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

英文中文

Modeling bounded rationality in discretionary lane-changing with the cumulative prospect theory 用累积前景理论对任意变道的有限理性建模

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-11-26 DOI: 10.1080/19427867.2025.2495940

Dewen Kong , Xi Zhang , Miao Wang , Lishan Sun , Yan Xu , Qingqing Wang

Discretionary lane-changing behavior, a prevalent expressway phenomenon, necessitates driver-centric analysis. Existing studies often assume perfect rationality, neglecting bounded rationality. This work divides decisions into perception and judgment phases. For perception, stated preference surveys and data-driven methods evaluate how drivers’ attention allocation and cognitive limits shape decisions. During judgment, cumulative prospect theory with logistic regression models heterogeneous reference points, achieving >90% accuracy—10% higher than expected utility models. Sensitivity analysis reveals: 1) 17% increased lane-changing propensity when risk aversion falls below loss sensitivity; 2) Loss aversion dominance in suboptimal lanes; 3) Systematic underestimation of high-probability events via probability distortion. These results quantitatively establish bounded rationality’s cognitive mechanisms in driving, offering theoretical foundations for human-machine collaborative systems and traffic management strategies addressing cognitive constraints. The dual-phase framework enhances behavioral realism while maintaining computational tractability in driving simulations.

任意变道行为是高速公路上普遍存在的现象，需要对其进行以驾驶员为中心的分析。现有的研究往往假设完全理性，而忽略了有限理性。这项工作将决策分为感知和判断两个阶段。对于感知，陈述偏好调查和数据驱动方法评估驾驶员的注意力分配和认知限制如何影响决策。在判断过程中，累积前景理论与逻辑回归模型异质参考点，达到了90%的准确率-比实用新型预期高10%。敏感性分析表明：1)当风险厌恶低于损失敏感性时，变道倾向增加17%；2)次优通道损失厌恶优势；3)通过概率失真对高概率事件的系统性低估。这些结果定量地建立了有限理性在驾驶中的认知机制，为解决认知约束的人机协作系统和交通管理策略提供了理论基础。双相框架增强了行为的真实感，同时保持了驾驶模拟的计算可追溯性。

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引用次数: 0

E-scooters as transit feeders: insights from revealed preferences of shared e-scooter users 电动滑板车作为交通工具：来自共享电动滑板车用户偏好的见解

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-11-26 DOI: 10.1080/19427867.2025.2496455

Mohammadjavad Javadinasr , Sina Asgharpour , Abolfazl (Kouros) Mohammadian

While e-scooters are increasingly used for short-distance trips, concerns persist regarding their effective integration with public transit. This study sheds light on the factors shaping revealed preferences for using e-scooters as transit access/egress mode by surveying 2,126 shared e-scooter users. We developed a Generalized Structural Equation Model (GSEM) to analyze the impacts of sociodemographic, transportation-related, and attitudinal factors on the frequency of e-scooter integration with transit. The results identified four attitudinal variables—transportation efficacy, variety seeking, environmental concern, and perceived safety—as the main drivers. Regarding demographic factors, experienced e-scooter users are more likely to integrate with transit, and women are less likely to use e-scooters in integration with transit. Furthermore, the findings underscored the role of transportation variables, households without personal vehicles, and access time to e-scooters under five minutes. The insights gained from the study help urban planners enhance e‑scooter–transit synergy and advance sustainable micromobility use.

尽管电动滑板车越来越多地用于短途出行，但人们仍然担心它们与公共交通的有效结合。这项研究通过调查2126名共享电动滑板车用户，揭示了影响人们使用电动滑板车作为交通进出方式偏好的因素。我们建立了一个广义结构方程模型（GSEM）来分析社会人口学、交通相关因素和态度因素对电动滑板车与交通整合频率的影响。结果确定了四个态度变量——运输效率、多样性寻求、环境关注和感知安全——是主要的驱动因素。在人口统计因素方面，经验丰富的电动滑板车用户更有可能与公共交通相结合，而女性则不太可能将电动滑板车与公共交通相结合。此外，调查结果强调了交通变量的作用，没有私家车的家庭，以及使用电动滑板车的时间少于5分钟。从该研究中获得的见解有助于城市规划者增强电动滑板车-交通的协同作用，并促进可持续的微型交通工具的使用。

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引用次数: 0

Applying hybrid dimension reduction and econometric model to investigate rider behaviors in roadway departure motorcycle crashes 应用混合降维和计量经济模型研究摩托车偏离道路碰撞中驾驶员行为

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-11-26 DOI: 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.

这项研究分析了2017年至2021年路易斯安那州记录的3234起摩托车偏离道路的事故。该数据集涵盖碰撞、车辆、环境和位置变量，能够全面评估影响伤害严重程度的因素。我们使用了两阶段混合方法：聚类对应分析首先降低维数并揭示潜在模式，然后将随机参数有序概率模型拟合到整个样本和每个聚类。模型通过随机参数和固定参数捕捉主要效应和重要交互效应。该综合方法提高了解释力，突出了高风险的碰撞概况，为有针对性的对策提供了明确的指导，旨在减少路易斯安那州摩托车手在道路偏离事件中造成的致命和严重伤害。

引用次数: 0

Integrated design and optimization of customized bus travel services for urban commuting 城市通勤定制公交出行服务集成设计与优化

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-11-26 DOI: 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.

定制公交（CB）出行服务为城市交通提供了一个很有前途的解决方案，它将私家车的便利性与公共交通的成本效益相结合。本研究提出了一种综合的城市通勤CB服务系统，优化站点位置和调度策略。首先，基于通勤需求的空间分布，采用k均值聚类算法确定公交站点的位置；考虑系统成本和乘客等待时间，将公交路线问题表述为混合整数非线性规划（MINLP）模型。为了有效地求解城市场景下的MINLP模型，采用了自适应大规模邻域搜索（ALNS）算法。最后，利用实际数据对系统进行了验证。结果表明，与传统出租车服务相比，CB服务显著减少了乘客的平均等待时间和车辆行驶时间，同时运营成本也降低了30%以上。

引用次数: 0

Exploring the complexity of daily activity schedules using spatial statistics and machine learning methods 利用空间统计和机器学习方法探索日常活动安排的复杂性

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 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.

日常活动的复杂性——个体活动的多样性和顺序——对于理解旅行行为至关重要。然而，影响这种复杂性的社会人口和土地利用因素的非线性空间相互作用仍然很少被探索。本研究整合了复杂性指标（包括熵和活动转换）、空间聚类（空间关联的局部指标）和随机森林模型来解决这一差距。利用2018年奥肯那根旅游调查数据，我们确定了不同的空间集群：高-高（个人及其邻居都表现出高复杂性的区域）、高-低、低-低和低-高复杂性。我们的研究结果强调了日常活动复杂性与诸如靠近中央商务区、便利设施、交通可达性、土地利用多样性、年龄和收入等因素之间的显著非线性关联。这种综合方法捕捉了复杂的空间相互作用，为不同地理和社会人口背景下活动复杂性的变化提供了新的见解，强调了在旅行行为分析中考虑非线性效应的重要性。

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引用次数: 0

Highway non-recurrent congestion prediction using a multi-step spatio-temporal deep learning approach 基于多步时空深度学习方法的公路非经常性拥堵预测

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 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.

公路非经常性拥堵的准确预测对现代交通系统至关重要。然而，这项研究仍处于早期阶段，往往局限于单步时间预测。为了解决这一限制，本研究提出了一种利用双流自动编码器序列到序列模型（DS-AE-Seq2Seq）的新方法，为高速公路NRC的定量、时空和多步骤预测创建了一个准确的工具。该模型创新地集成了一个自动编码器和一个Seq2Seq编码器，分别处理静态和时间序列数据。解码器使用两个流的结果生成时空预测。用美国I-5和I-405高速公路的数据对模型进行了测试。结果表明，该方法不仅优于基准，而且在极端交通条件下（包括严重伤害事件和各种服务水平）也具有较高的预测精度。此外，通过灵敏度分析，该模型在不同距离和预测范围内证明了可靠性。

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引用次数: 0

Reducing total waiting time at single charging station: a tradable credit-based peer-peer trading approach 减少单个充电站的总等待时间：基于可交易信用的点对点交易方法

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 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.

减少电动汽车在充电站的等待时间和提高服务体验是研究的关键领域。本文提出了一种基于可交易信用的点对点（P2P）交易方法，以最小化充电站的总等待时间。到达充电站后，每个车辆乘员被分配相同数量的积分。在排队等候时，车辆可以使用积分作为货币进行交易。该系统允许高载客量车辆有更大的购买力，可能取代低载客量车辆，后者得到相应的补偿。为该方法建立了一个完整的理论框架，并通过一系列的案例研究验证了其有效性。结果表明，该方法显著减少了充电站所有乘员的总等待时间。进一步解释了该方法性能优异的根本原因，并分析了系统内不同占用率车辆的性能。最后，从比较分析和敏感性分析实验中得出与管理相关的见解。

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引用次数: 0

Online crowdsourced delivery optimization problem for takeaway orders with balanced rider resources and uncertain travel time 骑手资源均衡且出行时间不确定的外卖订单在线众包配送优化问题

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 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.

外卖食品大多是在网上订购，然后送到。一些骑手可能会收到超出其能力的订单，造成资源不平衡。此外，不确定的出行时间也使乘客难以有效地完成配送。因此，针对具有骑手资源平衡和出行时间不确定的在线众包配送问题（OCD-BRUT），采用整数规划方法对配送路线进行优化。提出了一种带有有序序列优化算子的改进遗传算法（IGA）。在模拟和现实数据集上的数值实验表明，OCD-BRUT有效地平衡了骑手资源，特别是在大中型实例中。对于中小型实例，IGA与最优基线的平均差距为- 2.58%，而在大型实例中，IGA与最优基线的平均差距达到- 7.48%，表明IGA在高峰时段处理大量订单的效率较高。此外，还进行了几个关键参数的敏感性分析，以获得管理见解。

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引用次数: 0

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 中国郊区道路摩托车驾驶员非违例碰撞伤害严重程度的影响因素探讨：一个均值和方差异质性的随机参数logit模型

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 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.

摩托车非违章碰撞（MN-V）被证实与道路和环境因素更密切相关。然而，有限的研究探讨了这些决定因素如何影响MN-V碰撞的损伤严重程度。利用中国西安摩托车事故数据，本研究建立了多随机参数logit模型，结合均值和方差的异质性，研究了风险决定因素对MN-V碰撞中摩托车驾驶员损伤严重程度的影响。通过似然比测试揭示了损伤严重程度决定因素的整体时间不稳定性。有几个因素增加了严重伤害的可能性，包括年老（55岁）、与重型车辆碰撞、正面碰撞、弯道、干燥的路面和假期。此外，不利的骑行条件降低了严重伤害的概率，这归因于骑手在不利条件下的风险补偿机制。该研究为MN-V碰撞中摩托车损伤严重程度的机制提供了新的见解。

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引用次数: 0

Study on nonlinear dynamics of Bertrand game between automobile manufacturers under low-carbon policies on both sides of supply and demand 供需双方低碳政策下汽车制造商Bertrand博弈的非线性动力学研究

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-10-21 DOI: 10.1080/19427867.2025.2478173

Dan Zhao , Su-ya Jia , Fang-yue Zheng , Jin-huan Tang

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.

本文分析了双重信贷政策对供给侧生产率的牵引效应，以及碳包容效益和消费者低碳偏好对需求侧市场容量的调节效应。将非线性动力学理论与Bertrand博弈相结合，建立了新能源汽车定价的动态Bertrand博弈模型。结果表明：(1)过快的价格调整会导致汽车市场的混乱波动，插电式混合动力汽车（phev）比纯电动汽车（bev）更容易受到价格波动的影响。(2)供给侧双积分政策可以提高汽车厂商定价决策的稳定性，使纯电动汽车比插电式电动汽车更具市场竞争力。(3)从需求侧看，纯电动汽车与插电式混合动力汽车价格互补。(4)在政策设计中，应综合考虑信用交易价格与碳包容效益之间的平衡。

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引用次数: 0

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