Transportation Research Part E-Logistics and Transportation Review最新文献

{"title":"Optimizing hub networks for truck platooning under uncertainty in cost savings","authors":"Elif Zeynep Serper ,&nbsp;Sibel A. Alumur","doi":"10.1016/j.tre.2026.104692","DOIUrl":"10.1016/j.tre.2026.104692","url":null,"abstract":"<div><div>Platooning offers significant potential cost benefits for truckload transportation by utilizing vehicle-to-vehicle communication and automation through the formation and dissolution of platoons at hubs. This paper addresses optimization of platooning hub networks for transporting truckloads of commodities from their origins to destinations within the promised delivery times. Deterministic and stochastic optimization models are developed to design these networks with a minimum total cost, where each truckload of a commodity can be transported either directly along its shortest path from origin to destination or routed via platooning through hubs. The stochastic model incorporates uncertainty associated with the potential cost savings due to platooning. The Sample Average Approximation method is employed to solve the stochastic model. Using real-world data involving 1253 commodities across 39 U.S. cities, the computational analysis demonstrates significant cost savings and delivery performance improvements through platooning. On average, even under the highest hub operating costs, the proposed model achieves a 7.97% reduction in overall costs compared to the direct-shipment-only scenario, with the best-case improvement reaching 15.89%. Additionally, the platoon-enabled network significantly improves delivery performance, increasing the share of shipments delivered within one day by up to 42% compared to the direct-shipment-only case. Furthermore, the results demonstrate the stochastic model’s ability to adapt to cost uncertainties, making it a valuable tool for changing logistics environments.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104692"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000918","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 structure of global delay propagation in air transport","authors":"Josu Blanco,&nbsp;Antònia Tugores,&nbsp;José J. Ramasco,&nbsp;Massimiliano Zanin","doi":"10.1016/j.tre.2026.104688","DOIUrl":"10.1016/j.tre.2026.104688","url":null,"abstract":"<div><div>The modelling and understanding of how flight delays propagate between airports of a country or region is a major topic of research in air transport that has been tackled through different techniques in the last decade. Much less attention has been devoted to the large-scale structure of the propagation, i.e. if and how delays can propagate between regional networks and continents. By leveraging on two complementary analysis approaches, we show how such propagation takes place across the main world regions, describing how it is modulated by seasons, the number of flights connecting them and their relative distance. We further propose a methodology to detect which airports act as gateways for global-scale propagation, and discuss the operational applications of these findings.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104688"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072516","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":"Digital twin-based dynamic co-scheduling with AGV energy management in sea-rail intermodal automated container terminals","authors":"Jiaqi Li ,&nbsp;Daofang Chang ,&nbsp;Furong Wen ,&nbsp;Ilkyeong Moon","doi":"10.1016/j.tre.2026.104707","DOIUrl":"10.1016/j.tre.2026.104707","url":null,"abstract":"<div><div>To fully leverage the advantages of sea-rail intermodal transport, the automation upgrade of the railway center station (RCS) is essential for enabling seamless connectivity between the RCS and the terminal via automated guided vehicles (AGVs). This transformation introduces complex scheduling challenges for sea-rail intermodal automated container terminals (SRIACTs), including multi-directional container flows, coordination among diverse equipment, and AGV charging requirements with battery management. To address these challenges, this paper investigates the multi-equipment collaborative scheduling problem in SRIACTs with consideration of AGV charging. A mixed-integer programming model is formulated with sequencing, timing, and energy constraints, aiming to jointly minimize makespan and total charging time. To improve computational efficiency in large-scale cases, an improved genetic algorithm based on a decomposition-iteration framework is developed according to problem-specific features. Furthermore, to address operational uncertainties, a digital twin-based hybrid rescheduling framework is extended to enable real-time monitoring, disturbance detection, and rapid response to AGV status and battery levels, thereby enhancing system resilience and scheduling flexibility. Extensive numerical experiments are conducted to validate the effectiveness of the proposed algorithm and rescheduling framework. On this basis, comparative analyses are performed on bi-objective formulations and the flexible charging strategy. Additionally, sensitivity analyses examine the impacts of key factors, including objective weights, charging thresholds, rescheduling thresholds, and the number and layout of charging facilities. The findings provide valuable insights for terminal operators in formulating integrated scheduling strategies, optimizing AGV charging plans, and scientifically deploying charging infrastructure during the RCS automation process, thereby promoting sustainable and intelligent terminal operations.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104707"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072506","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 new robust capacitated hub interdiction problem under ambiguous demand and its benders decomposition","authors":"Meiyu Liu ,&nbsp;Shanshan Gao ,&nbsp;Naiqi Liu","doi":"10.1016/j.tre.2026.104716","DOIUrl":"10.1016/j.tre.2026.104716","url":null,"abstract":"<div><div>In this paper, we study the capacitated hub interdiction problem on a multiple allocation hub-and-spoke network and formulate it as a bi-level optimization model. The rational attacker in the upper-level model interdicts a subset of hubs and maximizes the damage to the defender in the lower-level model. Given that hubs operate with limited capacity, an interdiction would lead to unfulfilled demand, thereby incurring additional penalty costs. In reality, however, demand in urban logistics transportation tends to exhibit significant variation due to its inherent non-stationarity and spatial correlation. Therefore, we model demand uncertainty to make robust hub location and routing decisions. Methodologically, a sub-Gaussian-based ambiguity set is constructed using statistical methods, which involves the family of all probability distributions consistent with known mean, variance and support information about demand. We develop a distributionally robust optimization model for our capacitated hub interdiction problem under the constructed ambiguity set, and then reformulate it as a mixed integer linear programming model, which facilitates us to design an accelerated Benders decomposition algorithm. In particular, we derive robust solutions with both a priori and a posteriori probability guarantees. Case study on the well-known CAB dataset demonstrates the advantages of our optimization method in balancing robustness and conservatism. Furthermore, computational results on the TR dataset illustrate that our proposed algorithm outperforms the CPLEX solver.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104716"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072507","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":"Who should invest in EV charging infrastructure? Policy design under ZEV mandates","authors":"Ting Chen ,&nbsp;Kannan Govindan ,&nbsp;Wenting Yang ,&nbsp;Qiuwei Wang ,&nbsp;Lu Liu","doi":"10.1016/j.tre.2025.104642","DOIUrl":"10.1016/j.tre.2025.104642","url":null,"abstract":"<div><div>Governments worldwide have implemented multiple incentive measures to promote electric vehicle (EV) adoption, including consumer subsidies, infrastructure investment subsidies, and Zero Emission Vehicle (ZEV) mandates. Motivated by these policy instruments, various stakeholders (including automakers and EV component suppliers) are actively investing in charging infrastructure. Which subsidy policy is most cost-effective, and which entity’s infrastructure investment yields optimal outcomes? This study constructs a Stackelberg game model comprising government, component supplier, and competing fuel/electric vehicle manufacturers to explore optimal policy structures under three investment modes (i.e., supplier-led infrastructure investment, EV manufacturer-led investment, and traditional automaker-led investment). We systematically evaluate the performance in minimizing government expenditure while achieving adoption targets for each investment mode. More importantly, we extend our analysis to include ZEV mandates and their impact on subsidy effectiveness and government expenditure. Our results provide several interesting and counterintuitive insights. First, we show that supplier-led infrastructure investment (i.e., Mode S) consistently achieves the lowest policy expenditure and highest social welfare. Second, when automakers invest (Modes E and F), consumer subsidies alone are never optimal, contradicting widespread policy practice. Mode S consistently achieves the lowest expenditure and highest social welfare, yet Modes E and F generate superior economic benefits for firms—revealing a fundamental trade-off between public cost-efficiency and private profitability. Third, although ZEV mandates are intended to substitute for costly subsidies and reduce fiscal burden, our analysis reveals they may paradoxically increase government expenditure and discourage EV production when requirements become overly stringent.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104642"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940514","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":"Inventory-constrained online learning for revenue management with delayed feedback","authors":"Sheng Ji","doi":"10.1016/j.tre.2025.104649","DOIUrl":"10.1016/j.tre.2025.104649","url":null,"abstract":"<div><div>Delayed feedback is a prevalent challenge in modern logistics and transportation systems, especially on digital retail platforms. This paper investigates an online learning and pricing problem characterized by aggregated and anonymous delays. In this setting, neither demand nor revenue is immediately observable following a pricing decision; instead, these metrics become available to the retailer only after some stochastic delay. The retailer also faces an initial inventory constraint, creating a complex exploration-exploitation trade-off among learning demand, generating revenue, and managing inventory. To address this challenge, we propose a novel batch-based learning algorithm, referred to as Bandits with Dual Mirror Descent (BUD for short), which integrates mirror descent with bandit control. The algorithm employs a carefully designed batch structure to isolate the impact of delayed feedback, while combining Upper Confidence Bound (UCB) for pricing with dual updates for inventory management. Our theoretical analysis shows that the regret (defined as the revenue gap between the optimal policy and the learning algorithm) of BUD grows sublinearly with the selling horizon and matches the known lower bounds in both bandit with delays and online pricing problems. We conducted numerical experiments to demonstrate that the regret of BUD converges to 0 in various scenarios.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104649"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940516","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 event-based model and hybrid genetic search algorithm for an inland multi-size container transportation problem","authors":"Meiyan Chi ,&nbsp;Xiaoning Zhu ,&nbsp;Baicheng Yan ,&nbsp;Kris Braekers","doi":"10.1016/j.tre.2025.104639","DOIUrl":"10.1016/j.tre.2025.104639","url":null,"abstract":"<div><div>This paper addresses a complex multi-size Container Drayage Problem (CDP) in the hinterland of a seaport, where a fleet of identical trucks is used to transport containers between customer locations, a container terminal, and a depot, and in which the repositioning of empty containers is also considered. Each truck can carry either one 40-ft container or two 20-ft containers simultaneously. The main target of the CDP is to determine the trucking schedule that satisfies all transport demands while minimizing the total cost. The problem is described using an event-based graph that considers capacity, pairing, precedence, and time-window constraints implicitly, based on which a compact Mixed-Integer Linear Programming (MILP) model is proposed. To reduce the model scale and enhance computational efficiency, we introduce tailored model enhancement methods to eliminate infeasible event nodes and arcs based on time window feasibility checks. The results of numerical experiments prove that the event-based model can solve small-scale instances effectively. For large-scale instances, we develop a Hybrid Genetic Search (HGS) algorithm that incorporates a Dynamic Programming (DP)-optimized enumeration method to handle multi-size container loading schemes and time-window constraints effeciently. Extensive computational experiments show that our proposed algorithm significantly outperforms the commercial solver <span>CPLEX</span> on large-scale instances, demonstrating its scalability for real-world applications.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104639"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978646","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":"ASB-TDM: Towards flexible and user-friendly transportation demand management in polycentric metropolitan areas","authors":"Zhanbo Sun ,&nbsp;Huimin Tang ,&nbsp;Baichuan Zhu ,&nbsp;Qinxu Li ,&nbsp;Xuting Wang ,&nbsp;Rui Ma","doi":"10.1016/j.tre.2025.104652","DOIUrl":"10.1016/j.tre.2025.104652","url":null,"abstract":"<div><div>This paper proposes a novel, flexible and user-friendly approach, namely, attraction site-based transportation demand management (ASB-TDM) to alleviate road congestion and improve social welfare in trip-making, where both pull and push strategies are imposed on attraction sites in polycentric metropolitan areas. Under the ASB-TDM, attraction sites - as the objects under government regulation - are charged for causing excessive congestion at traffic hotspots or subsidized to attract consumers and balance network traffic. The impacts of ASB-TDM can be partially transferred to the travelers. There is a game between the government, attraction sites and travelers. Government, as the leader, aims to maximize social welfare, while the attraction sites pursue maximizing their own profits, and travelers choose the destination and path accordingly so as to maximize individual trip-making utilities. A tri-level programming model is established to study the game, which is then solved using a meta-heuristic solution approach integrating method of successive algorithm, diagonalization algorithm, and elitism-based genetic algorithm. Experiments were conducted based on a four-node network, the Nguyen-Dupuis network, and the Sioux Falls Network. Numerical results indicate that the oligopolistic competition among attraction sites can lead to improved social welfare and alleviated traffic congestion due to the dispersion effects of attraction sites in polycentric metropolitan areas, as the induced travel demand is diverted to underutilized areas (e.g., subcenters). The ASB-TDM scheme with government participation further enhances these benefits by optimizing the competitive dynamics through strategic subsidies and charges, leading to superior outcomes compared to the pure market-driven approach.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104652"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014567","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":"Two-stage virtual coupling high-speed railway train operation control method considering train operation curve optimization","authors":"Wencheng Huang ,&nbsp;Baiquan Tai","doi":"10.1016/j.tre.2026.104687","DOIUrl":"10.1016/j.tre.2026.104687","url":null,"abstract":"<div><div>In order to maintain the train tracking accuracy of the virtual coupled train formation (VCTF) while reducing energy consumption during operation, in this paper, we propose a two-stage virtual coupling high-speed train operation control method that considers optimizing the train operation curve. Based on train dynamics, we take minimizing energy consumption and operation time deviation as the main objectives for the train operation curve optimization in Stage I, establish a dynamic programming model with time discretization, and design an algorithm to satisfy the requirements of precise parking and on-time operation of the train, in which a variable time step solving algorithm is considered to improve the solving efficiency. The optimal energy-saving train operation curve obtained in Stage I is used as the expected operation curve of the leader train in the VCTF. In Stage II, we adopt the Distributed Model Predictive Control (DMPC) to establish the virtual coupling high-speed railway train operation control model. With the goal of minimizing the position error and speed error of the VCTF, and the train safety interval, train speed, train control performance and passenger comfort constraints, the designed DMPC ensures the stability and safety of the operation of VCTF. Finally, simulation analysis is conducted on three different scenarios, the research results show that the methodology proposed in this paper can effectively reduce the energy consumption of trains during operation while improving the tracking accuracy of trains in VCTF. In terms of energy saving, the proposed method is superior to previous heuristic algorithms and reinforcement learning methods, with improvements of 16.12 % and 2.52 %, respectively. In terms of tracking accuracy, the peak distance and speed errors of the VC trains do not exceed 2 m and 0.24 m/s. In terms of solving efficiency, the variable time step dynamic programming method can improve the solving speed by nearly 30 % while ensuring the solving accuracy. Moreover, the energy consumption of Train 0 and Train 1 increased by 1.7 % and 0.66 % respectively compared to the expected operation curve. Simulation results with different expected intervals show that setting an appropriate expected tracking interval in actual operation requires a balance between train operation safety and energy efficiency.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104687"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978614","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":"ISAC-empowered deep reinforcement learning scheme for eVTOL approach trajectory optimization with radar point cloud","authors":"Leyan Chen ,&nbsp;Yuhao Wang ,&nbsp;Shulu Chen ,&nbsp;Yong Sun ,&nbsp;Kai Wang","doi":"10.1016/j.tre.2026.104681","DOIUrl":"10.1016/j.tre.2026.104681","url":null,"abstract":"<div><div>Urban air mobility (UAM) has emerged as a promising solution to alleviate ground traffic congestion by enabling the use of low-altitude airspace for passenger and cargo transportation. Among UAM, electric vertical take-off and landing (eVTOL) aircraft are expected to play a central role in future urban transport systems due to their flexibility, zero-emission operation, and compatibility with existing infrastructure. However, ensuring the safe and efficient operation of eVTOLs during the approach and landing phase remains a major challenge, especially in urban environments where intelligent unmanned aerial vehicles (UAVs) simultaneously perform logistics and monitoring tasks at low altitudes. Reliable sensing and communication are therefore essential to guarantee operational safety, connectivity, and energy efficiency. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) framework for eVTOL approach trajectory optimization. The framework integrates radar point cloud (RPC) sensing with the three-dimensional radio knowledge map (RKM) to enhance environmental awareness and communication reliability in dense urban airspace. Based on this framework, a fusion deep point reinforcement learning (FDPRL) algorithm is developed to optimize eVTOL trajectories under age-of-information and energy constraints jointly. It includes the RPC feature extraction module for UAV sensing, the RKM feature extraction module for communication enhancement, and a decision-making module for trajectory control. Simulation results demonstrate that the proposed FDPRL achieves challenge performance, outperforming all the benchmarks, enabling the eVTOL to adaptively adjust its approach trajectory to avoid UAVs while maintaining efficient communication, thus achieving superior total communication capacity and maximum residual energy.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104681"},"PeriodicalIF":8.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978650","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}