Transportation Research Part C-Emerging Technologies最新文献

{"title":"Joint optimization of pricing, seat allocation and overbooking for high-speed railway system under demand uncertainty","authors":"Jing Xu ,&nbsp;Lianbo Deng ,&nbsp;Yihan Gao ,&nbsp;Wei Liu","doi":"10.1016/j.trc.2025.105492","DOIUrl":"10.1016/j.trc.2025.105492","url":null,"abstract":"<div><div>This paper examines the integrated optimization of pricing, seat allocation, and overbooking strategies in high-speed railway (HSR) operations. Overbooking helps address inefficiencies arising from empty seats due to passenger no-shows or last-minute cancellations. The complexity of the problem stems from two main factors: (i) the interdependence of pricing, seat allocation, and overbooking decisions, which jointly influence railway system performance; and (ii) the uncertainties associated with passenger demand and no-show behavior. To tackle these complexities, we develop a two-stage stochastic programming model aimed at maximizing expected railway profit. In the first stage, the model determines HSR pricing and seat allocation, including overbooking, while the second stage addresses potential denied boarding due to overbooking, based on the first-stage decisions. To solve the model, we employ a sample average approximation method and introduce a tailored progressive hedging algorithm (PHA). Additionally, we adapt commonly used surrogate-based optimization methods, such as Kriging and radial basis function models, for comparative analysis. Numerical studies on both a small-scale example and a real-world HSR line reveal that the proposed joint optimization significantly boosts railway profit across various demand and no-show scenarios, with the PHA solution approach outperforming surrogate-based methods in terms of both solution quality and computational efficiency.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105492"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785156","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":"Privacy risk of transportation location-Based data: Re-identification and de-anonymization","authors":"Yi Zhang,&nbsp;Yaobang Gong,&nbsp;Madeline Shi,&nbsp;Xianfeng Yang","doi":"10.1016/j.trc.2025.105495","DOIUrl":"10.1016/j.trc.2025.105495","url":null,"abstract":"<div><div>In the era of ubiquitous connectivity, vast quantities of location-based data are continuously generated, profoundly impacting both online services and offline research applications. Although this data drives significant advancements in fields such as autonomous vehicle trajectory control, customized recommendations, and travel behavior analysis, it simultaneously raises critical privacy concerns, notably the risk of individual re-identification from anonymized datasets. Previous research focused on pattern-based re-identification within isolated datasets, without effectively linking anonymized data to real-world identities and investigating the real-world implications of such privacy breaches. Moreover, the intrinsic mechanisms underlying the vulnerability of location-based datasets to de-anonymization attacks remain underexplored. This study addresses these gaps by proposing an inference model specifically tailored for individual re-identification and de-anonymization attacks on privacy-protected transportation location-based datasets. Additionally, a mathematical framework is introduced to quantify the de-anonymization rate and systematically analyze dataset vulnerability to such privacy attacks. Experimental evaluations conducted on datasets of varying sizes validate the effectiveness of our approach, demonstrating successful re-identification even when standard privacy protections are in place. Our findings emphasize the urgent need for enhanced privacy-preserving strategies and offer a comprehensive basis for understanding and mitigating privacy risks associated with location-based data.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105495"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785143","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":"Data-driven optimization for maritime logistics: integrating transport network mining with ship fleet routing","authors":"Yimeng Zhang ,&nbsp;Liang Huang ,&nbsp;Jiaci Wang ,&nbsp;He Lin ,&nbsp;Shuyang Zhu ,&nbsp;Mi Gan ,&nbsp;Xiaobo Liu ,&nbsp;Ruixue Ai","doi":"10.1016/j.trc.2025.105451","DOIUrl":"10.1016/j.trc.2025.105451","url":null,"abstract":"<div><div>We introduce a data-driven approach for optimizing maritime logistics by integrating the construction of maritime transport networks with the routing of the ship fleet. Utilizing data mining techniques, this approach identifies crucial nodes and routes from Automatic Identification System (AIS) data and builds a directed weighted transport network. Based on the obtained transport network, we then optimize ships’ routes using Mixed Integer Programming and Adaptive Large Neighborhood Search. This data-driven method provides a complete solution that improves maritime logistics from data mining to route optimization and enhances the operational autonomy of both autonomous and traditional ships. The results using real-world AIS data illustrate how data mining can be leveraged to develop a detailed transport network that significantly enhances fleet routing optimization. We evaluate our approach against two benchmarks in the literature and demonstrate that it enhances identification accuracy by over 14 %. Furthermore, through numerical analyses under various scenarios, such as route disruptions and varying levels of port congestion, our routing approach proves capable of managing large-scale operations and adapting to transport time variations. Compared to disruptions on routes, severe port congestion notably increases operational costs as it extends loading and unloading times and causes higher delay penalties.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105451"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567486","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 hybrid ranking and selection procedure to solve simulation-based discrete network design problems","authors":"Jinbiao Huo ,&nbsp;Zhiyuan Liu ,&nbsp;Ziyuan Gu ,&nbsp;Churong Chen","doi":"10.1016/j.trc.2025.105438","DOIUrl":"10.1016/j.trc.2025.105438","url":null,"abstract":"<div><div>With the rapid advancements of simulation techniques, simulation-based traffic management and control have gained increasing popularity. This study specifically targets the solution algorithm for simulation-based discrete network design problems (S-DNDPs). Previous research has often tackled S-DNDPs using surrogate-based optimization approaches. However, existing methods face challenges in accuracy due to discrete decision variables and insufficiently address the stochasticity and high computational costs of traffic simulators. Consequently, there is a pressing need to develop an efficient and effective method for S-DNDPs. To this end, this study introduces a novel simulation-based optimization framework, namely the ranking and selection (R&amp;S) procedure, to solve S-DNDPs. The R&amp;S procedure, deemed to be a variant of the enumeration method, does not rely on (approximate) gradient information of the S-DNDP. Instead, it explores all feasible solutions, intelligently allocates computational resources, and selects the optimal one accordingly. To efficiently solve S-DNDPs, a hybrid R&amp;S procedure is proposed by incorporating a Bayesian screening procedure in a popular R&amp;S procedure (i.e., the <em>optimal computing budget allocation</em>, OCBA). In addition, a self-adaptive scheme is developed to determine the computational budget in each iteration. This study demonstrated that in comparison with OCBA, the proposed R&amp;S procedure achieves a higher lower bound of the probability of correct selection, leading to a more efficient allocation of computational resources. To address the scalability issue, this study provides a simple yet effective extension of the hybrid R&amp;S procedure, enabling the method to be applied to large-scale problems where computational resources may not suffice to perform exhaustive evaluations. The proposed methods, including the hybrid R&amp;S procedure and its extension, are validated on a real-world S-DNDP. Experimental results demonstrate the superior performance of the proposed methods against benchmark methods.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105438"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796028","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 stochastic electric vehicle routing problem under uncertain energy consumption","authors":"Andrea Spinelli ,&nbsp;Dario Bezzi ,&nbsp;Ola Jabali ,&nbsp;Francesca Maggioni","doi":"10.1016/j.trc.2025.105480","DOIUrl":"10.1016/j.trc.2025.105480","url":null,"abstract":"<div><div>The increasing adoption of Electric Vehicles (EVs) for service and goods distribution operations has led to the emergence of Electric Vehicle Routing Problems (EVRPs), a class of vehicle routing problems addressing the unique challenges posed by the limited driving range and recharging needs of EVs. While the majority of EVRP variants have considered deterministic energy consumption, this paper focuses on the Stochastic Electric Vehicle Routing Problem with a Threshold recourse policy (SEVRP-T), where the uncertainty in energy consumption is considered, and a recourse policy is employed to ensure that EVs recharge at Charging Stations (CSs) whenever their State of Charge (SoC) falls below a specified threshold. We formulate the SEVRP-T as a two-stage stochastic mixed-integer second-order cone model, where the first stage determines the sequences of customers to be visited, and the second stage incorporates charging activities. The objective is to minimize the expected total duration of the routes, composed by travel times and recharging operations. To cope with the computational complexity of the model, we propose a heuristic based on an Iterated Local Search (ILS) procedure coupled with a Set Partitioning problem. To further speed up the heuristic, we develop two lower bounds on the corresponding first-stage customer sequences. Furthermore, to handle a large number of energy consumption scenarios, we employ a scenario reduction technique. Extensive computational experiments are conducted to validate the effectiveness of the proposed solution strategy and to assess the importance of considering the stochastic nature of the energy consumption. The research presented in this paper contributes to the growing body of literature on EVRP and provides insights into managing the operational deployment of EVs in logistics activities under uncertainty.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105480"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689775","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":"Consolidation of urban freight transportation and public transit systems: a generic strategic framework using Continuum Approximation","authors":"Mohammadhosein Pourgholamali ,&nbsp;Mohammad Miralinaghi ,&nbsp;Alireza Talebpour ,&nbsp;Samuel Labi","doi":"10.1016/j.trc.2025.105490","DOIUrl":"10.1016/j.trc.2025.105490","url":null,"abstract":"<div><div>This study proposes a consolidated transportation system (CTS) in which the public transit fleet is leveraged to transport packages in an urban area. Under the proposed CTS, the freight company pays a fee to use a transit fleet’s unused capacity for its last-mile delivery operations. A decision<del>-</del>making framework is developed to identify the set of transit lines and stations that optimize the CTS operation. This framework also identifies optimal service zones for the selected transit stations and the warehouses. The proposed decision-making framework is formulated as an optimization problem and solved using a Continuum Approximation (CA) technique and a customized Adaptive Large Neighborhood Search (ALNS). The performance of the proposed algorithm is evaluated by comparing its solutions with those obtained from CPLEX, demonstrating both accuracy and efficiency. The numerical results show some significant benefits for freight companies due to delivery cost reduction and revenue generation for transit agencies. Also, the effects of the different cost components on CTS’s economic feasibility are shown and measured through sensitivity analyses. It is shown that mini-depot opening costs and transit shipment fees not only determine the overall savings for freight companies but also directly affect the revenue generated for transit agencies. Furthermore, the analyses highlight inherent trade-offs between the economic benefits of the CTS and potential reductions in the level of service experienced by transit passengers. Overall, this study demonstrates the promising benefits of integrating public transit and last-mile delivery to enable a more efficient transportation system.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105490"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732481","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 line planning approach with passenger assignment considering cross-line operations and flexible train composition for a metro network","authors":"Zhikai Wang ,&nbsp;Andrea D’Ariano ,&nbsp;Shuai Su ,&nbsp;Tao Tang ,&nbsp;Boyi Su","doi":"10.1016/j.trc.2025.105489","DOIUrl":"10.1016/j.trc.2025.105489","url":null,"abstract":"<div><div>As metro systems in most major cities evolve from single-line to network-level operations, passenger accessibility improves significantly. However, interchange stations experience increased pressure due to high transfer demand. Additionally, the non-equilibrium distribution of passenger demand poses significant challenges for operators. One effective strategy to mitigate transfer issues and improve operational flexibility is the implementation of cross-line operations. Furthermore, the flexible train composition mode provides a promising avenue to accommodate imbalanced passenger demand. In light of this, the study focuses on the line planning problem with passenger assignment, considering cross-line operations and the flexible train composition mode. Given that these operational modes require closer cooperation among trains, traditional line planning formulations become inadequate. To tackle this challenge, a novel mathematical model utilizing aggregated decision variables is developed to represent cross-line and flexible train coupling and decoupling operations. Subsequently, we formulate the passenger assignment problem as a multi-commodity flow problem, which effectively captures passenger movements within the metro network. To enhance computational efficiency, this study first employs the Dantzig-Wolfe decomposition method to transform the original formulation into a path-based model. Next, a branch-and-Benders cut approach is proposed to solve the problem. To strengthen the Benders cuts, we further develop a linear programming problem to generate the closest Benders cuts. The proposed approach is validated using a real-world case from the Beijing metro network, which comprises seven operating lines and 14 interchange stations. The computational results demonstrate that our proposed algorithms significantly outperform the commercial optimizer CPLEX. Moreover, the proposed operational modes reduce operating costs by up to 47.56 %, while passenger traveling costs decrease by as much as 4.34 %. The number of used train units decreases by up to 71.48 %.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105489"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732482","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":"Game-theoretic incentives for federated learning in traffic prediction: Balancing resource allocation and prediction accuracy via Stackelberg contracts","authors":"Guowen Dai ,&nbsp;Dong Ngoduy ,&nbsp;Jinjun Tang ,&nbsp;Chuyun Zhao","doi":"10.1016/j.trc.2025.105474","DOIUrl":"10.1016/j.trc.2025.105474","url":null,"abstract":"<div><div>Federated learning-based traffic flow prediction has attracted growing interest in the field. Federated Learning (FL) provides a novel solution for privacy-preserving distributed training. However, designing a fair and efficient incentive mechanism to encourage collaboration among diverse participants remains a key challenge. This paper proposes an incentive mechanism for FL based on contract theory and the Stackelberg game. More specifically, our proposed method quantifies and differentiates rewards for participant contributions through contract design while using the Stackelberg game to balance resource allocation and profit competition between the server and participants. Additionally, this paper integrates an efficient local prediction model, WL-Transformer (Weighted Layer Transformer), to enhance participants’ local data modeling capabilities, thereby improving the accuracy and adaptability of the global model in traffic flow prediction tasks. Finally, experiments on the License Plate Recognition (LPR) dataset from Changsha, China, demonstrate the effectiveness of the proposed incentive mechanism in achieving high-accuracy traffic flow prediction.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105474"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689756","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":"Demand-driven timetabling and vehicle scheduling optimization for electric bus transit lines","authors":"Jinpeng Liang ,&nbsp;Chenghao Zhuang ,&nbsp;Haitao Liu ,&nbsp;Ziyou Gao","doi":"10.1016/j.trc.2025.105482","DOIUrl":"10.1016/j.trc.2025.105482","url":null,"abstract":"<div><div>Electric buses (EBs) have experienced rapid global expansion due to their substantial environmental and economic benefits. However, this transition presents new challenges for bus timetabling and vehicle scheduling, primarily arising from limited battery capacity and frequent charging requirements. This paper presents an integrated optimization framework for demand-driven timetabling and vehicle scheduling in EB transit lines that explicitly considers both passenger flow dynamics and battery constraints. We formulate a comprehensive mixed-integer programming (MIP) model using a space-time network representation to simultaneously optimize departure times, vehicle schedules, and passenger assignments. The objective function minimizes total costs, which encompass vehicle procurement, operational expenses, and passenger waiting times. To enhance computational efficiency, we reformulate the original MIP model into a path-based formulation and employ column generation (CG) techniques to decompose the problem into a restricted master problem (RMP) and pricing subproblems (PSP), which are solved iteratively. To obtain integer solutions, we develop a BP algorithm that integrates the CG procedure within a branch-and-bound framework. Computational experiments on both synthetic scenarios and a real-world case study demonstrate that our integrated approach achieves substantial reductions in overall costs and passenger waiting times compared to conventional two-stage optimization approaches that solve timetabling and vehicle scheduling sequentially.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105482"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689252","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 prediction to explanation: A machine learning and causal mediation framework for roadway crash risk with connected vehicle data","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Shaoyan Zhai,&nbsp;Abu Saif Md Nasim Uddin,&nbsp;Zubayer Islam","doi":"10.1016/j.trc.2025.105479","DOIUrl":"10.1016/j.trc.2025.105479","url":null,"abstract":"<div><div>Traditional safety analysis often relies on static roadway attributes and crash frequencies, lacking insight into the behavioral mechanisms through which the roadway environment influence crash occurrence. To address this gap, this study proposes a novel machine learning–guided causal framework that integrates crash risk classification with causal mediation analysis to uncover the behavioral pathways linking road environment factors to crash risk. First, an ensemble learning framework is developed to classify high-risk road segments using connected vehicle (CV) data, where a double-layer <em>meta</em>-soft voting model achieves the best performance (AUC = 0.879; F1 = 0.655). Second, a Double Machine Learning (DML)–based mediation analysis is employed to quantify the indirect effects of operating speed and speed deviation in transmitting the impact of contextual features (e.g., speed limit, sidewalk coverage, building density) on crash risk. The findings demonstrate that operating speed and speed deviation significantly mediate the effects of posted speed limits, whereas abrupt driving maneuvers (e.g., hard braking) do not serve as structural mediators. Notably, the strength and structure of these mediation pathways vary across functional road classes, highlighting spatial heterogeneity in behavioral responses. The results support a shift from reactive, regulation-centric strategies to behavior-aware safety interventions informed by CV data. Practical recommendations include incorporating CV-derived metrics into real-time safety monitoring and prioritizing adaptive speed controls on collector and local roads. By bridging predictive analytics and causal inference, this study enhances the methodological toolkit for traffic safety research and contributes to a greater understanding of how roadway design, driver behavior, and crash risk interact through quantifiable mechanisms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105479"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145613580","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}