Transportation Research Part C-Emerging Technologies最新文献

{"title":"Real-time identification of cooperative perception necessity in road traffic scenarios","authors":"Chengyuan Ma ,&nbsp;Hangyu Li ,&nbsp;Keke Long ,&nbsp;Hang Zhou ,&nbsp;Zhaohui Liang ,&nbsp;Pei Li ,&nbsp;Hongkai Yu ,&nbsp;Xiaopeng Li","doi":"10.1016/j.trc.2026.105547","DOIUrl":"10.1016/j.trc.2026.105547","url":null,"abstract":"<div><div>Cooperative perception (CP) has shown great potential in enhancing traffic safety with Vehicle-to-Everything (V2X) communications. However, its substantial communication burden makes resource-efficient CP crucial, especially when a single vehicle’s intelligence with adequate perception is sufficient to handle most traffic scenarios. Therefore, to reduce the resource consumption of CP, it is essential to identify the traffic conditions under which CP should be applied. This paper addresses this issue by identifying the necessity of CP among road users, and evaluating whether their sensory information is adequate for ensuring traffic safety. We propose a practical framework to assess CP necessity by leveraging bird’s-eye view data from roadside cameras. The framework begins with video-based object localization and tracking to identify the position and movement of each road user. Next, we use a stochastic motion prediction model to analyze the collision risks between pairs of road users. Simultaneously, pairwise perception analysis is used to assess the probability of one road user perceiving another, determining if a road user falls within a blind spot. Road users with both collision risk and potential perception blind spots are identified as requiring CP. Field tests are conducted using real-world scenarios at two complex intersections in Madison, WI, which include a diverse range of road users, such as various vehicles and vulnerable pedestrians and cyclists. The results demonstrate that the proposed framework can effectively identify the safety-challenging scenarios that require CP in complex traffic environments. With only 0.1% of situations in our field test requiring CP, implementing the proposed framework can save a significant amount of communication bandwidth and computational costs while ensuring the same level of safety. Our code and data will be made available upon the acceptance of this paper.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105547"},"PeriodicalIF":7.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102561","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":"Corrigendum to “A line planning approach with passenger assignment considering cross-line operations and flexible train composition for a metro network” [Transp. Res. Part C: Emerg. Technol. 183 (2026) 105489]","authors":"Zhikai Wang, Andrea D’Ariano, Shuai Su, Tao Tang, Boyi Su","doi":"10.1016/j.trc.2026.105544","DOIUrl":"https://doi.org/10.1016/j.trc.2026.105544","url":null,"abstract":"","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"39 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110848","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":"Corrigendum to “The VCG pricing policy with unit reserve prices for ride-sourcing is 3 4 -incentive compatibility” [Transp. Res. Part C: Emerg. Technol. 171 (2025) 104991]","authors":"Ruijie Li, Haiyuan Chen, Xiaobo Liu, Kenan Zhang","doi":"10.1016/j.trc.2026.105543","DOIUrl":"https://doi.org/10.1016/j.trc.2026.105543","url":null,"abstract":"","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"26 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110264","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":"Vertical federated learning for transport mode detection using multi-modality data","authors":"Ningkang Yang, Ramandeep Singh, Oleksandr Shtykalo, Iuliia Yamnenko, Constantinos Antoniou","doi":"10.1016/j.trc.2026.105546","DOIUrl":"https://doi.org/10.1016/j.trc.2026.105546","url":null,"abstract":"","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"8 1","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095873","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":"NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning","authors":"Edgar Ramirez-Sanchez ,&nbsp;Catherine Tang ,&nbsp;Yaosheng Xu ,&nbsp;Nrithya Renganathan ,&nbsp;Vindula Jayawardana ,&nbsp;Zhengbing He ,&nbsp;Cathy Wu","doi":"10.1016/j.trc.2026.105530","DOIUrl":"10.1016/j.trc.2026.105530","url":null,"abstract":"<div><div>The transportation sector accounts for nearly one-quarter of global greenhouse gas (GHG) emissions. Emerging technologies-such as eco-driving, connected vehicle control, and others-offer significant potential for emission reduction; however, officially validated, yet optimization-ready emission models are essential for guiding their design, deployment, and evaluation. The U.S. EPA’ Motor Vehicle Emission Simulator (MOVES) is the validated regulatory and industry standard for vehicle emissions in the U.S. Yet, its complexity, macroscopic focus, and high computational demands make it unsuitable and incompatible with control and optimization applications, and burdensome even for traditional analyses. Furthermore, its reliance on location-specific inputs limits its applicability beyond the U.S. As a result, researchers often resort to alternative models, leading to emission estimates that are neither comparable nor officially validated. To address this gap, we introduce <strong>NeuralMOVES</strong>, an open-source, lightweight surrogate model for CO₂ emissions with near-MOVES fidelity. NeuralMOVES transforms MOVES from a multi-software system requiring specialized expertise and hours of computation into a 2.4 MB Python package that runs in milliseconds and integrates seamlessly into optimization frameworks. Developed by reverse-engineering MOVES through over 200 million batch queries to generate a comprehensive microscopic emission dataset (MOVES_RE, 9.89 GB), NeuralMOVES uses machine learning to compress this dataset by over 4,000× while enabling continuous, differentiable, and real-time emission estimation. An extensive validation shows a mean absolute percentage error of 6.013% across over two million test scenarios, each representing a complete driving trajectory evaluated under specific environmental and vehicle conditions. We demonstrate NeuralMOVES in a dynamic eco-driving case study, showing that it integrates seamlessly into optimization pipelines, leads to different trajectories than alternative models, and captures parameter sensitivities that alternative models overlook. NeuralMOVES enables regulatory-grade, microscopic emission modeling for emerging transportation technologies worldwide and is available at: <span><span>https://github.com/edgar-rs/neuralMOVES</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105530"},"PeriodicalIF":7.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078148","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 deep reinforcement learning-linear control strategy for longitudinal control of connected and automated vehicles","authors":"Ziwei Yi ,&nbsp;Min Xu ,&nbsp;Shuaian Wang","doi":"10.1016/j.trc.2026.105541","DOIUrl":"10.1016/j.trc.2026.105541","url":null,"abstract":"<div><div>String stability is important to maintain the longitudinal control of connected and automated vehicles (CAVs). It prevents the amplification of the perturbations as they propagate through the platoon. A variety of methods based on the deep reinforcement learning (DRL) approach have been proposed for longitudinal control of CAVs, which show excellent performance. However, none of those methods consider string stability on theoretical grounds due to the lack of explicit mathematical models in the DRL approach. To address this problem, we integrate a novel linear controller in a DRL framework for longitudinal control of CAVs, referred to integrated DRL-linear control (IDL) strategy. It can guarantee string stability while striking a good balance among various benefits, including vehicle safety, comfort, and efficiency. We employ the twin delay depth deterministic policy gradient (TD3) algorithm, a promosing DRL, in the proposed framework for decision. Numerical simulation results demonstrate that the proposed approach ensures theoretical string stability while significantly enhancing vehicle safety, comfort, and efficiency compared to human-driven vehicles (HDVs) and a model-based cooperative adaptive cruise control (CACC) strategy. It also outperforms the deep deterministic policy gradient (DDPG) and pure TD3 strategies in terms of safety, comfort, and string stability. These results indicate that the proposed IDL strategy not only benefits from the advantages of the linear controller in analyzing theoretical string stability conditions but also retains the advantage of the DRL approach in terms of optimizing the trade-off between multiple benefits.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105541"},"PeriodicalIF":7.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078212","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":"Resilient multi-agent reinforcement learning for centralised tactical conflict resolution under uncertain perturbations and non-cooperative traffic in urban air mobility","authors":"Rodolphe Fremond ,&nbsp;Yan Xu ,&nbsp;Junjie Zhao ,&nbsp;Antonios Tsourdos ,&nbsp;Gokhan Inalhan","doi":"10.1016/j.trc.2026.105542","DOIUrl":"10.1016/j.trc.2026.105542","url":null,"abstract":"<div><div>This research investigates tactical conflict resolution for Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM) operations under degraded conditions and in the presence of non-cooperative UAS/UAM and manned Commercial Air Transportation and General Aviation (CAT/GA) intruders. The study adopts a centralised safety-net approach within UAS Traffic Management (UTM) architectures, envisioning ground-based conflict resolution services. We propose a set of Tactical Conflict Resolution Solvers (TCRS), each built upon a Multi-Agent Reinforcement Learning (MARL) core using a shared-policy transformer architecture and executed in a decentralised manner. To assess resilience of TCRS variants, we introduce domain-specific perturbations, including positioning noise, communication loss, and sensor-related defects. The TCRS operates with partial decision-making ability in non-cooperative traffic environments, while the perturbation model increases realism by simulating varying degrees of information availability. Results show that the perturbation-trained models achieve substantial safety gains compared with the baseline TCRS trained in ideal conditions. The most resilient variant; trained under multi-perturbation exposure and evaluated in non-cooperative environments, achieves a threefold reduction in critical safety violations compared with the baseline and remains robust under mixed cooperative/non-cooperative traffic with static intent. It exhibits a modest vulnerability under fully homogeneous non-cooperative scenarios with dynamic intent. Simulations involving concurrent CAT/GA and UAS operations further indicate that integrating UAS operations within the existing airspace classification remains hazardous for ground-based tactical conflict resolution when constrained by short look-ahead horizons and insufficient time to react.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105542"},"PeriodicalIF":7.6,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078071","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":"From voice to safety: Language AI powered pilot-ATC communication understanding for airport surface movement collision risk assessment","authors":"Yutian Pang,&nbsp;Andrew Paul Kendall,&nbsp;Alex Porcayo,&nbsp;Mariah Barsotti,&nbsp;Anahita Jain,&nbsp;John-Paul Clarke","doi":"10.1016/j.trc.2026.105540","DOIUrl":"10.1016/j.trc.2026.105540","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Surface movement collision risk is critical for airport safety. These models play a vital role in identifying and mitigating potential hazards during airport ground operations by providing warnings of near-miss incidents, thereby reducing the risk of accidents that could jeopardize human lives and financial assets. However, existing models, developed decades ago, have not fully integrated recent advancements in machine intelligence, where incorporating additional functionalities presents promising opportunities for improved risk assessment. This work provides a feasible solution to the existing airport surface safety monitoring capabilities (i.e., Airport Surface Surveillance Capability (ASSC)), namely language AI-based voice communication understanding for collision risk assessment. The proposed framework consists of two major parts, (a) rule-enhanced Named Entity Recognition (NER); (b) surface collision risk modeling. NER module generates information tables by processing voice communication transcripts, which serve as references for producing potential taxi plans and calculating the surface movement collision risk. We first collect and annotate our dataset based on open-sourced video recordings and safety investigation reports. Additionally, we refer to FAA Order JO 7110.65W and FAA Order JO 7340.2N to get the list of heuristic rules and phase contractions of communication between the pilot and the Air Traffic Controller (ATCo). Then, we propose the novel ATC Rule-Enhanced NER method, which integrates the heuristic rules into the model training and inference stages, resulting in a hybrid rule-based NER model. We show the effectiveness of this hybrid approach by comparing different setups with different token-level embedding models. For the risk modeling, we adopt the node-link airport layout graph from NASA FACET and model the aircraft taxi speed at each link as a log-normal distribution and derive the total taxi time distribution. Then, we propose a spatiotemporal formulation of the risk probability of two aircraft moving across potential collision nodes during ground movement. Furthermore, we propose the real-time implementation of such a method to obtain the lead time, with a comparison with a Petri-Net based method. We show the effectiveness of our approach through case studies, (a) the Haneda airport runway collision accident happened in January 2024; (b) the KATL taxiway collision happened in September 2024; (c) the Tenerife airport disaster in March 1977. We show that, by understanding the pilot-ATC communication transcripts and analyzing surface movement patterns, the proposed model estimates the surface movement collision probability within machine processing time, thus enabling proactive measures to possible collisions at a certain node, which improves airport safety. A study on validating the log-normal assumption of aircraft taxi speed distributions is also given. We provide the link to code and data repository &lt;span&gt;&lt;span","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105540"},"PeriodicalIF":7.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072502","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":"Dynamic charging optimization for electric buses under photovoltaic-storage-grid energy supply mode","authors":"Yuting Ji ,&nbsp;Yiming Bie ,&nbsp;Dongfang Ma","doi":"10.1016/j.trc.2026.105539","DOIUrl":"10.1016/j.trc.2026.105539","url":null,"abstract":"<div><div>With the growing emphasis on sustainable transportation, the photovoltaic-storage-grid energy supply (PSG-ES) mode has been adopted in electric bus (EB) systems, showing promising performance and strong potential. However, environmental uncertainties cause random fluctuations in both solar output and energy demand, posing challenges to stable system operation. Most existing studies use static or low-frequency dynamic models based on day-ahead forecasts. These methods struggle to adapt to real-time changes, limiting the economic and environmental benefits of the PSG-ES mode. To address this issue, we propose a minute-level dynamic charging scheduling method for multi-route EB systems under the PSG-ES mode. The problem is formulated as a Markov Decision Process, with a penalty function and an action correction mechanism introduced to handle complex operational constraints. To improve learning and adaptability, we develop a deep reinforcement learning algorithm, featuring multi-head networks and composite experience replay to address distribution shifts and policy conflicts. Experiments using real-world EB data show that the proposed method effectively manages supply–demand uncertainties and generates more cost-efficient and environmentally sustainable charging plans. Compared to static scheduling with day-ahead forecasts, it reduces charging costs by 7.48% and carbon emissions by 2.99%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105539"},"PeriodicalIF":7.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072503","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-RSRAE: Enhancing ship inspection operations through dual robust subspace recovery auto-encoder in port state control","authors":"Jiongchao Jin ,&nbsp;Xiaowei Gao ,&nbsp;Xiuju Fu ,&nbsp;Zheng Qin ,&nbsp;Tao Cheng ,&nbsp;Ran Yan","doi":"10.1016/j.trc.2026.105537","DOIUrl":"10.1016/j.trc.2026.105537","url":null,"abstract":"<div><div>Maritime transportation serves as the backbone of global trade, carrying more than 80% of the world’s cargo by volume. Ensuring shipping safety is a top priority for the maritime industry. To uphold safety standards, Port State Control (PSC) inspections, established by the International Maritime Organization (IMO), are conducted by national ports to verify that foreign visiting ships comply with international and local regulations and are adequately manned. Given the limited inspection resources at ports and the need to avoid excessive inspections that could disrupt the fast turnover of the maritime supply chain, accurately predicting a ship’s inspection in PSC, particularly the deficiency and detention conditions, is crucial for improving the reasonability of the ship inspection process. However, the existing models usually treat detention and deficiency prediction tasks separately, while advanced models such as deep learning are seldom developed for prediction. To address these limitations, we propose Dual-RSRAE, a novel multi-task Dual Robust Subspace Recovery Layer-based auto-encoder for ship risk prediction. This approach integrates the prediction of deficiencies and detentions within a unified, end-to-end pipeline, making it the first attempt to explore the inherent connections between these tasks. Our evaluation, conducted on 31,707 real PSC inspection records from the Asia-Pacific region, demonstrates that Dual-RSRAE outperforms state-of-the-art methods, achieving at least an 13% improvement in detention prediction and a 12% improvement in deficiency prediction accuracy.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105537"},"PeriodicalIF":7.6,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072510","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}