Transportation Research Part C-Emerging Technologies最新文献

{"title":"ACC, queue storage, and worrisome news for cities","authors":"Servet Lapardhaja ,&nbsp;Jean Doig Godier ,&nbsp;Michael J. Cassidy ,&nbsp;Xingan (David) Kan","doi":"10.1016/j.trc.2024.104809","DOIUrl":"10.1016/j.trc.2024.104809","url":null,"abstract":"<div><p>Rush-period traffic conditions in two idealized settings are forecast into the future, when most drivers will presumably rely on adaptive cruise control (ACC) while operating their cars. Field experiments emulating the full range of congested conditions confirm that, for a given traffic speed, the spacings for ACC-vehicles tend to be larger than those in present-day congestion, where vehicles are fully human-controlled. These larger spacings mean smaller densities, which mean, in turn, that queues will be less compacted than at present. The queues will therefore expand over greater distances in the future, as more ACC-controlled vehicles enter the scene. These wider-spread, uncompacted queues spell trouble for cities, where queue storage during a rush is often a problem already.</p><p>Simulations calibrated to the field-measured data were used to explore this unintended consequence of ACC for various foreseeable futures. Assumptions favorable to ACC were adopted throughout, to produce what are likely lower-bound estimates of future queue-storage problems. These lower bounds served as simple means to address forecast uncertainties. This is because our best-case outcomes for all futures examined are still far worse than the glowing predictions from elsewhere of how ACC may someday eliminate congestion. The first idealized setting was inspired by Downtown Los Angeles, where moderately high congestion already persists during each rush, but where physically long street links help with queue storage. We predict that, owing to ACC alone, rush-period vehicle hours traveled (VHT) on this first network will grow from present-day levels by as much as 12%. In the second setting, inspired by Midtown Manhattan where congestion is already severe and link lengths are short, VHT is predicted to grow by as much as 87%. Higher bottleneck capacities often promised of ACC are shown to be of little value when spillover queues constrain bottleneck flows from reaching those capacities. Adjusting onboard ACC controllers to produce smaller jam spacings was tested through simulation. The tests show how looming problems might be averted by this intervention, and futures thus improved.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141990581","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":"Multi-level traffic-responsive tilt camera surveillance through predictive correlated online learning","authors":"Tao Li ,&nbsp;Zilin Bian ,&nbsp;Haozhe Lei ,&nbsp;Fan Zuo ,&nbsp;Ya-Ting Yang ,&nbsp;Quanyan Zhu ,&nbsp;Zhenning Li ,&nbsp;Kaan Ozbay","doi":"10.1016/j.trc.2024.104804","DOIUrl":"10.1016/j.trc.2024.104804","url":null,"abstract":"<div><p>In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985831","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 generic stochastic hybrid car-following model based on approximate Bayesian computation","authors":"Jiwan Jiang ,&nbsp;Yang Zhou ,&nbsp;Xin Wang ,&nbsp;Soyoung Ahn","doi":"10.1016/j.trc.2024.104799","DOIUrl":"10.1016/j.trc.2024.104799","url":null,"abstract":"<div><p>Car following (CF) models are fundamental to describing traffic dynamics. However, the CF behavior of human drivers is highly stochastic and nonlinear. As a result, identifying the “best” CF model has been challenging and controversial despite decades of research. Introduction of automated vehicles has further complicated this matter as their CF controllers remain proprietary, though their behavior appears different than human drivers. This paper develops a stochastic learning approach to integrate multiple CF models, rather than relying on a single model. The framework is based on approximate Bayesian computation that probabilistically concatenates a pool of CF models based on their relative likelihood of describing observed behavior. The approach, while data-driven, retains physical tractability and interpretability. Evaluation results using two datasets show that the proposed approach can better reproduce vehicle trajectories for both human-driven and automated vehicles than any single CF model considered.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141978557","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":"Self-organized free-flight arrival for urban air mobility","authors":"Martin Waltz ,&nbsp;Ostap Okhrin ,&nbsp;Michael Schultz","doi":"10.1016/j.trc.2024.104806","DOIUrl":"10.1016/j.trc.2024.104806","url":null,"abstract":"<div><p>Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a self-organized vertiport arrival system based on deep reinforcement learning. The airspace around the vertiport is assumed to be circular, and the vehicles can freely operate inside. Each aircraft is considered an individual agent and follows a shared policy, resulting in decentralized actions that are based on local information. We investigate the development of the reinforcement learning policy during training and illustrate how the algorithm moves from suboptimal local holding patterns to a safe and efficient final policy. The latter is validated in simulation-based scenarios, including robustness analyses against sensor noise and a changing distribution of inbound traffic. Lastly, we deploy the final policy on small-scale unmanned aerial vehicles to showcase its real-world usability.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003279/pdfft?md5=81fe9ea676332304688fe99ee14e4f00&pid=1-s2.0-S0968090X24003279-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954092","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":"How the strength of social relationship affects pedestrian evacuation behavior: A multi-participant fire evacuation experiment in a virtual metro station","authors":"Xiaolu Xia ,&nbsp;Jieyu Chen ,&nbsp;Jin Zhang ,&nbsp;Nan Li","doi":"10.1016/j.trc.2024.104805","DOIUrl":"10.1016/j.trc.2024.104805","url":null,"abstract":"<div><p>In fire evacuation, social groups of pedestrians often maintain proximity, proceeding at a similar pace towards a common destination. However, the effect of social groups on pedestrian evacuation behavior is underexplored due to the lack of quantification of the social relationships and the subsequent inadequate assessment of their influence on pedestrian dynamics during evacuation. To address these issues, an immersive virtual reality (VR)-based multi-participant evacuation experiment was conducted in a virtual metro station. Social groups of different relationship strengths measured by trust were asked to evacuate from a simulated metro station fire emergency scene. Results showed that grouped pedestrians with stronger social relationships had lower stress response to emergency situations, and tended to stay closer to each other during evacuation. In addition, stronger social relationships also led to more coordinated evacuation decisions between grouped pedestrians. In terms of evacuation performance, stronger social relationship sightly delayed pedestrians’ initial response but reduced their overall evacuation time. By quantitatively measuring the strength of social relationships and comprehensively revealing its influence on pedestrians who evacuate in social groups, this study is expected to enhance the understanding of social group dynamics in pedestrian evacuation, and offer significant insights for emergency management in indoor environments, such as transportation facilities, where high footfall and complex crowd patterns demand efficient evacuations to avert massive injuries.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954093","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":"Overtaking on two-lane two-way rural roads: A personalized and reactive approach for automated vehicle","authors":"Benoit Vigne,&nbsp;Rodolfo Orjuela,&nbsp;Jean-Philippe Lauffenburger,&nbsp;Michel Basset","doi":"10.1016/j.trc.2024.104800","DOIUrl":"10.1016/j.trc.2024.104800","url":null,"abstract":"<div><p>Research on Connected and Automated Vehicles (CAV) has primarily focused on highway and urban environments, neglecting the significance and dangers of two-lane two-way rural roads. However, CAV driving strategies have the potential to improve significantly this network safety, particularly in critical maneuvers such as overtaking. This paper proposes an overall safety autonomous driving architecture particularly adapted to overtaking maneuver in a two-lane two-way rural road context. The proposed architecture considers vehicle connectivity to share their ego speeds and positions, enabling a rule-based decision-making process coupled with a Fuzzy Inference Systems (FIS) to manage the maneuver’s tasks and to ensure the feasibility of the maneuver. A safety-oriented abort task facilitates a return to the starting lane in case of potential collisions improving maneuver reactivity. Additionally, an original driving personalization is proposed through one driving style parameter modifying the trajectory shape and the maneuver initiation. Two low level controllers handle the vehicle control signals for braking, throttle, and steering wheel angle completing the architecture and allowing full autonomous driving. The algorithm is evaluated using a high fidelity simulation environment in different driving situations. The obtained results demonstrate its reliability and consistency in producing safe overtaking maneuvers regardless the generated situation. A Monte Carlo test highlights the correlation between driving style and comfort in most cases. However, the algorithm limited to two vehicles in the surrounding environment needs improvement to cope with more diverse driving situations.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003218/pdfft?md5=b69d80aad7a0139edf69c4ebd87fa7b2&pid=1-s2.0-S0968090X24003218-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964081","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":"Explainable active learning metamodeling for simulations: Method and experiments for ATM performance assessment","authors":"Christoffer Riis ,&nbsp;Francisco Antunes ,&nbsp;Tatjana Bolić ,&nbsp;Gérald Gurtner ,&nbsp;Andrew Cook ,&nbsp;Carlos Lima Azevedo ,&nbsp;Francisco Câmara Pereira","doi":"10.1016/j.trc.2024.104788","DOIUrl":"10.1016/j.trc.2024.104788","url":null,"abstract":"<div><p>The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Learning Metamodel), a three-step framework integrating active learning and SHAP (SHapley Additive exPlanations) values into simulation metamodels for supporting ATM decision-making. XALM efficiently uncovers hidden relationships among input and output variables in ATM simulators, which are usually of interest in policy analysis. Our experiments show that XALM’s predictive performance is comparable to that of the XGBoost metamodel with fewer simulations. Additionally, XALM exhibits superior explanatory capabilities compared to non-active learning metamodels.</p><p>Using the ‘Mercury’ (flight and passenger) ATM simulator, XALM is applied to a real-world scenario in Paris Charles de Gaulle airport, extending an arrival manager’s range and scope by analysing six variables. This case study illustrates the effectiveness of the proposed framework in enhancing simulation interpretability and understanding variable interactions. By addressing computational challenges and improving explainability, it complements traditional simulation-based analyses.</p><p>Lastly, we discuss two practical approaches for reducing the computational burden of the metamodelling further: we introduce a stopping criterion for active learning based on the inherent uncertainty of the metamodel, and we show how the simulations used for the metamodel can be reused across key performance indicators, thus decreasing the overall number of simulations needed.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003097/pdfft?md5=6787f77fd6b79519007f5138d071351e&pid=1-s2.0-S0968090X24003097-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953351","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":"Two-stream video-based deep learning model for crashes and near-crashes","authors":"Liang Shi,&nbsp;Feng Guo","doi":"10.1016/j.trc.2024.104794","DOIUrl":"10.1016/j.trc.2024.104794","url":null,"abstract":"<div><p>The use of videos for effective crash and near-crash prediction can significantly enhance the development of safety countermeasures and emergency response. This paper presents a two-stream hybrid model with temporal and spatial streams for crash and near-crash identification based on front-view video driving data. The novel temporal stream integrates optical flow and TimeSFormer, utilizing divided-space–time attention. The spatial stream employs TimeSFormer with space attention to complement spatial information that is not captured by the optical flow. An XGBoost classifier merges the two streams through late fusion. The proposed approach utilizes data from the Second Strategic Highway Research Program Naturalistic Driving Study, which encompasses 1922 crashes, 6960 near-crashes, and 8611 normal driving segments. The results demonstrate excellent performance, achieving an overall accuracy of 0.894. The F1 scores for crashes, near-crashes, and normal driving segments were 0.760, 0.892, and 0.923, respectively, indicating strong predictive power for all three categories. The proposed approach offers a highly effective and scalable solution for identifying crashes and near-crashes using front-view video driving data and has broad applications in the field of traffic safety.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952698","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":"Crowdshipping problem with dynamic compensations and transshipments","authors":"Ali Şardağ ,&nbsp;Kerim U. Kızıl ,&nbsp;Barış Yıldız","doi":"10.1016/j.trc.2024.104796","DOIUrl":"10.1016/j.trc.2024.104796","url":null,"abstract":"<div><p>Rapid urban growth and consequent increase in e-commerce demand make urban logistics a harder task than ever. The growing size of urban delivery operations not only entails operational challenges but also generates several negative externalities, such as increased traffic, pollution, noise, and accidents. This trend creates a pressing need for efficient delivery mechanisms that are more economical and environmentally friendly than existing systems. Crowdshipping, wherein ordinary members of the society partake in delivery operations for a small compensation, is one of the answers that cater to this need and has attracted considerable research interest recently. However, designing compensation mechanisms to prompt efficient participation from the public remains largely unexplored in the literature. In this study, we devise a dynamic compensation scheme for crowdshipping operations in a many-to-many express delivery framework, where the crowdshipper compensations are determined based on spatial and temporal distributions of the delivery demand and continually updated during the service time to leverage the crowd participation as needed. To address the resulting complex network management problem, we derive analytical solutions for compensation optimization and use these results along with effective pruning strategies to build a lookup table to simultaneously determine package routes and compensation offers in real time. Computational studies and extensive simulations conducted with real-world data show that our proposed approach can provide significant cost savings and considerably reduce operational costs and other transport-related negative externalities when compared to classical delivery modes, crowdshipping with static compensations, and crowdshipping without transshipment.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003176/pdfft?md5=93df452ea1ac877a96ef03a0328720aa&pid=1-s2.0-S0968090X24003176-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952699","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":"Climate-aware air traffic flow management optimization via column generation","authors":"Céline Demouge ,&nbsp;Marcel Mongeau ,&nbsp;Nicolas Couellan ,&nbsp;Daniel Delahaye","doi":"10.1016/j.trc.2024.104792","DOIUrl":"10.1016/j.trc.2024.104792","url":null,"abstract":"<div><p>Aviation is one of the global warming contributors. Its impact is due to CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> and non-CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> effects. Trajectory design is one of action levers for minimizing the environmental impact of air transportation. However, it affects the Air Traffic Management and should satisfy airspace constraints, especially airspace capacities. This paper proposes a climate-aware version of the Air Traffic Flow Management (ATFM), focusing on CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> and one particular non-CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> effect: condensation trails (<em>contrails</em>), although other non-CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> effects can be integrated. A deterministic ATFM optimization model is proposed, solved by a column generation approach. This problem is solved using different metrics, from simple to more complex and realistic ones. Numerical experiments are conducted both in the lateral case and when the cruise altitude becomes a decision variable. The impact of airspace capacities is also evaluated. The problem instances that are studied are built from realistic open-access data and made publicly available.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003139/pdfft?md5=d04a11b648ff9c7aaa9f85ff25680e65&pid=1-s2.0-S0968090X24003139-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951655","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}