Transportation Research Part C-Emerging Technologies最新文献

{"title":"Using connected and automated vehicles to attenuate disturbances in freeway traffic","authors":"Benjamin Coifman ,&nbsp;Yuan Liu","doi":"10.1016/j.trc.2025.105497","DOIUrl":"10.1016/j.trc.2025.105497","url":null,"abstract":"<div><div>Congested traffic is characterized by disturbances propagating upstream through the queued vehicles. This work seeks to use connected and automated vehicles (CAVs) to mitigate slow and stop waves in congested freeway traffic with the goals of improving safety by reducing unexpected deceleration waves while improving fuel efficiency and emissions by reducing the need to accelerate after disturbances pass. To smooth out traffic disturbances, the CAV implements a jam absorption strategy that integrates the instantaneous state information from the downstream vehicles to forecast the trajectory of the CAV’s leader and proactively respond to changes in state that have not yet reached the lead vehicle. Using empirical vehicle trajectory data, it is shown that the methodology can rapidly nullify stop and slow waves. Examples include the conversion of a 10 sec stop wave to steady and smoothly flowing traffic over a span of 15 CAVs.</div><div>One of the key innovations in this work is how the traffic state is forecasted using a model that is much simpler than prior work in this area while still being able to dynamically adapt to changes in the evolving traffic state. This paper also explores the impacts of varying the look ahead range, acceleration limits, and the parameters of the forecasting model. It is shown that the method is robust, still performing well when the forecasting model’s calibration is poor. While the different permutations result in small changes, in all the experiments the methodology continues working as intended: the first CAV was able to nullify the stop waves and subsequent CAVs continued to anneal the disturbances.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105497"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDKFormer: Contextual deviation knowledge-based transformer for long-tail trajectory prediction","authors":"Yuansheng Lian ,&nbsp;Ke Zhang ,&nbsp;Meng Li","doi":"10.1016/j.trc.2025.105430","DOIUrl":"10.1016/j.trc.2025.105430","url":null,"abstract":"<div><div>Predicting the future movements of surrounding vehicles is essential for ensuring the safe operation and efficient navigation of autonomous vehicles (AVs) in urban traffic environments. Existing vehicle trajectory prediction methods focus primarily on improving overall performance, yet they struggle to address long-tail scenarios effectively. This limitation often leads to poor predictions in rare cases, significantly increasing the risk of safety incidents. Taking Argoverse 2 motion forecasting dataset as an example, we first investigate the long-tail characteristics in trajectory samples from two perspectives, individual motion and group interaction, and deriving deviation features to distinguish abnormal from regular scenarios. On this basis, we propose CDKFormer, a contextual deviation knowledge-based Transformer model for long-tail trajectory prediction. CDKFormer integrates an attention-based scene context fusion module to encode spatiotemporal interaction and road topology. An additional deviation feature fusion module is proposed to capture dynamic deviations in the target vehicle’s status. We further introduce a dual query-based decoder, supported by a multistream decoder block, to sequentially decode heterogeneous scene deviation features and generate multimodal trajectory predictions. Extensive experiments demonstrate that CDKFormer achieves state-of-the-art performance, significantly enhancing prediction accuracy and robustness for long-tailed trajectories compared to existing methods, thus advancing the reliability of AVs in complex real-world environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105430"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chance-constrained eco-driving control of connected autonomous vehicles in mixed traffic environment at signalized intersections with uncertain signal timings","authors":"Yongjie Xue ,&nbsp;Dongxuan Bai ,&nbsp;Yu Zhou ,&nbsp;Chuan Ding ,&nbsp;Hai L. Vu ,&nbsp;Bin Yu","doi":"10.1016/j.trc.2025.105460","DOIUrl":"10.1016/j.trc.2025.105460","url":null,"abstract":"<div><div>At signalized intersections, Connected Autonomous Vehicles (CAV) technology allows vehicle trajectory optimization using signal phasing and timing (SPaT) information, thereby reducing energy consumption caused by unnecessary deceleration and acceleration both upstream and downstream of signalized intersections. However, in widely deployed actuated traffic signal controllers, accurate and timely SPaT information is often unavailable for CAVs due to continuous adjustments in phase durations based on real-time traffic flow. Instead, only predicted SPaT information with inherent uncertainties can be provided within a specified confidence interval. Moreover, CAVs will coexist with human-driven vehicles (HVs) in mixed traffic environment, where the uncertainties of start-up lost time of HVs also present significant challenges to the trajectory optimization of CAVs. Hence, this paper proposes a chance-constrained eco-driving control for CAVs in mixed traffic environment with consideration of uncertainties in SPaT information and HVs. The method introduces a risk coefficient to represent the confidence level of CAVs regarding the probability distribution of uncertainties. The stochastic model predictive control (SMPC) with chance constraints is constructed to ensure that CAVs do not rear-end the preceding vehicle or exceed maximum acceleration/deceleration. The variable step size for SMPC is designed to enhance the performance of the proposed method while reducing the computation time and ensuring the real-time control. Safety analysis is conducted to demonstrate that the proposed method prevents CAVs from running red phases even under high risk coefficients. Numerical simulation under unsaturated, saturated and oversaturated traffic flow indicate that the proposed method effectively smooths the trajectories of CAVs and benefits the traffic efficiency. Comparing results at CAV penetration rates of 20 % and 80 % with 0 % (i.e., fully HVs), the proposed method reduces energy consumption by an average of 5.77 % and 10.24 % in the unsaturated traffic flow, 17.50 % and 31.32 % in the saturated traffic flow, and 22.99 % and 20.30 % in the oversaturated traffic flow, respectively.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105460"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145611672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-mode control for dedicated-lane networks in mixed semi-autonomous vehicle traffic","authors":"Hao Guan ,&nbsp;Xiangdong Chen ,&nbsp;Qiang Meng ,&nbsp;Chin Long Mak","doi":"10.1016/j.trc.2025.105491","DOIUrl":"10.1016/j.trc.2025.105491","url":null,"abstract":"<div><div>The urban mobility landscape is evolving from traditional vehicles (TVs) to a mix including semi-autonomous vehicles (semi-AVs), which provide automation functions yet still require human oversight. A key characteristic of semi-AVs, often overlooked in existing mixed traffic studies, is their dual-mode driving capability, allowing control to switch between human drivers and semi-autonomous systems. Our study addresses this gap by focusing on mixed semi-AV traffic environments and leveraging the unique dual-mode driving capability of semi-AVs to enhance overall urban network efficiency. We propose a novel dual-mode control methodology for mixed semi-AV traffic, implementing dedicated lanes to avoid the complex interactions between autonomously driven semi-AVs (AD-SAVs) and manually driven vehicles. The critical AD-SAV proportion that maximizes network flow is identified by modeling the traffic dynamics of the dedicated-lane network using a three-dimensional macroscopic fundamental diagram (3D-MFD), providing a practical reference for effectively managing mixed traffic. A dual-mode pricing system is developed to dynamically adjust vehicle proportions through an adaptive pricing strategy. This strategy is encapsulated in an optimization model that utilizes a mixed logit model to determine the optimal pricing adjustments. Utilizing the analytical properties of the mixed traffic characterized by the 3D-MFD, an equivalent stage-wise reformulation model is constructed for real-time computation. By dynamically optimizing the proportion of AD-SAVs, our approach ensures efficient lane utilization, enhances overall traffic flow, and reduces congestion without requiring costly infrastructure modifications. Through extensive simulations, the proposed dual-mode control strategy demonstrates significant improvements in traffic efficiency, contributing a scalable, cost-effective solution for integrating semi-AVs into urban transportation networks.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105491"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crash prediction under limited CV coverage: an ensemble deep learning model integrating multi-source traffic data","authors":"Samgyu Yang,&nbsp;Mohamed Abdel-Aty,&nbsp;Lei Han","doi":"10.1016/j.trc.2025.105472","DOIUrl":"10.1016/j.trc.2025.105472","url":null,"abstract":"<div><div>This study presents a comprehensive crash prediction framework that integrates traditional microwave vehicle detection systems (MVDS) with emerging connected vehicle (CV) data to improve proactive traffic safety management. While MVDS data provide consistent, infrastructure-based traffic measurements, their spatial coverage and behavioral resolution are limited. In contrast, CV data offer high-resolution, continuous vehicle trajectories that capture detailed driving behavior, but suffer from low and uneven market penetration. To fully leverage the strengths of both sources, an ensemble deep learning model was developed, utilizing MVDS data for macroscopic, segment-level traffic patterns and CV data for microscopic, vehicle-level dynamics. Importantly, rather than relying on segment-level aggregated CV metrics, this study directly utilizes individual vehicle trajectories to preserve temporal and spatial fidelity, enabling the model to capture detailed behavior that often precedes crashes. Three modeling configurations, MVDS only, CV only, and MVDS + CV data, were evaluated across different crash types and roadway segment types. Results demonstrate that integrated data source model consistently outperforms single-source models, achieving higher sensitivity and lower false alarm rates, particularly for rear-end and sideswipe crashes. Furthermore, model performance was evaluated under varying CV market penetration rates. While CV-only model showed limited performance under low coverage (&lt;1%), it exhibited strong and stable results at 4 % penetration or higher, with sensitivity exceeding 0.79. These findings highlight the potential of CV data to support scalable crash prediction without relying on infrastructure-based sensors, especially as CV adoption expands. The proposed approach offers a robust and adaptable solution for enhancing roadway safety across diverse traffic environment.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105472"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated framework of routing and rebalancing for RoboTaxi systems","authors":"Aoyong Li ,&nbsp;Yaotian Tan ,&nbsp;Wei Zhang ,&nbsp;Kai Wang ,&nbsp;Xiaobo Qu","doi":"10.1016/j.trc.2025.105415","DOIUrl":"10.1016/j.trc.2025.105415","url":null,"abstract":"<div><div>The RoboTaxi service is considered a groundbreaking mode of transportation, offering autonomous ride services to passengers. Once a travel request is placed, the passenger is picked up promptly and transported directly to their destination. Following a trip, the RoboTaxi can immediately serve another customer, remain idle, or relocate within the city to anticipate future demand. The operation of RoboTaxi systems involves two key processes: routing and rebalancing. Routing determines which vehicle will serve a passenger and in what order. Rebalancing involves moving idle vehicles to strategic locations to improve passenger satisfaction for future requests. This relies on short-term demand prediction to ensure efficient resource allocation. While existing research has primarily considered these components independently, this study integrates them into a comprehensive framework designed to improve operator profitability and passenger satisfaction. In addition, different prediction methods are adopted to examine the impact of prediction accuracy on optimization results. The results indicate that the proposed framework achieves an approximate 12 % increase in operator profits and a significant improvement in the acceptance ratio.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105415"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised graph learning for OD flow semantic awareness: harnessing high-order relationships via trajectory chains and POI contexts","authors":"Tao Wu ,&nbsp;Xinyi Lin ,&nbsp;Jianxin Qin ,&nbsp;Qianqian Deng","doi":"10.1016/j.trc.2025.105433","DOIUrl":"10.1016/j.trc.2025.105433","url":null,"abstract":"<div><div>Origin-Destination (OD) flow semantic awareness enables the identification of trip patterns across diverse groups and spatiotemporal contexts, revealing spatial relationships and interactions. However, the lack of annotated semantic information in OD data poses a significant challenge, as it hinders the analysis of travel purposes. To address this challenge, we propose a novel self-supervised graph learning framework that leverages bicycle trajectory chains and Points of Interest (POIs) within a 15-minute walking radius. By integrating Graph Attention Networks (GAT) and Hypergraph Convolutional Networks (HGCN), our framework extracts spatial and high-order semantic features from mobility data without requiring labeled training data. A Transformer encoder further enriches node contextual features, enabling the inference of trip purposes and the identification of diverse spatiotemporal travel patterns. Empirical validation in Xiamen, China, demonstrates the framework’s effectiveness in uncovering meaningful OD flow semantics, providing new insights into urban mobility dynamics. The identified flow semantics successfully reveal the underlying mobility patterns and emphasize the synergistic potential of shared bikes within the public transportation network.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105433"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiperiod alternative service optimization responding to joint disruptions in multimodal transit systems","authors":"Hankun Zheng ,&nbsp;Huijun Sun ,&nbsp;Jianjun Wu ,&nbsp;Dario Pacciarelli ,&nbsp;Marcella Samà ,&nbsp;Liujiang Kang","doi":"10.1016/j.trc.2025.105419","DOIUrl":"10.1016/j.trc.2025.105419","url":null,"abstract":"<div><div>Besides disruptions within individual bus or metro transit systems, joint disruptions can occur in multimodal transit systems, which are typically more prolonged and extensive. This paper focuses on joint metro station closures and road disruptions over an entire day, divided into several time periods with various passenger demands and bus travel times. In response, alternative metro services are scheduled to skip the closed stations, while affected bus routes are adapted to provide alternative bus services. We simultaneously optimize adaptive bus routes, vehicle frequencies, and passenger demand assignment to develop multiperiod alternative metro and bus services. Notably, alternative service optimization within each time period is integrated to maintain bus link consistency, enhancing service reliability and passenger travel satisfaction. A novel discount-based strategy is introduced to balance bus link consistency with alternative service effectiveness. For the studied problem, we develop an integer non-linear programming model based on the set of candidate passenger paths, aiming to minimize total passenger and operation costs with discounts. Afterwards, to efficiently generate passenger paths and solve the model, we propose an innovative link-sequence-based column generation with station clustering. In our column generation, we iteratively solve an aggregated restricted master problem and simpler pricing subproblems to first generate passengers’ link sequences, which are subsequently expanded to passenger paths by solving a series of tailored expansion models. Additionally, a priority-based rule is incorporated into column generation to avoid generating duplicate link sequences. A station clustering procedure is developed to reduce the problem size of column generation, further improving computational efficiency. Finally, we validate our methodology using mid- and large-scale instances in Beijing, as well as performing comparative and sensitivity analyses.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105419"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145614118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic data generation for joint electric vehicle driving and charging events via deep generative networks","authors":"Zhi Li ,&nbsp;Wei Ma ,&nbsp;Monica Menendez ,&nbsp;Zhibin Chen ,&nbsp;Minghui Zhong","doi":"10.1016/j.trc.2025.105481","DOIUrl":"10.1016/j.trc.2025.105481","url":null,"abstract":"<div><div>The rapid growth of the global electric vehicle (EV) market has generated vast amounts of real-time data through smart vehicle devices, capturing detailed EV behavioral patterns, including spatiotemporal charging distribution, electricity consumption, origin-destination (OD) driving trajectories, and parking dynamics. This data offers transformative potential for refining transportation policy, strategically deploying charging infrastructure, and facilitating vehicle-to-grid (V2G) energy integration.</div><div>Recent advances in generative modeling offer solutions by creating synthetic datasets that maintain data utility while protecting privacy. However, existing studies often lack detailed joint generation of driving and charging events and are less effective in handling highly dependent and constrained event variables. To address these gaps, we propose a joint EV driving and charging model for generating large-scale spatiotemporal behavior data by combining Transformer architecture and Gibbs sampling, leveraging 15 months of data from 3777 EVs in Shanghai, China. Our model effectively captures inter-feature and temporal dependencies, enabling scenario-based data generation while addressing privacy challenges. Our codebase, pre-trained model, and synthetic dataset are publicly available at: <span><span>https://github.com/zhilee2023/EV-drive-charge-data-gen</span><svg><path></path></svg></span></div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105481"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aggressiveness-conservativeness trade-off in cooperative driving: An adaptive CBF approach under perception constraints","authors":"Qichao Liu ,&nbsp;Jian Wang","doi":"10.1016/j.trc.2025.105435","DOIUrl":"10.1016/j.trc.2025.105435","url":null,"abstract":"<div><div>Perception-constrained environments pose significant challenges to cooperative driving, where incomplete situational awareness can compromise both safety and efficiency. This paper presents a novel control-layer framework that addresses the safety-efficiency trade-off under limited perception by integrating an adaptive Control Barrier Function (ACBF) into a Model Predictive Control (MPC) formulation. The proposed ACBF mechanism dynamically adjusts safety margins according to the spatial structure of observable regions, enabling the system to balance conservativeness and aggressiveness in real time. To ensure computational tractability, we develop a convexification strategy for nonlinear ACBF constraints and implement an Alternating Direction Method of Multipliers (ADMM)-based parallel optimization algorithm. Furthermore, we provide a formal proof that the closed-loop system achieves asymptotic stability under the proposed MPC-ACBF scheme. Numerical experiments demonstrate that the proposed method achieves a 0 % collision rate across three representative scenarios, while reducing task completion time by over 15 % compared to conservative baselines and improving minimum inter-vehicle distances by more than 1.5 m relative to aggressive strategies. These results confirm that MPC-ACBF preserves high control efficiency while substantially enhancing safety and robustness under constrained perception.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105435"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}