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

{"title":"A model for queuing toll policies incorporating cost-sharing characteristics of carpooling in the transportation network with capacity constraints","authors":"Yanqin Chen ,&nbsp;Changxin Liao ,&nbsp;Jia Yao ,&nbsp;David Z.W. Wang ,&nbsp;Anthony Chen","doi":"10.1016/j.tre.2026.104700","DOIUrl":"10.1016/j.tre.2026.104700","url":null,"abstract":"<div><div>This paper introduces capacity constraints into a multi-modal transportation network, including solo driving, carpooling, and public transit, and proposes two queuing toll policies based on the cost-sharing characteristics of carpooling to eliminate physical queues. By incorporating traveler heterogeneity in the value of time, a multi-class user equilibrium model is developed to assess the impacts of capacity constraints and queuing toll policies on traffic equilibrium under three cases: 1) case 1, no tolls are charged to any travelers. Physical queues may occur on the saturated links; 2) case 2, the queuing tolls are charged exclusively to solo drivers, and it takes into account the scenario that the physical queues are not completely eliminated in the saturated link only with carpooling; 3) case 3, the queuing tolls are charged to solo drivers and carpooling travelers (i.e., carpooling drivers and riders). Then, an improved route swapping algorithm is proposed to solve the equilibrium model. Finally, numerical analysis based on the Winnipeg network is conducted to demonstrate the properties of the problem and the performance of the proposed model and algorithm. The results show that the proposed queuing toll policies can improve travel efficiency by encouraging carpooling and eliminating physical queues. Moreover, the differential impacts of the three cases on heterogeneous travelers’ mode choices and travel efficiency are explored. These findings provide a theoretical basis for the practical implementation of queuing toll policies.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104700"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110215","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":"Hierarchical planning and scheduling for bulk ports via network flow and deep reinforcement learning-guided constraint programming","authors":"Xuan Lu ,&nbsp;Yu Zhang ,&nbsp;Xuri Xin ,&nbsp;Hang Yang ,&nbsp;Huanhuan Li ,&nbsp;Lanbo Zheng ,&nbsp;Zaili Yang","doi":"10.1016/j.tre.2026.104714","DOIUrl":"10.1016/j.tre.2026.104714","url":null,"abstract":"<div><div>In this research, an integrated inbound and outbound operational planning and scheduling problem is addressed for complex and large bulk ports. The practice of moving homogeneous dry bulk cargoes on a fixed terminal is changing as raw materials of different types are transported from/to the same terminals. It raises a new research challenge where unloading, stacking, reclaiming, conveying and loading operations must be coordinated to import/export blended products according to the tight specifications of customers. This paper aims to maximise resource utilisation and to satisfy demands as early as possible. The essence of the problem is to design the routing of product flows throughout the port logistics network such that supply and demand are matched optimally. This study presents a new framework that enables the modelling of the planning part as a multi-commodity flow problem and the scheduling part as a constraint programming (CP) problem. A novel dual-engine optimisation method that synergistically combines CP with deep reinforcement learning (DRL) is proposed to accelerate the scheduling phase. The method leverages DRL agents to fix key variables, thereby effectively accelerating the optimisation process of the CP solver. Comprehensive numerical experiments are conducted on real data sets as well as instances derived from real scenarios to validate the effectiveness of the proposed approach, demonstrating significant improvements in port scheduling efficiency. Additionally, strategic management analyses offer actionable insights to support decision-making in bulk port operations. The proposed methods provide a generalised methodology adaptable to a broad range of complex combinatorial optimisation problems in port logistics and beyond, paving the way for more intelligent and sustainable dry bulk port management.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104714"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135122","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":"Enabling the multi-LR ability of drones in the multi-visit truck-drone routing problem with pickup and delivery","authors":"Sepehr Pasha,&nbsp;S.Mehdi Sajadifar","doi":"10.1016/j.tre.2026.104678","DOIUrl":"10.1016/j.tre.2026.104678","url":null,"abstract":"<div><div>The use of drones alongside trucks for parcel delivery has received considerable research attention, further stimulated by advancements in drone capacity and range that enhance operational viability relative to traditional methods. In this paper, we address a variant of the combined truck-drone routing problem, entailing multiple trucks collaborating with drones to meet the pickup and delivery demands of customers. In the proposed problem, drone energy consumption depends on the carried load; drones may serve multiple customers per flight, and each truck can launch and retrieve its drone multiple times at each customer node (multi-LR) to enhance overall utilization. We propose a mixed-integer linear programming model to minimize total cost, enhanced with problem-specific cuts, which are demonstrated through extensive computational experiments to effectively reduce runtime. The model includes flexible features that allow it to handle diverse operational constraints, such as restrictions on the number of flights performed and high-traffic areas. Given the complexity of the model, we develop an adapted algorithm from the literature, incorporating significant modifications along with a new acceleration strategy. The approach combines a maximum payload method in the first stage with an improved simulated annealing algorithm using problem-specific neighborhood operators in the second stage. Although our findings show that the multi-LR feature increases the number of flights performed, both the model and the adapted algorithm demonstrate its cost efficiency, achieving average transportation cost reductions of 14.51% compared to the system without multi-LR and 45.62% compared to the traditional truck-only system.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104678"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081788","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":"Assessing Markov property in driving behaviors: Insights from statistical tests","authors":"Zheng Li ,&nbsp;Haoming Meng ,&nbsp;Chengyuan Ma ,&nbsp;Ke Ma ,&nbsp;Xiaopeng Li","doi":"10.1016/j.tre.2026.104740","DOIUrl":"10.1016/j.tre.2026.104740","url":null,"abstract":"<div><div>The Markov property serves as a foundational assumption in most existing work on vehicle driving behavior, positing that future states depend solely on the current state, not the series of preceding states. This study validates the Markov properties of vehicle trajectories for both Autonomous Vehicles (AVs) and Human-driven Vehicles (HVs). A statistical method used to test whether time series data exhibits Markov properties is applied to examine whether the trajectory data possesses Markov characteristics. Kolmogorov–Smirnov test and Brown–Forsythe test are additionally introduced to characterize the differences in Markov properties between AVs and HVs. Based on several public trajectory datasets, we investigate the presence and order of the Markov property of different types of vehicles through rigorous statistical tests. Our findings reveal that AV trajectories generally exhibit stronger Markov properties compared to HV trajectories, with a higher percentage conforming to the Markov property and lower Markov orders. In contrast, HV trajectories display greater variability and heterogeneity in decision-making processes, reflecting the complex perception and information processing involved in human driving. These results have significant implications for the development of driving behavior models, traffic flow models, and traffic simulation systems. Our study also demonstrates the feasibility of using statistical methods to test the presence of Markov properties in driving trajectory data.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104740"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174550","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":"Returnable-after-deep-trial policies in e-commerce supply chains with opportunistic consumers","authors":"Zhengzheng Chen,&nbsp;Tiaojun Xiao","doi":"10.1016/j.tre.2026.104728","DOIUrl":"10.1016/j.tre.2026.104728","url":null,"abstract":"<div><div>In response to consumer desire for a deep experience of product performance, some online sellers have strengthened their traditional money back guarantee by implementing the “returnable after a deep trial” (RADT) policy. But such a policy has also encouraged opportunistic behavior. Considering such opportunistic behavior for consumers, we build an e-commerce supply chain model to investigate the price decisions and the return strategy (implementing RADT or not) of online sellers under two selling modes: agency selling and reselling. We show that the RADT is often expensive for consumers. Besides, counterintuitively, when the online seller allows for an increased degree of deep trial, the seller is highly likely to decrease the retail price and the refund amount, simultaneously. Moreover, opportunistic consumers also have strategic value: although consumers’ opportunistic behavior under the RADT seemingly harms the online seller, in fact, the seller may generate profits by strategically encouraging opportunistic behavior. Furthermore, under agency selling, when consumers’ benefit from opportunistic behavior is not extremely small, and most ordinary consumers directly return the product, the RADT is optimal; while under reselling, the RADT is optimal if most ordinary consumers directly return the product. Surprisingly, more opportunistic consumers increase the online seller’s incentive to adopt the RADT policy.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104728"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146724","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":"Geopolitical disruptions and sustainability imperatives: a structural model of value chain and supply chain transformation in maritime logistics","authors":"Chung-Shan Yang","doi":"10.1016/j.tre.2026.104733","DOIUrl":"10.1016/j.tre.2026.104733","url":null,"abstract":"<div><div>The maritime logistics sector faces growing pressure to adapt its operations in response to escalating geopolitical disruptions and sustainability imperatives. This study develops and empirically tests an integrated structural model to examine how geopolitical shocks and sustainability requirements influence value chain restructuring, supply chain reconfiguration, and overall supply chain performance. Drawing on global value chain theory and the dynamic capabilities framework, the model is evaluated using data from 154 senior executives in the maritime logistics sector. Measurement constructs are validated through confirmatory factor analysis (CFA), and hypotheses are tested using structural equation modeling (SEM). Results reveal that sustainability requirements exert a stronger influence on long-term value chain restructuring, while geopolitical shocks significantly affect both value chain restructuring and supply chain reconfiguration. In particular, geopolitical shocks display a significant direct effect on tactical reconfiguration and a stronger effect on strategic restructuring, which in turn cascades into downstream reconfiguration. Both restructuring and reconfiguration are found to significantly enhance supply chain performance. This research offers novel empirical evidence on adaptive capabilities in maritime logistics, contributing to the theoretical understanding of supply chain resilience and informing policy and managerial strategies in an era of systemic disruption.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104733"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146723","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":"Drone scheduling optimization for shore-to-ship delivery and waste recycling","authors":"Tingsong Wang ,&nbsp;Haiqing Zhou ,&nbsp;Xuecheng Tian","doi":"10.1016/j.tre.2026.104708","DOIUrl":"10.1016/j.tre.2026.104708","url":null,"abstract":"<div><div>Traditional shore-to-ship delivery and waste recycling predominantly rely on replenishment vessels, facing critical challenges such as low operational efficiency, high costs, and prolonged waiting times during multi-vessel operations. To address these challenges, this paper addresses the Drone Scheduling Problem in Shore-to-Ship Delivery and Recycling (DSP-SSDR). Specifically, we develop a mixed-integer programming (MIP) model to minimize the total completion time of all required tasks while accounting for practical constraints including drone load capacity, time windows, battery consumption, multiple trips, and dynamic base station return. To efficiently solve this problem, an adaptive large neighborhood search (ALNS) algorithm is proposed and improved, which dynamically adapts removal and repair operators and incorporates a two-layer local search to improve the solution quality. Numerical experiments using practical realistic test cases demonstrate that the proposed ALNS algorithm achieves superior performance compared to the commercial solver Gurobi, particularly in computational efficiency for large-scale instances.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104708"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135140","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":"Supply chain Holdings: The “Fire Extinguisher” or the “Fuel Booster” for supply chain Risks?","authors":"Duo Wang,&nbsp;Yunge Hu,&nbsp;Yanxi Li","doi":"10.1016/j.tre.2026.104703","DOIUrl":"10.1016/j.tre.2026.104703","url":null,"abstract":"<div><div>Based on relational theory, supply chain shareholding, as a novel feature of supply chains, has the potential to strengthen collaborative ties between upstream and downstream enterprises, effectively mitigating supply chain risks and acting as a “fire extinguisher.” Conversely, it may also intensify interdependencies among firms, making risk transmission within the supply network more likely, thereby functioning as a “fuel booster” for supply chain risks. This study reveals that supply chain holdings play a pivotal “fire extinguisher” role in corporate supply chain risk management, with such effects primarily driven by customer holdings. Specifically, supply chain holdings operate through three principal mechanisms: information transmission facilitation, business stability enhancement, and capital provision within supply chain networks. Heterogeneity analysis further demonstrates that the risk-alleviating effects are particularly pronounced under conditions of substantial supply chain information frictions, elevated external environmental risks, heightened financing constraints, diminished external bargaining power, or weakened shareholder control power. Moreover, the risk-mitigating effect of supply chain holdings persists even under unforeseen risk scenarios. This research not only expands the understanding of the economic consequences of supply chain holding from the perspective of supply chain risk management but also provides an important theoretical foundation for enterprises to effectively prevent supply chain risks and achieve high-quality development.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104703"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110246","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":"Machine learning algorithms and models for airport gate assignment problem: A systematic literature review","authors":"Hasnain Ali ,&nbsp;Kadir Dönmez ,&nbsp;Wei Lun Lim ,&nbsp;Sameer Alam","doi":"10.1016/j.tre.2026.104734","DOIUrl":"10.1016/j.tre.2026.104734","url":null,"abstract":"&lt;div&gt;&lt;div&gt;As global air traffic continues to grow, the efficient utilization of airport terminal gates has become critical for adhering to turnaround schedules, minimizing arrival delay propagation, and reducing missed passenger connections. The Gate Assignment Problem (GAP)—which involves allocating arriving (and departing) aircraft to gates under operational constraints—has traditionally been addressed using exact optimization methods, heuristics, and metaheuristics. However, these methods struggle to either scale or adapt to the uncertainty and complexity of real-world airport operations. In recent years, Machine Learning (ML) has emerged as a promising alternative or complement to classical methods, offering a fundamentally data-driven approach to prediction and adaptive decision-making. ML techniques have shown potential to anticipate disruptions before they occur, rapidly approximate optimal solutions, and learn complex, nonlinear patterns in historical gate assignments that are difficult to codify using handcrafted heuristics. Yet, despite increasing academic interest, the application of ML to GAP remains fragmented and poorly synthesized. Existing studies apply diverse ML techniques and hybrid models but rarely benchmark them against traditional or standalone counterparts, and rely on inconsistent evaluation practices—using non-standardized, often proprietary datasets with limited reproducibility—hindering comparative analysis and generalizability.&lt;/div&gt;&lt;div&gt;This paper presents a systematic literature review (SLR) of ML-based approaches for solving the GAP, covering 21 peer-reviewed studies published between 2016 and 2025. We organize our review around three guiding research questions: (i) the comparative strengths and limitations of ML methods versus traditional optimization techniques; (ii) the design and performance of hybrid ML–optimization frameworks; and (iii) the types of datasets and feature sets used in ML-based GAP studies, and the extent to which they reflect the complexity and variability of real-world airport operations. Following the Kitchenham approach, we synthesize findings from peer-reviewed studies, highlighting trends and gaps to guide future gate assignment research and system development. Our review reveals that ML-based techniques—particularly reinforcement learning and supervised delay predictors—offer strong potential for handling uncertainty and improving decision quality compared to traditional optimization methods. However, their effectiveness is often limited by data availability and lack of interpretability. Hybrid ML–optimization frameworks show promise in combining predictive and search capabilities, but current designs are ad hoc and rarely benchmarked against their standalone components. Most ML-based GAP studies rely on narrow, single-airport datasets that omit key operational dynamics, limiting generalizability and real-world relevance. To address these gaps, we propose future directions: (1) developing","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104734"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160932","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":"Techno-economic optimization for containership green transition under net-zero emission target: a predict-then-optimize framework","authors":"Leqi Zhou ,&nbsp;Jian Gang Jin ,&nbsp;Yuling Gao ,&nbsp;Çağatay Iris ,&nbsp;Tolga Bektaş ,&nbsp;Jiang Hang Chen","doi":"10.1016/j.tre.2026.104731","DOIUrl":"10.1016/j.tre.2026.104731","url":null,"abstract":"<div><div>Shipping companies are under growing pressure to meet net-zero emission target under tightening regulations. To tackle this challenge, this paper extends the techno-economic predict-then-optimize framework to make long-term strategic decisions that jointly minimize greenhouse gas emissions and lifecycle costs over the planning horizon. The framework integrates a vessel-specific fuel consumption prediction model for comprehensive estimation of future emissions, and an optimization model that strategically schedules and combines multiple emission reduction methods. The optimization explicitly accounts for the techno-economic trade-offs among slow steaming, maintenance, fuel switching, and capital investments for retrofits, while ensuring compliance with carbon intensity indicator (CII) regulations and the overall net-zero emission target. A case study on several containerships demonstrates the applicability of the model, with numerical experiments showing that the predict-then-optimize framework achieves a 67% carbon reduction by 2040. Further investigations of the strategies, including illustration of transition plans, comparisons of different strategies, evaluation of marginal costs for each method, and sensitivity analysis on interest rates and regulations, are conducted to provide insights into lifecycle cost-minimizing and emission regulation-compliant strategy design.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"209 ","pages":"Article 104731"},"PeriodicalIF":8.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146722","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}