Transportmetrica A-Transport Science最新文献

英文中文

A robust optimisation approach of train timetabling for freight transportation using high-speed railway 利用高速铁路货运列车时刻表的稳健优化方法

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2385874

Shengdong Li , Ming Zhu , Dajie Zuo , Chuijiang Guo , Li Shi

To ensure reliable services for high-speed freight rail transport, we address the issue of robust freight train timetabling by incorporating buffer times. Unlike passenger trains with prescribed timetables, high-speed freight train timetables need to be scheduled from the beginning and integrated into existing passenger schedules. To achieve reliable high-speed rail freight services, we introduce robust parameters that determine the buffer times for train operations and station stops. Using these, we develop an integer linear robust model aimed at maximizing timetable robustness, considering given control parameters, while minimizing travel and deviation times. We employ an integer Benders decomposition algorithm to solve this model efficiently. Our robust optimization method is validated through experiments using data from the Chengdu-Chongqing high-speed railway, demonstrating the efficacy and efficiency of our model and algorithm, especially in balancing efficiency and robustness.

为确保高速货运铁路运输的可靠服务，我们通过加入缓冲时间来解决货运列车时刻表编制的稳健性问题。与客运列车规定的时间不同，货运列车的缓冲时间是由列车运行时间决定的。

引用次数: 0

Cooperative lane-changing in mixed traffic: a deep reinforcement learning approach 混合交通中的合作变道：一种深度强化学习方法

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2343048

Xue Yao , Zhaocheng Du , Zhanbo Sun , Simeon C. Calvert , Ang ji

Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs are explicitly modelled, and different leader-follower compositions are considered in CLCMT, which provides a high-fidelity DRL learning environment. A feedback module is established to enable interactions between the decision-making layer and the manoeuvre control layer. Simulation results show that the increase in CAV penetration leads to safer, more comfort, and eco-friendly lane-changing behaviours. A CAV-CAV lane-changing scenario can enhance safety by 24.5%–35.8%, improve comfort by 8%–9%, and reduce fuel consumption and emissions by 5.2%–12.9%. The proposed CLCMT promises advantages in the lateral decision-making and motion control of CAVs.

深度强化学习（DRL）在自动驾驶汽车决策和执行控制方面取得了显著进展，从而改善了交通性能。本文介绍了一种基于 DRL 的机械...

引用次数: 0

A general coupled morning–evening traffic equilibrium model with rideshare, ride-hailing, and public transit services 包含共享出行、打车和公共交通服务的一般耦合早晚交通平衡模型

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2357160

Wei Gu , Michael Zhang , Maged Dessouky , Jong-Shi Pang

We develop a general equilibrium model to capture the complex interactions between different modes, such as solo driving, public transit, as well as rideshare and ride-hailing services such as Uber and Lyft, under a joint morning and evening commute framework. Formulated as a variational inequality (VI) and equivalently as a mixed complementarity problem (MiCP), the model allows (a) travelers to switch between different transportation modes and (b) passengers from different Origin-Destination (OD) pairs to share a ride together. The computational results on the Sioux-Falls network show that our model captures the possible mode switches and the coupling effects between morning and evening commutes. Furthermore, our numerical examples demonstrate that modelling morning and evening commutes separately tends to overestimate the travelers' disutility and the average Vehicle Miles Traveled (VMT) in the network.

我们建立了一个一般均衡模型，以捕捉不同模式之间复杂的相互作用，如单独驾驶、公共交通以及Uber等共享和打车服务。

引用次数: 0

A Lagrangian approach for variable speed limit implementation in C-ITS framework 在 C-ITS 框架内实施可变速度限制的拉格朗日方法

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2347604

Eléonore Fauchet , Kinjal Bhattacharyya , Pierre-Antoine Laharotte , Nour-Eddin El Faouzi

With the development of Cooperative-Intelligent Transport System (C-ITS) technologies, new strategies based on embedded technologies have emerged to manage road networks. This paper focuses on adapting to this connectivity context a Variable Speed Limit (VSL) system to detect shockwaves and anticipate their propagation based on the kinematic wave theory to dampen them. We provide an alternative framework to adapt the VSL strategy, well-suited for Eulerian approaches, into a Lagrangian context. While the Eulerian approach is based on Loop Detector (LD) and macroscopic traffic indicators (e.g. flow, density), our Lagrangian approach relies on Road Side Units (RSUs) that record the GPS traces shared by Connected Vehicles (CVs). Based on the combination of CV trajectories, Fundamental and Space-Time Diagrams theory, shockwave estimation and prediction processes directly operate on congestion waves, which release the estimation issue for traffic density. The simulation-based analysis reveals that the performance of the Lagrangian approach is comparable to the Eulerian configurations.

随着协同智能交通系统（C-ITS）技术的发展，出现了基于嵌入式技术的道路网络管理新策略。本文重点探讨了如何适应...

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引用次数: 0

Signal control for overflow prevention at intersections using partial connected vehicle data 利用部分联网车辆数据防止交叉路口溢流的信号控制

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2361648

Jing Zhao , Tianyu Yao , Cheng Zhang , Muhammad Awais Shafique

Various signal control methods assumed that the bottleneck capacity and traffic volume are known. However, both may be unknown in nonrecurrent congestion cases, such as traffic accidents. In this study, a signal control optimisation method is developed for overflow prevention considering unknown traffic volume and bottleneck dropped capacity. The proposed method predicts remaining space at the junction exits and arrival–departure curves using partial connected vehicle data. Subsequently, the signal control is reallocated using the model predictive control technique. The results of case study show that the model is valid to prevent overflow when the connected vehicle penetration rate exceeds 10%. The average delay of the proposed method is reduced by 48.56% and 24.49% compared with those of the adaptive signal control method considering the queue length of only the approach lanes and that considering the queue lengths of both the approach and exit lanes but without prediction, respectively.

各种信号控制方法都假定瓶颈处的通行能力和交通流量是已知的。然而，在交通事故等非经常性拥堵情况下，这两者可能都是未知的。在这种情况下...

引用次数: 0

Jointly modelling activity start time, travel mode, companionship, and destination location choices 联合模拟活动开始时间、旅行方式、同伴和目的地位置选择

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2372025

Shivam Khaddar , Varun Varghese , Mahmudur Rahman Fatmi , Makoto Chikaraishi

Activity-travel decisions, including time use (i.e. activity start time), travel choices (i.e. mode and companionship), and destination location choices are interdependent and complex in nature. They are often controlled by observed factors such as sociodemographic, vehicle ownership, built environment, land use, and unobserved factors like attitude, preference, or habits which typically are not captured in the survey data. To accommodate these complex interactions and capture trade-offs among the time use-travel-land use choices, this study proposes a joint discrete choice model by introducing unobserved factors that are common to them. The model results confirm the presence of complex interdependencies among these choice dimensions. In addition, this study highlights the contribution of observed and unobserved factors introduced in the model by calculating the total variance of utility differences. Lastly, this study provides behavioural insights on the activity-travel patterns, which can be further used to develop robust travel demand management policies.

活动-旅行决策，包括时间使用（即活动开始时间）、旅行选择（即方式和同伴）以及目的地位置选择，在本质上是相互依存和复杂的。因此...

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引用次数: 0

An uncertainty cognition-based game model for lane-changing process in mixed driving environment 基于不确定性认知的混合驾驶环境下变道过程博弈模型

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2368010

Yuqing Cao , Hao Sun , Panfei Sun

The non-instantaneous nature of lane-changing demands real-time adaptability for autonomous vehicles (AVs) to respond continuously changing traffic conditions. In the mixed environment where AVs coexist with human-driven vehicles (HVs), the lack of inter-vehicle information exchange necessitates the Nash Equilibrium as best response. In addition, the unpredictable intentions of HV introduce uncertainty, posing a challenge for the solution of equilibrium. This paper introduces an aggressiveness parameter reflecting human drivers' yielding tendencies to autonomous vehicles and enables human-like uncertainty cognition during lane changes. To meet the practical solution requirements of the uncertainty cognition-based game model, we propose Proactive Equilibrium Strategy Algorithm (PESA) based on two-stage Nash equilibrium and anticipation of the opponent's next-stage strategy. Utilising Next Generation Simulation (NGSIM) as environmental data, PESA shows safer and more efficient lane-changing behaviour and leads to more favourable post-lane-changing traffic conditions compared to actual data outcomes.

变道的非瞬时性要求自动驾驶汽车（AV）具有实时适应能力，以应对不断变化的交通状况。在混合环境中，自动驾驶汽车（AV）与其他自动驾驶汽车（AV）共同行驶。

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引用次数: 0

Car-following informed neural networks for real-time vehicle trajectory imputation and prediction 用于实时车辆轨迹估算和预测的汽车跟踪信息神经网络

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2374523

Yu-Hang Yin , Xing Lü , Shu-Kai Li , Li-Xing Yang , Ziyou Gao

Vehicle trajectory information is a crucial part of improving the efficiency and the safety of the ITS. Data missing or irregular sampling in the real-world road traffic makes it hard to obtain accurate real-time vehicle trajectories. In this paper, we focus on trajectory imputation and prediction tasks with small data (magnitude set as $ 10^1 $

10^{1}

and $ 10^2 $

10^{2}

). Limited by insufficient data, the simulation results of the existing data-driven algorithms are unsatisfactory. With car-following models integrated as prior physical information to constrain the training process, we design the car-following-informed neural network (CFINN). A multi-head self-attention layer is attached to the fully connected network layer to extract vehicle features. Different from the structure of most neural networks in regression analysis, an extra physics-based dataset is constructed in the CFINN. The loss function consists of two parts including the given trajectory's fitting error and the generated trajectory's residual error. We embed the gated recurrent unit-based encoder–decoder layer to the CFINN framework for trajectory predictions. The rationality and the superiority of our model are validated on the NGSIM dataset and the HighD dataset. Compared with baseline models in both single-vehicle and queue-typed trajectory imputation experiments, lower error can be achieved via the CFINN and coefficients of car-following models can be calibrated. According to driving regimes derived from CFINN-based trajectory prediction experiments, we discuss the impact of cut-in behaviours on the target vehicle and carry out the kinetic analysis. The novel neural network model driven by both data and physical knowledge provides technical support in vehicle status assessments and trajectory predictions.

车辆轨迹信息是提高智能交通系统效率和安全性的关键部分。现实世界道路交通中的数据缺失或不规则采样导致很难获得准确的车辆轨迹信息。

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引用次数: 0

An integrated approach for an aircraft routing and fuel tankering problem 飞机航线和油箱问题的综合解决方法

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2392164

Hyemin Yoo , Junhyeok Lee , Ilkyeong Moon

In the highly competitive airline industry, it is crucial for airlines to minimise operating costs and strengthen their competitiveness. Fuel costs constitute a substantial portion of airline operating costs and are directly related to airlines' profits. In this research, we support the airlines' fuel management by presenting a model that integrates aircraft routing and fuel tankering decisions. The effectiveness of fuel tankering can be enhanced by considering the aircraft routing decisions together, leading to a significant cost reduction for airlines. The integrated model is developed as a mixed-integer linear programming model. The symmetry-breaking methods and decomposition-based heuristic algorithm are also proposed to alleviate the computational burden. A set of computational results illustrates the significant cost-saving effects of the proposed model and the heuristic algorithm.

在竞争激烈的航空业，最大限度地降低运营成本、增强竞争力对航空公司来说至关重要。燃油成本占航空公司运营成本的很大一部分。

引用次数: 0

On-demand airport slot management: tree-structured capacity profile and coadapted fire-break setting and slot allocation 按需机场机位管理：树状结构的容量分布和共同适应的防火墙设置与机位分配

IF 3.1 2区工程技术 Q2 TRANSPORTATION

Transportmetrica A-Transport Science

Pub Date : 2026-01-02 DOI: 10.1080/23249935.2024.2393224

Chunzheng Wang , Lei Yang , Minghua Hu , Yanjun Wang , Zheng Zhao

We propose an on-demand airport slot management (ODASM) approach to guide slot fire-break capacity setup and airport slot allocation. The ODASM consists of a tree-structured capacity profile and a coadapted fire-break setting and slot allocation model. The tree-structured capacity profile that is constructed using a decision tree aims to capture various fire-break capacity settings and their corresponding delays. It provides diversified fire-break schemes and delay references in the airport slot allocation process. The coadapted fire-break setting and slot allocation model are able to generate good coadaptation. That is, the fire-breaks are set to adapt to the preferences of airlines' requests to minimise the total displacements, and the airlines' requests are modified to adapt to the fire-breaks to satisfy a predetermined delay level. The approach has been tested at Shanghai Pudong Airport, and the results suggest that the ODASM can generate a moderate level of rescheduling while resulting in the expected delay that does not exceed a given acceptable delay level. It has been demonstrated that the ODASM can offer substantial advantages in contrast to a classic slot allocation method where the capacity are set as the maximum of the declared capacity. Moreover, due to the refined relationship between fire-break configuration and delay, the computation process of slot optimisation only takes a few minutes, which is completely viable for the application. This approach is compatible with the current slot guidelines while showing significant potential to improve the collaboration among stakeholders in slot management.

我们提出了一种按需机场机位管理（ODASM）方法，用于指导机位防火墙容量设置和机场机位分配。ODASM 包括一个树状结构的容量曲线和一个...

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