Transportation Research Part C-Emerging Technologies最新文献

{"title":"Online algorithms for the multi-vehicle inventory-routing problem with real-time demands","authors":"Luca Bertazzi ,&nbsp;Guilherme O. Chagas ,&nbsp;Leandro C. Coelho ,&nbsp;Demetrio Laganà ,&nbsp;Francesca Vocaturo","doi":"10.1016/j.trc.2024.104892","DOIUrl":"10.1016/j.trc.2024.104892","url":null,"abstract":"<div><div>The increasing availability of sophisticated information and communication technology has stimulated new research within the distribution logistics area in the last few decades. Real-time information is crucial to ensure not only the competitiveness of a company but also its survival in the e-commerce era. Companies try to offer delivery to their customers within a few hours of receiving a request. In addition, real-time information can be exploited in systems that operate under emergencies, where response time is critical. We model and solve a multi-vehicle inventory-routing problem in which new service requests are revealed dynamically over time, in real-time or online. For this problem, we propose a class of online algorithms based on iteratively solving integer programming models. These models are solved through a tailored branch-and-cut method, in which several families of valid inequalities are separated and dynamically introduced in the model or through a matheuristic to speed up the solution process. We carry out a competitive analysis that allows us to prove the competitive ratio of the online algorithms we propose and, therefore, to evaluate their performance with respect to the optimal solution of the offline problem, in the worst case. An extensive computational experience on benchmark instances shows that these algorithms are also effective on average and require short computational time when the matheuristic is applied to solve the integer programming models. Additional tests on large real-world instances indicate that the proposed solution methods achieve performance that remains reasonable for the size of these instances.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104892"},"PeriodicalIF":7.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650788","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":"CosIn: A statistical-based algorithm for computation of space-speed time delay in pedestrian motion","authors":"Jinghui Wang ,&nbsp;Wei Lv ,&nbsp;Shuchao Cao ,&nbsp;Zhensheng Wang","doi":"10.1016/j.trc.2024.104912","DOIUrl":"10.1016/j.trc.2024.104912","url":null,"abstract":"<div><div>Precise assessment of Space-speed time delay (TD) is critical for distinguishing between anticipation and reaction behaviors within pedestrian motion. Besides, the TD scale is instrumental in the evaluation of potential collision tendency of the crowd, thereby providing essential quantitative metrics for assessing risk. In this consideration, this paper introduced the CosIn algorithm for evaluating TD during pedestrian motion, which includes both the CosIn-1 and CosIn-2 algorithms. CosIn-1 algorithm analytically calculates TD, replacing the numerical method of discrete cross-correlation, whereas the CosIn-2 algorithm estimates the TD from a statistical perspective. Specifically, the CosIn-1 algorithm addresses the precise computation of TD for individual pedestrians, while the CosIn-2 algorithm is employed for assessing TD at the crowd scale, concurrently addressing the imperative of real-time evaluation. Efficacy analyses of the CosIn-1 and CosIn-2 algorithms are conducted with data from single-file pedestrian experiments and crowd-crossing experiments, respectively. During this process, the discrete cross-correlation method was employed as a baseline to evaluate the performance of both algorithms, which demonstrated notable accuracy. This algorithm facilitate the precise evaluation of behavior patterns and collision tendency within crowds, thereby enabling us to understand the crowds dynamics from a new perspective.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104912"},"PeriodicalIF":7.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650787","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":"On the service differentiation for parking sharing","authors":"Zhuoye Zhang ,&nbsp;Fangni Zhang ,&nbsp;Wei Liu ,&nbsp;Hai Yang","doi":"10.1016/j.trc.2024.104915","DOIUrl":"10.1016/j.trc.2024.104915","url":null,"abstract":"<div><div>This paper models and optimizes a two-sided market of shared parking where the parking sharing platform rents spare parking spaces from owners and provides them to parkers. Different parkers may derive a different utility or benefit from renting and using a parking space from the platform and their willingness-to-pay for the parking sharing service may differ. In this context, we consider that the platform can provide differentiated services to parkers, i.e., priority and normal services. The priority service will secure the rights to be matched with the parking supplies firstly, but may involve a higher service price. We model the parking supply–demand equilibrium for such a two-sided market with differentiated services and compare it against that under single-type (homogeneous) service. We also analyze how the supply–demand equilibrium varies with the platform’s pricing strategies (service prices and rent paid to parking owners). Then, we discuss and compare the parking sharing platform’s pricing strategies under different economic objectives (i.e., maximize net revenue or social benefit) and under different service structures (i.e., single-type service or differentiated services). We found that differentiated services can help improve platform revenue and social welfare.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104915"},"PeriodicalIF":7.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650786","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 User-Driven Prioritisation Process implementation and optimisation for ATFM hotspot resolution","authors":"Andrea Gasparin ,&nbsp;Lorenzo Castelli ,&nbsp;Tatjana Bolić ,&nbsp;Gérald Gurtner ,&nbsp;Nadine Pilon","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":"170 ","pages":"Article 104894"},"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":"Hongliang Lu ,&nbsp;Chao Lu ,&nbsp;Haoyang Wang ,&nbsp;Jianwei Gong ,&nbsp;Meixin Zhu ,&nbsp;Hai Yang","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":"169 ","pages":"Article 104899"},"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":"Pricing for multi-modal pickup and delivery problems with heterogeneous users","authors":"Mark Beliaev ,&nbsp;Negar Mehr ,&nbsp;Ramtin Pedarsani","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":"169 ","pages":"Article 104864"},"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":"LFF: An attention allocation-based following behavior framework in lane-free environments","authors":"Xingyu Chen ,&nbsp;Weihua Zhang ,&nbsp;Haijian Bai ,&nbsp;Heng Ding ,&nbsp;Mengfan Li ,&nbsp;Wenjuan Huang","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":"169 ","pages":"Article 104883"},"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":"Data poisoning attacks on traffic state estimation and prediction","authors":"Feilong Wang ,&nbsp;Xin Wang ,&nbsp;Yuan Hong ,&nbsp;R. Tyrrell Rockafellar ,&nbsp;Xuegang (Jeff) Ban","doi":"10.1016/j.trc.2024.104577","DOIUrl":"10.1016/j.trc.2024.104577","url":null,"abstract":"<div><div>Data has become ubiquitous nowadays in transportation, including vehicular data and infrastructure-generated data. The growing reliance on data poses potential cybersecurity issues to transportation systems, among which the so-called “data poisoning” attacks by adversaries are becoming increasingly critical. Such attacks aim to compromise a system’s performance by adding systematic and malicious noises, perturbations, or deviations to the dataset used by the system. Formal investigations of data poisoning attacks are essential for understanding the attacks and developing effective defense methods. This study develops a general data poisoning attack model for traffic state estimation and prediction (TSEP) that is a basic application in transportation. We first formulate data poisoning attacks as a general sensitivity analysis of parameterized optimization problems<span> over parameter changes (i.e., data perturbations) and study the Lipschitz continuity property of the solution with the presence of general (equality and inequality) constraints. Then, we develop attack models that fit a broader spectrum of learning applications (such as TSEP) by extending existing models that only focus on learning problems with no or equality constraints (widely used in the cybersecurity field). Since the solution of such general problems is often continuous but not differentiable with data changes, we apply the generalized implicit function theorem to compute the semi-derivatives that express how the TSEP solution responds to data perturbations. The semi-derivatives enable us to evaluate TSEP models’ vulnerability (at each data point) and solve the proposed attack model. We demonstrate the generality and effectiveness of the proposed method on two TSEP models using mobile sensing data.</span></div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"168 ","pages":"Article 104577"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661459","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 origin–destination matrix estimation on large networks—A joint origin–destination-path-choice formulation","authors":"Yumin Cao ,&nbsp;Hans van Lint ,&nbsp;Panchamy Krishnakumari ,&nbsp;Michiel Bliemer","doi":"10.1016/j.trc.2024.104850","DOIUrl":"10.1016/j.trc.2024.104850","url":null,"abstract":"<div><div>This paper presents a novel approach to data-driven time-dependent origin–destination (OD) estimation using a joint origin–destination-path choice formulation, inspired by the well-known equivalence of doubly constraint gravity models and multinomial logit models for joint O–D choice. This new formulation provides a theoretical basis and generalizes an earlier contribution. Although including path choice increases the dimensionality of the problem, it also dramatically improves the quality of the data one can <em>directly</em> use to solve it (e.g. measured path travel times versus coarse centroid-to-centroid travel times); and opens up possibilities to combine different assimilation techniques in a single framework: (1) fast shortest path set computation using static (e.g. road type) and dynamic (speed, travel time) link properties; (2) predicting a “prior OD matrix” using the resulting path-shares and (estimated or measured) production and attraction totals; and (3) scaling/constraining this prior using link flows (informative of demand). If the resulting system of equations has insufficient rank, we use principal component analysis to reduce the dimensionality, solve this reduced problem, and transform that solution back to a full OD matrix. Comprehensive tests and sensitivity analysis on 7 networks with different sizes and characteristics give an empirical underpinning of the extended equivalence principle; demonstrate good accuracy and reliability of the OD estimation method overall; and suggest that the method is robust with respect to major assumptions and contributing factors.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"168 ","pages":"Article 104850"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661378","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":"Privacy-preserving data fusion for traffic state estimation: A vertical federated learning approach","authors":"Qiqing Wang,&nbsp;Kaidi Yang","doi":"10.1016/j.trc.2024.104743","DOIUrl":"10.1016/j.trc.2024.104743","url":null,"abstract":"<div><div>This paper proposes a privacy-preserving data fusion<span> method for traffic state estimation (TSE). Unlike existing works that assume all data sources<span> to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in the collaboration and data sharing between multiple data owners, such as municipal authorities (MAs) and mobility providers (MPs). To this end, we propose a novel vertical federated learning (FL) approach, FedTSE, that enables multiple data owners to collaboratively train and apply a TSE model without having to exchange their private data. To enhance the applicability of the proposed FedTSE in common TSE scenarios with limited availability of ground-truth data, we further propose a privacy-preserving physics-informed FL approach, i.e., FedTSE-PI, that integrates traffic models into FL. Real-world data validation shows that the proposed methods can protect privacy while yielding similar accuracy to the oracle method without privacy considerations.</span></span></div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"168 ","pages":"Article 104743"},"PeriodicalIF":7.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661369","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}