Transportation Research Part C-Emerging Technologies最新文献

{"title":"Overcoming computational challenges in air transportation: A quantum computing perspective of the status quo and future applicability","authors":"Zhuoming Du,&nbsp;Sebastian Wandelt,&nbsp;Xiaoqian Sun","doi":"10.1016/j.trc.2025.105505","DOIUrl":"10.1016/j.trc.2025.105505","url":null,"abstract":"<div><div>Recent research breakthroughs in quantum computing, such as Microsoft’s topological qubits, hold the promise of revolutionizing complex optimization problems, particularly in the air transportation industry. This study aims to estimate the mid-term scalability of quantum computing in air transportation, focusing on prevalent optimization problems including network design, airline scheduling, and gate assignment. These problems are computationally intensive and often intractable for classical computers due to their highly combinatorial nature. We develop a framework to assess the potential scalability of quantum algorithms for these problems, considering factors such as qubit count and error rates. Our findings suggest that significant advancements in quantum hardware and algorithms are necessary before quantum computing can outperform classical methods in this domain. Therefore, while quantum computing offers a promising tool for solving complex optimization problems in air transportation, its real-world application remains a distant goal. We believe that our work helps guiding researchers and industry professionals in their pursuit of quantum-enhanced air transport solutions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105505"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842860","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":"The dynamic system optimal departure time choice problem for a bottleneck with a stochastic capacity: Model formulation and solution algorithm","authors":"Yao Li ,&nbsp;Jie Wang ,&nbsp;Zijun Wu ,&nbsp;Jiancheng Long","doi":"10.1016/j.trc.2025.105510","DOIUrl":"10.1016/j.trc.2025.105510","url":null,"abstract":"<div><div>This paper concerns a novel dynamic system optimal departure time choice (DSO-DTC) problem that takes the capacity uncertainty at a bottleneck into account. The existing traditional analytical methods are unable to yield satisfactory results without stringent conditional assumptions. We innovatively discretize and reformulate it with linear programming (LP) and nonlinear programming (NLP), respectively. While the LP problem can be solved exactly with a standard solver like CPLEX, its complexity grows dramatically when the underlying discretization becomes a little finer. Therefore, we propose a sensitivity analysis-based (SAB) algorithm for the NLP problem instead, and further refine this algorithm with sophisticated strategies. Our experimental study demonstrates that the algorithm not only achieves superior solution efficiency and quality but also exhibits enhanced scalability in terms of discretization accuracy when compared to the benchmark solver CPLEX. Besides, the model enables us to efficiently study the impact of bottleneck capacity uncertainty on the performance of the bottleneck and on the efficiency of tolling strategies, which can be hardly achieved by traditional bottleneck model analysis methods.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105510"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927802","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":"2026-03-01","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":"Integrated optimization for vehicle trajectory reconstruction under cooperative perception environment","authors":"Tianheng Zhu,&nbsp;Wangzhi Li,&nbsp;Yiheng Feng","doi":"10.1016/j.trc.2026.105522","DOIUrl":"10.1016/j.trc.2026.105522","url":null,"abstract":"<div><div>Vehicle trajectories provide detailed information about vehicle movements and interactions, which are essential for various transportation applications. However, collecting complete vehicle trajectory data requires high costs. Reconstructing complete vehicle trajectories from partial observations is thus a more cost-effective alternative. Previous studies on trajectory reconstruction primarily focused on vehicle longitudinal behaviors, usually neglecting lane-change (LC) maneuvers. This study proposes an integrated optimization-based vehicle trajectory reconstruction model that considers LC and overtaking behaviors under a cooperative perception environment with very low market penetration rates (MPRs) of connected and automated vehicles (CAVs) and varying packet loss rates (PLRs) of vehicle-to-everything (V2X) communication. A Mixed Integer Linear Programming (MILP) problem is constructed with the objective of minimizing the errors between reconstructed trajectories and observed trajectories, which converts the trajectory reconstruction problem into a joint trajectory generation problem. Moreover, this study considers a cooperative perception environment where partial observed trajectories are collected from CAV perception sensors. Different from other studies that implemented oversimplified detection models to generate observed trajectories without considering the real-world complexity and variability of detection patterns from perception sensors, in this study, we adopt distance-dependent true positive rates (TPRs) as detection performance metric to mimic CAV detection, computed using BEVFusion detection outputs on the nuScenes dataset. The proposed formulation streamlines the entire process and can be applied to various road geometries and traffic conditions. Numerical studies using both NGSIM highway and urban arterial datasets demonstrate the model’s effectiveness in reconstructing vehicle trajectories under 2%-5% CAV MPRs with varying PLRs. Additional sensitivity analysis was conducted to evaluate the impact of 1) vehicle occlusion in CAV detection model; 2) varying traffic conditions (i.e., demand levels); and 3) weights of different terms in the objective function on the trajectory reconstruction accuracy. Under similar reconstruction rates of unobserved trajectories and road segment lengths, the proposed method outperforms existing studies by a significant margin in terms of both longitudinal position accuracy and LC time prediction. The source code is publicly available at <span><span>https://github.com/Purdue-CART-Lab/CP-TrajRecon-Opt</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105522"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978353","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":"2026-03-01","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}

{"title":"An optimization model for en-route express service scheduling in modular autonomous transit systems","authors":"Shuyan Xiao ,&nbsp;Yufeng Zhang ,&nbsp;Lixing Yang","doi":"10.1016/j.trc.2026.105535","DOIUrl":"10.1016/j.trc.2026.105535","url":null,"abstract":"<div><div>Modular autonomous transit systems (MATS) present a promising direction for the advancement of urban mobility due to their remarkable ability to flexibly allocate capacity across both spatial and temporal dimensions. Although scholars have extensively explored optimizing the coupling and decoupling of modules and creating adaptable service strategies for MATS, the capacity for modules to overtake has largely been neglected, which could reduce MATS efficiency. In this paper, we introduce a novel operational strategy allowing certain modules to detach from their scheduled trip and, by bypassing stops, create an unscheduled express service, thus leading to an en-route express service. The potential overtaking actions of both decoupled and non-decoupled modules, due to skipping stops, and passengers transfer within the module, add significant complexity to the model. To address this, we develop a mixed integer nonlinear programming (MINLP) model with an objective to minimize the total cost for both passengers and the operator of the transit system, and we determine optimal decoupling/coupling strategies and schedules for the en-route express services. To enhance computational efficiency, we recast the original nonlinear model into a mixed integer quadratic program (MIQP) and introduce an outer approximation (OA) algorithm to solve it effectively. The results of our illustrative and large-scale experiments reveal that the proposed OA algorithm significantly enhances computational efficiency compared to CPLEX solvers. Compared to two benchmark systems with fixed capacity buses—local (all-stop) service and stop-skipping service, the proposed en-route express service reduces the total cost by 15.1% and 12.9%, and lowers the average passenger service time cost by 19.7% and 33.8%, respectively, underscoring the advantages of the en-route express service for MATS. These findings contribute to the development of more efficient MATS operations by introducing an en-route decoupling strategy that leverages overtaking capabilities to create adaptive express services. The work highlights the significant potential and importance of developing urban mobility systems that are more adaptable and responsive to urban travelers, while optimizing the utilization of available resources.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105535"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072511","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":"A fundamental diagram-consistent fluid queue model for dynamic throughput under heavy traffic congestion","authors":"Yuyan (Annie) Pan ,&nbsp;Xianbiao (XB) Hu ,&nbsp;Xuesong (Simon) Zhou","doi":"10.1016/j.trc.2026.105533","DOIUrl":"10.1016/j.trc.2026.105533","url":null,"abstract":"<div><div>The efficient operation of transportation systems is a critical priority for policymakers, particularly given the increasingly emphasis on efficiency gains to meet growing travel demands without relying solely on capacity expansion. During peak hours at freeway bottleneck locations, a flow drop may be observed, and this drop is influenced by traffic stream attributes such as merging and diverging vehicles, resulting in significant efficiency losses. However, queue-based models, widely used for estimating delays and queue lengths, often oversimplify congestion dynamics by assuming a constant outflow rate, leading to inconsistencies when compared with FD-based observations of over-congested states. In this manuscript, we introduce a novel Fundamental Diagram-Consistent Fluid Queue (FDQ) framework for analyzing and mitigating traffic efficiency losses during heavy congestion. We extend the traditional fluid queue model by incorporating a stationary density-flow relationship observed empirically at key bottlenecks. Unlike classical queue-based models, our framework allows the flow throughput to evolve with local traffic state transitions, especially the shift from semi-congested to fully congested regimes. We start with triangular FD and show how to analytically derive FD-consistent dynamic flow throughput, as well as the associated traffic states such as the queue profile and waiting time. Such framework is then utilized to understand efficiency loss mechanisms and explore the potential for increased system efficiency through targeted inflow control. Two types of FDQ models were developed: one with flow throughput in polynomial form (FDQ-PN) and another in piecewise form (FDQ-PW). The FDQ framework is also extended to work with quadratic FD. Validation and numerical analyses were performed using datasets from Los Angeles I-405 and <em>Phoenix</em> I-10. The results demonstrate that the proposed framework substantially improves the accuracy of traffic state estimation. Furthermore, the demand–supply coupled inflow control is shown to offer a more significant efficiency gain than adjusting demand or supply alone.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105533"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022859","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 ,&nbsp;Ramandeep Singh ,&nbsp;Oleksandr Shtykalo ,&nbsp;Iuliia Yamnenko ,&nbsp;Constantinos Antoniou","doi":"10.1016/j.trc.2026.105546","DOIUrl":"10.1016/j.trc.2026.105546","url":null,"abstract":"<div><div>Transport mode detection (TMD) is vital for intelligent transportation systems and urban computing. However, most existing approaches rely on data from a single modality, such as GPS trajectories or inertial measurement units. This limits their effectiveness in dynamic real-world scenarios. While some studies have improved transport mode detection by combining multiple modalities, heterogeneities in sampling frequency, spatial precision, signal coverage, and occasional missing-modality conditions, together with the requirement for strict temporal alignment, constrain the fused data to the resolution of the weakest modality and lead to substantial information loss. Furthermore, centralizing data from all modalities is often impractical, as the different data types are held by separate parties that may lack labeled data and may also be unwilling to share their raw data due to privacy or commercial concerns. To address these issues, we propose a semi-supervised vertical federated learning (VFL) framework for TMD that uses IMU, GPS, and mobile phone network data. In this framework, each party independently trains an attention-based autoencoder to encode local features. The hidden features are then sent to a central server for classification, and the classification loss is propagated back for local model updating. Given the high-frequency nature of IMU data, we have designed a dynamic sub-segment sampling strategy to adapt to transport mode detection tasks with different temporal resolutions. In addition, the framework adopts distillation and representation alignment to mitigate the impact of missing or weak modalities, and it supports both single-modality and multi-modality inference. We compared the proposed model with several state-of-the-art models, exploring the effectiveness of the different components of the VFL framework. The results demonstrate that our model consistently outperforms multiple baselines, and the proposed VFL framework effectively enhances local inference performance, which can be extended to various model architectures.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105546"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","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":"Corrigendum to “The VCG pricing policy with unit reserve prices for ride-sourcing is 34-incentive compatibility” [Transp. Res. Part C: Emerg. Technol. 171 (2025) 104991]","authors":"Ruijie Li ,&nbsp;Haiyuan Chen ,&nbsp;Xiaobo Liu ,&nbsp;Kenan Zhang","doi":"10.1016/j.trc.2026.105543","DOIUrl":"10.1016/j.trc.2026.105543","url":null,"abstract":"","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105543"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","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":"Decarbonizing freight transportation: Joint optimization of intermodal service scheduling and cargo routing","authors":"Zeyu Liu","doi":"10.1016/j.trc.2026.105513","DOIUrl":"10.1016/j.trc.2026.105513","url":null,"abstract":"<div><div>With the rapid growth of freight transportation, Greenhouse Gas (GHG) emissions will increase explosively due to the heavy reliance on trucking. To reach the carbon-neutral goal by 2050, it is crucial to fully utilizing intermodal capabilities, which reduces GHG emissions and economic costs for large volumes of cargo across long distances. Yet, existing optimization models for intermodal transportation lack operational details, typically using broad averages of emission and cost metrics or relying on inflexible scheduling, leading to suboptimal results. In this study, we propose a holistic mixed integer model to optimize container-level transportation in a multi-layered intermodal network, with routing and scheduling decisions of individual containers and vehicles. To address the computational challenges, we establish structural properties and develop novel decomposition methods using variable duplication, relaxation, and symmetry breaking. Real-world intermodal network data in the United States are collected to enable comprehensive experiments. Model behaviors and algorithm performances are investigated through sensitivity analyses and benchmarking. The proposed algorithm leads to more than 50% and 60% improvements in solution quality and efficiency, respectively. Additionally, we compute large-scale scenarios to render future projections of the United States freight transportation sector from 2025 to 2050. The joint effect of synchromodality and clean energy technology foresees up to 154 million tons of reductions in GHG emissions by 2050.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105513"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978355","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}