Transportation Research Part C-Emerging Technologies最新文献

{"title":"Ensemble methods for route choice","authors":"","doi":"10.1016/j.trc.2024.104803","DOIUrl":"10.1016/j.trc.2024.104803","url":null,"abstract":"<div><p>Understanding travellers’ route preferences allows for the calculation of traffic flow on network segments and helps in assessing facility requirements, costs, and the impact of network modifications. Most research employs logit-based choice methods to model the route choices of individuals, but machine learning models are gaining increasing interest. However, all of these methods typically rely on a single ‘best’ model for predictions, which may be sensitive to measurement errors in the training data. Moreover, predictions from discarded models might still provide insights into route choices. The ensemble approach combines outcomes from multiple models using various pattern recognition methods, assumptions, and/or data sets to deliver improved predictions. When configured correctly, ensemble models offer greater prediction accuracy and account for uncertainties. To examine the advantages of ensemble techniques, a data set from the I-35 W Bridge Collapse study in 2008, and another from the 2011 Travel Behavior Inventory (TBI), both in Minneapolis–St. Paul (The Twin Cities) are used to train a set of route choice models and combine them with ensemble techniques. The analysis considered travellers’ socio-demographics and trip attributes. The trained models are applied to two datasets, the Longitudinal Employer-Household Dynamics (LEHD) commute trips and TBI morning peak trips, for validation. Predictions are also compared with the loop detector records on freeway links. Traditional Multinomial Logit and Path-Size Logit models, along with machine learning methods such as Decision Tree, Random Forest, Extra Tree, AdaBoost, Support Vector Machine, and Neural Network, serve as the foundation for this study. Ensemble rules are tested in both case studies, including hard voting, soft voting, ranked choice voting, and stacking. Based on the results, heterogeneous ensembles using soft voting outperform the base models and other ensemble rules on testing sets.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003243/pdfft?md5=38db06abbc21d7a29bd4a8c9436738ca&pid=1-s2.0-S0968090X24003243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049279","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":"Routing and scheduling of trains and engines in a railway marshalling station yard","authors":"","doi":"10.1016/j.trc.2024.104826","DOIUrl":"10.1016/j.trc.2024.104826","url":null,"abstract":"<div><p>In a busy railway marshalling station, train and engine traffic management in the receiving/departure yard plays a crucial role in efficient and stable operations. Traditionally, a track assignment problem (TAP) is solved to assign tracks to trains for berthing at a receiving/departure yard. However, the TAP does not encompass shunting operations in the yard (e.g., engine replacement and train disassembly), which can result in additional scheduling challenges for dispatchers and route conflicts between operations. This paper investigates a train and engine routing and scheduling problem (TERSP) in a receiving/departure yard of railway marshalling stations, which involves simultaneously assigning routes and scheduling route-setting start times for both train and shunting operations to be conducted in the yard. By introducing the concepts of task, activity, and pattern, we transform the original problem into assigning pre-generated patterns incorporating both route and route-setting start time alternatives to activities. The transformed problem is formulated into a compact binary integer linear programming model with a linear number of constraints and the objective of minimizing the total time deviation of all involved tasks. An improved technique that relies on listing all maximal (bi)cliques in a constructed graph is designed to effectively model the time coherence and track section occupation constraints. A heuristic that gradually expands the patterns for the identified key activities by adding more start time alternatives is applied to remedy an infeasible model caused by potential route conflicts. In addition, a rolling horizon algorithm that decomposes the original problem into consecutive smaller stages using either a time-rolling or a train-rolling rule is developed to efficiently solve instances. Finally, numerical experiments based on the physical layouts and real timetables of a receiving yard and a departure yard of a large marshalling station in China are conducted to assess the performance and applicability of our proposed approaches. The results demonstrate that our approaches typically find (near-)optimal solutions within several minutes for the investigated instances by simultaneously addressing different classes of yard operations and resources.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049281","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":"Pickup and delivery problem with electric vehicles and time windows considering queues","authors":"","doi":"10.1016/j.trc.2024.104829","DOIUrl":"10.1016/j.trc.2024.104829","url":null,"abstract":"<div><p>The electric vehicle, as a green and sustainable technology, has gained tremendous development and application recently in the logistics distribution system. However, the increasing workload and limited infrastructure capacity pose challenges for electric vehicles in the pickup and delivery operating system, including task allocation, electric vehicle routing, and queue scheduling. To address these issues, this paper introduces a pickup and delivery problem with electric vehicles and time windows considering queues, which considers queue scheduling for multiple electric vehicles when operating at the same site. A novel mixed integer linear programming model is proposed to minimize the cost of travel distance and queue time. An adaptive hybrid neighborhood search algorithm is developed to solve the moderately large-scale problem. Experimental results demonstrate the effectiveness of the model and adaptive hybrid neighborhood search algorithm. The competitive performance of the developed algorithm is further confirmed by finding 9 new best solutions for the pickup and delivery problem with electric vehicles and time windows benchmark instances. Moreover, the results and sensitivity analysis of objective weight costs highlight the impact and importance of considering queues in the studied problem and obtain some management insights.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044408","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":"Hybrid car following control for CAVs: Integrating linear feedback and deep reinforcement learning to stabilize mixed traffic","authors":"","doi":"10.1016/j.trc.2024.104773","DOIUrl":"10.1016/j.trc.2024.104773","url":null,"abstract":"<div><p>This paper introduces a novel hybrid car-following strategy for connected automated vehicles (CAVs) to mitigate traffic oscillations while simultaneously improving CAV car-following (CF) distance-maintaining efficiencies. To achieve this, our proposed control framework integrates two controllers: a linear feedback controller and a deep reinforcement learning controller. Firstly, a cutting-edge linear feedback controller is developed by non-linear programming to maximally dampen traffic oscillations in the frequency domain while ensuring both local and string stability. Based on that, deep reinforcement learning (DRL) is employed to complement the linear feedback controller further to handle the unknown traffic disturbance quasi-optimally in the time domain. This unique approach enhances the control stability of the traditional DRL approach and provides an innovative perspective on CF control. Simulation experiments were conducted to validate the efficacy of our control strategy. The results demonstrate superior performance in terms of training convergence, driving comfort, and dampening oscillations compared to existing DRL-based controllers.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142040378","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":"Reasoning graph-based reinforcement learning to cooperate mixed connected and autonomous traffic at unsignalized intersections","authors":"","doi":"10.1016/j.trc.2024.104807","DOIUrl":"10.1016/j.trc.2024.104807","url":null,"abstract":"<div><p>Cooperation at unsignalized intersections in mixed traffic environments, where Connected and Autonomous Vehicles (CAVs) and Manually Driving Vehicles (MVs) coexist, holds promise for improving safety, efficiency, and energy savings. However, the mixed traffic at unsignalized intersections present huge challenges like MVs’ uncertainties, the chain reaction and diverse interactions. Following the thought of the situation-aware cooperation, this paper proposes a Reasoning Graph-based Reinforcement Learning (RGRL) method, which integrates a Graph Neural Network (GNN) based policy and an environment providing mixed traffic with uncertain behaviors. Firstly, it graphicly represents the observed scenario as a situation using the interaction graph with connected but uncertain (bi-directional) edges. The situation reasoning process is formulated as a Reasoning Graph-based Markov Decision Process which infers the vehicle sequence stage by stage so as to sequentially depict the entire situation. Then, a GNN-based policy is constructed, which uses Graph Convolution Networks (GCN) to capture the interrelated chain reactions and Graph Attentions Networks (GAT) to measure the attention of diverse interactions. Furthermore, an environment block is developed for training the policy, which provides trajectory generators for both CAVs and MVs. A reward function that considers social compliance, collision avoidance, efficiency and energy savings is also provided in this block. Finally, three Reinforcement Learning methods, D3QN, PPO and SAC, are implemented for comparative tests to explore the applicability and strength of the framework. The test results demonstrate that the D3QN outperformed the other two methods with a larger converged reward while maintaining a similar converged speed. Compared to multi-agent RL (MARL), the RGRL approach showed superior performance statistically, reduced the number of severe conflicts by 77.78–94.12 %. The RGRL reduced average and maximum travel times by 13.62–16.02 %, and fuel-consumption by 3.38–6.98 % in medium or high Market Penetration Rates (MPRs). Hardware-in-the-loop (HIL) and Vehicle-in-the-loop (VehIL) experiments were conducted to validate the model effectiveness.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044407","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":"TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management","authors":"","doi":"10.1016/j.trc.2024.104818","DOIUrl":"10.1016/j.trc.2024.104818","url":null,"abstract":"<div><p>Transit users’ lifecycle behavior pattern transition reflects the continuous and multi-phase changes in how frequently and regularly users utilize public transit over their lifetime. Predicting transit users’ lifecycle behavior pattern transition is vital for enhancing the efficiency and responsiveness of transportation systems. Thus, this study incorporates lifecycle analysis in predicting long-term sequential behavioral pattern transition processes to go beyond just examining user churning at a single point in time. Specifically, this study proposes the TripChain2RecDeepSurv, a novel model that pioneers the individual-level analysis of lifecycle behavior status transitions (LBST) within public transit systems. The TripChain2RecDeepSurv is composed of (1) the TripChain2Vec module for encoding transit users’ trip chains; (2) the self-attention Transformer module for exploring the latent features related to spatiotemporal patterns; (3) the recurrent deep survival analysis module for predicting LBSTs. We demonstrate TripChain2RecDeepSurv’s predictive performance for empirical analysis by employing Shenzhen Bus data. Our model achieves a 74.39% accuracy rate in churn determination and over 80% accuracy in status sequence identification on the churn path. In addition, our findings highlight the segmented nature of Kaplan-Meier curves and identify the optimal intervention time against the user churning process. Meanwhile, the proposed model provides individual-level heterogeneity analysis, which emphasizes the significance of customizing user engagement strategies, advocating for interventions that extend users’ engagement in patterns with high-frequency transit usage to curb the transition to less frequent travel usage.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044594","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":"Distributed optimization for multi-commodity urban traffic control","authors":"","doi":"10.1016/j.trc.2024.104823","DOIUrl":"10.1016/j.trc.2024.104823","url":null,"abstract":"<div><p>A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed-gradient projection algorithm. The intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020397","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":"Ship sailing speed optimization considering dynamic meteorological conditions","authors":"","doi":"10.1016/j.trc.2024.104827","DOIUrl":"10.1016/j.trc.2024.104827","url":null,"abstract":"<div><p>Sailing speed optimization is a cost-effective strategy to improve ship energy efficiency and a viable way to fulfill emission reduction requirements. This study develops a novel ship sailing speed optimization method that considers dynamic meteorological conditions. We first develop an artificial neural network model for vessel fuel consumption rate (FCR) prediction based on a fusion dataset of ship noon reports and public meteorological data. Then, based on the predicted FCRs, the method repeatedly formulates a multistage graph based on the most recent forecasts, and optimal speeds for the remaining voyage are obtained until the vessel reaches the destination port. The computational efficiency of the optimization process is enhanced by progressively removing nodes without connections to successor nodes, starting from the penultimate stage. We examine the proposed method on two 11-day voyages of a dry bulk carrier. Results show that the proposed method demonstrates significant reductions in fuel consumption by 5.35% compared with a constant sailing speed scheme and by 7.34% compared with a static speed optimization model. In addition, the proposed model achieves similar fuel savings to those achieved by speed optimization based on actual meteorological conditions, enabling shipping companies to optimize ship sailing speeds in the absence of actual meteorological conditions. The proposed method can be applied to various types of vessels due to its flexibility and adaptability, making it a valuable tool for the shipping industry to reduce greenhouse gas (GHG) emissions, thereby supporting the International Maritime Organization (IMO)’s goal of reaching net-zero GHG emissions by around 2050.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142020482","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":"Lateral conflict resolution data derived from Argoverse-2: Analysing safety and efficiency impacts of autonomous vehicles at intersections","authors":"","doi":"10.1016/j.trc.2024.104802","DOIUrl":"10.1016/j.trc.2024.104802","url":null,"abstract":"<div><p>With the increased deployment of autonomous vehicles (AVs) in mixed traffic flow, ensuring safe and efficient interactions between AVs and human road users is important. In urban environments, intersections have various conflicts that can greatly affect driving safety and traffic efficiency. This study uses road test data to examine the possible safety and efficiency impacts of intersection conflict resolution involving AVs. The contribution comprises two main aspects. Firstly. we prepare and open a high-quality lateral conflict resolution dataset derived from the Argoverse-2 data, specifically targeting urban intersections. A rigorous data processing pipeline is applied to extract pertinent scenarios, rectify anomalies, enhance data quality, and annotate conflict regimes. This effort yields 5000+ AV-involved and 16000 AV-free cases, covering rich conflict regimes and balanced traffic states. Secondly, we employ surrogate safety measures to assess the safety impact of AVs on human-driven vehicles (HVs) and pedestrians. In addition, a novel concept of Minimum Recurrent Clearance Time (MRCT) is proposed to quantify the traffic efficiency impacts of AVs during conflict resolution. The results show that, for AV–HV and HV–HV conflict resolution processes, the differences in selected safety and efficiency measures for human drivers are statistically insignificant. In contrast, pedestrians demonstrate diverse behaviour adjustments. Some pedestrians behave more conservatively when interacting with AVs than with HVs. Notably, the efficiency of AV-involved conflict resolution is significantly lower than in AV-free instances due to the conservative driving style of AVs. This efficiency gap is particularly large when AVs pass through the conflict point after human drivers in unprotected left turns. These observations offer a perspective on how AVs potentially affect the safety and efficiency of mixed traffic. The processed dataset is openly available via <span><span>https://github.com/RomainLITUD/conflict_resolution_dataset</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003231/pdfft?md5=36afa5b8da2e246f19bba4039b7a316b&pid=1-s2.0-S0968090X24003231-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002414","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":"Distributed virtual formation control for railway trains with nonlinear dynamics and collision avoidance constraints","authors":"","doi":"10.1016/j.trc.2024.104808","DOIUrl":"10.1016/j.trc.2024.104808","url":null,"abstract":"<div><p>To improve the model accuracy and control efficiency for the movements of a virtual formation, this paper investigates distributed optimal control for the virtual formation control system in railways. Adopting the relative distance braking mode, a coupled optimal control problem with nonlinear train dynamics and constraints regarding collision avoidance and jerk is formulated for the virtual formation. To handle the non-convex constrained problem efficiently, a distributed augmented Lagrangian based alternating direction inexact newton (ALADIN) method under the model predictive control (MPC) framework is developed. For the execution of the distributed computational process, the copied variables are introduced to reformulate the original coupled problem in an objective separable form. By exploiting the problem separability, the ALADIN method decomposes the reformulation into a coordinated quadratic programming problem of small-scale and several local nonlinear programming problems that can be calculated in parallel, thereby facilitating real-time control and relieving communication burden. Numerical experiments on a metro line are carried out to verify the effectiveness of the proposed model and method. Experimental results demonstrate that high-performance tracking control for virtually coupled train units can be achieved in real time.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993640","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}