Pub Date : 2022-10-18DOI: 10.1080/19439962.2022.2129891
Md Nasim Khan, Mohamed M. Ahmed
Abstract The main focus of this study was to develop a robust prediction model based on deep learning capable of providing timely predictions of injury and fatal crashes in adverse weather on rural mountainous freeways. This study leveraged a promising deep learning technique named ResNet18. To apply the proposed deep learning model, the numeric crash data were converted to images utilizing a cutting-edge method, called DeepInsight. In addition, considering the imbalanced nature of the crash data, this study leveraged two data balancing techniques, namely Random Under Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE); and experimented with several data sampling ratios. The best prediction performance was found using a ratio of 1:2:2 (Fatal:Injury:PDO) coupled with both RUS and SMOTE, which produced an overall prediction accuracy of 99.3% and 80.5% for fatal and injury crashes, respectively. This study also investigated the importance of variables on crash severity, which revealed that driver residency, vehicle damage extent, airbag deployment, driver conditions, weather, and road surface conditions were the most important variables contributing to the severity of crashes. The proposed deep learning framework can provide an accurate prediction of fatal and injury crashes, which is crucial to ensuring effective traffic collision management.
{"title":"A novel deep learning approach to predict crash severity in adverse weather on rural mountainous freeway","authors":"Md Nasim Khan, Mohamed M. Ahmed","doi":"10.1080/19439962.2022.2129891","DOIUrl":"https://doi.org/10.1080/19439962.2022.2129891","url":null,"abstract":"Abstract The main focus of this study was to develop a robust prediction model based on deep learning capable of providing timely predictions of injury and fatal crashes in adverse weather on rural mountainous freeways. This study leveraged a promising deep learning technique named ResNet18. To apply the proposed deep learning model, the numeric crash data were converted to images utilizing a cutting-edge method, called DeepInsight. In addition, considering the imbalanced nature of the crash data, this study leveraged two data balancing techniques, namely Random Under Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE); and experimented with several data sampling ratios. The best prediction performance was found using a ratio of 1:2:2 (Fatal:Injury:PDO) coupled with both RUS and SMOTE, which produced an overall prediction accuracy of 99.3% and 80.5% for fatal and injury crashes, respectively. This study also investigated the importance of variables on crash severity, which revealed that driver residency, vehicle damage extent, airbag deployment, driver conditions, weather, and road surface conditions were the most important variables contributing to the severity of crashes. The proposed deep learning framework can provide an accurate prediction of fatal and injury crashes, which is crucial to ensuring effective traffic collision management.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"53 1","pages":"795 - 825"},"PeriodicalIF":2.6,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88552022","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 : 2022-10-12DOI: 10.1080/19439962.2022.2128959
Liu Yang, Yunzhou Song, Zhenxin Hu, Zi-Shuang Wang, X. Li
Abstract Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.
{"title":"Recognition of typical driving stressors and driver stress level in a Chinese sample","authors":"Liu Yang, Yunzhou Song, Zhenxin Hu, Zi-Shuang Wang, X. Li","doi":"10.1080/19439962.2022.2128959","DOIUrl":"https://doi.org/10.1080/19439962.2022.2128959","url":null,"abstract":"Abstract Drivers are adversely affected in decision-making and behavior under excessive stress, thus increasing road crash risks. In this study, the Driver Stress Inventory (DSI) was used to identify typical driving stress scenarios and explore the characteristics of drivers among different stress levels. A total of 1881 drivers took part in the survey. The Precedence Chart was used to rank the importance of driving stressors involved in the scale. K-means cluster was adopted to classify drivers’ stress into three levels, namely low, medium and high-stress. Finally, the Kruskal-Wallis test and Mantel-Haenszel test were employed to analyze the similarities and differences of demographic statistical characteristics under different stress levels. The results of the study indicate that various unexpected scenarios caused by the abnormal behavior of other road users are the most typical stressors. Drivers in the high-stress group tended to be younger and less experienced. Professional drivers reported higher stress than nonprofessional drivers. In addition, high-stress drivers were more prone to be involved in traffic crashes.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"23 2 PT. 1 1","pages":"774 - 794"},"PeriodicalIF":2.6,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82926549","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 : 2022-10-11DOI: 10.1080/19439962.2022.2129892
Wei Lin, Heng Wei, John E. Ash
Abstract The characteristics of intersection crashes are not only affected by the subject intersection where the crash occurs but also are correlated with environmental conditions of neighboring analysis zones. There are few studies on intersection crash analysis to solve certain spatial effects on microscopic safety issues by proactively incorporating highway safety improvement measures into the long-term transportation planning process. The objective of this paper is to develop a heuristic traffic safety analysis system where spatial spillovers analysis is integrated into roadway safety assessment to incorporate micro variables and macro variables. With K-means clustering technique in a GIS environment, 8 hotspot counties are identified from 88 counties in Ohio, which have high intersection crash propensity. The rest of counties are identified as general counties. Then, an innovative integrated Generalized Linear Model is adopted to identify 11 and 20 significant variables that contribute to the intersection crash propensity in hotspot counties and general counties, respectively. To verify compatibility of intersection crash frequency models with macro-level and micro-level measurement, Reading Road in Cincinnati, Hamilton County (hotspot county) and I-71 in Mason City and Lebanon City of Warren County (general county) are used as examples for the test, and the results show a good consistence.
{"title":"Modeling spatial spillover effect on intersection crash propensity: a case study at the county level in Ohio","authors":"Wei Lin, Heng Wei, John E. Ash","doi":"10.1080/19439962.2022.2129892","DOIUrl":"https://doi.org/10.1080/19439962.2022.2129892","url":null,"abstract":"Abstract The characteristics of intersection crashes are not only affected by the subject intersection where the crash occurs but also are correlated with environmental conditions of neighboring analysis zones. There are few studies on intersection crash analysis to solve certain spatial effects on microscopic safety issues by proactively incorporating highway safety improvement measures into the long-term transportation planning process. The objective of this paper is to develop a heuristic traffic safety analysis system where spatial spillovers analysis is integrated into roadway safety assessment to incorporate micro variables and macro variables. With K-means clustering technique in a GIS environment, 8 hotspot counties are identified from 88 counties in Ohio, which have high intersection crash propensity. The rest of counties are identified as general counties. Then, an innovative integrated Generalized Linear Model is adopted to identify 11 and 20 significant variables that contribute to the intersection crash propensity in hotspot counties and general counties, respectively. To verify compatibility of intersection crash frequency models with macro-level and micro-level measurement, Reading Road in Cincinnati, Hamilton County (hotspot county) and I-71 in Mason City and Lebanon City of Warren County (general county) are used as examples for the test, and the results show a good consistence.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"38 1","pages":"826 - 851"},"PeriodicalIF":2.6,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79077065","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 : 2022-10-07DOI: 10.1080/19439962.2022.2129893
Hang Qi, Xiaohua Zhao, Yiping Wu, Yang Ding, Yang Bian
Abstract Considering the fact that driving behavior data possesses characteristics of strong real-time, poor stability, and continuous change, this study proposes the Individual Driving Behavior Graph Construction Method (DBGCM), which visually presents the time trajectory of driving behavior to explore safety-ecological (SAF-ECO) characteristics of individual drivers. The results can be applied in the analysis of driving safety ecology and as a reference for driving behavior optimization. This study is based on the micro-driving behavior data collected by the on-board diagnostic devices (OBD), which can create a graph on individual driver behavior characteristics via nodes and time axis as its elements. Additionally, the method of Longest Common Subsequence (LCSS) is proposed to identify the similarity among different driving behavior graphs. The data results of taxi drivers under different SAF-ECO levels lead to the conclusion that the driving behavior characteristics graph analysis is consistent with the SAF-ECO classification. The similarity of graphs among “safe and non-eco” drivers is higher than that within other categories. Finally, the research discusses in detail the data requirements, method verification, and future applications. The reasonable coupling characteristic description of “SAF-ECO” driving behavior is conducive to the enhancement of drivers’ self-management ability, driving education, and customization for drivers.
{"title":"Graph method for driving behavior optimization based on “SAF-ECO” description of behavior characteristics","authors":"Hang Qi, Xiaohua Zhao, Yiping Wu, Yang Ding, Yang Bian","doi":"10.1080/19439962.2022.2129893","DOIUrl":"https://doi.org/10.1080/19439962.2022.2129893","url":null,"abstract":"Abstract Considering the fact that driving behavior data possesses characteristics of strong real-time, poor stability, and continuous change, this study proposes the Individual Driving Behavior Graph Construction Method (DBGCM), which visually presents the time trajectory of driving behavior to explore safety-ecological (SAF-ECO) characteristics of individual drivers. The results can be applied in the analysis of driving safety ecology and as a reference for driving behavior optimization. This study is based on the micro-driving behavior data collected by the on-board diagnostic devices (OBD), which can create a graph on individual driver behavior characteristics via nodes and time axis as its elements. Additionally, the method of Longest Common Subsequence (LCSS) is proposed to identify the similarity among different driving behavior graphs. The data results of taxi drivers under different SAF-ECO levels lead to the conclusion that the driving behavior characteristics graph analysis is consistent with the SAF-ECO classification. The similarity of graphs among “safe and non-eco” drivers is higher than that within other categories. Finally, the research discusses in detail the data requirements, method verification, and future applications. The reasonable coupling characteristic description of “SAF-ECO” driving behavior is conducive to the enhancement of drivers’ self-management ability, driving education, and customization for drivers.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"57 1","pages":"852 - 875"},"PeriodicalIF":2.6,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88283443","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}
Abstract Railroad safety is one critical concern for the railroad industry. Extracting useful information from railroad safety-related textual materials is one significant and essential task. To better understand the contributing factors to the railroad accidents, previous studies have primarily focused on the structured fields, while few of them have developed a thorough analysis of the narratives. In addition, due to the difficulty of understanding the terminologies in the accidents’ narratives, it is challenging to extensively use these narratives as a time-consuming and labor-intensive task. Therefore, this study proposed a novel deep learning approach to consistently leverage the values behind these railroad accident narratives. The proposed method modified the classical Bidirectional Encoder Representations for Transformers (BERT) with the connection of a Deep Neural Network (DNN). To validate the superiority of the proposed BERT-DNN, several additional text classification methods were employed in the real-world railroad accident database. Results demonstrate the proposed method in this study can assign congruous accident causes based on the railroad accidents’ narratives precisely and outperforms previous state-of-the-art text classification approaches. The analytical results, along with proposed methodological framework, can contribute to an in-deep understanding of accident causes for practitioners and academics, and ultimately enhance rail operation safety.
{"title":"Railroad accident causal analysis with unstructured narratives using bidirectional encoder representations for transformers","authors":"Bingxue Song, Xiaoping Ma, Yong Qin, Hao Hu, Zhipeng Zhang","doi":"10.1080/19439962.2022.2128956","DOIUrl":"https://doi.org/10.1080/19439962.2022.2128956","url":null,"abstract":"Abstract Railroad safety is one critical concern for the railroad industry. Extracting useful information from railroad safety-related textual materials is one significant and essential task. To better understand the contributing factors to the railroad accidents, previous studies have primarily focused on the structured fields, while few of them have developed a thorough analysis of the narratives. In addition, due to the difficulty of understanding the terminologies in the accidents’ narratives, it is challenging to extensively use these narratives as a time-consuming and labor-intensive task. Therefore, this study proposed a novel deep learning approach to consistently leverage the values behind these railroad accident narratives. The proposed method modified the classical Bidirectional Encoder Representations for Transformers (BERT) with the connection of a Deep Neural Network (DNN). To validate the superiority of the proposed BERT-DNN, several additional text classification methods were employed in the real-world railroad accident database. Results demonstrate the proposed method in this study can assign congruous accident causes based on the railroad accidents’ narratives precisely and outperforms previous state-of-the-art text classification approaches. The analytical results, along with proposed methodological framework, can contribute to an in-deep understanding of accident causes for practitioners and academics, and ultimately enhance rail operation safety.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"73 1","pages":"717 - 736"},"PeriodicalIF":2.6,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76475681","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}
Abstract Freeway crash prediction models are the basic of traffic safety research, yet crash occurrence and the influencing factors change over time. In order to make sure the implemented safety models fit the current traffic environment, this study conducts a comparative analysis of 2017 and 2020 datasets collected from freeways in Suzhou, China. Considering the spatial correlation among analysis units and the hierarchical data structure, a Bayesian conditional autoregressive negative binomial (CAR-NB) model and a Bayesian hierarchical CAR-NB (HCAR-NB) model were used to explore the safety influencing factors, and a traditional NB model was developed for further comparison. To update the HCAR-NB model from 2017 to 2020, Bayesian inference with informative priors was used to improve its goodness of fit and efficiency. Preliminary results showed that 1) the HCAR-NB model outperformed the NB model and CAR-NB model in prediction accuracy, and 2) the number of crashes was significantly correlated with average speed, speed variance, road segment length, number of lanes, and presence of ramps. The potential for safety improvement (PSI) method was applied to the modeling results to identify hotspots for the two years. The results confirmed that the hotspots spatiotemporally shifted among the freeways. The proposed crash prediction model and updating method are expected to assist implementation of informed countermeasures for freeway safety improvement.
{"title":"Traffic safety analysis and model updating for freeways using Bayesian method","authors":"Xuesong Wang, Qi Zhang, Xiaohan Yang, Yingying Pei, Jinghui Yuan","doi":"10.1080/19439962.2022.2128957","DOIUrl":"https://doi.org/10.1080/19439962.2022.2128957","url":null,"abstract":"Abstract Freeway crash prediction models are the basic of traffic safety research, yet crash occurrence and the influencing factors change over time. In order to make sure the implemented safety models fit the current traffic environment, this study conducts a comparative analysis of 2017 and 2020 datasets collected from freeways in Suzhou, China. Considering the spatial correlation among analysis units and the hierarchical data structure, a Bayesian conditional autoregressive negative binomial (CAR-NB) model and a Bayesian hierarchical CAR-NB (HCAR-NB) model were used to explore the safety influencing factors, and a traditional NB model was developed for further comparison. To update the HCAR-NB model from 2017 to 2020, Bayesian inference with informative priors was used to improve its goodness of fit and efficiency. Preliminary results showed that 1) the HCAR-NB model outperformed the NB model and CAR-NB model in prediction accuracy, and 2) the number of crashes was significantly correlated with average speed, speed variance, road segment length, number of lanes, and presence of ramps. The potential for safety improvement (PSI) method was applied to the modeling results to identify hotspots for the two years. The results confirmed that the hotspots spatiotemporally shifted among the freeways. The proposed crash prediction model and updating method are expected to assist implementation of informed countermeasures for freeway safety improvement.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"201 1","pages":"737 - 759"},"PeriodicalIF":2.6,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76962102","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 : 2022-10-03DOI: 10.1080/19439962.2022.2128958
Luis Miguel Martín-delosReyes, P. Lardelli-Claret, M. Rivera-Izquierdo, E. Jiménez-Mejías, V. Martínez-Ruíz
Abstract The relationship between license-related infractions (LRIs) and the severity of road crashes has been scarcely addressed in previous research. This study estimates the association between each LRI and the severity of driver injuries and the partial severity of the crash (i.e., crash severity after excluding the severity of the driver’s own injuries) in a cohort comprising 78,720 drivers who were considered responsible for crashes in the Spanish National Register for Road Traffic Accident Victims, from 2014 to 2017. Adjusted Relative Risk Ratios for each LRI and severity level were obtained through multinomial logistic regression models. Age- and sex-adjusted estimates revealed an increased severity for almost all LRIs. Additional adjustment for seat belt use showed a decrease in the magnitude of the associations, particularly regarding driver injury severity, suggesting that part of these associations was related to increased vulnerability of the driver. Adjustment for other vehicle- and environment-related variables showed a further decrease in the associations but remained significant for “never having obtained a license” and other specific LRIs. These results support the need for maintaining police surveillance and legal measures to identify these subgroups of drivers, remove them from the road and adopt strategies for their safe return to driving.
{"title":"Measuring and understanding the association between license-related infractions and road crash severity","authors":"Luis Miguel Martín-delosReyes, P. Lardelli-Claret, M. Rivera-Izquierdo, E. Jiménez-Mejías, V. Martínez-Ruíz","doi":"10.1080/19439962.2022.2128958","DOIUrl":"https://doi.org/10.1080/19439962.2022.2128958","url":null,"abstract":"Abstract The relationship between license-related infractions (LRIs) and the severity of road crashes has been scarcely addressed in previous research. This study estimates the association between each LRI and the severity of driver injuries and the partial severity of the crash (i.e., crash severity after excluding the severity of the driver’s own injuries) in a cohort comprising 78,720 drivers who were considered responsible for crashes in the Spanish National Register for Road Traffic Accident Victims, from 2014 to 2017. Adjusted Relative Risk Ratios for each LRI and severity level were obtained through multinomial logistic regression models. Age- and sex-adjusted estimates revealed an increased severity for almost all LRIs. Additional adjustment for seat belt use showed a decrease in the magnitude of the associations, particularly regarding driver injury severity, suggesting that part of these associations was related to increased vulnerability of the driver. Adjustment for other vehicle- and environment-related variables showed a further decrease in the associations but remained significant for “never having obtained a license” and other specific LRIs. These results support the need for maintaining police surveillance and legal measures to identify these subgroups of drivers, remove them from the road and adopt strategies for their safe return to driving.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"216 1","pages":"761 - 773"},"PeriodicalIF":2.6,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74765889","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 : 2022-09-21DOI: 10.1080/19439962.2022.2119455
Xuewei Li, J. Rong, Zhenlong Li, Xiaohua Zhao, Jianming Ma, Jiaxia Yang
Abstract To clarify the effect of the cooperative vehicle infrastructure system (CVIS) application on drivers’ visual performance, a total of 37 drivers were recruited to drive the simulated roadway in a freeway work zone under baseline and cooperative vehicle environments. Drivers’ attention and concentration on the forward roadway, attention distraction, and attention distribution in both scenarios were analyzed. The results indicated that the CVIS application changed drivers’ information-processing mode in the forward roadway as manifested by higher glance frequency and shorter average dwell time. In addition, more off-road distractions were observed in the range of 500 m in front of the work zone, but focusing on human–machine interfaces (HMIs) was not the main cause. In conclusion, the change in the driver’s attention allocation and the diversion was clarified with the proposed visual link diagram. This paper provides a comprehensive approach to visual assessment of CVIS and contributes to the customized design and optimization of future CVIS-HMI.
{"title":"Effects of cooperative vehicle infrastructure system on driver’s attention––A simulator study on work zone","authors":"Xuewei Li, J. Rong, Zhenlong Li, Xiaohua Zhao, Jianming Ma, Jiaxia Yang","doi":"10.1080/19439962.2022.2119455","DOIUrl":"https://doi.org/10.1080/19439962.2022.2119455","url":null,"abstract":"Abstract To clarify the effect of the cooperative vehicle infrastructure system (CVIS) application on drivers’ visual performance, a total of 37 drivers were recruited to drive the simulated roadway in a freeway work zone under baseline and cooperative vehicle environments. Drivers’ attention and concentration on the forward roadway, attention distraction, and attention distribution in both scenarios were analyzed. The results indicated that the CVIS application changed drivers’ information-processing mode in the forward roadway as manifested by higher glance frequency and shorter average dwell time. In addition, more off-road distractions were observed in the range of 500 m in front of the work zone, but focusing on human–machine interfaces (HMIs) was not the main cause. In conclusion, the change in the driver’s attention allocation and the diversion was clarified with the proposed visual link diagram. This paper provides a comprehensive approach to visual assessment of CVIS and contributes to the customized design and optimization of future CVIS-HMI.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"5 1","pages":"541 - 562"},"PeriodicalIF":2.6,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81699090","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 : 2022-09-20DOI: 10.1080/19439962.2022.2123582
R. Tamakloe, J. Hong, J. Kim, D. Park
Abstract Fatal crashes involving Powered Two-Wheelers (PTWs) are a major health concern in South Korea due to the increase in their usage in the logistics industry. Owing to the way, they are operated on the roadways, most riders end up in fatal crashes. Interestingly, little research exists regarding the impact of risk factors on fatal crashes involving at-fault PTW riders. This study employs a copula-based regression technique to simultaneously model the relationship between crash-risk factors and crash outcome metrics of fatal PTW rider-at-fault crashes, namely the number of crash casualties (casualty size) and the number of vehicles involved in a crash (crash size) at intersections and non-intersection segments. The proposed method was superior compared to the SEM-based bivariate regression approach, and the estimation results showed that there exists a positive relationship between both outcome variables. From the analysis, it was identified that while "other violations" comprising speeding and wrongful overtaking had varying effects on crash size outcomes at the intersection and non-intersection segments, variables such as daytime, winter, head-on collisions, and pedestrian involvement had positive impact on the crash consequence metrics irrespective of the crash location. Insights drawn from the study are used in recommending appropriate countermeasures for improving PTW safety.
{"title":"Factors affecting fatal PTW at-fault crash outcome metrics at intersections and non-intersections in South Korea","authors":"R. Tamakloe, J. Hong, J. Kim, D. Park","doi":"10.1080/19439962.2022.2123582","DOIUrl":"https://doi.org/10.1080/19439962.2022.2123582","url":null,"abstract":"Abstract Fatal crashes involving Powered Two-Wheelers (PTWs) are a major health concern in South Korea due to the increase in their usage in the logistics industry. Owing to the way, they are operated on the roadways, most riders end up in fatal crashes. Interestingly, little research exists regarding the impact of risk factors on fatal crashes involving at-fault PTW riders. This study employs a copula-based regression technique to simultaneously model the relationship between crash-risk factors and crash outcome metrics of fatal PTW rider-at-fault crashes, namely the number of crash casualties (casualty size) and the number of vehicles involved in a crash (crash size) at intersections and non-intersection segments. The proposed method was superior compared to the SEM-based bivariate regression approach, and the estimation results showed that there exists a positive relationship between both outcome variables. From the analysis, it was identified that while \"other violations\" comprising speeding and wrongful overtaking had varying effects on crash size outcomes at the intersection and non-intersection segments, variables such as daytime, winter, head-on collisions, and pedestrian involvement had positive impact on the crash consequence metrics irrespective of the crash location. Insights drawn from the study are used in recommending appropriate countermeasures for improving PTW safety.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"135 1","pages":"681 - 716"},"PeriodicalIF":2.6,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78607009","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 : 2022-09-14DOI: 10.1080/19439962.2022.2123581
Subasish Das, R. Tamakloe, Boniphace Kutela, Ahmed Hossain
Abstract Frontage roads are the supporting roadways that are along freeways and fully controlled principal arterial roadway networks in the U.S. These roads are designed in a way to provide access between the freeways, principal arterials, and surrounding business entities. For Texas, these roadways are the leading design resolution for providing access along rural freeways and principal arterial roadways. These roadways are generally two-ways for rural and less developed urban areas and are mostly one-way for urban and city-centered roadways. Although frontage roadways possess major safety concerns, the safety performance of these roadways has not been well studied. This study collected six years of frontage road crash data from Texas to determine the patterns of associated factors by applying a dimension reduction method known as cluster correspondence analysis (CCA). The results revealed four clusters for each of the two datasets based on crash injury types. For fatal and injury crashes, the major clusters are distraction-related crashes at signalized intersections, segment-related crashes at dark unlighted conditions, yield signed intersection locations and segments with no TCDs, and intersection crashes on undivided roadways. For the no injury crash dataset, the key clusters are segment crashes in dark conditions and rain, crashes at signalized intersections with both drivers going straight, segment crashes with both drivers going straight with marked lanes or no TCDs, and intersection-related collisions on undivided roadways. Based on the evaluation results, suitable safety countermeasures and policy initiatives to reduce frontage road crash frequencies can be singled out.
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