Transportation Research Part C-Emerging Technologies最新文献_第6页

Integrating first-and-last-mile feeder services with urban public transportation considering transfer time uncertainty 考虑换乘时间不确定性，将首末英里支线服务与城市公共交通相结合

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-03-01 Epub Date: 2025-12-26 DOI: 10.1016/j.trc.2025.105499

Bo Sun, Qiang Meng

{"title":"Integrating first-and-last-mile feeder services with urban public transportation considering transfer time uncertainty","authors":"Bo Sun, Qiang Meng","doi":"10.1016/j.trc.2025.105499","DOIUrl":"10.1016/j.trc.2025.105499","url":null,"abstract":"<div><div>This study proposes an integrated first-and-last-mile feeder service (IFLMFS) that coordinates shared mobility solutions with urban public transportation (UPT). Riders can request travel services in the form of a single first-mile (FM) trip prior to UPT, or a single last-mile (LM) trip following UPT usage, or a combination of both, to connect with UPT hubs seamlessly. We formulate the IFLMFS problem as a tailored path-based set-packing (PSC) model. For riders requiring both FM and LM connections, new coupling constraints are introduced into the PSC model to jointly optimize routing decisions, which ensures the chronological consistency between FM and LM routes. To account for the transfer time uncertainty during UPT, arising from factors such as riders’ walking speed variations and boarding behaviors, an effective robust optimization approach is adopted and the PSC model is reformulated with tractable robust counterparts. To efficiently process real-time travel requests, we develop a Column Generation method embedded within a Rolling Horizon Framework (CG-RHF). To accelerate CG-RHF, a time-based pruning strategy is created to tighten the solution space and a decomposition strategy for pricing subproblems is designed across different UPT hubs in parallel. To validate the computational performance of the proposed CG-RHF, we benchmark it against an exact branch-price-and-cut algorithm and a customized adaptive large neighborhood search metaheuristic. Extensive numerical experiments from Singapore’s metro network demonstrate that the integrated service can efficiently deliver high-quality solutions across a variety of rider travel scenarios.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105499"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824270","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}

引用次数: 0

Distributionally robust optimization of sailing speed, bunkering, and fuel switching for dual-fuel liner services 双燃料班轮航速、加油和燃料切换的分布鲁棒优化

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-03-01 Epub Date: 2026-01-16 DOI: 10.1016/j.trc.2026.105528

Ping He , Lingxiao Wu , Jian Gang Jin , Shaorui Zhou , Frederik Schulte

{"title":"Distributionally robust optimization of sailing speed, bunkering, and fuel switching for dual-fuel liner services","authors":"Ping He , Lingxiao Wu , Jian Gang Jin , Shaorui Zhou , Frederik Schulte","doi":"10.1016/j.trc.2026.105528","DOIUrl":"10.1016/j.trc.2026.105528","url":null,"abstract":"<div><div>To reduce CO<sub>2</sub> and SO<sub>2</sub> emissions, shipping companies have started deploying LNG or methanol dual-fuel ships on liner services. Unlike traditional container ships, these dual-fuel ships can use multiple types of fuels during a voyage, allowing them to comply with emission regulations while reducing operational costs through fuel switching and speed optimization. Given the significant fluctuations in bunker prices across different ports, decisions regarding fuel switching, refueling, and sailing speeds must account for price uncertainty. We develop a distributionally robust chance-constrained programming model based on the Wasserstein uncertainty set to minimize operating costs under this uncertainty. We divide each port-to-port sailing leg into sub-legs, considering regional emission requirements or canal segments. This segmentation enables the optimization of fuel usage proportions, sailing speeds, and refueling strategies for each sub-leg. The model is then reformulated as a tractable mixed-integer second-order conic programming model. We validate the model using real-world data from COSCO Shipping. Numerical experiments demonstrate that the model can identify optimal solutions for real-scale instances within practical computational time. Furthermore, the robust solutions significantly outperform those obtained using the traditional sample average approximation method. Our results suggest that the joint optimization of fuel management and sailing speeds for dual-fuel ships can effectively reduce operating costs without increasing emissions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105528"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978354","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}

引用次数: 0

Modeling proactive avoidance behaviors in pedestrian flows considering congestion anticipation 考虑拥堵预期的行人流主动回避行为建模

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.trc.2026.105532

Pei-Yang Wu , Ying-En Ge , Zhuanglin Ma , Ren-Yong Guo

{"title":"Modeling proactive avoidance behaviors in pedestrian flows considering congestion anticipation","authors":"Pei-Yang Wu , Ying-En Ge , Zhuanglin Ma , Ren-Yong Guo","doi":"10.1016/j.trc.2026.105532","DOIUrl":"10.1016/j.trc.2026.105532","url":null,"abstract":"<div><div>This paper investigates proactive avoidance behaviors in pedestrian flows by means of real-world experiments and a potential field model. Typical movement patterns of pedestrians related to the proactive avoidance behaviors are provided. Based on the observed behaviors, a potential field model is proposed to combine tactical-level and operational-level modeling frameworks. This model allows pedestrians to select and move toward temporary destinations instead of moving directly to their final destinations. Pedestrian movements are guided by the potential values associated with different positions in the focused space. Three types of sub-potentials are involved to reflect the effects of route attributes, spatiotemporal congestion anticipation, and potential conflicts on pedestrian movements. Numerical experiments demonstrate that the proposed model can effectively reproduce pedestrians’ proactive avoidance behaviors. The model is validated by comparing the simulation results with existing experimental data and our own experimental data. This investigation provides an alternative explanation for pedestrian movement mechanisms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105532"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014571","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}

引用次数: 0

A graph vertex-coloring-based parallel block coordinate descent method for solving the traffic assignment problem 一种基于图顶点着色的并行块坐标下降法求解交通分配问题

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-11-20 DOI: 10.1016/j.trc.2025.105439

Kai Zhang , Zhiyuan Liu , Honggang Zhang , Yicheng Zhang , Yuk Ming Tang , Xiaowen Fu

{"title":"A graph vertex-coloring-based parallel block coordinate descent method for solving the traffic assignment problem","authors":"Kai Zhang , Zhiyuan Liu , Honggang Zhang , Yicheng Zhang , Yuk Ming Tang , Xiaowen Fu","doi":"10.1016/j.trc.2025.105439","DOIUrl":"10.1016/j.trc.2025.105439","url":null,"abstract":"<div><div>Traffic assignment is an essential component of the traditional four-step transportation planning methodology and significantly contributes to the prediction of traffic flow distribution and optimization of traffic planning. Existing algorithms for solving the user equilibrium traffic assignment problem typically rely on equal intervals and random sampling strategies to divide a set of origin–destination (OD) pairs. However, these sampling strategies fail to address the path overlap issue among OD pairs and often depend on sensitivity analyses to partition the OD set, hindering the efficiency of task parallelism. To address this challenge, the OD grouping problem was formulated as a vertex-coloring problem, which was translated into an integer linear programming (ILP) model. The largest degree first algorithm was proposed to solve the OD grouping problem, enabling the identification of OD pairs within each block with minimal path overlap. Thereafter, the results of the OD grouping based on vertex coloring were incorporated into the parallel block coordinate descent (PBCD) method, increasing the number of OD subproblems within each block and enhancing the parallel computation. An adaptive algorithm is further proposed to address the OD-based restricted subproblem depending on the number of paths for a given OD pair. The proposed method is evaluated based on various large-scale transportation networks and compared with existing algorithms, demonstrating its effectiveness in reducing path overlap within blocks and improving the efficiency of solving traffic assignment problems in large-scale networks.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105439"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553973","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}

引用次数: 0

On-demand DAR system considering traffic dynamics and network partitioning 考虑流量动态和网络划分的按需雷达系统

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-12-11 DOI: 10.1016/j.trc.2025.105493

Cong Quoc Tran, Shang Jiang, Mehdi Keyvan-Ekbatani

{"title":"On-demand DAR system considering traffic dynamics and network partitioning","authors":"Cong Quoc Tran, Shang Jiang, Mehdi Keyvan-Ekbatani","doi":"10.1016/j.trc.2025.105493","DOIUrl":"10.1016/j.trc.2025.105493","url":null,"abstract":"<div><div>On-demand shared mobility systems (e.g., pooled ride-hailing, dial-a-ride, and carpooling) could enhance urban mobility by providing more sustainable door-to-door transportation options, connecting communities to public transport hubs and combining the advantages of public transportation and private services. Among these, dial-a-ride (DAR) systems represent a classical form of shared mobility; however, most DAR models traditionally rely on traffic-independent travel times. The challenges lie in the complexity of incorporating traffic conditions and stakeholders’ involvement in the decision-making process, particularly under dynamic traffic settings. This study proposes a congestion-aware DAR framework to maximize the system’s profit while considering the dynamic nature of traffic conditions, real-time passenger requests and their sharing preferences. The proposed framework is able to capture the heterogeneity of traffic patterns by incorporating a regional dynamic traffic assignment (DTA) with a novel network partitioning strategy. Solution algorithms (i.e., quasi-dynamic partition selection, MSA-based algorithm for the regional DTA problem and dynamic on-demand DAR algorithm) are developed and numerically tested using the central business district network of Christchurch City, New Zealand, considering specific traffic scenarios. Numerical tests and comparative results demonstrate the practical applicability and operational efficiency (i.e., utilization rates of vehicles and rejection rates of customers’ requests) of the regional-based dynamic on-demand DAR system compared to the traditional dynamic DAR approach. Managerial insights and potential research directions are also provided.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105493"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731236","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}

引用次数: 0

VLM-MPC: Model predictive controller augmented vision language model for autonomous driving VLM-MPC：自动驾驶模型预测控制器增强视觉语言模型

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-12-05 DOI: 10.1016/j.trc.2025.105487

Keke Long , Haotian Shi , Jiaxi Liu , Chaowei Xiao , Xiaopeng Li

{"title":"VLM-MPC: Model predictive controller augmented vision language model for autonomous driving","authors":"Keke Long , Haotian Shi , Jiaxi Liu , Chaowei Xiao , Xiaopeng Li","doi":"10.1016/j.trc.2025.105487","DOIUrl":"10.1016/j.trc.2025.105487","url":null,"abstract":"<div><div>Motivated by the emergent reasoning capabilities of Vision Language Models (VLMs) and their potential to improve the comprehensibility of autonomous driving systems, this paper introduces a closed-loop autonomous driving controller called VLM-MPC, which combines a VLM with Model Predictive Controller (MPC) to evaluate how model-based control could enhance VLM decision-making. The proposed VLM-MPC is structured into two asynchronous components: The upper level VLM generates driving parameters (e.g., desired speed, desired headway) for lower-level control based on front camera images, ego vehicle state, traffic environment conditions, and reference memory; The lower-level MPC controls the vehicle in real-time using these parameters, considering engine lag and providing state feedback to the entire system. Experiments based on the nuScenes dataset and Carla simulation validated the effectiveness of the proposed VLM-MPC across various environments (e.g., night, rain, fog, and intersections). The results demonstrate that the VLM-MPC consistently maintains Post Encroachment Time (PET) above safe thresholds, in contrast to some scenarios where the VLM-based control posed collision risks. Additionally, the VLM-MPC enhances smoothness compared to the real-world trajectories and VLM-based control. By comparing behaviors under different environmental settings, we highlight the VLM-MPC’s capability to understand the environment and make reasoned inferences. Moreover, we validate the contributions of two key components, the reference memory and the environment encoder, to the stability of responses through ablation tests.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105487"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689760","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}

引用次数: 0

V2X-VLM: End-to-End V2X cooperative autonomous driving through large vision-Language models V2X- vlm：基于大型视觉语言模型的端到端V2X协同自动驾驶

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-11-30 DOI: 10.1016/j.trc.2025.105457

Junwei You , Zhuoyu Jiang , Zilin Huang , Haotian Shi , Rui Gan , Keshu Wu , Xi Cheng , Xiaopeng Li , Bin Ran

{"title":"V2X-VLM: End-to-End V2X cooperative autonomous driving through large vision-Language models","authors":"Junwei You , Zhuoyu Jiang , Zilin Huang , Haotian Shi , Rui Gan , Keshu Wu , Xi Cheng , Xiaopeng Li , Bin Ran","doi":"10.1016/j.trc.2025.105457","DOIUrl":"10.1016/j.trc.2025.105457","url":null,"abstract":"<div><div>Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving by leveraging information from both ego-vehicle and infrastructure sensors. However, effectively fusing heterogeneous visual and semantic information while ensuring robust trajectory planning remains a significant challenge. This paper introduces V2X-VLM, a novel end-to-end (E2E) cooperative autonomous driving framework based on vision-language models (VLMs). V2X-VLM integrates multiperspective camera views from vehicles and infrastructure with text-based scene descriptions to enable a more comprehensive understanding of driving environments. Specifically, we propose a contrastive learning-based mechanism to reinforce the alignment of heterogeneous visual and textual characteristics, which enhances the semantic understanding of complex driving scenarios, and employ a knowledge distillation strategy to stabilize training. Experiments on a large real-world dataset demonstrate that V2X-VLM achieves state-of-the-art trajectory planning accuracy, significantly reducing L2 error and collision rate compared to existing cooperative autonomous driving baselines. Ablation studies validate the contributions of each component. Moreover, the evaluation of robustness and efficiency highlights the practicality of V2X-VLM for real-world deployment to enhance overall autonomous driving safety and decision-making.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105457"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651056","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}

引用次数: 0

In-plant autonomous mobile robot scheduling and routing problem considering battery consumption model 考虑电池消耗模型的厂内自主移动机器人调度与路由问题

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-12-15 DOI: 10.1016/j.trc.2025.105488

Zhaofang Mao , Zhanghuang Xie , Kan Fang , Dian Huang , Zhao Zhao

{"title":"In-plant autonomous mobile robot scheduling and routing problem considering battery consumption model","authors":"Zhaofang Mao , Zhanghuang Xie , Kan Fang , Dian Huang , Zhao Zhao","doi":"10.1016/j.trc.2025.105488","DOIUrl":"10.1016/j.trc.2025.105488","url":null,"abstract":"<div><div>The evolving landscape of larger and increasingly intricate systems calls for the adoption of diverse and adaptable autonomous mobile robots (AMRs) to meet the movement requirements within a factory. To examine the battery consumption characteristics of AMRs, our study introduces a novel perspective by investigating the relationship between carrying capacity and battery consumption. We propose to model this relationship using simulation techniques, thus enabling our study to better aligns with real-world applications. The primary objective of this study is to develop optimal charging strategies within the constraints of limited charging stations while simultaneously minimizing overall tardiness. We develop a mixed integer programming (MIP) model for the AMR scheduling and routing problem with battery consumption model (AMRSRP-BCM). A dynamic operator sequence reheating simulated annealing hyper-heuristic (DOS-RSAH) algorithm is designed to solve this problem. Extensive experiments are conducted to demonstrate the robustness of our model and algorithm while sensitivity analysis is presented to show the application of our study, and valuable insights are gained.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105488"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785157","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}

引用次数: 0

Learning to reschedule platforms: A graph neural network based deep reinforcement learning method for the train platforming and rescheduling problem⁎ 站台重新调度学习：一种基于图神经网络的列车站台和重新调度问题的深度强化学习方法

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-11-22 DOI: 10.1016/j.trc.2025.105453

Hongxiang Zhang , Andrea D’Ariano , Yongqiu Zhu , Yaoxin Wu , Liuyang Hu , Gongyuan Lu

{"title":"Learning to reschedule platforms: A graph neural network based deep reinforcement learning method for the train platforming and rescheduling problem⁎","authors":"Hongxiang Zhang , Andrea D’Ariano , Yongqiu Zhu , Yaoxin Wu , Liuyang Hu , Gongyuan Lu","doi":"10.1016/j.trc.2025.105453","DOIUrl":"10.1016/j.trc.2025.105453","url":null,"abstract":"<div><div>The train platforming schedule is the crucial plan for guiding trains to travel through a railway station without spatial and temporal conflicts. When trains are delayed in arriving at the station due to disturbances or disruptions, it raises the Train Platforming and Rescheduling Problem (TPRP), one of the hot topics in railway traffic management. It focuses on allocating platforms and time slots for trains to reduce delays and ensure operational efficiency in a station. This paper introduces a novel graph neural network based deep reinforcement learning method to address this problem, named Learning to Reschedule Platforms (L2RP). We formulate the solving process of TPRP as a customized Markov decision process. Meanwhile, we integrate a microscopic discrete-event train operation simulation model to serve as the agent exploration environment, which provides states, executes actions, and completes transitions. Then, we design a hybrid graph neural network based policy network to derive high-quality actions under each graph encoded state.</div><div>The policy network is trained with the reward function designed to minimize total train knock-on delays and platform changes. The experiments on real-world instances show that the proposed L2RP method can produce high-quality solutions for instances of various scenarios within stably short solving times.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105453"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575243","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}

引用次数: 0

Optimising pre-disaster evacuation strategies using shared autonomous vehicles: A dynamic and stochastic approach 使用共享自动驾驶汽车优化灾前疏散策略：动态和随机方法

IF 7.6 1区工程技术

Transportation Research Part C-Emerging Technologies Pub Date : 2026-02-01 Epub Date: 2025-11-22 DOI: 10.1016/j.trc.2025.105437

Yunlong Wang, Minh Kieu

{"title":"Optimising pre-disaster evacuation strategies using shared autonomous vehicles: A dynamic and stochastic approach","authors":"Yunlong Wang, Minh Kieu","doi":"10.1016/j.trc.2025.105437","DOIUrl":"10.1016/j.trc.2025.105437","url":null,"abstract":"<div><div>Classical mass evacuation strategies from disasters often rely on static planning and manual operations, which struggle to handle the uncertainty and time-critical nature of large-scale disasters. This study aims to develop a dynamic and intelligent evacuation strategy using Shared Autonomous Vehicles (SAVs) to improve the efficiency and equity of emergency transport for populations without private vehicles. We propose a Transformer-Based Deep Reinforcement Learning with Branch and Bound (TB-DRL) approach to address the SAV Evacuation Dispatching Problem under uncertain demand, traffic, and SAVs availability. The TB-DRL’s multi-head attention layers learn spatiotemporal correlations between demand nodes and SAV locations, while the hierarchical mask progressively prunes infeasible or low-value actions, shortening decision time without compromising optimality. Based on Auckland’s 345 km road graph and 38,993 residents dataset, the results show that TB-DRL lowers total evacuation cost by 8.53 %-27.45 % in dynamic tests and by up to 18.55 % and 16.19 % in static and dynamic city-scale scenarios, while keeping the mean route-cost gap below 1.7 %. Pareto analysis demonstrates that TB-DRL can shift smoothly between cost-driven and demand-driven objectives, while sensitivity experiments across assembly node locations, random SAV’s state and congestion levels confirm the robustness of the strategy to uncertain evacuation scenarios. The simulation based on real population data shows that using more SAVs helps reduce congestion in the later evacuation stage. However, their empty return trips can create local congestion. When SAVs handle 60 % of the evacuation tasks, the total evacuation time and efficiency improve most significantly.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"183 ","pages":"Article 105437"},"PeriodicalIF":7.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145575244","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}

引用次数: 0