.6,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77933372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1080/19439962.2022.2098442
A. S. Hasan, Md. Asif Bin Kabir, M. Jalayer, Subasish Das
Abstract In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway’s geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models’ performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.
{"title":"Severity modeling of work zone crashes in New Jersey using machine learning models","authors":"A. S. Hasan, Md. Asif Bin Kabir, M. Jalayer, Subasish Das","doi":"10.1080/19439962.2022.2098442","DOIUrl":"https://doi.org/10.1080/19439962.2022.2098442","url":null,"abstract":"Abstract In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway’s geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models’ performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"8 1","pages":"604 - 635"},"PeriodicalIF":2.6,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89697419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-15DOI: 10.1080/19439962.2022.2098441
Subasish Das, Eun Sug Park, Sobhan Sarkar
Abstract A significant association between crash severity and operating speed is known to exist. However, the findings related to the speed-crash association are inconclusive. Some studies found that higher speed is associated with a higher number of crashes, whereas other studies found the opposite result. Some of the critical issues in this research problem result from study design, the definition of operating speed measures, types and granularity of operating speed measures, spatial correlation, and design standards of different roadway facilities. The road safety profession will benefit greatly from informative research on the impact of vehicle operating speed, roadway design elements, and traffic volume on crash outcomes. This study investigated the speed-crash association in both annual and daily level datasets to determine how roadway characteristics interact with various speed measures to impact the likelihood of crash occurrences on both annual and daily levels. For annual models, the average operating speed is positively associated with both fatal and injury and property damage only (PDO) crashes. However, for daily models, this association is mostly negative and insignificant. The standard deviation of operating speed is positively associated with crash occurrences for both daily and annual models. The findings of this study can provide additional insights into the speed-crash association literature.
{"title":"Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways","authors":"Subasish Das, Eun Sug Park, Sobhan Sarkar","doi":"10.1080/19439962.2022.2098441","DOIUrl":"https://doi.org/10.1080/19439962.2022.2098441","url":null,"abstract":"Abstract A significant association between crash severity and operating speed is known to exist. However, the findings related to the speed-crash association are inconclusive. Some studies found that higher speed is associated with a higher number of crashes, whereas other studies found the opposite result. Some of the critical issues in this research problem result from study design, the definition of operating speed measures, types and granularity of operating speed measures, spatial correlation, and design standards of different roadway facilities. The road safety profession will benefit greatly from informative research on the impact of vehicle operating speed, roadway design elements, and traffic volume on crash outcomes. This study investigated the speed-crash association in both annual and daily level datasets to determine how roadway characteristics interact with various speed measures to impact the likelihood of crash occurrences on both annual and daily levels. For annual models, the average operating speed is positively associated with both fatal and injury and property damage only (PDO) crashes. However, for daily models, this association is mostly negative and insignificant. The standard deviation of operating speed is positively associated with crash occurrences for both daily and annual models. The findings of this study can provide additional insights into the speed-crash association literature.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"1 1","pages":"584 - 603"},"PeriodicalIF":2.6,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76099067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.1080/19439962.2022.2098440
Qifeng Yu, Kesi You, Jinxian Weng
Abstract To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.
{"title":"Verification analysis of relationship between driving failure probability and traffic accident rate","authors":"Qifeng Yu, Kesi You, Jinxian Weng","doi":"10.1080/19439962.2022.2098440","DOIUrl":"https://doi.org/10.1080/19439962.2022.2098440","url":null,"abstract":"Abstract To enable the reliability theory to be further applied in roadway geometry design and safety evaluation, it is necessary to explore and establish the relationship between the failure index in reliability theory and the traffic accident rate. Based on the collected data, the risk ranking-based verification method and regression prediction model-based verification method considering the probability of driving failure were used to verify the relationship between the probability of driving failure and traffic accident rate. The Spearman’s rank correlation coefficient was calculated and the results show that there is a moderate correlation between the driving failure probability of the selected road segment and the traffic accident rate. Four regression prediction models for both two-lane and four-lane roads were established considering the probability of driving failure and four significant variables including segment length, annual average daily traffic volume, speed limit, and curve radius. The results show that the established regression prediction models can fit accident data well. This research verified and established the relationship between the probability of driving failure and the road traffic accident rate and pointed out that the traffic accident rate has a positive correlation with the probability of driving failure.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"113 ","pages":"563 - 583"},"PeriodicalIF":2.6,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72430831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-14DOI: 10.1080/19439962.2022.2098891
Renteng Yuan, Xin Gu, Zhipeng Peng, Q. Xiang
Abstract This study aims to explore the variability of risk factors affecting injury severity in rear-end crashes when different struck vehicle groups are involved. Two types of rear-end crash data, vehicle-strike-car data, vehicle-strike-truck data, are extracted from the Fatality Analysis Reporting System (FARS). Two likelihood ratio (LR) tests are firstly performed to validate the struck vehicle group variations, and then two separate random thresholds random parameters hierarchical ordered probit (RRHOP) models (Model 1 and Model 2) are established to capture unobserved heterogeneity. The results of LR test show significant differences in the effects of factors included in each model. Moreover, the model results suggest that SUVs, vans, and large trucks as striking vehicles are significant related to injury severity in both models with different effects. Factors such as speeding related, pickup, model year (struck vehicle), disabled damage, adverse weather, speed limit (≥60 mile/h), and young driver (struck vehicle) are found to be statistically significant in only model 1. These results provide a better understanding of differences in contributing factors of rear-end crashes, which help to propose effective countermeasures to mitigate its injury severity.
{"title":"Analysis of factors affecting occupant injury severity in rear-end crashes by different struck vehicle groups: A random thresholds random parameters hierarchical ordered probit model","authors":"Renteng Yuan, Xin Gu, Zhipeng Peng, Q. Xiang","doi":"10.1080/19439962.2022.2098891","DOIUrl":"https://doi.org/10.1080/19439962.2022.2098891","url":null,"abstract":"Abstract This study aims to explore the variability of risk factors affecting injury severity in rear-end crashes when different struck vehicle groups are involved. Two types of rear-end crash data, vehicle-strike-car data, vehicle-strike-truck data, are extracted from the Fatality Analysis Reporting System (FARS). Two likelihood ratio (LR) tests are firstly performed to validate the struck vehicle group variations, and then two separate random thresholds random parameters hierarchical ordered probit (RRHOP) models (Model 1 and Model 2) are established to capture unobserved heterogeneity. The results of LR test show significant differences in the effects of factors included in each model. Moreover, the model results suggest that SUVs, vans, and large trucks as striking vehicles are significant related to injury severity in both models with different effects. Factors such as speeding related, pickup, model year (struck vehicle), disabled damage, adverse weather, speed limit (≥60 mile/h), and young driver (struck vehicle) are found to be statistically significant in only model 1. These results provide a better understanding of differences in contributing factors of rear-end crashes, which help to propose effective countermeasures to mitigate its injury severity.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"42 1 1","pages":"636 - 657"},"PeriodicalIF":2.6,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82763770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-26DOI: 10.1080/19439962.2022.2086952
Alainie Sawtelle, M. Shirazi, P. Gårder, J. Rubin
Abstract Lane departure crashes account for approximately 34% of all roadway crashes and over 70% of all roadway fatalities in Maine. Despite an 18% decrease in average daily traffic volume, the half of the year with colder weather, from November to April, comprises over 64% of the yearly lane-departure crashes. The purpose of this study is to explore to what extent seasonal (i.e., winter vs. non-winter) and monthly weather variations impact lane departure crashes on rural Maine roads. We used a negative binomial model with panel data to analyze monthly crashes on Interstates, minor arterials, major collectors, and minor collectors from 2015 to 2019 for winter and non-winter periods. The data include monthly average daily traffic, geometric characteristics, and weather variables. The research results indicate that the seasonal variability as reflected in various weather variables significantly impact the frequency of lane-departure crashes during the winter period. The marginal effect analysis shows that as the number of days with more than 1 inch of snowfall, or rainfall increases by 1%, the average number of lane-departure crashes increases approximately by 0.51% and 0.09% on Interstate roadways, respectively.
{"title":"Exploring the impact of seasonal weather factors on frequency of lane-departure crashes in Maine","authors":"Alainie Sawtelle, M. Shirazi, P. Gårder, J. Rubin","doi":"10.1080/19439962.2022.2086952","DOIUrl":"https://doi.org/10.1080/19439962.2022.2086952","url":null,"abstract":"Abstract Lane departure crashes account for approximately 34% of all roadway crashes and over 70% of all roadway fatalities in Maine. Despite an 18% decrease in average daily traffic volume, the half of the year with colder weather, from November to April, comprises over 64% of the yearly lane-departure crashes. The purpose of this study is to explore to what extent seasonal (i.e., winter vs. non-winter) and monthly weather variations impact lane departure crashes on rural Maine roads. We used a negative binomial model with panel data to analyze monthly crashes on Interstates, minor arterials, major collectors, and minor collectors from 2015 to 2019 for winter and non-winter periods. The data include monthly average daily traffic, geometric characteristics, and weather variables. The research results indicate that the seasonal variability as reflected in various weather variables significantly impact the frequency of lane-departure crashes during the winter period. The marginal effect analysis shows that as the number of days with more than 1 inch of snowfall, or rainfall increases by 1%, the average number of lane-departure crashes increases approximately by 0.51% and 0.09% on Interstate roadways, respectively.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"551 1","pages":"445 - 466"},"PeriodicalIF":2.6,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81217789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-15DOI: 10.1080/19439962.2022.2086953
Linlin Jing, W. Shan, Yingyu Zhang
Abstract Studies that measure individual differences leading to risky driving behaviors in pre-crash phase will make contributions to accidents prevention. The primary concern here is what induces drivers to engage in risky behaviors. In this research, 441 valid questionnaires were distributed to examine the impact of risk preference, risk perception, and their interaction on risky driving behaviors, and the moderating effects of gender, age, and driving experience were measured accordingly. Results from the ordered logit regression model analysis demonstrate that the data fit well with our theoretical model. Risk preference and risk perception both predict risky driving behaviors with risk perception having greater predictability, and their interaction significantly affects risky driving behaviors when gender and age variables were added to the model separately. Gender and driving experience moderate the influence of risk perception on risky driving behaviors. The predictive effect of risk perception on risky driving behaviors was more significant for females than males. The effect of risk perception on risky driving behaviors was more pronounced for drivers with 1-3 years of driving experience compared with others. These interesting findings suggest that interventions need to be directed to all parts of the causal chain.
{"title":"Risk preference, risk perception as predictors of risky driving behaviors: the moderating effects of gender, age, and driving experience","authors":"Linlin Jing, W. Shan, Yingyu Zhang","doi":"10.1080/19439962.2022.2086953","DOIUrl":"https://doi.org/10.1080/19439962.2022.2086953","url":null,"abstract":"Abstract Studies that measure individual differences leading to risky driving behaviors in pre-crash phase will make contributions to accidents prevention. The primary concern here is what induces drivers to engage in risky behaviors. In this research, 441 valid questionnaires were distributed to examine the impact of risk preference, risk perception, and their interaction on risky driving behaviors, and the moderating effects of gender, age, and driving experience were measured accordingly. Results from the ordered logit regression model analysis demonstrate that the data fit well with our theoretical model. Risk preference and risk perception both predict risky driving behaviors with risk perception having greater predictability, and their interaction significantly affects risky driving behaviors when gender and age variables were added to the model separately. Gender and driving experience moderate the influence of risk perception on risky driving behaviors. The predictive effect of risk perception on risky driving behaviors was more significant for females than males. The effect of risk perception on risky driving behaviors was more pronounced for drivers with 1-3 years of driving experience compared with others. These interesting findings suggest that interventions need to be directed to all parts of the causal chain.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"117 1","pages":"467 - 492"},"PeriodicalIF":2.6,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88408763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-31DOI: 10.1080/19439962.2022.2076756
Xiaochi Ma, Jian Lu, Xian Liu, Weibin Qu
Abstract Real-time crash risk prediction is a hot topic of emerging technology. Due to the lack of basic risk formation theory, previous studies focussed on the application of complex models to improve the accuracy of prediction, ignoring the interpretation of variables, while the traditional statistical analysis method can interpret variables, but the prediction accuracy is poor, which falls into a dilemma of trade-off. In this study, based on the traffic flow information of elevated expressway, an improved genetic programming (GP) approach with elite gene bank is applied to obtain an explicit traffic flow crash risk function to solve the above trade-off problem. Logistic regression and backward-propagation neural network combined with partial dependency plot were used as baseline methods to examine the interpretability and accuracy of GP. It is found that GP prediction model has been proved to be able to select important variables and solve the trade-off dilemma, which has good interpretability and accuracy. The results show that crash risk in the traffic flow mainly comes from the traffic volume, speed of the upstream section, and the speed of the current section. Furthermore, the error of GP comes from the unobserved heterogeneity and crash mechanism theory is proposed.
{"title":"A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy","authors":"Xiaochi Ma, Jian Lu, Xian Liu, Weibin Qu","doi":"10.1080/19439962.2022.2076756","DOIUrl":"https://doi.org/10.1080/19439962.2022.2076756","url":null,"abstract":"Abstract Real-time crash risk prediction is a hot topic of emerging technology. Due to the lack of basic risk formation theory, previous studies focussed on the application of complex models to improve the accuracy of prediction, ignoring the interpretation of variables, while the traditional statistical analysis method can interpret variables, but the prediction accuracy is poor, which falls into a dilemma of trade-off. In this study, based on the traffic flow information of elevated expressway, an improved genetic programming (GP) approach with elite gene bank is applied to obtain an explicit traffic flow crash risk function to solve the above trade-off problem. Logistic regression and backward-propagation neural network combined with partial dependency plot were used as baseline methods to examine the interpretability and accuracy of GP. It is found that GP prediction model has been proved to be able to select important variables and solve the trade-off dilemma, which has good interpretability and accuracy. The results show that crash risk in the traffic flow mainly comes from the traffic volume, speed of the upstream section, and the speed of the current section. Furthermore, the error of GP comes from the unobserved heterogeneity and crash mechanism theory is proposed.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"65 1","pages":"421 - 443"},"PeriodicalIF":2.6,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81092342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-06DOI: 10.1080/19439962.2022.2070812
Mouyid Islam, F. Mannering
Abstract Asleep/fatigued driving has proven to be a serious and persistent highway-safety problem. This study investigates aspects of this problem by studying the temporal changes in driver-injury severities in single-vehicle crashes that involve asleep/fatigued driving. To do this, random parameters logit models with unobserved heterogeneity in means and variances were estimated to compare injury-severities in asleep/fatigued crashes in Florida in 2014 and 2019. The estimated models are based on available police-reported crash data that include a wide variety of factors related to the spatial, temporal, and weather characteristics as well as vehicle characteristics, traffic information, harmful events, roadway attributes, and driver characteristics. The model estimates show that there were many statistically significant factors determining driver-injury severities resulting from asleep/fatigued driving, and that the effect of these factors on driver-injury severities has changed significantly over time, with many explanatory variables producing temporally shifting marginal effects. While asleep/fatigued driving crashes remain a serious safety concern, the empirical findings indicate (using model prediction simulations) that the resulting injury severities in crashes involving asleep/fatigued driving have declined between 2014 and 2019, likely reflecting the effectiveness of safety campaigns and ongoing improvements in vehicle safety technologies and highway safety features.
{"title":"An empirical analysis of how asleep/fatigued driving-injury severities have changed over time","authors":"Mouyid Islam, F. Mannering","doi":"10.1080/19439962.2022.2070812","DOIUrl":"https://doi.org/10.1080/19439962.2022.2070812","url":null,"abstract":"Abstract Asleep/fatigued driving has proven to be a serious and persistent highway-safety problem. This study investigates aspects of this problem by studying the temporal changes in driver-injury severities in single-vehicle crashes that involve asleep/fatigued driving. To do this, random parameters logit models with unobserved heterogeneity in means and variances were estimated to compare injury-severities in asleep/fatigued crashes in Florida in 2014 and 2019. The estimated models are based on available police-reported crash data that include a wide variety of factors related to the spatial, temporal, and weather characteristics as well as vehicle characteristics, traffic information, harmful events, roadway attributes, and driver characteristics. The model estimates show that there were many statistically significant factors determining driver-injury severities resulting from asleep/fatigued driving, and that the effect of these factors on driver-injury severities has changed significantly over time, with many explanatory variables producing temporally shifting marginal effects. While asleep/fatigued driving crashes remain a serious safety concern, the empirical findings indicate (using model prediction simulations) that the resulting injury severities in crashes involving asleep/fatigued driving have declined between 2014 and 2019, likely reflecting the effectiveness of safety campaigns and ongoing improvements in vehicle safety technologies and highway safety features.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"9 1","pages":"397 - 420"},"PeriodicalIF":2.6,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79683811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.1080/19439962.2022.2069896
Anik Das, Md Nasim Khan, Mohamed M. Ahmed, S. Wulff
Abstract This study investigated lane-changing characteristics with regard to drivers’ aggressiveness in rain and clear weather utilizing the SHRP2 Naturalistic Driving Study (NDS) dataset. An innovative methodology was developed to identify lane-changing events and extract corresponding parameters from the SHRP2 NDS database. Initially, K-means and K-medoids clustering methods were examined to classify drivers into non-aggressive and aggressive categories considering six features related to driving behavior, and K-means clustering was adopted based on the average silhouette width method (ASWM). Two-level mixed-effects linear regression models were calibrated to assess the contributing factors that affect lane-changing durations, which revealed that different vehicle kinematics, traffic, driver, and roadway characteristics, as well as weather conditions combined with other factors, were significant in the calibrated models for both driver types. The results revealed that the lane-changing duration associated with heavy rain decreased with a higher speed limit for aggressive drivers. Furthermore, the lane-changing duration associated with light/moderate rain decreased with the number of lanes for non-aggressive drivers. The study findings could be leveraged to incorporate drivers’ aggressiveness into microsimulation lane-changing model calibration and validation as well as could have significant implications in improving safety in Connected and Autonomous Vehicles (CAV).
{"title":"Cluster analysis and multi-level modeling for evaluating the impact of rain on aggressive lane-changing characteristics utilizing naturalistic driving data","authors":"Anik Das, Md Nasim Khan, Mohamed M. Ahmed, S. Wulff","doi":"10.1080/19439962.2022.2069896","DOIUrl":"https://doi.org/10.1080/19439962.2022.2069896","url":null,"abstract":"Abstract This study investigated lane-changing characteristics with regard to drivers’ aggressiveness in rain and clear weather utilizing the SHRP2 Naturalistic Driving Study (NDS) dataset. An innovative methodology was developed to identify lane-changing events and extract corresponding parameters from the SHRP2 NDS database. Initially, K-means and K-medoids clustering methods were examined to classify drivers into non-aggressive and aggressive categories considering six features related to driving behavior, and K-means clustering was adopted based on the average silhouette width method (ASWM). Two-level mixed-effects linear regression models were calibrated to assess the contributing factors that affect lane-changing durations, which revealed that different vehicle kinematics, traffic, driver, and roadway characteristics, as well as weather conditions combined with other factors, were significant in the calibrated models for both driver types. The results revealed that the lane-changing duration associated with heavy rain decreased with a higher speed limit for aggressive drivers. Furthermore, the lane-changing duration associated with light/moderate rain decreased with the number of lanes for non-aggressive drivers. The study findings could be leveraged to incorporate drivers’ aggressiveness into microsimulation lane-changing model calibration and validation as well as could have significant implications in improving safety in Connected and Autonomous Vehicles (CAV).","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"51 1","pages":"2137 - 2165"},"PeriodicalIF":2.6,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84364997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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