Transportation Research Part C-Emerging Technologies最新文献

{"title":"Integrated optimization of demand-oriented timetabling and rolling stock circulation planning with flexible train compositions and multiple service routes on urban rail lines","authors":"Xinyu Bao ,&nbsp;Qi Zhang ,&nbsp;Haodong Yin ,&nbsp;Entai Wang ,&nbsp;Yaling Xiao","doi":"10.1016/j.trc.2025.105071","DOIUrl":"10.1016/j.trc.2025.105071","url":null,"abstract":"<div><div>This study investigates an integrated train operation plan problem with flexible train compositions and multiple service routes, which allows trains to couple, decouple and turn around at intermediate turnaround stations based on virtual coupling technology. An integrated optimization model for demand-oriented train timetables and rolling stock circulation plans is developed with the consideration of train service routes and the number of train services, by minimizing both passenger waiting time and operation costs. Specifically, with the path-based space–time network representation, we formulate a novel rolling stock circulation model, which accounts for coupling and decoupling operations and stabling track capacity constraints with lower complexity than the assignment model. In addition, we improve the linear dynamic passenger flow loading model, which extends the operational application with multiple service routes and characterizes various passenger waiting behaviors. To solve the proposed model, an adaptive simulated annealing (ASA) algorithm is designed to obtain high-quality solutions using flow-oriented and random operators. Lastly, the performance of the proposed models and algorithms is verified by small-scale numerical experiments. Then, the efficiency of the proposed approaches is further demonstrated through a real-world case study based on Beijing Subway Changping Line, showing a 42% reduction in total passenger waiting time, a decrease of 16 rolling stock, and a 10% reduction in variable operation costs compared to the current operation mode.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105071"},"PeriodicalIF":7.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577539","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":"TSGDiff: Traffic state generative diffusion model using multi-source information fusion","authors":"Huipeng Zhang,&nbsp;Honghui Dong,&nbsp;Zhiqiang Yang","doi":"10.1016/j.trc.2025.105081","DOIUrl":"10.1016/j.trc.2025.105081","url":null,"abstract":"<div><div>Accurate analysis and prediction of traffic states are fundamental and crucial for intelligent transportation systems, playing a significant role in enhancing the efficiency and safety of traffic systems. Advances in deep learning have promoted the development of traffic prediction. However, some traditional prediction methods primarily rely on historical traffic data to sequentially predict future traffic trends. While some also incorporate one or more influencing factors, such as weather and day of the week, as covariates, they often lack a unified fusion approach to model the impact of these covariates on future traffic states, and they are prone to error accumulation in long-term predictions. To address these challenges, we propose TSGDiff, a novel traffic state generative diffusion model using multi-source information fusion. The proposed method leverages an innovative diffusion model framework and integrates various sources of information, such as traffic data, weather, and weekdays, to enhance the accuracy of traffic state prediction. TSGDiff transforms historical spatiotemporal information and future environment information into feature representations using an attention-based spatiotemporal extraction module and a traffic semantic encoding module, respectively. These feature representations serve as guiding conditions for the diffusion model to generate traffic states. By incorporating the prediction horizon as an input parameter, TSGDiff directly generates future traffic states point-to-point, thereby avoiding error accumulation inherent in iterative prediction methods. To adapt the diffusion model to graph structure road network data, we introduce a Graph Attention U-Net (GAUNet) to capture the spatial correlations in traffic data. Experiments on real-world Beijing traffic datasets demonstrate that TSGDiff significantly outperforms baseline models for long-term predictions and performs comparably for short-term predictions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105081"},"PeriodicalIF":7.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577674","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":"Manage morning commute problem of household travels under single-step toll: A comparison study","authors":"Yuan Zhang,&nbsp;Hui Zhao,&nbsp;Rui Jiang,&nbsp;Ying Shang","doi":"10.1016/j.trc.2025.105075","DOIUrl":"10.1016/j.trc.2025.105075","url":null,"abstract":"<div><div>In general, road tolls could alleviate travel congestion for commuters during rush hour and improve the efficiency of individual transport trips. But for the household commuters, whether the road toll management framework can achieve the same effect is still unclear. From this perspective, it is meaningful to understand the difference between the impacts of road toll pricing on individual trips and on household trips. Based on this consideration, this paper investigates the influence of single-step toll strategy on household trips using the ADL model and the braking model. The optimal school-work time gap, optimal toll value and optimal toll interval are obtained for optimizing the total system travel cost. To have a deeper insight into the difference between the three types of models, the ADL, braking and Laih (<span><span>Jia et al., 2016</span></span>) models are studied extensively, leading to the following conclusions: (i) Under the optimal strategies in the three single-step household toll models, the optimal school-work time gap ranges and the toll intervals differ, but the optimal toll values are the same; (ii) The ADL model performs optimally in optimizing the total system travel cost, followed by the Laih model; (iii) the total congestion cost decreases with school-work time gap under the optimal strategy of the single-step toll models, and the ADL model performs optimally in this regard. Therefore, management insights can be derived, in which traffic authorities should set proper staggering hours, toll price and interval, as well as cultivate proper travel habits of household travelers, thereby helping to reduce road congestion and improve social welfare. Finally, a comparison of the three toll models through numerical experiments validates our findings.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105075"},"PeriodicalIF":7.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549594","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":"Improved multi-agent deep reinforcement learning-based integrated control for mixed traffic flow in a freeway corridor with multiple bottlenecks","authors":"Lei Han ,&nbsp;Lun Zhang ,&nbsp;Haixiao Pan","doi":"10.1016/j.trc.2025.105077","DOIUrl":"10.1016/j.trc.2025.105077","url":null,"abstract":"<div><div>A major challenging issue related to the emerging mixed traffic flow system, composed of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs), is the lack of adequate traffic control measures, especially in a large freeway corridor with multiple bottlenecks. Multi-agent deep reinforcement learning exhibits significant advantages, such as fast response, high flexibility, strong adaptability, low computational burden, and collaborative optimization. These features enable it to achieve superior efficiency and robustness in handling dynamically changing traffic environments and large-scale traffic control problems. Inspired by this, we propose a novel Integrated Traffic Control (ITC) strategy based on an Improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (IPMATD3) algorithm in the mixed traffic environment (abbreviated as IPMATD3-based ITC). Specifically, the proposed IPMATD3-based ITC approach seeks to coordinate multiple Ramp Metering (RM) and Variable Speed Limit (VSL) controllers along a freeway corridor, with the objectives of improving traffic mobility and efficiency, enhancing safety, and reducing emissions. The proposed method utilized a centralized training with decentralized execution paradigm to learn the joint actions of all traffic controllers in a high-dimensional state and action spaces. A hybrid reward function is developed by synchronously considering the above objectives to optimize traffic control performance. Then, the rank-based prioritized experience replay mechanism is incorporated into the conventional MATD3 algorithm to improve learning efficiency. A real-world freeway corridor is selected to test the proposed control method. Moreover, its performance is compared with the several state-of-the-art methods. The simulation results demonstrate that the proposed method achieves remarkable control performance at a 10% CAV Penetration Rate (PR), effectively reducing the spatiotemporal extent of freeway traffic congestion. The proposed method outperforms other approaches in improving freeway traffic efficiency, mobility, safety, and environmental sustainability. Increasing the PR can improve the performance of various methods and benefit traffic operations. However, when the PR reaches higher levels, the marginal benefits of further increases become less pronounced.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105077"},"PeriodicalIF":7.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549666","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 real-time synthesized driving risk quantification model based on driver risk perception-response mechanism","authors":"Leipeng Zhu ,&nbsp;Zhiqing Zhang ,&nbsp;Jingyang Yu ,&nbsp;Yongnan Zhang ,&nbsp;Jinxiu Fu","doi":"10.1016/j.trc.2025.105073","DOIUrl":"10.1016/j.trc.2025.105073","url":null,"abstract":"<div><div>Risk factors within the driver-vehicle–road system are dynamically coupled, with the driver being the most critical factor contributing to system destabilization. However, current traffic risk assessment models struggle to accurately measure the dynamic risk caused by the driver, limiting their applicability in increasingly complex driving environments. Based on the artificial potential field theory, the paper begins its investigation with the driver’s risk perception-response mechanism, and incorporates the effects of risk gain and attenuation to develop a driving behavior dynamic risk quantification model (behavior field). This model is then superimposed with enhanced kinetic and potential fields to construct a real-time synthesized driving risk quantification model under the dynamic coupling of the driver-vehicle–road system, which is validated in various traffic scenarios. The results suggest that: (a) The driving behavior dynamic risk quantification model accurately represents the underlying risks during the driver’s perception, judgment, and decision-making phases. It effectively captures the risk differences between different traffic scenarios and drivers, demonstrating high applicability and sensitivity. (b) The kinetic and potential fields that account for the risk diffusion effect are more consistent with the actual risk distribution characteristics. They can also efficiently represent the risk evolution patterns of influencing factors across diverse scenarios. (c) Compared with the conventional driving safety field and risk evaluation metrics (e.g., steering entropy, jerk, and time to collision), the synthesized driving risk real-time quantification model effectively captures the dynamic coupling of objective traffic environment risks and subjective driving behavior risks on a multidimensional spatiotemporal scale. It provides more robust risk prediction results (R² = 0.988, root mean square error = 0.007). This research can provide a theoretical reference for the automatic analysis of comprehensive traffic risk and the development of more intelligent advanced driver assistance systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105073"},"PeriodicalIF":7.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549667","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":"Electric truck routing and platooning problem considering vehicle charging and driver assignment on highway networks","authors":"Xiaoyuan Yan ,&nbsp;Min Xu ,&nbsp;Xiaotong Sun","doi":"10.1016/j.trc.2025.105072","DOIUrl":"10.1016/j.trc.2025.105072","url":null,"abstract":"<div><div>Driven by the development of electricity-powered trucks and the connected and autonomous vehicle (CAV) technology, the electric truck (ET) routing and platooning has received considerable attention. To reap the labor cost savings and environmental benefits, this study makes the first attempt to investigate the ET routing and platooning problem considering vehicle charging and flexible assignment of the drivers. The objective is to determine the optimal routes and schedules of the trucks and the drivers that minimize the total operational cost to complete a group of freight transportation tasks while considering the features of the electric trucks, i.e., the limited driving range and charging demands due to limited battery capacity. A mixed-integer linear programming (MILP) model that can effectively determine the itineraries of the trucks and drivers and incorporate specific characteristics of ET platooning is formulated for the proposed problem. By exploring the essential features of the platooning process and its optimal solution structures, a tailor-designed edge set covering algorithm dedicated to platooning-related optimization problem is proposed to address the problem. Numerical experiments are conducted to evaluate the proposed model and solution method against three benchmark methods and quantify the benefits of the ET platooning. Sensitivity analysis is also carried out to explore the impacts of several major influential factors on the system performance and derive managerial insights.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105072"},"PeriodicalIF":7.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143548744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic multi-region MFD stochastic user equilibrium: Formulation and parameter estimation in a large-scale case study","authors":"Lawrence Christopher Duncan ,&nbsp;Thomas Kjær Rasmussen ,&nbsp;David Paul Watling ,&nbsp;Otto Anker Nielsen","doi":"10.1016/j.trc.2025.105008","DOIUrl":"10.1016/j.trc.2025.105008","url":null,"abstract":"<div><div>Multi-region Macroscopic Fundamental Diagram (MFD) traffic equilibrium models have been developed as a more easily calibratable, maintainable, and computationally efficient alternative to traditional link-network traffic assignment models with full disaggregate network representation. There are four gaps in the research into these models that we highlight: i) the lack of stochasticity accounted for in the modelling of regional path choice, ii) the estimation of parameters of regional path choice models within the traffic equilibrium, iii) regional path choices being based on region travel times actually experienced (rather than instantaneous travel times), and iv) the paucity of real-life case studies. Motivated by these gaps, this paper presents a new dynamic multi-region MFD Stochastic User Equilibrium (SUE) model, and applies it in a real-life case study. The traffic dynamics are described by a new traffic propagation model utilising features of a space–time graph. Regional path choices can be based on region travel times actually experienced. The model produces continuous equilibrated regional path choice probability outputs, thereby facilitating the development of a rigorous statistical estimation procedure for calibrating parameters from tracked regional path choice data. This estimation procedure is operationalised in a large-scale and detailed multi-region MFD system, with 39 underlying rural and urban regions and 96 directional, superimposed motorway regions, 135 regions in total. Results provide empirical evidence to support hypotheses that regional path choice modelling should consider stochasticity, regional path overlap, multiple attributes, and experienced region travel times. Numerical experiments also demonstrate continuity, differences between the instantaneous and experienced dynamic models, relative insensitivity to the time-slice grain, and realism of the model.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105008"},"PeriodicalIF":7.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards an efficient electric bus system: Multi-phase optimization model for incremental electrification of bus network with uncertain energy consumption","authors":"Behnam Davazdah Emami ,&nbsp;Yiling Zhang ,&nbsp;Alireza Khani ,&nbsp;Ying Song","doi":"10.1016/j.trc.2025.105011","DOIUrl":"10.1016/j.trc.2025.105011","url":null,"abstract":"<div><div>Electrifying bus transit systems emerges as a practical solution to environmental degradation resulting from the unprecedented level of mobility nowadays. In the U.S., with the intensified efforts to expand EV infrastructure, a special emphasis is now placed on providing emission-free transit services. This initiative is central to America’s push towards a net-zero-emissions future. In response, a growing number of cities have started replacing diesel buses with battery electric buses (BEBs). However, technological, operational, and economic barriers related to charging infrastructure and power supplies make the electrification of bus systems a gradual process, where only a part of the system is electrified at each stage. Moreover, due to the limited battery capacities of BEBs and their stochastic discharge rates influenced by factors like weather, traffic, and road conditions, BEBs often require daytime charging to be able to continue operating throughout the day. Therefore, transit agencies need to develop an integrated strategy that can address various costs of electrification and minimize the planning and operational costs. This study proposes a framework to facilitate the incremental electrification of bus systems. We formulate the problem as a two-stage stochastic mixed-integer linear programming model. The first stage optimizes long-term strategical decisions related to fleet sizing, charging station siting, and charging-station-route assignments under random BEB charging demand and time-of-use electricity tariffs. The second-stage optimizes the charging operations of the BEB fleet for a realized charging demand scenario while maintaining the service schedule for passenger convenience. We also develop a Benders decomposition method to solve the problem with better computational efficiency than existing solvers. To validate the proposed model, we test it on a real-world bus network to design an incremental electrification plan. We show the efficacy of the solution approach and study the managerial insights including the deployment of fast charging and potential battery technology enhancement in the future.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105011"},"PeriodicalIF":7.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512574","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 mobility permits allocation policy in congested highways","authors":"Liming Li ,&nbsp;Jinpeng Liang ,&nbsp;Chenghao Zhuang ,&nbsp;Yue Bao ,&nbsp;Ziyou Gao","doi":"10.1016/j.trc.2025.105048","DOIUrl":"10.1016/j.trc.2025.105048","url":null,"abstract":"<div><div>Highway congestion during peak periods poses a significant challenge for transportation authorities, necessitating the effective management of highway access across origin–destination (OD) pairs. This paper presents a data-driven mobility permit allocation policy designed to optimize highway access under stochastic demand. We model the problem using stochastic nonlinear programming with the dual targets of minimizing total travel time and ensuring a minimum proportion of highway access for each OD pair. The model is reformulated into a target-based framework that applies quadratic penalties to deviations from both efficiency and fairness targets. We develop an efficient online algorithm that allocates permits in response to each demand scenario and theoretically establish its convergence properties. Numerical experiments using both synthetic and real-world data from the Beijing Capital Airport Highway demonstrate that our proposed method achieves near-optimal system performance, with total travel times only 1% higher than System Optimal solution while reducing travel times by up to 22% compared to User Equilibrium solution. These results highlight our approach’s ability to achieve system-optimal efficiency while maintaining fair access across different OD pairs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105048"},"PeriodicalIF":7.6,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508985","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 artificial potential field approach for virtual coupling train control with complete braking curve supervision","authors":"Yuqing Ji ,&nbsp;Egidio Quaglietta ,&nbsp;Rob M.P. Goverde ,&nbsp;Dongxiu Ou","doi":"10.1016/j.trc.2025.105050","DOIUrl":"10.1016/j.trc.2025.105050","url":null,"abstract":"<div><div>In response to the escalating demand for rail transport, the concept of Virtual Coupling (VC) train operations is progressively gaining ground within the railway sector. The concept of VC aims at reducing safe train separation to less than the absolute braking distance by letting trains move synchronously in radio-connected convoys. One of the major concerns associated with VC is ensuring safe train separation considering realistic risk factors, such as heterogeneous train braking performances and varying track conditions. To address such a safe train separation problem under VC, this paper proposes a novel train control model based on the Artificial Potential Field (APF) method to safely supervise the complete braking process of trains moving in a VC convoy. The proposed model uses a homogeneous strip representation of train length and a Dynamic Safety Margin (DSM) to take into account accurate train dynamics as well as potential risk factors, due to different train acceleration/braking rates, communication delays, unexpected emergency train braking applications, and position measurement errors. The method has been applied to the case of a high-speed line in China. Results show that the APF-based control method can effectively adapt to real-time variations in train dynamics and the operational environment to safely supervise the complete train braking process and avoid collisions even in the case of unplanned emergency braking applications. The proposed APF-based approach shows promising real-time performance which can further contribute to advancing the state of the art on safe train control under VC signalling.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105050"},"PeriodicalIF":7.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487544","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}