Transportation Research Part C-Emerging Technologies最新文献

{"title":"A User-Driven Prioritisation Process implementation and optimisation for ATFM hotspot resolution","authors":"","doi":"10.1016/j.trc.2024.104894","DOIUrl":"10.1016/j.trc.2024.104894","url":null,"abstract":"<div><div>The current and forecast air traffic levels lead to demand-capacity imbalances, which are dealt with by delaying flights through the allocation of air traffic flow management (ATFM) slots. To mitigate the delay impact on airspace users (AUs) and passengers, <em>User Driven Prioritisation Process (UDPP)</em> solutions are under development, with the goal to enhance flexibility for airlines to prioritise their own flights in the ATFM regulations. UDPP solutions are developed in collaboration with AUs, achieving high maturity level and even operational use at some airports.</div><div>While UDPP solutions in reality are still based on manual or semi-automated procedures, in this paper we show that when an airline has an accurate delay cost model at disposal, the prioritisation process can be fully automated via an integer programming model that provides the prioritisation that optimises the AUs’ UDPP exploitation. We use this automated process and the implementation of the UDPP mechanism to provide an estimation of the benefits of UDPP in terms of cost with respect to the current ATFM regulation process.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593523","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":"Scenario-level knowledge transfer for motion planning of autonomous driving via successor representation","authors":"","doi":"10.1016/j.trc.2024.104899","DOIUrl":"10.1016/j.trc.2024.104899","url":null,"abstract":"<div><div>For autonomous vehicles, transfer learning can enhance performance by making better use of previously learned knowledge in newly encountered scenarios, which holds great promise for improving the performance of motion planning. However, previous practices using transfer learning are data-level, which is mainly achieved by introducing extra data and expanding experience. Such data-level consideration depends heavily on the quality and quantity of data, failing to take into account the scenario-level features behind similar scenarios. In this paper, we provide a scenario-level knowledge transfer framework for motion planning of autonomous driving, named SceTL. By capitalizing on successor representation, a general scenario-level knowledge among similar scenarios can be captured and thereby recycled in different traffic scenarios to empower motion planning. To verify the efficacy of our framework, a method that combines SceTL and classic artificial potential field (APF), named SceTL-APF, is proposed to conduct global planning for navigation in static scenarios. Meanwhile, a local planning method combining SceTL and motion primitives (MP), SceTL-MP, is developed for dynamic scenarios. Both simulated and realistic data are used for verification. Experimental results demonstrate that SceTL can facilitate the scenario-level knowledge transfer for both SceTL-APF and SceTL-MP, characterized by better adaptivity and faster computation speed compared with existing motion planning methods.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586487","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":"LFF: An attention allocation-based following behavior framework in lane-free environments","authors":"","doi":"10.1016/j.trc.2024.104883","DOIUrl":"10.1016/j.trc.2024.104883","url":null,"abstract":"<div><div>With the rapid advancement of autonomous driving technology, current autonomous vehicles (AVs) typically rely on lane markings and parameters for operation despite their advanced perception capabilities. This research aims to develop a Lane-Free Following (LFF) framework to address behavior planning for AVs in environments lacking clear lane markings. The LFF utilizes decision modules, such as Monitoring Zones, Focus Zones, and Passing Corridors, to dynamically select the most appropriate following strategy. It integrates a Multi-Target Following Model (MT-IDM) and an attention allocation mechanism to optimize acceleration control by adjusting attention concentration levels. Initially, we examine the stability of multi-target following and determine the stability region on a two-dimensional plane using specific stability criteria. Subsequently, the LFF is integrated with the lateral model of the Intelligent Agent Model (IAM), and calibrated and validated using lane-free traffic data from Hefei, China, and Chennai, India. Simulation results demonstrate the LFF’s high accuracy across various vehicle types. In simulations conducted on open boundary roads and virtual circular roads with varying widths and traffic densities, the LFF showed enhanced driving comfort and efficiency. This optimization of road widths and densities improved traffic flow and road space utilization compared to traditional lane-based traffic. In congested start conditions on circular roads, we compared the uniform attention allocation mode (LFF-UA), the concentrated attention allocation mode (LFF-CA), and the High-Speed Social Force Model (HSFM). Results indicated that the HSFM excels in velocity and flow, offering faster startup efficiency. The LFF-UA, while maintaining efficiency, evenly distributed attention to neighboring preceding vehicles, enhancing driving safety and reducing fuel consumption and emissions. This research addresses current issues in mixed traffic environments and provides theoretical references for the future application of connected autonomous vehicles in lane-free environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571534","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":"Pricing for multi-modal pickup and delivery problems with heterogeneous users","authors":"","doi":"10.1016/j.trc.2024.104864","DOIUrl":"10.1016/j.trc.2024.104864","url":null,"abstract":"<div><div>In this paper, we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while price matching users with their desired delivery modes. More precisely, we consider that orders are demanded by a heterogeneous population of users with varying trade-offs between price and latency. To capture how prices affect the behavior of heterogeneous selfish users choosing between multiple delivery modes, we construct a congestion game taking place over a form of star network, where each source–sink pair is composed of parallel links connecting users with their preferred delivery method. Using the unique geometry of this network, we prove that one can set prices explicitly to induce any desired network flow, i.e, given a desired allocation strategy, we have a closed-form solution for the delivery prices. We conclude by performing a case study on a meal delivery problem with multiple courier modalities using data from real world instances.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571533","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":"T3C: A traffic-communication coupling control approach for autonomous intersection management system","authors":"","doi":"10.1016/j.trc.2024.104886","DOIUrl":"10.1016/j.trc.2024.104886","url":null,"abstract":"<div><div>Autonomous intersection management (AIM) system requires communication protocols with low delay and high reliability. However, most previous studies optimize the connected automated vehicle’s (CAV) communication and control systems individually, ignoring their collaboration and cascade effects. To address this gap, we present the Traffic-Communication Coupling Control (T3C) approach for joint optimization of CAV trajectories and communication networking. The roadside unit (RSU) periodic intervention mechanism and the edge-end collaborative computing architecture are utilized to adapt the AIM system’s multi-type computational tasks. The approach creates a relay CAV identity assignment module to provide a linkage pattern between communication networking and CAV control. Following that, CAVs utilize a distributed trajectory planning approach to plan their trajectory states, with parallel distributed model predictive control applied on a rolling horizon. The RSU collects and transmits the trajectory states to the mobile edge computing (MEC), which optimizes communication networking. To quickly solve the networking scheme, the task is divided into two sub-problems: backbone network generation based on the traffic-information flow coupling mechanism and information flow distribution. These two sub-problems are handled using the adjacency matrix masking optimization approach and enhanced adaptive large neighborhood search (ALNS) algorithm, respectively. Numerical studies are carried out to confirm the effectiveness of the proposed approach in various vehicle arrival rate scenarios. The results demonstrate that T3C can ensure stable low-delay communication while improving traffic efficiency, particularly in high vehicle arrival rate scenarios. Specifically, T3C achieves a low travel delay ratio of 28.38%–53.67% at the cost of an average transmission delay of 13.90 ms–24.95 ms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571532","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 data-driven mixed-integer linear programming approach for real-time rescheduling of urban rail transit under rolling stock faults","authors":"","doi":"10.1016/j.trc.2024.104893","DOIUrl":"10.1016/j.trc.2024.104893","url":null,"abstract":"<div><div>Urban rail transit operations are susceptible to unexpected disturbances or disruptions, with rolling stock faults being a particularly common cause. Therefore, this paper focuses on the integrated rescheduling of the train timetable and rolling stock circulation in an urban rail transit line under rolling stock faults. Three typical scenarios arising from such faults are studied simultaneously, i.e., delay, out-of-service, and rescue. Taking general key practical constraints and scenario-specific constraints into account, multi-objective mathematical models are formulated for each scenario to optimize various dispatching measures, such as retiming, cancellation, short-turning, and backup rolling stock utilization. For computational tractability, the proposed models are transformed into equivalent mixed-integer linear programming (MILP) reformulations using some linearization techniques. In order to satisfy the real-time requirements of train rescheduling, a data-driven approach is developed to accelerate the solving process by fixing some decision variables in advance. Specifically, the prediction of binary variable values is treated as a classification task. After creating a dataset including different rolling stock faults and their respective optimal solutions generated by GUROBI, the correlations between optimal solutions and instance features are extracted through supervised learning based on the multilayer perceptron. By generalizing the extracted correlations to unseen instances, high-quality solutions can be found in a short time. Finally, numerical experiments are carried out based on the Beijing Yizhuang Metro Line. Compared to directly solving the original model using GUROBI, the proposed solution approach can reduce the average computation time by up to 91.49% with an average optimality gap of only 0.77%.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553555","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":"Cooperative bus eco-approaching and lane-changing strategy in mixed connected and automated traffic environment","authors":"","doi":"10.1016/j.trc.2024.104907","DOIUrl":"10.1016/j.trc.2024.104907","url":null,"abstract":"<div><div>In mixed traffic environments, existing bus eco-approaching and lane-changing methods fail to adequately consider the uncontrollability of human-driven vehicles and their interactions with surrounding vehicles during lane changes, often leading to increased fuel consumption and emissions. To address this issue, this paper proposes a two-stage cooperative bus motion control model during lane-changing and approaching stops: the first stage finds the optimal positions and timings for bus lane changes in a mixed traffic environment; the second stage constructs a Model Predictive Control (MPC)-based cooperative lane change controller, which couples the lateral and longitudinal movements of buses. Using Lyapunov stability theory, the stability of this controller is demonstrated. A solution algorithm that integrates the Pontryagin Minimum Principle (PMP) and the Broyden–Fletcher–Goldfarb–Shanno Sequential Quadratic Programming (BFGS-SQP) method is proposed. The proposed model is tested on real-world cases with the Simulation of Urban Mobility (SUMO). The results show that, compared to traditional strategies, the cooperative strategy improves lane-changing efficiency by 14.95 %, reduces fuel consumption by 17.24 %, and increases traffic stability by 25.21 %. Under high-traffic conditions, the proposed strategy can significantly reduce lane-changing time by 25.18 %. The higher coefficient values in the objective function drive buses to adopt more proactive actions to quickly complete the space creation process. The results in a whole bus line show the proposed method increases the lane-changing success rate by 25.42 % and reduces the waiting time by 37.35 %.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553552","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":"Integrated optimization of traffic signals and vehicle trajectories for mixed traffic at signalized intersections: A two-level hierarchical control framework","authors":"","doi":"10.1016/j.trc.2024.104884","DOIUrl":"10.1016/j.trc.2024.104884","url":null,"abstract":"<div><div>With the rapid advancement of connected and automated vehicle (CAV) technology, the integrated optimization of traffic signals and vehicle trajectories has emerged as a promising approach to enhance intersection performance. However, the complexity of this integrated optimization problem requires substantial computational resources, rendering existing methods impractical for real-time applications. To address this challenge, this paper presents a two-level hierarchical control framework for mixed traffic environments, consisting of both CAVs and human-driven vehicles (HVs), offering a computationally efficient solution without compromising performance. At the upper level, we introduce a platoon-based mixed integer linear programming (PMILP) model to jointly optimize signal timing and desired arrival times, with the main objective of minimizing traffic delay. Building upon the optimized desired arrival times, a Nash-based distributed model predictive control (DMPC) method is developed at the lower level to optimize CAV trajectories, enabling vehicles to pass through intersections at free-flow speeds without stopping and minimizing acceleration fluctuations. Numerical experiments are conducted to assess the performance of the proposed method against three alternatives. Method 1 uses actuated signal control (ASC) for traffic signals, and the Intelligent Driver Model (IDM) for all vehicles. Method 2 combines the controlled optimization of phases (COP) for signal control with DMPC for CAVs and IDM for HVs. Method 3 applies the proposed PMILP method for traffic signals, predictive cruise control (PCC) for CAVs, and IDM for HVs. The results demonstrate that the proposed integrated optimization approach significantly reduces traffic delay, fuel consumption, and idling time, while simultaneously enhancing driving comfort across different CAV penetration rates and degrees of saturation. Notably, the proposed method achieves these improvements with a high level of computational efficiency.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553554","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":"Airspace network design for urban UAV traffic management with congestion","authors":"","doi":"10.1016/j.trc.2024.104882","DOIUrl":"10.1016/j.trc.2024.104882","url":null,"abstract":"<div><div>To support the safe and widespread use of unmanned aerial vehicles (UAVs) in urban environments, industry stakeholders and regulatory authorities are partnering to develop urban airspace traffic management systems (UTMs). UTM system providers face strategic decisions in how to design and manage airspace available to UAV flights. We consider a provider that plans to open an urban airspace in which UAV flights are routed above existing roads in 3D corridors corresponding to segmented altitude levels. The provider aims to select a subset of the road network to form an air-network with the goal of providing safe and cost effective service for UAV traffic. The air-network selected must provide routes that respect UAV technology restrictions, and must have adequate capacity to support the expected flight volume. We develop a 3D airspace network design model that selects a subset of roads whose 3D projection into the sky will be used for routing flights. The constrained system optimum (CSO) traffic assignment model is used to evaluate the quality of the network; the CSO user constraints represent battery restrictions while minimizing the total travel time ensures realistic routing in the face of congestion. To incorporate the 3D nature of flights, we use simulation to calibrate a Bureau of Public Roads capacity parameter that reflects the multiple vertical layers of airspace made available when a road is selected for the network. We introduce a methodology to derive candidate maps for urban areas and use it on open-source data to build a case study for Chicago city center. We assess the impact of budget, congestion, minimum-path deviation, and demand patterns on network designs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531853","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":"Spatiotemporal implicit neural representation as a generalized traffic data learner","authors":"","doi":"10.1016/j.trc.2024.104890","DOIUrl":"10.1016/j.trc.2024.104890","url":null,"abstract":"<div><div>Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel paradigm to address the STTD learning problem by parameterizing STTD as an implicit neural representation. To discern the underlying dynamics in low-dimensional regimes, coordinate-based neural networks that can encode high-frequency structures are employed to directly map coordinates to traffic variables. To unravel the entangled spatial–temporal interactions, the variability is decomposed into separate processes. We further enable modeling in irregular spaces such as sensor graphs using spectral embedding. Through continuous representations, our approach enables the modeling of a variety of STTD with a unified input, thereby serving as a generalized learner of the underlying traffic dynamics. It is also shown that it can learn implicit low-rank priors and smoothness regularization from the data, making it versatile for learning different dominating data patterns. We validate its effectiveness through extensive experiments in real-world scenarios, showcasing applications from corridor to network scales. Empirical results not only indicate that our model has significant superiority over conventional low-rank models, but also highlight that the versatility of the approach extends to different data domains, output resolutions, and network topologies. Comprehensive model analyses provide further insight into the inductive bias of STTD. We anticipate that this pioneering modeling perspective could lay the foundation for universal representation of STTD in various real-world tasks. <strong>PyTorch implementations of this project is publicly available at:</strong> <span><span>https://github.com/tongnie/traffic_dynamics</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531854","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}