Pub Date : 2025-05-28DOI: 10.1080/19427867.2024.2399422
Tianpei Tang , Jun Chen , Yuntao Guo , Dian Sheng , Xinghua Li , Panagiotis Ch. Anastasopoulos
Transitioning to a fully Level-5 autonomous vehicle (AV) environment presents numerous challenges, notably influenced by public adoption intention. Previous studies have shown limitations in scope, population, and methodology. This study expands the Technology Acceptance Model to investigate AVs adoption intention across various city sizes in China. Through surveys in China, 2,662 responses were gathered in 2021 from mega, large, and small-to-medium cities. Using Multiple Indicators and Multiple Causes models, the study examines influencing factors on AVs adoption intention and population heterogeneities. Key findings emphasize the importance of adoption attitude, information provision, and perceived AV usefulness. Additionally, the impact of financial incentives, convenience, and several other factors varies across city sizes. The insights gained from the study can be utilized to develop more cost-effective policies and strategies tailored to different subgroups of the population to fully utilize the potential benefits of AVs while minimizing unintended consequences across diverse urban settings.
{"title":"Intention to adopt autonomous vehicles in China: a comparative study among residents in different-sized cities","authors":"Tianpei Tang , Jun Chen , Yuntao Guo , Dian Sheng , Xinghua Li , Panagiotis Ch. Anastasopoulos","doi":"10.1080/19427867.2024.2399422","DOIUrl":"10.1080/19427867.2024.2399422","url":null,"abstract":"<div><div>Transitioning to a fully Level-5 autonomous vehicle (AV) environment presents numerous challenges, notably influenced by public adoption intention. Previous studies have shown limitations in scope, population, and methodology. This study expands the Technology Acceptance Model to investigate AVs adoption intention across various city sizes in China. Through surveys in China, 2,662 responses were gathered in 2021 from mega, large, and small-to-medium cities. Using Multiple Indicators and Multiple Causes models, the study examines influencing factors on AVs adoption intention and population heterogeneities. Key findings emphasize the importance of adoption attitude, information provision, and perceived AV usefulness. Additionally, the impact of financial incentives, convenience, and several other factors varies across city sizes. The insights gained from the study can be utilized to develop more cost-effective policies and strategies tailored to different subgroups of the population to fully utilize the potential benefits of AVs while minimizing unintended consequences across diverse urban settings.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 910-929"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110016","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}
Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.
{"title":"A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system","authors":"Dauha Elamrani Abou Elassad , Zouhair Elamrani Abou Elassad , Abdel Majid Ed-Dahbi , Othmane El Meslouhi , Mustapha Kardouchi , Moulay Akhloufi , Nusrat Jahan","doi":"10.1080/19427867.2024.2392063","DOIUrl":"10.1080/19427867.2024.2392063","url":null,"abstract":"<div><div>Road accidents are an inevitable aspect of daily life, and predicting crashes is crucial for minimizing disruptions and advancing intelligent transportation technologies. This study aims to design an ensemble fusion decision system using various base classifiers and a meta-classifier to improve crash prediction efficiency within the driver-vehicle-environment system. We adopted a data-driven strategy to analyze four categories of features—driver demographics, vehicle telemetry, driver inputs, and environmental conditions—collected from a driving simulator. Optimized modeling strategies using AdaBoost, XGBoost, GBM, LightGBM, and CatBoost were implemented. Moreover, statistical logit models were also used to assess the likelihood of crashes and the correlations among key variables. Furthermore, three resampling strategies, SMOTE-TL, SMOTE-ENN, and ADASYN, were employed to address class imbalance. The best performance was achieved with GBM, XGBoost, and AdaBoost as base classifiers, SMOTE-TL for balancing, and CatBoost as the meta-classifier, with 89.78% precision, 95.69% recall, and 92.64% F1-score.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 827-843"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220915","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 : 2025-05-28DOI: 10.1080/19427867.2024.2391662
Dongsheng Gao , Xiaoqiang Zhang , Yun Yang
This paper presents a systematic investigation of the train choice preference heterogeneity of High-Speed Railway (HSR) passengers under market segmentation to understand their train choice comprehensively. A stated preference survey was conducted for the Nanning-Guangzhou Railway and Nanning-Beihai Railway. Latent Class Analysis (LCA) was employed to identify homogeneous subgroups and segment the passenger market of each line into three segments: private travelers with a long total duration (PTLTD), business travelers (BT), and private travelers with a short total duration (PTSTD). Mixed Logit (ML) models were constructed for each subgroup sample to assess passengers' preferences in train choice. The results show that each class exhibited unique characteristics and preferences, and train fare and running time, departure date and time, and train frequency were statistically significant factors affecting train choice. This study can furnish theoretical and decisional support for HSR operators to design train operating schemes and flexible fare systems.
本文对细分市场下高铁乘客的列车选择偏好异质性进行了系统研究，以了解他们的列车选择偏好。
{"title":"An empirical study on train choice preferences of high-speed railway passengers: the case of Nanning-Guangzhou railway and Nanning-Beihai railway","authors":"Dongsheng Gao , Xiaoqiang Zhang , Yun Yang","doi":"10.1080/19427867.2024.2391662","DOIUrl":"10.1080/19427867.2024.2391662","url":null,"abstract":"<div><div>This paper presents a systematic investigation of the train choice preference heterogeneity of High-Speed Railway (HSR) passengers under market segmentation to understand their train choice comprehensively. A stated preference survey was conducted for the Nanning-Guangzhou Railway and Nanning-Beihai Railway. Latent Class Analysis (LCA) was employed to identify homogeneous subgroups and segment the passenger market of each line into three segments: private travelers with a long total duration (PTLTD), business travelers (BT), and private travelers with a short total duration (PTSTD). Mixed Logit (ML) models were constructed for each subgroup sample to assess passengers' preferences in train choice. The results show that each class exhibited unique characteristics and preferences, and train fare and running time, departure date and time, and train frequency were statistically significant factors affecting train choice. This study can furnish theoretical and decisional support for HSR operators to design train operating schemes and flexible fare systems.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 805-815"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220916","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 : 2025-05-28DOI: 10.1080/19427867.2024.2398336
Yulong Pei , Yuhang Wen , Sheng Pan
Accurately predicting traffic accident severity is crucial for road safety. However, existing studies lack interpretability in revealing the relationship between accident severity and key factors. To address this issue, we propose a new interpretable analytical framework. The framework utilizes XGBoost and SHAP to select effective factors. Then the AISTGCN model is constructed by improving the STGCN through the local attention mechanism to predict the severity of the accident. Finally, DeepLIFT is used to interpret the forecasts and identify key factors. Our experiments using real-world UK accident data demonstrate that our proposed AISTGCN outperforms baseline models in outcome prediction with an accuracy of 0.8772. The computation time was reduced and the reliability of predictions was enhanced through screening for effective factors. Furthermore, DeepLIFT provides more reasonable explanations when explaining accidents of different severity, indicating that vehicle count significantly impacts. Our framework aids in developing effective safety measures to reduce accidents.
{"title":"Traffic accident severity prediction based on interpretable deep learning model","authors":"Yulong Pei , Yuhang Wen , Sheng Pan","doi":"10.1080/19427867.2024.2398336","DOIUrl":"10.1080/19427867.2024.2398336","url":null,"abstract":"<div><div>Accurately predicting traffic accident severity is crucial for road safety. However, existing studies lack interpretability in revealing the relationship between accident severity and key factors. To address this issue, we propose a new interpretable analytical framework. The framework utilizes XGBoost and SHAP to select effective factors. Then the AISTGCN model is constructed by improving the STGCN through the local attention mechanism to predict the severity of the accident. Finally, DeepLIFT is used to interpret the forecasts and identify key factors. Our experiments using real-world UK accident data demonstrate that our proposed AISTGCN outperforms baseline models in outcome prediction with an accuracy of 0.8772. The computation time was reduced and the reliability of predictions was enhanced through screening for effective factors. Furthermore, DeepLIFT provides more reasonable explanations when explaining accidents of different severity, indicating that vehicle count significantly impacts. Our framework aids in developing effective safety measures to reduce accidents.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 895-909"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220874","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 : 2025-05-28DOI: 10.1080/19427867.2024.2391168
Lei Gong , Pengfei Han , Tian Lei , Baicheng Li , Qin Luo , Cheng Zhu
Transfer behavior is a critical factor influencing the travel efficiency of public transportation passengers. To address the potential group heterogeneity, the present work developed an integrated Classification and Regression Tree-Multiple-Cox Proportional Hazards (CART-Multi-Cox) model for transfer behavior analysis using smart card data in Shenzhen, China. Specifically, passengers are first grouped into different types based on transfer behavior features, and the influence of various independent variables on the transfer duration of different passenger groups is then examined. The results reveal that the proposed CART-Multi-Cox model is able to account for the heterogeneity effect and provides a deeper understanding about passengers’ transfer behavior and its underlying influencing mechanism. The findings offer valuable references for refined transfer behavior management and help enhancing the competitiveness of public transportation.
{"title":"Analyzing the transfer duration of public transport passengers using classification and regression tree-multiple-Cox proportional hazards (CART-Multi-Cox) model","authors":"Lei Gong , Pengfei Han , Tian Lei , Baicheng Li , Qin Luo , Cheng Zhu","doi":"10.1080/19427867.2024.2391168","DOIUrl":"10.1080/19427867.2024.2391168","url":null,"abstract":"<div><div>Transfer behavior is a critical factor influencing the travel efficiency of public transportation passengers. To address the potential group heterogeneity, the present work developed an integrated Classification and Regression Tree-Multiple-Cox Proportional Hazards (CART-Multi-Cox) model for transfer behavior analysis using smart card data in Shenzhen, China. Specifically, passengers are first grouped into different types based on transfer behavior features, and the influence of various independent variables on the transfer duration of different passenger groups is then examined. The results reveal that the proposed CART-Multi-Cox model is able to account for the heterogeneity effect and provides a deeper understanding about passengers’ transfer behavior and its underlying influencing mechanism. The findings offer valuable references for refined transfer behavior management and help enhancing the competitiveness of public transportation.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 789-804"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220877","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 : 2025-04-21DOI: 10.1080/19427867.2024.2379702
Hongrui Zhang , Yonggang Wang , Shengrui Zhang , Jingtao Li , Qushun Wang , Bei Zhou
To improve the accuracy of lane-change intention prediction and analyze the influence of driving styles on prediction outcomes, the T-Encoder-Sequence model is proposed in this paper. It integrates the Transformer’s encoder module with various recurrent neural network (RNN) models and introduces a multimodal fusion input structure. Building on this, a risk indicator model, capable of reflecting driver stress, is established to calculate the model’s input parameters. Consequently, the model can simultaneously capture global information and consider the impact of vehicle classes on drivers. Furthermore, the k-means++ algorithm is employed to categorize vehicle trajectories into conservative, conventional, and aggressive types for further analysis. The results demonstrate that training the model with risk indicator parameters markedly enhances prediction performance. Under identical input parameters, the T-Encoder-Sequence model exhibits notably superior prediction efficacy compared to the original model. The T-Encoder-Sequence model, trained with risk indicator parameters, demonstrates substantial advantages compared to other studies.
{"title":"Improved time series models for the prediction of lane-change intention","authors":"Hongrui Zhang , Yonggang Wang , Shengrui Zhang , Jingtao Li , Qushun Wang , Bei Zhou","doi":"10.1080/19427867.2024.2379702","DOIUrl":"10.1080/19427867.2024.2379702","url":null,"abstract":"<div><div>To improve the accuracy of lane-change intention prediction and analyze the influence of driving styles on prediction outcomes, the T-Encoder-Sequence model is proposed in this paper. It integrates the Transformer’s encoder module with various recurrent neural network (RNN) models and introduces a multimodal fusion input structure. Building on this, a risk indicator model, capable of reflecting driver stress, is established to calculate the model’s input parameters. Consequently, the model can simultaneously capture global information and consider the impact of vehicle classes on drivers. Furthermore, the k-means++ algorithm is employed to categorize vehicle trajectories into conservative, conventional, and aggressive types for further analysis. The results demonstrate that training the model with risk indicator parameters markedly enhances prediction performance. Under identical input parameters, the T-Encoder-Sequence model exhibits notably superior prediction efficacy compared to the original model. The T-Encoder-Sequence model, trained with risk indicator parameters, demonstrates substantial advantages compared to other studies.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 4","pages":"Pages 747-761"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141815149","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}
To study the time-of-day variations and temporal stabilities of factors influencing single-vehicle crashes on rural highways, random parameters logit models with heterogeneity in means and variances under different time periods of the day and from year to year were estimated to identify significant factors. The potential crash-influencing factors in drivers, vehicles, roads, and the environment were analyzed to dissect the correlation and variability between the influencing factors and crash injury severity. Likelihood ratio tests were conducted to assess the transferability of model estimation results from different times of the day and from year to year. The results showed that the effect of factors that determine injury severity varied significantly across time-of-day/time-period combinations. Overall temporal instability was observed in the study. However, several explanatory variables showed temporally stable effects in terms of their impact on resulting injury severities. Such as male, driver age (<30), truck, non-dry road surface, and visibility (100–200 m).
{"title":"Temporal instability of factors affecting injury severity in single-vehicle crashes on rural highways","authors":"Yaping Wang , Fulu Wei , Yanyong Guo , Yongqing Guo","doi":"10.1080/19427867.2024.2366731","DOIUrl":"10.1080/19427867.2024.2366731","url":null,"abstract":"<div><div>To study the time-of-day variations and temporal stabilities of factors influencing single-vehicle crashes on rural highways, random parameters logit models with heterogeneity in means and variances under different time periods of the day and from year to year were estimated to identify significant factors. The potential crash-influencing factors in drivers, vehicles, roads, and the environment were analyzed to dissect the correlation and variability between the influencing factors and crash injury severity. Likelihood ratio tests were conducted to assess the transferability of model estimation results from different times of the day and from year to year. The results showed that the effect of factors that determine injury severity varied significantly across time-of-day/time-period combinations. Overall temporal instability was observed in the study. However, several explanatory variables showed temporally stable effects in terms of their impact on resulting injury severities. Such as male, driver age (<30), truck, non-dry road surface, and visibility (100–200 m).</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 4","pages":"Pages 578-594"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505414","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 : 2025-04-21DOI: 10.1080/19427867.2024.2375473
Nidhi Kathait , Amit Agarwal
Bicycle-sharing services have received worldwide attention as a sustainable form of transportation. This study explores a priori acceptance of public bicycle-sharing system (PBSS) in India, which is still in the early phases of adopting PBSS. The effects of psychological factors, such as perceived usefulness (PU), perceived ease of use (PEoU), perceived fun (PF), health value (HV), and environment values (EV), on intention to use PBSS are studied, utilizing an extended technology acceptance model. 747 samples were collected from online questionnaires in Dehradun, India. Results of Structural Equation Modelling revealed that intention to use PBSS is strongly predicted by PU and PF together, while PF mediates the influence of PEoU. Furthermore, EV and HV generate positive behavior intention to use PBSS through PU, PF, and PEoU. The study suggests promoting PBSS as a green and active transportation mode offering high PU and enjoyment, with theoretical and practical implications discussed.
{"title":"User intention to adopt public bicycle sharing system: a priori acceptance approach","authors":"Nidhi Kathait , Amit Agarwal","doi":"10.1080/19427867.2024.2375473","DOIUrl":"10.1080/19427867.2024.2375473","url":null,"abstract":"<div><div>Bicycle-sharing services have received worldwide attention as a sustainable form of transportation. This study explores a priori acceptance of public bicycle-sharing system (PBSS) in India, which is still in the early phases of adopting PBSS. The effects of psychological factors, such as perceived usefulness (PU), perceived ease of use (PEoU), perceived fun (PF), health value (HV), and environment values (EV), on intention to use PBSS are studied, utilizing an extended technology acceptance model. 747 samples were collected from online questionnaires in Dehradun, India. Results of Structural Equation Modelling revealed that intention to use PBSS is strongly predicted by PU and PF together, while PF mediates the influence of PEoU. Furthermore, EV and HV generate positive behavior intention to use PBSS through PU, PF, and PEoU. The study suggests promoting PBSS as a green and active transportation mode offering high PU and enjoyment, with theoretical and practical implications discussed.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 4","pages":"Pages 687-701"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141615060","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 : 2025-04-21DOI: 10.1080/19427867.2024.2366730
Emmanuel Kofi Adanu , Reuben Tamakloe , Richard Dzinyela , William Agyemang
Hit-and-run crashes often have severe consequences for vulnerable road users. In light of governmental efforts to promote pedestrian-friendly urban environments, the significance of these crashes cannot be overstated. In this study, we assessed the factors that influence the injury severity of pedestrians involved in hit-and-run crashes in Ghana. Historical crash data (1469 observations) spanning from 2013 to 2020 was used in this study. An injury-severity model was developed using random parameters logit approach to assess what crash factors significantly affect the injury outcome of the crashes. It was observed that hit-and-run crashes that occur on dark and unlit roadways were more likely to result in fatal injuries. Also, female pedestrians were less likely to be killed in hit-and-run crashes. These findings provide the basis for developing and implementing appropriate countermeasures, such as punitive laws for drivers who leave the crash scene and protective laws for those who help their victims.
{"title":"Assessing the factors associated with pedestrian injury severity in hit-and-run crashes in Ghana","authors":"Emmanuel Kofi Adanu , Reuben Tamakloe , Richard Dzinyela , William Agyemang","doi":"10.1080/19427867.2024.2366730","DOIUrl":"10.1080/19427867.2024.2366730","url":null,"abstract":"<div><div>Hit-and-run crashes often have severe consequences for vulnerable road users. In light of governmental efforts to promote pedestrian-friendly urban environments, the significance of these crashes cannot be overstated. In this study, we assessed the factors that influence the injury severity of pedestrians involved in hit-and-run crashes in Ghana. Historical crash data (1469 observations) spanning from 2013 to 2020 was used in this study. An injury-severity model was developed using random parameters logit approach to assess what crash factors significantly affect the injury outcome of the crashes. It was observed that hit-and-run crashes that occur on dark and unlit roadways were more likely to result in fatal injuries. Also, female pedestrians were less likely to be killed in hit-and-run crashes. These findings provide the basis for developing and implementing appropriate countermeasures, such as punitive laws for drivers who leave the crash scene and protective laws for those who help their victims.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 4","pages":"Pages 567-577"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141529185","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 : 2025-04-21DOI: 10.1080/19427867.2024.2369827
Zhipeng Peng , Hengyan Pan , Renteng Yuan , Yonggang Wang
Ride-hailing is increasingly important in urban public transportation, yet research on its traffic safety remains limited. This study examined risk factors for crash severity among ride-hailing drivers, including demographics, financial burden, phone usage, fatigue, and risky driving behaviors. Data were collected from 2,182 drivers via a self-reported survey. Recognizing potential differences between full-time and part-time drivers, the data were divided accordingly, and two Bayesian network models were generated. The results indicated that both types of drivers suffer from heavy financial burdens and severe fatigue, with certain factors related to risky behaviors or phone usage increasing the likelihood of serious crashes. However, the specific risk factors leading to severe crashes varied between the two groups. Furthermore, the study confirmed that several combinations of risk factors exhibit nonlinear amplification effects on crash severity across different driver groups. These findings may support the design of evidence-based interventions to mitigate crash severity among ride-hailing drivers.
{"title":"A comparative analysis of risk factors influencing crash severity between full-time and part-time riding-hailing drivers in China","authors":"Zhipeng Peng , Hengyan Pan , Renteng Yuan , Yonggang Wang","doi":"10.1080/19427867.2024.2369827","DOIUrl":"10.1080/19427867.2024.2369827","url":null,"abstract":"<div><div>Ride-hailing is increasingly important in urban public transportation, yet research on its traffic safety remains limited. This study examined risk factors for crash severity among ride-hailing drivers, including demographics, financial burden, phone usage, fatigue, and risky driving behaviors. Data were collected from 2,182 drivers via a self-reported survey. Recognizing potential differences between full-time and part-time drivers, the data were divided accordingly, and two Bayesian network models were generated. The results indicated that both types of drivers suffer from heavy financial burdens and severe fatigue, with certain factors related to risky behaviors or phone usage increasing the likelihood of serious crashes. However, the specific risk factors leading to severe crashes varied between the two groups. Furthermore, the study confirmed that several combinations of risk factors exhibit nonlinear amplification effects on crash severity across different driver groups. These findings may support the design of evidence-based interventions to mitigate crash severity among ride-hailing drivers.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 4","pages":"Pages 612-627"},"PeriodicalIF":3.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505412","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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