Transportation Letters-The International Journal of Transportation Research最新文献

英文中文

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 Q2 TRANSPORTATION

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

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。

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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 Q2 TRANSPORTATION

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

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.

准确预测交通事故的严重程度对道路安全至关重要。然而，现有研究在揭示事故严重程度与关键因素之间的关系方面缺乏可解释性。...

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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 Q2 TRANSPORTATION

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

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.

换乘行为是影响公共交通乘客出行效率的关键因素。为解决潜在的群体异质性问题，本研究开发了一种综合的...

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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 Q2 TRANSPORTATION

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

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.

为了提高变道意图预测的准确性，分析驾驶风格对预测结果的影响，本文提出了T-Encoder-Sequence模型。它将Transformer的编码器模块与各种循环神经网络（RNN）模型集成在一起，并引入了多模态融合输入结构。在此基础上，建立了能够反映驾驶员压力的风险指标模型，并计算了模型的输入参数。因此，该模型可以同时捕获全局信息并考虑车辆类别对驾驶员的影响。此外，采用k-means++算法将车辆轨迹分为保守型、常规型和激进型，以供进一步分析。结果表明，用风险指标参数训练模型能显著提高预测性能。在相同的输入参数下，T-Encoder-Sequence模型的预测效果明显优于原始模型。与其他研究相比，使用风险指标参数进行训练的T-Encoder-Sequence模型具有很大的优势。

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引用次数: 0

Temporal instability of factors affecting injury severity in single-vehicle crashes on rural highways 农村公路单车碰撞事故中受伤严重程度影响因素的时间不稳定性

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 10.1080/19427867.2024.2366731

Yaping Wang , Fulu Wei , Yanyong Guo , Yongqing Guo

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).

为研究农村公路单车碰撞事故影响因素的日时变化和时间稳定性，采用具有均值和方差异质性的随机参数Logit模型...

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引用次数: 0

User intention to adopt public bicycle sharing system: a priori acceptance approach 用户采用公共自行车共享系统的意愿：先验接受法

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 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.

自行车共享服务作为一种可持续的交通方式受到了全世界的关注。本研究探讨了印度对公共自行车共享系统（PBSS）的先验接受度，即...

引用次数: 0

Assessing the factors associated with pedestrian injury severity in hit-and-run crashes in Ghana 评估加纳交通肇事逃逸事故中行人受伤严重程度的相关因素

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 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.

肇事逃逸往往会给易受伤害的道路使用者带来严重后果。考虑到政府为促进行人友好型城市环境所做的努力，这些撞车事故的重要性不言而喻。

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引用次数: 0

A comparative analysis of risk factors influencing crash severity between full-time and part-time riding-hailing drivers in China 影响中国专职和兼职顺风车司机碰撞严重性的风险因素比较分析

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 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.

打车服务在城市公共交通中的地位日益重要，但对其交通安全的研究仍然有限。本研究探讨了滴滴顺风车碰撞严重性的风险因素。

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引用次数: 0

Spatiotemporal instability analysis in single-vehicle crash severity on different roadway classifications 不同道路分类中单车碰撞严重程度的时空不稳定性分析

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 10.1080/19427867.2024.2370672

Fulu Wei , Yaping Wang , Yongqing Guo , Yanyong Guo

The severity of crashes on national/provincial roads, urban roads, and rural roads was analyzed. Random parameters logit models with heterogeneity in means and variances under different time periods of the day and roadway classifications were employed. Analysis of factors influencing injury severity of single-vehicle crashes in the driver, vehicle, road, and environment to explore correlation and difference between influencing factors and crash severity. The results showed that the effect of factors that determine injury severity varied significantly across different roadway classifications. Specifically, driver age (<30) and summer were only significantly associated with single-vehicle crashes on urban roads. Dawn/dusk was only significant in single-vehicle crashes on rural roads. Meanwhile, overall spatiotemporal instability was observed in the study. While several indicators were also reported to show relative spatial or temporal stability such as male, driver age (>55), motorcycle, traffic control, and visibility (<100 m).

分析了国道/省道、城市道路和乡村道路上车祸的严重程度。随机参数 logit 模型具有不同时间段下均值和方差的异质性...

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引用次数: 0

Mitigating biases in big mobility data: a case study of monitoring large-scale transit systems 减少流动性大数据中的偏差：大型公交系统监测案例研究

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-04-21 DOI: 10.1080/19427867.2024.2379703

Feilong Wang , Xuegang (Jeff) Ban , Peng Chen , Chenxi Liu , Rong Zhao

Big mobility data (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmark data from governmental agencies. Spatio-temporal discrepancies between BMD and benchmark are observed and their impacts on transportation applications are investigated, emphasizing the urgent need to address these biases to prevent misguided policymaking. This study further proposes and tests a bias mitigation method. It is shown that the mitigated BMD could generate valuable insights into large-scale public transit systems across 100+ US counties, revealing regional disparities of the recovery of transit systems from the COVID-19. This study underscores the importance of caution when using BMD in transportation research and presents effective mitigation strategies that would benefit practitioners.

大移动数据（BMD）在研究人类移动性和评估交通系统性能方面显示出许多优势。然而，人们对移动大数据的质量仍然知之甚少。本研究...

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