Transportation Letters-The International Journal of Transportation Research最新文献_第5页

Predicting and explaining parking space sharing behaviors using LightGBM and SHAP with individual heterogeneity considered 考虑个体异质性的LightGBM和SHAP车位共享行为预测与解释

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2392332

Ange Wang , Jiyao Wang , Xiao Wen , Dengbo He , Ran Tu

引用次数: 0

Toward mainstreaming care activities in transportation: a time use and mobility segmentation approach 将护理活动纳入交通主流：时间利用和流动性细分方法

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2393536

Sebastián Henríquez , Juan Antonio Carrasco , Sebastián Astroza

{"title":"Toward mainstreaming care activities in transportation: a time use and mobility segmentation approach","authors":"Sebastián Henríquez , Juan Antonio Carrasco , Sebastián Astroza","doi":"10.1080/19427867.2024.2393536","DOIUrl":"10.1080/19427867.2024.2393536","url":null,"abstract":"<div><div>This paper assesses the importance of incorporating care dimensions into activity-travel segmentation to understand daily life mobility strategies. The data came from six neighbourhoods in Concepción, Chile, and included detailed information on activity-travel time use and interaction and classification schemes to identify care purposes. The study uses self-organizing maps to build incremental behavioural segments from weekly mobility and time use variables, adding care activities to assess their role in this segmentation. The results identify groups with a higher burden on care than others, emphasizing the role of transport mode and time use patterns. The result remarks caregiving activities hidden within other categories, identifying groups of caregivers, including domestic workers and women who work and have intense accompanying activities with children. The results highlight the differences between mobility patterns between different segments to make more invisible care and other related activities disproportionately performed by groups of women.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 858-868"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220878","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}

引用次数: 0

Network effects from the provision of charging stations in the market diffusion of electric vehicles 在电动汽车市场普及过程中提供充电站的网络效应

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2398323

Xiaoling Liu , Xu Guo , Xiaohua Sun , Helen Song-Turner

引用次数: 0

En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation 集装箱码头间运输电动卡车的途中充电调度策略

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2389609

Yuxiong Ji , Pengliang Cao , Kum Fai Yuen , Yujing Zheng , Yu Shen

{"title":"En-route recharging scheduling strategy for electric trucks serving inter-terminal container transportation","authors":"Yuxiong Ji , Pengliang Cao , Kum Fai Yuen , Yujing Zheng , Yu Shen","doi":"10.1080/19427867.2024.2389609","DOIUrl":"10.1080/19427867.2024.2389609","url":null,"abstract":"<div><div>Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size.</div></div>","PeriodicalId":48974,"journal":{"name":"Transportation Letters-The International Journal of Transportation Research","volume":"17 5","pages":"Pages 777-788"},"PeriodicalIF":3.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220917","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}

引用次数: 0

Intention to adopt autonomous vehicles in China: a comparative study among residents in different-sized cities 在中国采用自动驾驶汽车的意愿：不同规模城市居民的比较研究

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2399422

Tianpei Tang , Jun Chen , Yuntao Guo , Dian Sheng , Xinghua Li , Panagiotis Ch. Anastasopoulos

{"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}

引用次数: 0

A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system 用于道路碰撞预测的人在环集合融合框架：应对来自驾驶员-车辆-环境系统的不平衡异构数据

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2392063

Dauha Elamrani Abou Elassad , Zouhair Elamrani Abou Elassad , Abdel Majid Ed-Dahbi , Othmane El Meslouhi , Mustapha Kardouchi , Moulay Akhloufi , Nusrat Jahan

{"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}

引用次数: 0

An empirical study on train choice preferences of high-speed railway passengers: the case of Nanning-Guangzhou railway and Nanning-Beihai railway 高速铁路乘客列车选择偏好实证研究：以南宁-广州铁路和南宁-北海铁路为例

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2391662

Dongsheng Gao , Xiaoqiang Zhang , Yun Yang

{"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}

引用次数: 0

Traffic accident severity prediction based on interpretable deep learning model 基于可解释深度学习模型的交通事故严重性预测

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2398336

Yulong Pei , Yuhang Wen , Sheng Pan

{"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}

引用次数: 0

Analyzing the transfer duration of public transport passengers using classification and regression tree-multiple-Cox proportional hazards (CART-Multi-Cox) model 利用分类和回归树-多重-Cox 比例危险（CART-Multi-Cox）模型分析公共交通乘客的换乘持续时间

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-05-28 DOI: 10.1080/19427867.2024.2391168

Lei Gong , Pengfei Han , Tian Lei , Baicheng Li , Qin Luo , Cheng Zhu

{"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}

引用次数: 0

Improved time series models for the prediction of lane-change intention 预测变道意向的改进时间序列模型

IF 3.3 3区工程技术

Transportation Letters-The International Journal of Transportation Research Pub Date : 2025-04-21 DOI: 10.1080/19427867.2024.2379702

Hongrui Zhang , Yonggang Wang , Shengrui Zhang , Jingtao Li , Qushun Wang , Bei Zhou

{"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}

引用次数: 0