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

英文中文

Real-time pricing decision for electric carsharing systems with parking reservation using a fluid queueing network-based optimization 基于流体排队网络优化的带车位预约的电动汽车共享系统实时定价决策

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2534570

Hongxun Liu , Weiyao Xu , Boyu Hou , Lu Hu , Shiyun Tong

In this paper, we integrate the fluid queueing theory and rolling horizon approach to investigate the real-time pricing scheme for an electric carsharing system (ECS) with parking reservation in dynamic stochastic environments. Specifically, we first employ an exponential model to capture the nonlinear interrelationship among the elastic demand, travel price, and travel time. The travel time is affected by exogenous congestion and endogenous congestion. Then, we develop a novel queueing network model for ECS. In this model, all the arriving and serving processes are non-homogeneous Poisson processes, and idle probabilities are introduced to consider the stochasticity of the vehicle departure process. Next, we propose a rolling optimization framework based on the queueing network model to maximize the expected operating profit by optimizing hourly service pricing. The pricing decision in each time-bound optimization phase is achieved by a fast convergence algorithm (Improved Simultaneous Perturbation Stochastic Approximation). Numerical experiments in the Chengdu case reveal some interesting findings.

本文将流体排队理论与滚动地平线方法相结合，研究了动态随机环境下带车位预约的电动汽车共享系统的实时定价方案。具体而言，我们首先采用指数模型来捕捉弹性需求、出行价格和出行时间之间的非线性相互关系。出行时间受外生拥堵和内生拥堵的影响。然后，我们建立了一个新的ECS排队网络模型。在该模型中，所有到达和服务过程都是非齐次泊松过程，并引入空闲概率来考虑车辆出发过程的随机性。接下来，我们提出了一个基于排队网络模型的滚动优化框架，通过优化小时服务定价来实现期望运营利润最大化。采用一种快速收敛算法（改进的同步摄动随机逼近算法）来实现每个限时优化阶段的定价决策。成都案例的数值实验揭示了一些有趣的发现。

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引用次数: 0

Reinforcement learning control in complex freeway weaving sections under mixed traffic and platoon formation 混合交通下高速公路复杂交织路段的强化学习控制

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2527489

Anastasios Bithas , Emmanouil Kampitakis , Eleni I. Vlahogianni

Ramp Metering (RM) and Variable Speed Limits (VSL) are two widely studied and implemented ITS methods that can improve freeway traffic conditions under high traffic flows. In this work, a novel reinforcement learning (RL) control approach is introduced, which integrates RM and VSL within mixed traffic conditions, while allowing for the spontaneous formation of connected autonomous vehicle (CAV) platoons. The RL agent is trained in a microscopic simulation environment representing a freeway weaving section, under diverse traffic demands and CAV market penetration rates. The results indicate the superiority of the proposed method compared to feedback control (ALINEA) and no control, reducing the cumulative Total Time Spent by up to 13.49% and 40.55% respectively. Finally, as the CAV market penetration rate increases, VSL becomes more effective than RM, and the agent’s margin of improvement decreases due to the capacity gain from the platoon formation.

匝道计量（RM）和可变限速（VSL）是两种被广泛研究和实施的智能交通系统（ITS）方法，可以改善高交通流量下的高速公路交通状况。在这项工作中，引入了一种新的强化学习（RL）控制方法，该方法在混合交通条件下集成了RM和VSL，同时允许自发形成连接的自动驾驶车辆（CAV）队列。RL代理在代表高速公路编织路段的微观模拟环境中进行训练，该环境具有不同的交通需求和CAV市场渗透率。结果表明，与反馈控制（ALINEA）和无控制相比，该方法具有明显的优越性，累计总耗时分别减少13.49%和40.55%。最后，随着CAV市场渗透率的增加，VSL比RM更有效，agent的改进边际由于组队带来的容量增益而减小。

引用次数: 0

The Electric mobility ecosystem: a literature review of transport electrification to define research paths for Africa 电动交通生态系统：交通电气化的文献综述，以确定非洲的研究路径

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2535491

Chinenye L. Okafor , Joubert Van Eeden , MJ Booysen , Megan Bruwer

Electric mobility offers significant benefits, yet Africa lags in the transition. To support a just and sustainable shift, this scoping review assessed global and African research trends in the electric mobility ecosystem (EME) from 2000 to 2024. Using bibliometric analysis, the study mapped scholarly outputs and research networks across 12 EME elements. EV design, battery technology, energy supply, and charging infrastructure were the most studied topics, while policy, financing, and ownership were underrepresented. China, the USA, and India led global research; no African country ranked in the top 20. African contributions were minimal, highlighting gaps in capacity, collaboration, and funding. The review calls for greater interdisciplinary work and policy-focused research in Africa, along with stronger collaboration between Global North and South institutions. Addressing these gaps is essential for an inclusive and effective electric mobility transition.

电动交通带来了巨大的好处，但非洲在转型方面滞后。为了支持公正和可持续的转变，本范围审查评估了2000年至2024年全球和非洲电动交通生态系统（EME）的研究趋势。利用文献计量学分析，该研究绘制了12个EME要素的学术产出和研究网络。电动汽车设计、电池技术、能源供应和充电基础设施是研究最多的主题，而政策、融资和所有权的代表性不足。中国、美国和印度主导了全球研究；没有一个非洲国家进入前20名。非洲的贡献极少，突出了能力、合作和资金方面的差距。该评估报告呼吁在非洲开展更多跨学科工作和以政策为重点的研究，同时加强全球南北机构之间的合作。解决这些差距对于实现包容和有效的电动交通转型至关重要。

引用次数: 0

Examining nonlinear effect of influencing factors on traffic flow at urban road intersections using explainable machine learning 利用可解释机器学习研究城市道路交叉口交通流影响因素的非线性效应

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2528765

Xiao Pan , Yalin Ke , Jing Sun , Chunbo Zhang

Traffic congestion is a common problem in cities. Within the urban transportation system, road traffic flow plays a crucial role in determining the overall state of urban traffic conditions. This paper employs the eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) model to analyze the factors affecting traffic flow at intersections in the central districts of Shijiazhuang. A nonlinear analysis of traffic flow across various periods is conducted from three aspects, i.e. built environment, service facilities, and economic attribute. The results demonstrate temporal heterogeneity in the influencing degree of factors on traffic flow. The influence of factors on traffic flow, including distances to the city center, road density, and the number of catering and shopping facilities, are significant. There are complex and nonlinear relationships between traffic flow and influencing factors. These research results can offer valuable guidance for enhancing the operational efficiency of urban road traffic.

交通拥堵是城市的普遍问题。在城市交通系统中，道路交通流对城市交通状况的整体状态起着至关重要的作用。采用极值梯度Boosting-SHapley加性解释（XGBoost-SHAP）模型对石家庄市中心城区十字路口交通流的影响因素进行了分析。从建筑环境、服务设施和经济属性三个方面对不同时期的交通流进行非线性分析。结果表明，各因素对交通流的影响程度存在时间异质性。到市中心的距离、道路密度、餐饮和购物设施的数量等因素对交通流的影响是显著的。交通流与影响因素之间存在复杂的非线性关系。研究结果可为提高城市道路交通运行效率提供有价值的指导。

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引用次数: 0

Risk assessment of traffic violations in autonomous vehicles considering driving behavior 考虑驾驶行为的自动驾驶汽车交通违规风险评估

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2530584

Zhaowen Pang , Zhenbin Chen , Bin Sun , Jiayi Lu , Tianyang Gong , Xinjie Feng , Shichun Yang , Yaoguang Cao

The advent of autonomous vehicles brings profound changes to transportation while introducing new safety challenges, especially regarding traffic regulation compliance. This paper proposes a novel comprehensive risk assessment method considering driving behaviors to quantify traffic violation risks posed by autonomous systems. Grounded in Chinese traffic regulations, the method analyzes and defines key indicators of autonomous driving behavior and classifies risks into real-time and cumulative categories. By incorporating environmental safety entropy, a comprehensive risk profile is produced. Through simulation analysis using specific scenarios and datasets, combined with validation through on-road tests conducted with autonomous vehicles, the findings affirm the efficacy and practicality of this approach in quantifying the risks associated with autonomous driving. This research offers a scientific framework for quantifying risks and informing policy related to autonomous driving. It also aids in optimizing safety and reducing violations, enhancing the reliability of autonomous vehicle technology.

自动驾驶汽车的出现给交通运输带来了深刻的变化，同时也带来了新的安全挑战，特别是在交通法规合规方面。本文提出了一种考虑驾驶行为的综合风险评估方法，以量化自动驾驶系统的交通违规风险。该方法基于中国交通法规，分析和定义自动驾驶行为的关键指标，并将风险分为实时和累积两类。通过引入环境安全熵，得到了一个综合的风险概况。通过对特定场景和数据集的模拟分析，以及对自动驾驶汽车进行的道路测试验证，研究结果证实了该方法在量化自动驾驶相关风险方面的有效性和实用性。本研究为自动驾驶相关风险量化和政策制定提供了科学框架。它还有助于优化安全性，减少违规行为，提高自动驾驶汽车技术的可靠性。

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引用次数: 0

Low-carbon travel intention: examining the influence of built environment and policy perceptions in China using a structural equation modeling approach 低碳出行意愿：基于结构方程模型的中国建筑环境与政策认知影响研究

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2523370

Yingjie Sheng , Anning Ni , Lijie Liu , Linjie Gao , Yi Zhang , Guangnian Xiao , Yutong Zhu

Promoting low-carbon travel is critical for decarbonizing the transportation sector. This study extends the Theory of Planned Behavior (TPB) by integrating subjective perceptions of the built environment and transportation policies to understand their influence on travelers’ low-carbon travel intentions. Using structural equation modeling (SEM) based on survey data from Shanghai, China, the results show that traveler attitude has the strongest direct effect on intentions. Encouraging policies are found to be more effective than restrictive ones, underscoring the importance of positive incentives. To address population heterogeneity, machine learning models and SHapley Additive exPlanations (SHAP) analysis are applied to identify key demographic factors, including age, income, and fuel costs, for targeted policy design. The findings provide theoretical and practical insights for policymakers aiming to promote sustainable travel behavior through tailored strategies.

促进低碳旅行对于使交通运输部门脱碳至关重要。本研究通过整合建筑环境和交通政策的主观感知来扩展计划行为理论（TPB），以了解它们对旅行者低碳出行意愿的影响。利用结构方程模型（SEM）对上海地区的调查数据进行分析，结果表明游客态度对旅游意向的直接影响最大。研究发现，鼓励政策比限制政策更有效，强调了积极奖励的重要性。为了解决人口异质性问题，应用机器学习模型和SHapley加性解释（SHAP）分析来确定关键的人口因素，包括年龄、收入和燃料成本，以制定有针对性的政策。研究结果为旨在通过量身定制的策略促进可持续旅行行为的政策制定者提供了理论和实践见解。

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引用次数: 0

Analysis of conflict risk causes in urban expressway diverging areas: considering unobserved heterogeneity and their interactions 城市快速路分流区冲突风险成因分析：考虑未观测异质性及其相互作用

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2537173

Jiaqiang Wen , Nengchao Lyu , Yanyong Guo , Jianatihan Jinsihan

Urban expressway diverging areas face higher conflict risks due to complex weaving movements. This study aims to explore the determinants influencing conflict risk in diverging areas. High-resolution trajectory data totaling 32.1 hours from six expressway diverging areas in Wuhan were collected using unmanned aerial vehicles. By incorporating vehicle kinematic information, a new method for identifying traffic conflicts in a two-dimensional plane was defined. Based on this, a correlated random parameters multinomial logit model with heterogeneity in means was developed by relaxing fixed constraints on the means of random parameters, and allowing potential correlations among random parameters. The results show that exit configuration, speed difference between lanes, acceleration, and vehicle in exit significantly influence conflict risk, with their estimates exhibiting random effects. The influencing factors for lateral and longitudinal conflict risks differ. The findings provide insights for improving traffic safety in expressway diverging areas.

城市快速路分流区由于交织运动复杂，面临着较高的冲突风险。本研究旨在探讨影响不同地区冲突风险的决定因素。利用无人机采集武汉市6个高速公路分流区32.1小时高分辨率轨迹数据。结合车辆运动信息，定义了一种二维平面上的交通冲突识别方法。在此基础上，通过放宽随机参数均值的固定约束，允许随机参数之间存在潜在的相关性，建立了具有均值异质性的相关随机参数多项logit模型。结果表明，出口配置、车道间速度差、加速度和出口车辆对冲突风险有显著影响，且它们的估计具有随机效应。横向和纵向冲突风险的影响因素不同。研究结果为提高高速公路分流区交通安全水平提供了参考。

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引用次数: 0

Higher sense of power, less dangerous driving behaviors: perceived control as mediator and subjective social status as moderator 权力感越高，危险驾驶行为越少：知觉控制为中介，主观社会地位为调节因子

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2533009

Tianxiao Wang , Anqi Xie , Yan Ge , Weina Qu

To promote road safety, it is essential to determine the various self-perception factors influencing dangerous driving behaviors and explore the inner mechanisms. The main purpose of this study was to investigate the effects of sense of power, perceived control, and subjective social status on dangerous driving behaviors in a sample of drivers located in China. A total of 426 drivers completed the questionnaires on sense of power, perceived control, and subjective social status and the driver behavior questionnaire (DBQ). The results of path analysis showed that (1) sense of power positively predicted perceived control and negatively predicted dangerous driving behaviors; (2) perceived control negatively predicted errors and lapses of DBQ and mediated the relationship between them and sense of power; and (3) subjective social status moderated the effect of sense of power on perceived control. The present study expands the scope of the influencing factors of dangerous driving behaviors while providing practical guidance in the domain of safe driving.

为了促进道路安全，必须确定影响危险驾驶行为的各种自我知觉因素，并探索其内在机制。本研究的主要目的是探讨权力感、控制感和主观社会地位对中国司机危险驾驶行为的影响。共426名驾驶员完成了权力感、控制感、主观社会地位问卷和驾驶员行为问卷（DBQ）。路径分析结果表明：(1)权力感正向预测控制知觉，负向预测危险驾驶行为；(2)知觉控制负向预测DBQ错误和过失，并介导它们与权力感的关系；(3)主观社会地位调节了权力感对控制感的影响。本研究拓展了危险驾驶行为影响因素的范围，为安全驾驶领域提供了实践指导。

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引用次数: 0

Assessing the impact of penalty strategies for cancellation behavior on the sustainability of demand-responsive transit service 评估取消行为的惩罚策略对需求响应型交通服务可持续性的影响

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2532274

Hongfei Wang , Hongzhi Guan , Huanmei Qin , Pengfei Zhao , Weiwei Liang

The increasing urbanization has led to a growing demand for flexible transportation solutions, making demand-responsive transit (DRT) a promising alternative to traditional public transport. Nevertheless, passenger cancellation behavior poses the challenges to the reliability and sustainability of DRT services. To assess the impact of cancellation behavior, a two-phase optimization framework is established in this study. Penalty strategies can be divided into time-based penalty strategy and fixed-penalty strategy. Additionally, we construct a hybrid heuristic algorithm that combines variable neighborhood search (VNS) and rolling horizon in response to dynamic characteristics. Numerous experiments on a realistic network in Beijing are further performed to evaluate the impact of cancellation behavior on the DRT service. At the same level of penalty cost, DRT service with time-based penalty strategy was superior to the service with fixed penalty strategy. This study is sconducive to the economic, social, and environmental sustainability of DRT services.s.

日益增长的城市化导致对灵活交通解决方案的需求不断增长，使需求响应式交通（DRT）成为传统公共交通的一个有希望的替代方案。然而，乘客的取消行为对DRT服务的可靠性和可持续性提出了挑战。为了评估取消行为的影响，本研究建立了一个两阶段优化框架。惩罚策略可分为基于时间的惩罚策略和固定惩罚策略。此外，我们构造了一种结合可变邻域搜索（VNS）和滚动地平线的混合启发式算法来响应动态特性。为了进一步评估取消行为对DRT服务的影响，在北京的实际网络上进行了大量的实验。在相同惩罚成本水平下，基于时间的惩罚策略的DRT服务优于固定惩罚策略的DRT服务。本研究有助于提高DRT服务的经济、社会和环境可持续性。

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引用次数: 0

A post-disaster emergency logistics location-routing optimization method considering road risks 考虑道路风险的灾后应急物流选址路径优化方法

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2026-01-02 DOI: 10.1080/19427867.2025.2528818

Yikai Luo , Yuge Wang , Yan Li , Jinhua Xu , Hui Hu

Sudden natural disasters pose significant threats to lives, property, and infrastructure, underscoring the critical need to determine optimal locations for emergency reserve depots and plan efficient transportation routes. Therefore, this paper focuses on the location-routing optimization in emergency logistics following a disaster. In addition to the traditional considerations of rescue time and cost, we propose a novel approach that incorporates risk factors of transportation routes. The aim is to avoid secondary disasters during transport and improve rescue efficiency. Using the 2008 Wenchuan earthquake as a case, results show the hybrid machine learning CPSO-XGBoost approach employed as our road segment risk identification model achieves 0.9668 accuracy. Compared to NSGA-II, the improved ACONSGA-II algorithm shows superior optimization capability. Unlike traditional emergency location-routing models that overlook road risks, the proposed approach identifies safer location-routing solutions for material transport while maintaining rescue time and cost.

突发自然灾害对生命、财产和基础设施构成重大威胁，因此迫切需要确定应急储备仓库的最佳地点并规划有效的运输路线。因此，本文主要研究灾害后应急物流中的选址路径优化问题。在传统的考虑救援时间和成本的基础上，我们提出了一种考虑运输路线风险因素的新方法。目的是避免运输过程中的二次灾害，提高救援效率。以2008年汶川地震为例，采用混合机器学习CPSO-XGBoost方法作为道路路段风险识别模型，准确率达到0.9668。与NSGA-II相比，改进的ACONSGA-II算法具有更强的优化能力。与忽略道路风险的传统应急位置路由模型不同，该方法确定了更安全的物料运输位置路由解决方案，同时保持了救援时间和成本。

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引用次数: 0

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