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

英文中文

Linear regression coupled Wasserstein generative adversarial network for direct demand modeling of ride-hailing trips in Chicago and Austin 线性回归耦合瓦瑟斯坦生成对抗网络，用于芝加哥和奥斯汀的打车出行直接需求建模

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: 10.1080/19427867.2024.2372944

Md Hishamur Rahman , Masnun Abrar , Shakil Mohammad Rifaat

Accurate estimation of ride-hailing demand and understanding of its influencing factors are necessary for modern-day transportation planning. Although modern machine learning techniques improve the predictive accuracy of zone-to-zone direct demand models, they lack inference ability due to their complex model structure. Furthermore, zone-to-zone direct demand models suffer from limited sample size for machine learning due to fixed number of origin-destination (OD) pairs. To overcome these limitations, we propose a linear regression coupled Wasserstein generative adversarial network (LR-WGAN) for direct demand modeling of zone-to-zone ride-hailing trips that captures both linear explanatory components and non-linear patterns and improves the predictive accuracy by generating data to expand the OD sample size. The proposed LR-WGAN is found to significantly improve the predictive accuracy of OD ride-hailing demand in Chicago and Austin. Furthermore, the linear explanatory component of the model is utilized to draw inferences regarding the relationship between OD ride-hailing demand and predictor variables.

准确估算打车需求并了解其影响因素对于现代交通规划非常必要。尽管现代机器学习技术提高了对打车需求的估算能力，但仍存在一些问题。

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引用次数: 0

A novel car-following model for adaptive cruise control vehicles using enhanced intelligent driver model 使用增强型智能驾驶员模型的新型自适应巡航控制车辆跟车模型

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: 10.1080/19427867.2024.2376409

Jun Bai , Suyi Mao , Jaeyoung Jay Lee

This paper proposes Enhanced Intelligent Driver Model for Adaptive Cruise Control (EIDM-ACC) vehicles, a novel car-following model that dynamically adjusts desired speed and considers acceleration inertia. The EIDM-ACC model is compared with two widely used models for simulating ACC vehicles – the ACC model developed by the PATH Project (PATH-ACC) at the University of California Transportation Institute and Continuous Asymmetric Optimal Velocity Relative Velocity (CAOVRV) model. Three models are calibrated and cross-validated using real vehicle trajectory data from the OpenACC dataset. Results show that the EIDM-ACC outperforms the other two models in small and large fluctuation stages. In addition, EIDM-ACC has better performance in capturing the instability and energy consumption of ACC vehicles, and also has advantages over the other two models in terms of safety.

本文提出了自适应巡航控制（EIDM-ACC）车辆的增强型智能驾驶员模型，该模型是一种动态调整期望速度并考虑加速度惯性的新型车辆跟随模型。将EIDM-ACC模型与两种广泛应用于ACC车辆仿真的模型——美国加州大学交通研究所PATH项目开发的ACC模型（PATH-ACC）和连续非对称最优速度相对速度（CAOVRV）模型进行了比较。使用来自OpenACC数据集的真实车辆轨迹数据对三个模型进行校准和交叉验证。结果表明，EIDM-ACC模型在小波动和大波动阶段均优于其他两种模型。此外，EIDM-ACC在捕捉ACC车辆的不稳定性和能耗方面具有更好的性能，在安全性方面也优于其他两种车型。

引用次数: 0

A method based on Vision Transformer and multiple image information for vehicle lane-changing recognition in mixed traffic and connected environment 基于视觉变换器和多图像信息的混合交通和互联环境下车辆变道识别方法

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: 10.1080/19427867.2024.2377900

Peng Ji , Chuang Zhang , Zichen Zhang

To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point.

为了提高自动驾驶汽车在混合交通和互联环境中的安全性，识别人类驾驶车辆的变道意图是自动驾驶汽车的关键。本文提出了一种新的LCI识别方法，该方法从目标车辆及其相邻车辆的行驶状态和相对运动中提取特征。该方法对时间序列数据进行短时傅里叶变换、格拉曼角和场和格拉曼角差场，生成三幅灰度图像，并通过图像处理技术将其合并为一幅信息融合图像（IFI）。然后将ifi分类为保持车道、左变车道和右变车道三种类型，使用视觉转换模型和迁移学习来加快收敛速度并降低训练成本。实验结果表明，该方法优于传统方法，在变道点前识别LCI 3s的准确率达到95.65%。

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引用次数: 0

A tripartite evolutionary game study on the governance of online catering riders’ traffic violations from the perspective of collaborative regulation 协同规制视角下网络餐饮骑手交通违法治理的三方进化博弈研究

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: 10.1080/19427867.2024.2378612

Liang Xiao , Hongyong Li , Fumao Yu , Yuqi Wang

In the online catering industry, delivery riders’ traffic violations significantly threaten public transportation safety, inadequately addressed by current regulations from the government and online platforms. This paper proposes a government-led collaborative regulatory mechanism with active platform participation. A tripartite evolutionary game model, including the government, platforms, and riders, evaluates this approach. Findings suggest that collaborative regulation forms when the combined start-up and subsidy costs of government-led regulation are less than the total benefits of collaboration and when the cost difference between active and passive platform participation is less than the total benefit of active participation. Effective regulation occurs when combined penalties for riders’ violations exceed the profit difference between illegal and compliant deliveries. Reducing government regulation start-up costs and increasing penalties and enforcement probabilities can promote compliance among riders. Excessive subsidies to platforms do not effectively control violations, indicating the need for balanced subsidy allocation for optimal regulatory effectiveness.

在网络餐饮行业，外卖骑手的交通违规行为严重威胁着公共交通安全，而目前政府和网络平台的监管规定并未充分解决这一问题。本文提出一种政府主导、平台积极参与的协同监管机制。一个三方博弈进化模型，包括政府、平台和乘客，对这种方法进行了评估。研究结果表明，当政府主导的监管启动成本和补贴成本之和小于协作总收益，且平台主动参与和被动参与的成本差小于主动参与总收益时，协同监管就形成了。当对骑手违规行为的综合处罚超过违规和合规交付之间的利润差时，有效的监管就会出现。降低政府监管启动成本，增加处罚和执行概率，可以促进乘客的合规。对平台的过度补贴并不能有效控制违规行为，这表明需要平衡补贴分配以获得最佳监管效果。

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引用次数: 0

Optimizing social costs in post-pandemic humanitarian distribution models 在大流行病后人道主义物资分配模式中优化社会成本

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: 10.1080/19427867.2024.2370669

Tianyang Cai , Yusen Ye , Hong Yan

In response to the COVID-19 pandemic, the Red Cross Society of China played a crucial role in distributing medical donations, but the initial efforts were inefficient and neglected medical personnel’s welfare. This study proposes a time-sensitive humanitarian distribution model that optimizes the social costs by integrating logistics and deprivation costs that cares about human suffering. We ues the Gini coefficient to evaluate delays in distribution, aiding trade-off analysis between logistics efforts and social welfare. Our findings show that the proposed model improves the Gini coefficient by an average of 33.96% across 500 scenarios. Additionally, investing 23.7% more in logistics costs reduces the Gini coefficient by 0.1, enhancing the social welfare of medical supplies distribution. Sensitivity analysis examines the impact of time delay and cost investment on the Gini coefficient, offering insights into balancing logistics investments and social welfare.

在应对 COVID-19 大流行时，中国红十字会在分发医疗捐款方面发挥了关键作用，但初期工作效率低下，忽视了医务人员的作用。

引用次数: 0

Integrated optimization of train line planning and timetabling: a new method of changing train operation zone and direct-service of cross-line ODs 列车线路规划和时刻表编制的综合优化：改变列车运行区和跨线 OD 直接服务的新方法

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: 10.1080/19427867.2024.2374171

Ruxin Wang , Lei Nie , Yuyan Tan

Cross-line operation increases accessibility to the railway network. However, its long-distance operation is quite difficult due to the transport resource limitations. To provide good transportation plans for operators and more direct services for cross-line passengers, we propose a new method of changing operation zones of cross-line trains, which can be summarized as the integrated train line planning and timetabling problem. A model is established to minimize deviation of same-line trains from ideal timetables and maximize cross-line origin-destination direct services. A Lagrangian relaxation approach and an improved priority-rule heuristic algorithm are developed to solve large-scale problems and obtain feasible solutions. Reality-based instances are conducted on the Beijing-Shanghai high-speed railway and its connecting lines to verify the performance of the model and solution approach. The results show the benefit of changing train operation zones, and the model and approach can help to generate a good balance between operating profit and passenger demand.

跨线运营增加了铁路网的通达性。然而，由于运输资源的限制，它的长途运营相当困难。为了给运营商提供良好的运输计划，为跨线旅客提供更直接的服务，我们提出了一种新的跨线列车运营区域变更方法，可归纳为列车线路规划与调度一体化问题。建立了以同线列车与理想时刻表偏差最小和始发直达线服务最大化为目标的模型。提出了一种求解大规模问题的拉格朗日松弛法和一种改进的优先规则启发式算法。以京沪高速铁路及其连接线为实例，验证了模型和求解方法的有效性。结果表明，改变列车运营区域的效益，该模型和方法有助于在运营利润和乘客需求之间取得良好的平衡。

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引用次数: 0

Exploring the impact of time pressure on motorized two-wheeler riders’ over-speeding behavior 探究时间压力对电动两轮车骑行者超速行为的影响

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: 10.1080/19427867.2024.2368348

Ishant Sharma , Monik Gupta , Sabyasachee Mishra , Nagendra R. Velaga

Despite being inexpensive and the most accessible travel mode, motorized two-wheelers (MTW) are more prone to crashes than other travel modes. Over-speeding is one of the principal causes of such MTW-related crashes. Past studies are nonexistent in exploring the impact of time pressure and psychological constructs on MTW riders’ over-speeding behavior. Therefore, this paper captures the impact of the time pressure situations and identifies psychological segments (based on travel time-related anxiety) on the over-speeding behavior among MTW riders. A two-step modeling approach, including latent class analysis and multinomial logit model (MNL), was utilized for a stated preference survey of 513 Indian MTW riders. The latent class analysis identified three different psychological segments, i.e. minimally anxious, moderately anxious, and highly anxious. Results showed that over-speeding behavior is more likely to increase with travel time-related anxiety levels and hurriedness. The findings are expected to offer potential policy implications for mitigating speeding-related violations.

尽管机动两轮车（MTW）价格低廉，而且是最方便的出行方式，但与其他出行方式相比，机动两轮车更容易发生碰撞事故。超速是造成此类交通事故的主要原因之一。

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引用次数: 0

Private parking space owners’ choice behavior of different sharing modes: hybrid choice model using justified latent variables 私人车位车主对不同共享模式的选择行为：使用合理潜变量的混合选择模型

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-03-16 DOI: 10.1080/19427867.2024.2366399

Zengrui Wang , Xia Luo , Yiyuan Zhang , Qixin Mai

Private parking spaces account for a large proportion of potential parking resources, whose utilization rate could be raised by sharing them. To investigate private parking space owners’ sharing choice behavior, this paper applies a framework of Combined Technology Acceptance Model and the Theory of Planned Behavior for a more comprehensive consideration of psychological attitudes on these suppliers in urban residential areas. A web-based survey was conducted to collect data. The justified factors are then incorporated in hybrid choice model (HCM) as latent variables. HCM results showed that incorporation of latent variables such as perceived risk provides better fitting effect than traditional multinominal models, and that revenues and community participance are influential factors for sharing choice. Our findings indicate behavioral intention framework could serve as argumentation of rational selection of latent variables. These findings could also support better implementation of shared parking from managerial and operational perspectives.

私人车位占潜在停车资源的很大一部分，通过共享可以提高其利用率。为了研究私人停车位所有者共享停车位的行为，我们采用了以下方法。

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引用次数: 0

Modeling travel mode choice behavior for planned special events using CPT-based HI-MADM approach 使用基于 CPT 的 HI-MADM 方法模拟计划中的特别活动的出行方式选择行为

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-03-16 DOI: 10.1080/19427867.2024.2354015

Hongjun Cui , Wanru Sun , Minqing Zhu , Xinwei Ma , Jinping Xie

Planned special events (PSEs) can significantly increase traffic demand, posing greater challenges than daily operations. In response, host cities often invest heavily in transportation upgrades. This study proposes a model integrating cumulative prospect theory (CPT) and multiple-attribute decision-making to understand mode choice behavior of PSE attendees. This integration enables direct extraction of traveler preference information, offering a more suitable description of the decision-making process. The decision matrix considers mode and attribute heterogeneity. The reference point for CPT is derived from previous PSE trips or commutes. Furthermore, this paper assesses asymmetric nonlinear sensitivity and distorted perceptions of different attributes. Analysis of mode-choice behavior for PSE participation, using survey data from Tianjin, China, validates the approach. It better characterizes PSE choice behaviors and demonstrates varying sensitivity to gains and losses, dependent on different attributes and distances. Thus, event planners should prioritize improving travel time and convenience to increase public transport mode share.

计划中的特别活动会大幅增加交通需求，比日常运作带来更大的挑战。作为回应，主办城市往往在交通升级方面投入巨资。本研究提出了一个整合累积前景理论和多属性决策的模型来理解PSE参与者的模式选择行为。这种集成可以直接提取旅行者偏好信息，为决策过程提供更合适的描述。决策矩阵考虑了模式和属性的异质性。CPT的参考点来源于以前的PSE旅行或通勤。此外，本文还评估了不同属性的非对称非线性敏感性和扭曲感知。利用中国天津的调查数据，对PSE参与的模式选择行为进行了分析，验证了该方法的有效性。它更好地描述了PSE选择行为，并展示了不同属性和距离对收益和损失的不同敏感性。因此，活动策划者应该优先考虑改善出行时间和便利性，以增加公共交通方式的份额。

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Regression approach to analyze the travel characteristics of university students 分析大学生旅行特点的回归方法

IF 3.3 3区工程技术 Q2 TRANSPORTATION

Transportation Letters-The International Journal of Transportation Research

Pub Date : 2025-03-16 DOI: 10.1080/19427867.2024.2366327

Bisma Khalid , Zia ur Rehman , Farhan Haider , Ammad Hassan Khan , Qaiser Naheed Hashmi , Ali Raza , Muhammad Sohail Jameel

This study at the University of Engineering and Technology (UET), Lahore, examined students’ travel behavior, mode choice and constraints. Data from 1,449 students were analyzed using various statistical methods, including descriptive analysis, latent class analysis (LCA), multinomial logistic regression (MNL), correlation, and linear regression. The model’s accuracy was evaluated by calculating the percent error. Walking was the most popular mode of transportation within the campus, preferred by 37% of students. Most students traveled 1-15 km, taking 10-15 minutes, with off-campus students typically making three trips to their destination. Safety concerns significantly influenced 37% of students’ travel behavior due to built-in constraints.

拉合尔工程技术大学（UET）的这项研究调查了学生的出行行为、模式选择和约束。采用描述性分析、潜类分析（LCA）、多项逻辑回归（MNL）、相关分析和线性回归等多种统计方法对1449名学生的数据进行分析。通过计算误差百分比来评价模型的精度。步行是校园内最受欢迎的交通方式，37%的学生更喜欢步行。大多数学生的行程为1-15公里，耗时10-15分钟，校外学生通常需要三次才能到达目的地。由于内在约束，安全问题显著影响了37%的学生的旅行行为。

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