Pub Date : 2023-03-27DOI: 10.1080/19439962.2023.2189762
Zhen Gao, Jingning Xu, Rongjie Yu, Lei Han
Developing real-time crash risk models has been a hot research topic as it could identify crash precursors and thus triggering active traffic management strategies. Currently, crash risk identification models were mainly developed based upon supervised learning techniques, which requires large sample size of historical crash data. However, crashes are rare events in the real world, where the performance of supervised learning methods can be severely degraded to deal with the imbalanced sample. Besides, the data heterogeneity issue is another critical challenge. In this study, the unsupervised learning approach has been introduced to address unbalanced samples and data heterogeneity issues, and the experimental results has verified the effectiveness of the method. Data from the Shanghai urban expressway system were utilized for the empirical analyses. Several unsupervised learning methods were tested, among which, Angle-Based Outlier Detection (ABOD) model showed the best performance with 80.4% sensitivity and 25.4% false alarm rate (FAR). Considering the varying traffic flow distribution, dynamic ABOD with sliding window is further proposed, which improves the sensitivity by 6.3% and reduces the FAR by 8.1%. Finally, the proposed model is used to construct personalized road-level models, which achieve good performance despite the small sample size and severe sample imbalance.
{"title":"Utilizing angle-based outlier detection method with sliding window mechanism to identify real-time crash risk","authors":"Zhen Gao, Jingning Xu, Rongjie Yu, Lei Han","doi":"10.1080/19439962.2023.2189762","DOIUrl":"https://doi.org/10.1080/19439962.2023.2189762","url":null,"abstract":"Developing real-time crash risk models has been a hot research topic as it could identify crash precursors and thus triggering active traffic management strategies. Currently, crash risk identification models were mainly developed based upon supervised learning techniques, which requires large sample size of historical crash data. However, crashes are rare events in the real world, where the performance of supervised learning methods can be severely degraded to deal with the imbalanced sample. Besides, the data heterogeneity issue is another critical challenge. In this study, the unsupervised learning approach has been introduced to address unbalanced samples and data heterogeneity issues, and the experimental results has verified the effectiveness of the method. Data from the Shanghai urban expressway system were utilized for the empirical analyses. Several unsupervised learning methods were tested, among which, Angle-Based Outlier Detection (ABOD) model showed the best performance with 80.4% sensitivity and 25.4% false alarm rate (FAR). Considering the varying traffic flow distribution, dynamic ABOD with sliding window is further proposed, which improves the sensitivity by 6.3% and reduces the FAR by 8.1%. Finally, the proposed model is used to construct personalized road-level models, which achieve good performance despite the small sample size and severe sample imbalance.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135823028","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 : 2022-12-13DOI: 10.1080/19439962.2022.2153953
Wang Xiang, Y. He, Qunjie Peng, X. Li, Qingwan Xue, Guiqiu Xu
Abstract A high incidence of traffic accidents is often observed in freeway exit ramp areas. Slowing down, wandering, and changing lanes suddenly and continually in a short interval near the exit ramp are important reasons for accidents. Helping drivers start changing lanes sooner and more efficiently in freeway exit ramp areas is a feasible solution to vehicle interweaving. This paper aims to optimize the current guiding sign system and improve drivers’ lane-changing behavior before the exit ramp. Three guiding sign optimization measures (sign symbols, ground signs and voice prompts) had been considered before five guiding sign plans were made for driving simulation experiments: original sign (OS) plan, new type sign (NTS) plan, ground guiding sign (GOS) plan, voice prompt (VOS) plan, and voice-ground sign (VGOS) plan. The decisions, reactions, and operation processes of 43 Chinese drivers were compared to confirm the optimal guiding sign plan. The results showed that updating sign symbols, adding ground signs and voice prompts all contributed to the drivers’ shorter response time, earlier arrival at the lane-changing location, higher average speed and greater longitudinal distance of lane-changing. These findings can help freeway designers optimize the guiding sign system for freeway exit ramps.
{"title":"Optimizing the guiding sign system to improve drivers’ lane-changing behavior at freeway exit ramp","authors":"Wang Xiang, Y. He, Qunjie Peng, X. Li, Qingwan Xue, Guiqiu Xu","doi":"10.1080/19439962.2022.2153953","DOIUrl":"https://doi.org/10.1080/19439962.2022.2153953","url":null,"abstract":"Abstract A high incidence of traffic accidents is often observed in freeway exit ramp areas. Slowing down, wandering, and changing lanes suddenly and continually in a short interval near the exit ramp are important reasons for accidents. Helping drivers start changing lanes sooner and more efficiently in freeway exit ramp areas is a feasible solution to vehicle interweaving. This paper aims to optimize the current guiding sign system and improve drivers’ lane-changing behavior before the exit ramp. Three guiding sign optimization measures (sign symbols, ground signs and voice prompts) had been considered before five guiding sign plans were made for driving simulation experiments: original sign (OS) plan, new type sign (NTS) plan, ground guiding sign (GOS) plan, voice prompt (VOS) plan, and voice-ground sign (VGOS) plan. The decisions, reactions, and operation processes of 43 Chinese drivers were compared to confirm the optimal guiding sign plan. The results showed that updating sign symbols, adding ground signs and voice prompts all contributed to the drivers’ shorter response time, earlier arrival at the lane-changing location, higher average speed and greater longitudinal distance of lane-changing. These findings can help freeway designers optimize the guiding sign system for freeway exit ramps.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"392 1","pages":"1029 - 1056"},"PeriodicalIF":2.6,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86823612","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 : 2022-12-08DOI: 10.1080/19439962.2022.2153952
W. Agyemang, E. Adanu, Jun Liu, Steven Jones
Abstract Over the years, the uncontrolled interaction of human and high-speed vehicular activities at settlement areas along highways in Ghana has resulted in many pedestrian fatalities and injuries. This phenomenon has been attributed to the land-use and right-of-way planning and lack of pedestrian crossing facilities for safe crossing of highways. The slow response to developing strategies to reduce pedestrian fatalities along the nation’s highways has led to many public protests. To advance a data-driven and evidence-based approach to finding appropriate countermeasures, this study investigated the factors associated with pedestrian injury outcomes of inter-urban highway crashes in Ghana. Latent class multinomial logit modeling method was employed to account for unobserved heterogeneity in a five-year pedestrian-vehicle crash data recorded on highways in Ghana. The model estimation results show that speeding, hit and run and crashes that involve buses were more likely to result in fatal injury while crashes that occurred at highway sections with no shoulder were more likely to result in hospitalized injury. The findings of the study provide basis for the development of appropriate countermeasures to reduce the number of pedestrian deaths and injuries on high-speed inter-urban highways in Ghana and other countries with similar characteristics in the sub-region.
{"title":"A latent class multinomial logit analysis of factors associated with pedestrian injury severity of inter-urban highway crashes","authors":"W. Agyemang, E. Adanu, Jun Liu, Steven Jones","doi":"10.1080/19439962.2022.2153952","DOIUrl":"https://doi.org/10.1080/19439962.2022.2153952","url":null,"abstract":"Abstract Over the years, the uncontrolled interaction of human and high-speed vehicular activities at settlement areas along highways in Ghana has resulted in many pedestrian fatalities and injuries. This phenomenon has been attributed to the land-use and right-of-way planning and lack of pedestrian crossing facilities for safe crossing of highways. The slow response to developing strategies to reduce pedestrian fatalities along the nation’s highways has led to many public protests. To advance a data-driven and evidence-based approach to finding appropriate countermeasures, this study investigated the factors associated with pedestrian injury outcomes of inter-urban highway crashes in Ghana. Latent class multinomial logit modeling method was employed to account for unobserved heterogeneity in a five-year pedestrian-vehicle crash data recorded on highways in Ghana. The model estimation results show that speeding, hit and run and crashes that involve buses were more likely to result in fatal injury while crashes that occurred at highway sections with no shoulder were more likely to result in hospitalized injury. The findings of the study provide basis for the development of appropriate countermeasures to reduce the number of pedestrian deaths and injuries on high-speed inter-urban highways in Ghana and other countries with similar characteristics in the sub-region.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"16 1","pages":"1008 - 1028"},"PeriodicalIF":2.6,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81876051","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 : 2022-12-01DOI: 10.1080/19439962.2022.2147613
Bosong Fan, C. Shao, Yutong Liu, Juan Li
Abstract Urban rail transit emergencies in China’s large cities are frequent occurrences but currently, operation managers lack effective analysis tools that can help in reducing them. In this study we present a knowledge graph tool, developed using historical emergency text information from Beijing’s urban rail transit fault logs from which an information model is developed enabling key information to be mined and subsequently analyzed so that interrelationships within the text can be determined. The knowledge graph tool assists urban rail transit operation managers to analyze more effectively, through knowledge query and semantic search, the relations and attributes of emergencies enabling more insight into their root causes. Compared with traditional first and second order text parsing algorithms, the extended high order parsing algorithm proposed in this paper has better performance in the extraction of both phrases and inter-phrase relations, with an extraction accuracy of more than 85%. Furthermore, compared with traditional failure mode effect analysis methods, the extended method proposed in this paper can also calculate phrase attributes and therefore provide a reference for quantitative risk calculations.
{"title":"A new approach in developing an urban rail transit emergency knowledge graph based on operation fault logs","authors":"Bosong Fan, C. Shao, Yutong Liu, Juan Li","doi":"10.1080/19439962.2022.2147613","DOIUrl":"https://doi.org/10.1080/19439962.2022.2147613","url":null,"abstract":"Abstract Urban rail transit emergencies in China’s large cities are frequent occurrences but currently, operation managers lack effective analysis tools that can help in reducing them. In this study we present a knowledge graph tool, developed using historical emergency text information from Beijing’s urban rail transit fault logs from which an information model is developed enabling key information to be mined and subsequently analyzed so that interrelationships within the text can be determined. The knowledge graph tool assists urban rail transit operation managers to analyze more effectively, through knowledge query and semantic search, the relations and attributes of emergencies enabling more insight into their root causes. Compared with traditional first and second order text parsing algorithms, the extended high order parsing algorithm proposed in this paper has better performance in the extraction of both phrases and inter-phrase relations, with an extraction accuracy of more than 85%. Furthermore, compared with traditional failure mode effect analysis methods, the extended method proposed in this paper can also calculate phrase attributes and therefore provide a reference for quantitative risk calculations.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"49 1","pages":"1057 - 1085"},"PeriodicalIF":2.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75880240","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 : 2022-11-29DOI: 10.1080/19439962.2022.2147612
Qingwen Xue, Ke Wang, J. Lu, Yingying Xing, Xin Gu, Meng Zhang
Abstract Lane change (LC) behavior has critical effects on traffic flows and safety due to its complex interactions with surrounding vehicles. To ensure safe lane changes and prevent potential crashes, it is important to recognize the potential crash risk of lane change in real time. This study proposes an improved risk estimation (IRE) model to evaluate the potential collision risk of lane change (LCR) vehicle groups. The safety margin is introduced to consider the deceleration capability of vehicles to measure the reaction time of drivers during the LC. Then the IRE model is established, incorporating the collision probability and collision severity measured based on the safety margin. The trajectory data, extracted from the highD dataset, are used and 1536 LC samples are investigated. We compare the LCR under different contextual factors, including vehicle types (cars and trucks), two lane change directions (left and right lane change, LLC and RLC), and traffic flows (low and high traffic). It was found that truck drivers keep higher LCR compared with car drivers due to limited brake capacity, and the left lane change results in higher LCR compared with the right lane change. Additionally, lane change is associated with higher crash risk in high traffic flow, as compared to low traffic flow. The understanding of the crash risk of lane change behavior under different contextual factors, can be useful for real-time crash prediction and devising traffic management strategies.
{"title":"An improved risk estimation model of lane change using naturalistic vehicle trajectories","authors":"Qingwen Xue, Ke Wang, J. Lu, Yingying Xing, Xin Gu, Meng Zhang","doi":"10.1080/19439962.2022.2147612","DOIUrl":"https://doi.org/10.1080/19439962.2022.2147612","url":null,"abstract":"Abstract Lane change (LC) behavior has critical effects on traffic flows and safety due to its complex interactions with surrounding vehicles. To ensure safe lane changes and prevent potential crashes, it is important to recognize the potential crash risk of lane change in real time. This study proposes an improved risk estimation (IRE) model to evaluate the potential collision risk of lane change (LCR) vehicle groups. The safety margin is introduced to consider the deceleration capability of vehicles to measure the reaction time of drivers during the LC. Then the IRE model is established, incorporating the collision probability and collision severity measured based on the safety margin. The trajectory data, extracted from the highD dataset, are used and 1536 LC samples are investigated. We compare the LCR under different contextual factors, including vehicle types (cars and trucks), two lane change directions (left and right lane change, LLC and RLC), and traffic flows (low and high traffic). It was found that truck drivers keep higher LCR compared with car drivers due to limited brake capacity, and the left lane change results in higher LCR compared with the right lane change. Additionally, lane change is associated with higher crash risk in high traffic flow, as compared to low traffic flow. The understanding of the crash risk of lane change behavior under different contextual factors, can be useful for real-time crash prediction and devising traffic management strategies.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"14 1","pages":"963 - 986"},"PeriodicalIF":2.6,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87768318","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 : 2022-11-24DOI: 10.1080/19439962.2022.2147614
Subasish Das, Zihang Wei, Anandi Dutta
Abstract The hybrid electric vehicle (HEV) is a critical transportation disruptive technology that is expected to be widely adopted in the current and future marketplace. Many nations are promoting the success of HEVs. As the technologies and designs of these vehicles are significantly different from conventional vehicles, it is also important to understand the technical and body-related issues associated with these vehicles. This study used the National Highway Traffic Safety Administration’s vehicle owner’s complaint database to explore the potential issues associated with HEVs. The acquired dataset was divided into two groups based on their involvement in traffic crashes. The study applied association rule mining and text mining methods to analyze vehicle consumer complaint data. The results of association rule mining showed a significant association between hybrid electric all-wheel-drive vehicles manufactured between 2010 and 2021 that do not have anti-lock brakes and cruise control in the crash-related vehicle complaints dataset. Non-HEV vehicles, manufactured between 1992 and 1999, with cruise control and anti-braking systems as well as 5-10 cylinders, appeared frequently in the crash-related complaint dataset. Mileage-related issues and comparatively older HEVs (2000-2009) are dominant in non-crash-related data. The results from the text mining method show that brakes, mileage, failure, and crash are key features for consumer complaints related to crashes and brakes, battery, power, and recall are the key features for consumer complaints not related to crashes. The sentiment analysis results show slightly higher negative sentiments in complaint reports associated with crashes. The findings of this study can provide some insights into this unexplored research area.
{"title":"Rules mining on hybrid electric vehicle consumer complaint database","authors":"Subasish Das, Zihang Wei, Anandi Dutta","doi":"10.1080/19439962.2022.2147614","DOIUrl":"https://doi.org/10.1080/19439962.2022.2147614","url":null,"abstract":"Abstract The hybrid electric vehicle (HEV) is a critical transportation disruptive technology that is expected to be widely adopted in the current and future marketplace. Many nations are promoting the success of HEVs. As the technologies and designs of these vehicles are significantly different from conventional vehicles, it is also important to understand the technical and body-related issues associated with these vehicles. This study used the National Highway Traffic Safety Administration’s vehicle owner’s complaint database to explore the potential issues associated with HEVs. The acquired dataset was divided into two groups based on their involvement in traffic crashes. The study applied association rule mining and text mining methods to analyze vehicle consumer complaint data. The results of association rule mining showed a significant association between hybrid electric all-wheel-drive vehicles manufactured between 2010 and 2021 that do not have anti-lock brakes and cruise control in the crash-related vehicle complaints dataset. Non-HEV vehicles, manufactured between 1992 and 1999, with cruise control and anti-braking systems as well as 5-10 cylinders, appeared frequently in the crash-related complaint dataset. Mileage-related issues and comparatively older HEVs (2000-2009) are dominant in non-crash-related data. The results from the text mining method show that brakes, mileage, failure, and crash are key features for consumer complaints related to crashes and brakes, battery, power, and recall are the key features for consumer complaints not related to crashes. The sentiment analysis results show slightly higher negative sentiments in complaint reports associated with crashes. The findings of this study can provide some insights into this unexplored research area.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"41 1","pages":"987 - 1007"},"PeriodicalIF":2.6,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90730895","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 : 2022-11-22DOI: 10.1080/19439962.2022.2147611
yicheng Lu, Kai Cheng, Yue Zhang, Xinqiang Chen, Y. Zou
Abstract The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Cars and trucks have different sizes and operation characteristics, but very few traffic conflict analysis studies considered different vehicle types at freeway merging sections. Thus, the main objective of this study is to analyze lane-changing conflicts between different vehicle types at freeway merging sections. Vehicle trajectories are extracted from the Unmanned Aerial Vehicle (UAV) video data which are collected in Shanghai, China. Time-to-collision (TTC) is utilized as the surrogate safety measure (SSM) to analyze lane-changing conflicts. Results show that TTC values of car-car conflicts are the smallest, while truck-truck conflicts have the largest TTC values. Although traffic conflicts frequently occur at the on-ramp and additional rightmost lane, the spatial distribution of lane-changing conflicts is significantly different between different vehicle types. The findings of this study are useful for transportation management agencies to design proper strategies to improve traffic safety at freeway merging sections.
{"title":"Analysis of lane-changing conflict between cars and trucks at freeway merging sections using UAV video data","authors":"yicheng Lu, Kai Cheng, Yue Zhang, Xinqiang Chen, Y. Zou","doi":"10.1080/19439962.2022.2147611","DOIUrl":"https://doi.org/10.1080/19439962.2022.2147611","url":null,"abstract":"Abstract The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Cars and trucks have different sizes and operation characteristics, but very few traffic conflict analysis studies considered different vehicle types at freeway merging sections. Thus, the main objective of this study is to analyze lane-changing conflicts between different vehicle types at freeway merging sections. Vehicle trajectories are extracted from the Unmanned Aerial Vehicle (UAV) video data which are collected in Shanghai, China. Time-to-collision (TTC) is utilized as the surrogate safety measure (SSM) to analyze lane-changing conflicts. Results show that TTC values of car-car conflicts are the smallest, while truck-truck conflicts have the largest TTC values. Although traffic conflicts frequently occur at the on-ramp and additional rightmost lane, the spatial distribution of lane-changing conflicts is significantly different between different vehicle types. The findings of this study are useful for transportation management agencies to design proper strategies to improve traffic safety at freeway merging sections.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"7 1","pages":"943 - 961"},"PeriodicalIF":2.6,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83044013","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 : 2022-11-11DOI: 10.1080/19439962.2022.2137869
S. Wang, Jingfeng Ma, Hongliang Ding, Yuhuan Lu
Abstract Despite the benefits of cycling being widely accepted, bicycle safety—especially severe injury—has received increasing attention due to the vulnerability of bicyclists on the road. Factors contributing to varying bicycle injury severity have been identified in the literature. For the zonal factors, variables related to sociodemographic and household characteristics, built environments, land use, and traffic conditions are considered. However, it is rare that the heterogeneity and hierarchal features of bicycle injury severity are simultaneously considered. This study contributes to the literature by investigating the heterogeneous effects of zonal factors on varying bicycle injury severity, using a 3-year crash data set from the Lower Layer Super Output Areas of London. A combination of latent class clustering and partial proportional odds methods was developed. First, five subgroups of bicycle crashes were identified based on the latent class clustering method. Afterward, partial proportional models were developed separately for different clusters. Results indicate that a series of factors is found to be associated with the occurrence of severe bicycle injuries. However, effects of these factors could be distinctive among different clusters. For example, some factors only have significant impacts in the specific crash clusters. Furthermore, heterogeneous effects of the same factors in one or different clusters are discovered. The findings of this study can be helpful for the development of cycle infrastructures, traffic management, and safety education that can enhance the risk perception of bicyclists and reduce the occurrence of severe bicycle injuries.
{"title":"Exploring the heterogeneous effects of zonal factors on bicycle injury severity: latent class clustering analysis and partial proportional odds models","authors":"S. Wang, Jingfeng Ma, Hongliang Ding, Yuhuan Lu","doi":"10.1080/19439962.2022.2137869","DOIUrl":"https://doi.org/10.1080/19439962.2022.2137869","url":null,"abstract":"Abstract Despite the benefits of cycling being widely accepted, bicycle safety—especially severe injury—has received increasing attention due to the vulnerability of bicyclists on the road. Factors contributing to varying bicycle injury severity have been identified in the literature. For the zonal factors, variables related to sociodemographic and household characteristics, built environments, land use, and traffic conditions are considered. However, it is rare that the heterogeneity and hierarchal features of bicycle injury severity are simultaneously considered. This study contributes to the literature by investigating the heterogeneous effects of zonal factors on varying bicycle injury severity, using a 3-year crash data set from the Lower Layer Super Output Areas of London. A combination of latent class clustering and partial proportional odds methods was developed. First, five subgroups of bicycle crashes were identified based on the latent class clustering method. Afterward, partial proportional models were developed separately for different clusters. Results indicate that a series of factors is found to be associated with the occurrence of severe bicycle injuries. However, effects of these factors could be distinctive among different clusters. For example, some factors only have significant impacts in the specific crash clusters. Furthermore, heterogeneous effects of the same factors in one or different clusters are discovered. The findings of this study can be helpful for the development of cycle infrastructures, traffic management, and safety education that can enhance the risk perception of bicyclists and reduce the occurrence of severe bicycle injuries.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"77 1","pages":"918 - 942"},"PeriodicalIF":2.6,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88551557","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 : 2022-11-03DOI: 10.1080/19439962.2022.2137867
Jingyuan Shi, Muhammad Hussain, Dandan Peng
Abstract This study aims at analyzing the factors of aberrant driving behaviors and road accidents among Chinese ride-hailing drivers. Four hundred and twenty ride-hailing drivers (Male = 65%) completed a web-based questionnaire survey that assessed personal attributes, work-condition factors, aberrant driving behaviors, and self-reported road accidents in the last three years. A 10-item violations Driver Behavior Questionnaire (DBQ) scale was adopted to explore the aberrant driving behaviors of ride-hailing drivers. The ordinal regression model was used to examine the effects of personal attributes and work-condition factors on aberrant driving behaviors. A binary logistic regression model was employed to investigate the predictors of road accidents. The descriptive statistics indicate that among ride-hailing drivers, the traditional taxi drivers were found to be more involved in aberrant driving behaviors than private car drivers. The results from the Principal Component Analysis (PCA) reveal that ride-hailing drivers were involved in "risky violations." Male and young ride-hailing drivers were found to be more involved in risky violations than their counterparts. Furthermore, it is revealed that a one-unit increase in risky violations increased the probability of being involved in road accidents by 60%. Furthermore, a one-unit increase in work-condition factors increased the likelihood of being involved in road accidents by 41%. The findings in this study can help better understand the aberrant driving behaviors of ride-hailing drivers and contribute to a more effective policy for reducing the road accidents caused by ride-hailing drivers.
{"title":"A study of aberrant driving behaviors and road accidents in Chinese ride-hailing drivers","authors":"Jingyuan Shi, Muhammad Hussain, Dandan Peng","doi":"10.1080/19439962.2022.2137867","DOIUrl":"https://doi.org/10.1080/19439962.2022.2137867","url":null,"abstract":"Abstract This study aims at analyzing the factors of aberrant driving behaviors and road accidents among Chinese ride-hailing drivers. Four hundred and twenty ride-hailing drivers (Male = 65%) completed a web-based questionnaire survey that assessed personal attributes, work-condition factors, aberrant driving behaviors, and self-reported road accidents in the last three years. A 10-item violations Driver Behavior Questionnaire (DBQ) scale was adopted to explore the aberrant driving behaviors of ride-hailing drivers. The ordinal regression model was used to examine the effects of personal attributes and work-condition factors on aberrant driving behaviors. A binary logistic regression model was employed to investigate the predictors of road accidents. The descriptive statistics indicate that among ride-hailing drivers, the traditional taxi drivers were found to be more involved in aberrant driving behaviors than private car drivers. The results from the Principal Component Analysis (PCA) reveal that ride-hailing drivers were involved in \"risky violations.\" Male and young ride-hailing drivers were found to be more involved in risky violations than their counterparts. Furthermore, it is revealed that a one-unit increase in risky violations increased the probability of being involved in road accidents by 60%. Furthermore, a one-unit increase in work-condition factors increased the likelihood of being involved in road accidents by 41%. The findings in this study can help better understand the aberrant driving behaviors of ride-hailing drivers and contribute to a more effective policy for reducing the road accidents caused by ride-hailing drivers.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"72 1","pages":"877 - 894"},"PeriodicalIF":2.6,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72933281","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 : 2022-11-02DOI: 10.1080/19439962.2022.2137868
Xiaolong Zhang, Jianling Huang, Yang Bian, Xiaohua Zhao, Tangshan Han
Abstract With the rise of the transportation mode of shared electric bikes (shared e-bikes) in China, shared e-bike related accidents have gradually increased. To facilitate the design of safety policies, it is important to understand the factors that influence shared e-bike riders’ traffic accidents to facilitate intervention strategies. For this purpose, the structural equation model (SEM) with mediation analysis was applied by incorporating seven latent factors: traffic accidents, traffic violation behaviors, attitude toward safety responsibility, and attitude toward rule violations, risk perception, perceptive-motor skills, and safety skills. A questionnaire survey of a sample of 406 shared e-bike riders in China was conducted to obtain self-reported survey data. The results reveal that traffic violation behaviors and attitude toward safety responsibility had a statistically significant consequence on traffic accidents. Attitude toward rule violations, perceptive-motor skills, and safety skills can predict shared e-bike riders’ traffic accidents when the traffic violation behaviors are used as a mediator. Moreover, risk perception could also be used to predict shared e-bike riders’ traffic accidents when using attitudes toward safety responsibility or rule violations and traffic violation behaviors as a mediator. This paper lays a foundation for policymakers and traffic managers to develop effective intervention strategies and improve shared e-bike safety.
{"title":"Shared e-bike riders’ psychology contribution to self-reported traffic accidents: a structural equation model approach with mediation analysis","authors":"Xiaolong Zhang, Jianling Huang, Yang Bian, Xiaohua Zhao, Tangshan Han","doi":"10.1080/19439962.2022.2137868","DOIUrl":"https://doi.org/10.1080/19439962.2022.2137868","url":null,"abstract":"Abstract With the rise of the transportation mode of shared electric bikes (shared e-bikes) in China, shared e-bike related accidents have gradually increased. To facilitate the design of safety policies, it is important to understand the factors that influence shared e-bike riders’ traffic accidents to facilitate intervention strategies. For this purpose, the structural equation model (SEM) with mediation analysis was applied by incorporating seven latent factors: traffic accidents, traffic violation behaviors, attitude toward safety responsibility, and attitude toward rule violations, risk perception, perceptive-motor skills, and safety skills. A questionnaire survey of a sample of 406 shared e-bike riders in China was conducted to obtain self-reported survey data. The results reveal that traffic violation behaviors and attitude toward safety responsibility had a statistically significant consequence on traffic accidents. Attitude toward rule violations, perceptive-motor skills, and safety skills can predict shared e-bike riders’ traffic accidents when the traffic violation behaviors are used as a mediator. Moreover, risk perception could also be used to predict shared e-bike riders’ traffic accidents when using attitudes toward safety responsibility or rule violations and traffic violation behaviors as a mediator. This paper lays a foundation for policymakers and traffic managers to develop effective intervention strategies and improve shared e-bike safety.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"184 1","pages":"895 - 917"},"PeriodicalIF":2.6,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80491792","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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