Pub Date : 2025-11-26DOI: 10.1080/19427867.2025.2516422
Subasish Das , Monire Jafari , Richard Dzinyela , Md Nasim Khan
This study analyzed 3,234 motorcycle roadway‑departure crashes recorded in Louisiana from 2017 to 2021. The dataset spans crash, vehicle, environmental, and location variables, enabling a comprehensive assessment of factors shaping injury severity. We used a two‑stage hybrid approach: cluster correspondence analysis first reduces dimensionality and uncovers latent patterns, then random parameters ordered probit models are fitted to the full sample and to each cluster. Six distinct clusters emerge, and the models capture both main effects and important interaction effects through random and fixed parameters. The combined method improves explanatory power and highlights high‑risk crash profiles, offering clear guidance for targeted countermeasures aimed at reducing fatal and serious injuries among Louisiana motorcyclists involved in roadway‑departure events.
{"title":"Applying hybrid dimension reduction and econometric model to investigate rider behaviors in roadway departure motorcycle crashes","authors":"Subasish Das , Monire Jafari , Richard Dzinyela , Md Nasim Khan","doi":"10.1080/19427867.2025.2516422","DOIUrl":"10.1080/19427867.2025.2516422","url":null,"abstract":"<div><div>This study analyzed 3,234 motorcycle roadway‑departure crashes recorded in Louisiana from 2017 to 2021. The dataset spans crash, vehicle, environmental, and location variables, enabling a comprehensive assessment of factors shaping injury severity. We used a two‑stage hybrid approach: cluster correspondence analysis first reduces dimensionality and uncovers latent patterns, then random parameters ordered probit models are fitted to the full sample and to each cluster. Six distinct clusters emerge, and the models capture both main effects and important interaction effects through random and fixed parameters. The combined method improves explanatory power and highlights high‑risk crash profiles, offering clear guidance for targeted countermeasures aimed at reducing fatal and serious injuries among Louisiana motorcyclists involved in roadway‑departure events.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 10","pages":"Pages 1914-1934"},"PeriodicalIF":3.3,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665523","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 : 2025-11-26DOI: 10.1080/19427867.2025.2491532
Xu Wang , Tong Zhou , Rongjian Dai , Bingbing Xue , Yingchao Sun
Customized bus (CB) travel services present a promising solution for urban transportation, blending the convenience of private cars with the cost efficiency of public transit. This study proposes a comprehensive CB service system for urban commuting, optimizing both station locations and scheduling strategies. First, K-means clustering algorithm is applied to determine bus station locations based on the spatical distribution of commuting demand. The bus routing problem is then formulated as a mixed-integer nonlinear programming (MINLP) model, considering both system costs and passenger waiting times. To efficiently solve the MINLP model in urban scenarios, an adaptive large-scale neighborhood search (ALNS) algorithm is employed. Finally, the proposed system is validated using real-world data. Results indicate that the CB service notably reduces average passenger waiting time and vehicle travel time compared to traditional taxi services, while also achieving over a 30% reduction in operating costs.
{"title":"Integrated design and optimization of customized bus travel services for urban commuting","authors":"Xu Wang , Tong Zhou , Rongjian Dai , Bingbing Xue , Yingchao Sun","doi":"10.1080/19427867.2025.2491532","DOIUrl":"10.1080/19427867.2025.2491532","url":null,"abstract":"<div><div>Customized bus (CB) travel services present a promising solution for urban transportation, blending the convenience of private cars with the cost efficiency of public transit. This study proposes a comprehensive CB service system for urban commuting, optimizing both station locations and scheduling strategies. First, K-means clustering algorithm is applied to determine bus station locations based on the spatical distribution of commuting demand. The bus routing problem is then formulated as a mixed-integer nonlinear programming (MINLP) model, considering both system costs and passenger waiting times. To efficiently solve the MINLP model in urban scenarios, an adaptive large-scale neighborhood search (ALNS) algorithm is employed. Finally, the proposed system is validated using real-world data. Results indicate that the CB service notably reduces average passenger waiting time and vehicle travel time compared to traditional taxi services, while also achieving over a 30% reduction in operating costs.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 10","pages":"Pages 1771-1784"},"PeriodicalIF":3.3,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145665515","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 : 2025-10-21DOI: 10.1080/19427867.2025.2470545
Mostaq Ahmed , Mahmudur Fatmi
Daily activity complexity—the diversity and sequence of activities individuals perform—is crucial for understanding travel behavior. However, the non-linear spatial interactions of socio-demographic and land use factors influencing this complexity remain less explored. This study integrates a complexity indicator (encompassing entropy and activity transitions), spatial clustering (Local Indicators of Spatial Association), and random forest modeling to address this gap. Using the 2018 Okanagan Travel Survey data, we identify distinct spatial clusters: High-High (areas where individuals and their neighbors both exhibit high complexity), High-Low, Low-Low, and Low-High complexity. Our results highlight significant non-linear associations between daily activity complexity and factors such as proximity to central business districts, amenities, transit accessibility, land use diversity, age, and income. This combined approach captures intricate spatial interactions, providing novel insights into how activity complexity varies across different geographic and socio-demographic contexts, emphasizing the importance of considering non-linear effects in travel behavior analysis.
{"title":"Exploring the complexity of daily activity schedules using spatial statistics and machine learning methods","authors":"Mostaq Ahmed , Mahmudur Fatmi","doi":"10.1080/19427867.2025.2470545","DOIUrl":"10.1080/19427867.2025.2470545","url":null,"abstract":"<div><div>Daily activity complexity—the diversity and sequence of activities individuals perform—is crucial for understanding travel behavior. However, the non-linear spatial interactions of socio-demographic and land use factors influencing this complexity remain less explored. This study integrates a complexity indicator (encompassing entropy and activity transitions), spatial clustering (Local Indicators of Spatial Association), and random forest modeling to address this gap. Using the 2018 Okanagan Travel Survey data, we identify distinct spatial clusters: High-High (areas where individuals and their neighbors both exhibit high complexity), High-Low, Low-Low, and Low-High complexity. Our results highlight significant non-linear associations between daily activity complexity and factors such as proximity to central business districts, amenities, transit accessibility, land use diversity, age, and income. This combined approach captures intricate spatial interactions, providing novel insights into how activity complexity varies across different geographic and socio-demographic contexts, emphasizing the importance of considering non-linear effects in travel behavior analysis.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1549-1565"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468806","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 : 2025-10-21DOI: 10.1080/19427867.2025.2477005
Jing Li , Hao Yang , Saiedeh Razavi , Harith Abdulsattar
Accurate forecasting of highway Non-Recurrent Congestion (NRC) is critical for modern transportation systems. However, this research remains in its early stages and is frequently constrained to single-step temporal prediction. To address this limitation, this research presents a novel approach that leverages the Dual-Stream Autoencoder Sequence-to-Sequence model (DS-AE-Seq2Seq) to create an accurate tool for quantitative, spatio-temporal, and multi-step prediction of highway NRC. The proposed model innovatively integrates an Autoencoder and a Seq2Seq encoder to process static and time-series data, respectively. The decoder generates spatio-temporal predictions using the outcomes of both streams. The model is tested with data collected from highway I-5 and I-405, USA. Results show that it not only outperforms benchmarks but also exhibits high prediction accuracy under extreme traffic conditions, including severe injury incidents and various levels of service. Additionally, the model demonstrated reliability, through a sensitivity analysis, across different distances and prediction horizons.
{"title":"Highway non-recurrent congestion prediction using a multi-step spatio-temporal deep learning approach","authors":"Jing Li , Hao Yang , Saiedeh Razavi , Harith Abdulsattar","doi":"10.1080/19427867.2025.2477005","DOIUrl":"10.1080/19427867.2025.2477005","url":null,"abstract":"<div><div>Accurate forecasting of highway Non-Recurrent Congestion (NRC) is critical for modern transportation systems. However, this research remains in its early stages and is frequently constrained to single-step temporal prediction. To address this limitation, this research presents a novel approach that leverages the Dual-Stream Autoencoder Sequence-to-Sequence model (DS-AE-Seq2Seq) to create an accurate tool for quantitative, spatio-temporal, and multi-step prediction of highway NRC. The proposed model innovatively integrates an Autoencoder and a Seq2Seq encoder to process static and time-series data, respectively. The decoder generates spatio-temporal predictions using the outcomes of both streams. The model is tested with data collected from highway I-5 and I-405, USA. Results show that it not only outperforms benchmarks but also exhibits high prediction accuracy under extreme traffic conditions, including severe injury incidents and various levels of service. Additionally, the model demonstrated reliability, through a sensitivity analysis, across different distances and prediction horizons.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1611-1627"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469217","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 : 2025-10-21DOI: 10.1080/19427867.2025.2480127
Hongbo Yi , Yugang Liu , Jiali Li , Shuai Zheng , Yinjie Luo
Reducing the waiting time of electric vehicles (EVs) at charging stations and enhancing service experience are critical areas for research. This paper proposes a tradable credit-based peer-to-peer (P2P) trading approach to minimize the total waiting time at charging stations. Upon arrival at the charging station, each vehicle occupant is allocated an equal number of credits. While waiting in queue, vehicles can trade their positions using credits as currency. This system allows high-occupancy vehicles to have greater purchasing power, potentially displacing low-occupancy vehicles, which are compensated accordingly. A comprehensive theoretical framework is developed for the proposed method, and its effectiveness is validated through a series of case studies. The results indicate that the proposed approach significantly reduces the total waiting time for all occupants at the charging station. Furthermore, the underlying reasons for the approach’s strong performance are explained, and the performances of vehicles with different occupancy levels within the system is analyzed. Finally, insights with managerial relevance are derived from the comparative analysis and sensitivity analysis experiments.
{"title":"Reducing total waiting time at single charging station: a tradable credit-based peer-peer trading approach","authors":"Hongbo Yi , Yugang Liu , Jiali Li , Shuai Zheng , Yinjie Luo","doi":"10.1080/19427867.2025.2480127","DOIUrl":"10.1080/19427867.2025.2480127","url":null,"abstract":"<div><div>Reducing the waiting time of electric vehicles (EVs) at charging stations and enhancing service experience are critical areas for research. This paper proposes a tradable credit-based peer-to-peer (P2P) trading approach to minimize the total waiting time at charging stations. Upon arrival at the charging station, each vehicle occupant is allocated an equal number of credits. While waiting in queue, vehicles can trade their positions using credits as currency. This system allows high-occupancy vehicles to have greater purchasing power, potentially displacing low-occupancy vehicles, which are compensated accordingly. A comprehensive theoretical framework is developed for the proposed method, and its effectiveness is validated through a series of case studies. The results indicate that the proposed approach significantly reduces the total waiting time for all occupants at the charging station. Furthermore, the underlying reasons for the approach’s strong performance are explained, and the performances of vehicles with different occupancy levels within the system is analyzed. Finally, insights with managerial relevance are derived from the comparative analysis and sensitivity analysis experiments.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1682-1692"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468805","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 : 2025-10-21DOI: 10.1080/19427867.2025.2487455
Yanfang Ma , Jialei Li , Jinzhao Xue , Zongmin Li
The takeaway food is mostly ordered online, and delivered. Some riders may receive orders beyond their capacity, causing resource imbalance. Additionally, uncertain travel time makes it difficult for riders to complete deliveries effectively. Thus, an integer programming is formulated for the online crowdsourced delivery problem with balanced rider resources and uncertain travel time (OCD-BRUT) to optimize rider delivery routes. An improved genetic algorithm (IGA) with order sequence optimization operator is developed. Numerical experiments on both simulated and real-world datasets demonstrate that the OCD-BRUT effectively balances rider resources, especially in medium and large instances. For small to medium instances, the average gap between the IGA and the optimal baseline is −2.58%, while the average gap reaches −7.48% in large-scale instances, indicating IGA’s efficiency in handling numerous orders in rush hours. Besides, a sensitivity analysis of several key parameters is also performed to derive managerial insights.
{"title":"Online crowdsourced delivery optimization problem for takeaway orders with balanced rider resources and uncertain travel time","authors":"Yanfang Ma , Jialei Li , Jinzhao Xue , Zongmin Li","doi":"10.1080/19427867.2025.2487455","DOIUrl":"10.1080/19427867.2025.2487455","url":null,"abstract":"<div><div>The takeaway food is mostly ordered online, and delivered. Some riders may receive orders beyond their capacity, causing resource imbalance. Additionally, uncertain travel time makes it difficult for riders to complete deliveries effectively. Thus, an integer programming is formulated for the online crowdsourced delivery problem with balanced rider resources and uncertain travel time (OCD-BRUT) to optimize rider delivery routes. An improved genetic algorithm (IGA) with order sequence optimization operator is developed. Numerical experiments on both simulated and real-world datasets demonstrate that the OCD-BRUT effectively balances rider resources, especially in medium and large instances. For small to medium instances, the average gap between the IGA and the optimal baseline is −2.58%, while the average gap reaches −7.48% in large-scale instances, indicating IGA’s efficiency in handling numerous orders in rush hours. Besides, a sensitivity analysis of several key parameters is also performed to derive managerial insights.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1719-1738"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469215","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 : 2025-10-21DOI: 10.1080/19427867.2025.2474317
Yuntao Ye , Jie He , Xintong Yan , Chenwei Wang , Pengcheng Qin
Motorcyclist non-violation (MN-V) crashes were confirmed to be more closely associated with roadway and environmental factors. However, limited studies have explored how these determinants affect injury severity in MN-V crashes. Using motorcycle crash data from Xi’an, China, this study developed multiple random parameters logit models, incorporating heterogeneity in means and variances for investigating the impact of risk determinants on the injury severities sustained by motorcyclists involved in MN-V crashes. An overall temporal instability in the injury severity determinants was revealed via likelihood ratio tests. Several factors were observed to increase the likelihood of severe injuries, including old (age >55), collision with heavy vehicles, head-on collision, curves, dry road surfaces, and holidays. Additionally, unfavorable riding conditions were demonstrated to decrease the probability of severe injuries, which was attributed to the riders’ risk compensation mechanism in adverse conditions. This study provides new insights into the mechanism of motorcyclist injury severity in MN-V crashes.
{"title":"Exploring determinants of motorcyclist non-violation crash injury severities on suburban roads of China: a random parameter logit model with heterogeneity in means and variances","authors":"Yuntao Ye , Jie He , Xintong Yan , Chenwei Wang , Pengcheng Qin","doi":"10.1080/19427867.2025.2474317","DOIUrl":"10.1080/19427867.2025.2474317","url":null,"abstract":"<div><div>Motorcyclist non-violation (MN-V) crashes were confirmed to be more closely associated with roadway and environmental factors. However, limited studies have explored how these determinants affect injury severity in MN-V crashes. Using motorcycle crash data from Xi’an, China, this study developed multiple random parameters logit models, incorporating heterogeneity in means and variances for investigating the impact of risk determinants on the injury severities sustained by motorcyclists involved in MN-V crashes. An overall temporal instability in the injury severity determinants was revealed via likelihood ratio tests. Several factors were observed to increase the likelihood of severe injuries, including old (age >55), collision with heavy vehicles, head-on collision, curves, dry road surfaces, and holidays. Additionally, unfavorable riding conditions were demonstrated to decrease the probability of severe injuries, which was attributed to the riders’ risk compensation mechanism in adverse conditions. This study provides new insights into the mechanism of motorcyclist injury severity in MN-V crashes.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1599-1610"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468804","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}
This paper analyzes the traction effect of dual credit policy on supply-side productivity, as well as the regulatory effect of carbon inclusive benefits and consumer’s low-carbon preference on demand-side market volume. By combining nonlinear dynamics theory with Bertrand game, this paper establishes a dynamic Bertrand game model of new energy vehicle pricing. The results indicate that: (1) A too fast price adjustment can lead to chaotic fluctuations in the automobile market, and plug-in hybrid electric vehicles (PHEVs) are more susceptible to price fluctuations compared to battery electric vehicles (BEVs). (2) The dual credit policy on the supply side can improve the stability of pricing decisions for automobile manufacturers, making BEVs more competitive in the market than PHEVs. (3) From the demand side, the prices of BEVs and PHEVs are complementary. (4) In policy design, the balance between credit trading price and carbon inclusive benefits should be comprehensively considered.
{"title":"Study on nonlinear dynamics of Bertrand game between automobile manufacturers under low-carbon policies on both sides of supply and demand","authors":"Dan Zhao , Su-ya Jia , Fang-yue Zheng , Jin-huan Tang","doi":"10.1080/19427867.2025.2478173","DOIUrl":"10.1080/19427867.2025.2478173","url":null,"abstract":"<div><div>This paper analyzes the traction effect of dual credit policy on supply-side productivity, as well as the regulatory effect of carbon inclusive benefits and consumer’s low-carbon preference on demand-side market volume. By combining nonlinear dynamics theory with Bertrand game, this paper establishes a dynamic Bertrand game model of new energy vehicle pricing. The results indicate that: (1) A too fast price adjustment can lead to chaotic fluctuations in the automobile market, and plug-in hybrid electric vehicles (PHEVs) are more susceptible to price fluctuations compared to battery electric vehicles (BEVs). (2) The dual credit policy on the supply side can improve the stability of pricing decisions for automobile manufacturers, making BEVs more competitive in the market than PHEVs. (3) From the demand side, the prices of BEVs and PHEVs are complementary. (4) In policy design, the balance between credit trading price and carbon inclusive benefits should be comprehensively considered.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1628-1642"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469213","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 : 2025-10-21DOI: 10.1080/19427867.2025.2474310
Dongdong Song , Chenzhu Wang , Said M. Easa , Renteng Yuan , Fei Chen , Jianchuan Cheng , Yitao Yang , Le Tian
Lacking protection compared to drivers of other vehicles, motorcyclists accounted for most casualties and fatalities. This study explores how non-motorcycle drivers affect motorcyclists’ injury outcomes in motorcycle-vehicle collisions. The motorcycle-vehicle crashes from the United Kingdom for 2016–2020 are used to estimate two alternative logit models to account for possible unobserved heterogeneities. The models are a latent class multinomial logit with class probability functions and a random threshold-parameter generalized ordered logit. With three possible injury severity levels (fatal injury, severe injury, and minor injury), the characteristics of motorcyclist, driver, roadway, environment, vehicle, and collision are considered potential determinants. Then, the temporal instability issues are revealed through the likelihood ratio tests and out-of-sample predictions based on the two models. Showing good ${rho ^2}$ values of over 0.370, the latent class model’s estimation results are leveraged to quantify the effects of the contributing factors. Moreover, the marginal effects are also calculated to reveal the existing temporal instability, while some variables reflect the temporal instability in the influence trend and degree. The critical factors increasing the risk levels are male motorcyclists, higher speed limit, older ages of motorcyclists and vehicles, fine weather, single carriageway, and head-on collision type. Overall, subtle variations in the injury severity predictions exist in alternative heterogeneity modeling approaches, suffering from the modeling mechanism of different structural frameworks in capturing the unobserved heterogeneities.
{"title":"Alternative outcome frameworks to model injury severity outcomes of motorcyclists colliding with other vehicles","authors":"Dongdong Song , Chenzhu Wang , Said M. Easa , Renteng Yuan , Fei Chen , Jianchuan Cheng , Yitao Yang , Le Tian","doi":"10.1080/19427867.2025.2474310","DOIUrl":"10.1080/19427867.2025.2474310","url":null,"abstract":"<div><div>Lacking protection compared to drivers of other vehicles, motorcyclists accounted for most casualties and fatalities. This study explores how non-motorcycle drivers affect motorcyclists’ injury outcomes in motorcycle-vehicle collisions. The motorcycle-vehicle crashes from the United Kingdom for 2016–2020 are used to estimate two alternative logit models to account for possible unobserved heterogeneities. The models are a latent class multinomial logit with class probability functions and a random threshold-parameter generalized ordered logit. With three possible injury severity levels (fatal injury, severe injury, and minor injury), the characteristics of motorcyclist, driver, roadway, environment, vehicle, and collision are considered potential determinants. Then, the temporal instability issues are revealed through the likelihood ratio tests and out-of-sample predictions based on the two models. Showing good ${rho ^2}$<span><math><mrow><msup><mi>ρ</mi><mn>2</mn></msup></mrow></math></span> values of over 0.370, the latent class model’s estimation results are leveraged to quantify the effects of the contributing factors. Moreover, the marginal effects are also calculated to reveal the existing temporal instability, while some variables reflect the temporal instability in the influence trend and degree. The critical factors increasing the risk levels are male motorcyclists, higher speed limit, older ages of motorcyclists and vehicles, fine weather, single carriageway, and head-on collision type. Overall, subtle variations in the injury severity predictions exist in alternative heterogeneity modeling approaches, suffering from the modeling mechanism of different structural frameworks in capturing the unobserved heterogeneities.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1582-1598"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469212","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 : 2025-10-21DOI: 10.1080/19427867.2025.2479190
Manel Ouni , Rafaa Mraihi
Road traffic accidents are responsible for 1.35 million deaths, and road safety is the part of sustainable development goals, which aims to provide a safe, accessible, affordable, and sustainable transport system by 2030. Despite significant efforts made by the Tunisia government to reduce traffic accidents, there is still a need to understand the root causes of these accidents. This study examines the relationship between road traffic fatalities, economic growth, total mileage of highways, private vehicle ownership, and urbanization in Tunisia from 2000 to 2023. This study employs novel estimation techniques, including dynamic autoregressive distributed lag (DARDL) simulations and Kernel-based Regularized Least Squares (KRLS), to capture the counterfactual shocks in road traffic fatalities. The DARDL results revealed that 1% increase in economic growth, urbanization, and private car ownership leads to a 0.316%, 0.371% and 0.347% rise in road traffic accidents in the long-run, respectively. The KRLS results indicate that economic growth and private vehicle ownership have a positive marginal effect on road traffic fatalities. Specifically, an increase in private vehicle ownership consistently raises traffic fatalities across all percentiles. In contrast, the effect of total highway mileage is initially positive, leading to an increase in accidents, but turns negative in the later stages. Additionally, the frequency domain causality results show that economic growth granger causes road accident for frequencies corresponding to long-term (0.93–1.00) and medium-term (1.03–1.50) horizons. Urbanization granger causes road accident in the medium term (1.69 to 1.97) and short term (2.01 to 2.63). Private vehicle Granger-causes road accident for frequencies between 0.00 and 0.89 in the long run and between 1.03 and 1.73 in the medium term. The governments should include traffic education as part of syllabus from primary to higher studies. Government should create awareness about the loss due to road crashes.
{"title":"Impact of socioeconomic determinants on road traffic accidents in Tunisia: insights from the dynamic ARDL and machine learning approaches","authors":"Manel Ouni , Rafaa Mraihi","doi":"10.1080/19427867.2025.2479190","DOIUrl":"10.1080/19427867.2025.2479190","url":null,"abstract":"<div><div>Road traffic accidents are responsible for 1.35 million deaths, and road safety is the part of sustainable development goals, which aims to provide a safe, accessible, affordable, and sustainable transport system by 2030. Despite significant efforts made by the Tunisia government to reduce traffic accidents, there is still a need to understand the root causes of these accidents. This study examines the relationship between road traffic fatalities, economic growth, total mileage of highways, private vehicle ownership, and urbanization in Tunisia from 2000 to 2023. This study employs novel estimation techniques, including dynamic autoregressive distributed lag (DARDL) simulations and Kernel-based Regularized Least Squares (KRLS), to capture the counterfactual shocks in road traffic fatalities. The DARDL results revealed that 1% increase in economic growth, urbanization, and private car ownership leads to a 0.316%, 0.371% and 0.347% rise in road traffic accidents in the long-run, respectively. The KRLS results indicate that economic growth and private vehicle ownership have a positive marginal effect on road traffic fatalities. Specifically, an increase in private vehicle ownership consistently raises traffic fatalities across all percentiles. In contrast, the effect of total highway mileage is initially positive, leading to an increase in accidents, but turns negative in the later stages. Additionally, the frequency domain causality results show that economic growth granger causes road accident for frequencies corresponding to long-term (0.93–1.00) and medium-term (1.03–1.50) horizons. Urbanization granger causes road accident in the medium term (1.69 to 1.97) and short term (2.01 to 2.63). Private vehicle Granger-causes road accident for frequencies between 0.00 and 0.89 in the long run and between 1.03 and 1.73 in the medium term. The governments should include traffic education as part of syllabus from primary to higher studies. Government should create awareness about the loss due to road crashes.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 9","pages":"Pages 1665-1681"},"PeriodicalIF":3.3,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469214","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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