Communications in Transportation Research最新文献

{"title":"Harnessing multimodal large language models for traffic knowledge graph generation and decision-making","authors":"","doi":"10.1016/j.commtr.2024.100146","DOIUrl":"10.1016/j.commtr.2024.100146","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controllability test for nonlinear datatic systems","authors":"","doi":"10.1016/j.commtr.2024.100143","DOIUrl":"10.1016/j.commtr.2024.100143","url":null,"abstract":"<div><div>Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending it to datatic (i.e., data-driven) control systems is still a challenging task due to the absence of system models. In this study, we propose a general controllability test method for nonlinear systems with datatic description, where the system behaviors are merely described by data. In this situation, the state transition information of a dynamic system is available only at a limited number of data points, leaving the behaviors beyond these points unknown. Different from traditional exact controllability, we introduce a new concept called <em>ϵ</em>-controllability, which extends the definition from point-to-point form to point-to-region form. Accordingly, our focus shifts to checking whether the system state can be steered to a closed state ball centered on the target state, rather than exactly at that target state. Given a known state transition sample, the Lipschitz continuity assumption restricts the one-step transition of all the points in a state ball to a small neighborhood of the subsequent state. This property is referred to as one-step controllability backpropagation, i.e., if the states within this neighborhood are <em>ϵ</em>-controllable, those within the state ball are also <em>ϵ</em>-controllable. On its basis, we propose a tree search algorithm called maximum expansion of controllable subset (MECS) to identify controllable states in the dataset. Starting with a specific target state, our algorithm can iteratively propagate controllability from a known state ball to a new one. This iterative process gradually enlarges the <em>ϵ</em>-controllable subset by incorporating new controllable balls until all <em>ϵ</em>-controllable states are searched. Besides, a simplified version of MECS is proposed by solving a special shortest path problem, called Floyd expansion with radius fixed (FERF). FERF maintains a fixed radius of all controllable balls based on a mutual controllability assumption of neighboring states. The effectiveness of our method is validated in three datatic control systems whose dynamic behaviors are described by sampled data.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent vehicle platooning transit","authors":"","doi":"10.1016/j.commtr.2024.100145","DOIUrl":"10.1016/j.commtr.2024.100145","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-functional simulation platform for on-demand ride service operations","authors":"","doi":"10.1016/j.commtr.2024.100141","DOIUrl":"10.1016/j.commtr.2024.100141","url":null,"abstract":"<div><div>On-demand ride services or ride-sourcing services have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various optimization algorithms, including reinforcement learning approaches, have been developed to help ride-sourcing platforms design better operational strategies to achieve higher efficiency. However, due to cost and reliability issues, it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride-sourcing platforms. Acting as a proper test bed, a simulation platform for ride-sourcing systems will thus be essential for both researchers and industrial practitioners. While previous studies have established simulators for their tasks, they lack a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems to the completeness of tasks they can implement. To address the challenges, we propose a novel simulation platform for ride-sourcing systems on real transportation networks. It provides a few accessible portals to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated on real-world data-based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control","authors":"","doi":"10.1016/j.commtr.2024.100142","DOIUrl":"10.1016/j.commtr.2024.100142","url":null,"abstract":"<div><div>Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency than model-free RL by utilizing a virtual environment model. However, obtaining sufficiently accurate representations of environmental dynamics is challenging because of uncertainties in complex systems and environments. An inaccurate environment model may degrade the sample efficiency and performance of model-based RL. Furthermore, while model-based RL can improve sample efficiency, it often still requires substantial training time to learn from scratch, potentially limiting its advantages over model-free approaches. To address these challenges, this paper introduces a knowledge-informed model-based residual reinforcement learning framework aimed at enhancing learning efficiency by infusing established expert knowledge into the learning process and avoiding the issue of beginning from zero. Our approach integrates traffic expert knowledge into a virtual environment model, employing the intelligent driver model (IDM) for basic dynamics and neural networks for residual dynamics, thus ensuring adaptability to complex scenarios. We propose a novel strategy that combines traditional control methods with residual RL, facilitating efficient learning and policy optimization without the need to learn from scratch. The proposed approach is applied to connected automated vehicle (CAV) trajectory control tasks for the dissipation of stop-and-go waves in mixed traffic flows. The experimental results demonstrate that our proposed approach enables the CAV agent to achieve superior performance in trajectory control compared with the baseline agents in terms of sample efficiency, traffic flow smoothness and traffic mobility.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142531463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaborative electric vehicle routing with meet points","authors":"","doi":"10.1016/j.commtr.2024.100135","DOIUrl":"10.1016/j.commtr.2024.100135","url":null,"abstract":"<div><div>In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. The core of this mechanism is based on exchanging goods at meet points, which is optimally planned en route. We propose a Collaborative Electric Vehicle Routing Problem with Meet Points (CoEVRPMP) considering constraints such as time windows, opportunity charging, and meet-point synchronization. The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model. We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming. The viability and scalability of the collaborative method are demonstrated through numerical case studies, including a real-world case and a large-scale experiment with up to 500 customers. The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs. Moreover, collaboration helps to reduce the environmental footprint by decreasing travel distance.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000180/pdfft?md5=483104cc66217184d47dccaf267f5baf&pid=1-s2.0-S2772424724000180-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the application of blockchain technology in crowdsource autonomous driving map updating","authors":"","doi":"10.1016/j.commtr.2024.100140","DOIUrl":"10.1016/j.commtr.2024.100140","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000234/pdfft?md5=19494040fa438c8f34552841ca5654a3&pid=1-s2.0-S2772424724000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of decentralized traffic signal controllers on stabilizing heterogeneous urban grid network","authors":"","doi":"10.1016/j.commtr.2024.100137","DOIUrl":"10.1016/j.commtr.2024.100137","url":null,"abstract":"<div><p>Macroscopic Fundamental Diagrams (MFDs) are valuable for designing and evaluating network-wide traffic management schemes. Since obtaining empirical MFDs can be expensive, analytical methodologies are crucial to estimate variations in MFD shapes under different control strategies and predict their efficacy in mitigating congestion. Analyses of urban grid networks' abstractions can provide an inexpensive methodology to obtain a qualitative understanding of impacts of control policies. However, existing abstractions are valid only for simple intersection layouts with unidirectional and single-lane links and two conflicting movement groups. Naturally, the real intersections are more complex, with multiple incoming and outgoing lanes, heterogeneous incoming links' capacities and several conflicting movement groups. To this end, we consider a grid network with differences in capacities of horizontal and vertical directions, allowing us to investigate the characteristics of control policies that can avoid pernicious gridlock in heterogeneous networks. We develop a new, more comprehensive network abstraction of such grid networks to analyze and compare the impacts of two families of decentralized Traffic Signal Controllers (TSCs) on the network's stability. The obtained theoretical insights are verified using microsimulation results of grid networks with multiple signalized intersections. The analyses suggest that considering both upstream and downstream congestion information in deciding signal plans can encourage more evenly distributed traffic in the network, making them more robust and effective at all congestion levels. The study provides a framework to understand general expectations from decentralized control policies when network inhomogeneity arises due to variations in incoming link capacities and turning directions.</p></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000209/pdfft?md5=6cc32fde3214132b4de6a5ce630ad5b1&pid=1-s2.0-S2772424724000209-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fleet data based traffic modeling","authors":"","doi":"10.1016/j.commtr.2024.100138","DOIUrl":"10.1016/j.commtr.2024.100138","url":null,"abstract":"<div><p>Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the number of vehicles participating in the traffic – in other words, the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management – is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level. To tackle this gap, an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure. By optimally scaling the Origin-Destination matrices of the sample fleet, an appropriate model can be approximated to provide traffic flow data beside average speeds. The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model. The method has been tested through two different real-world traffic networks, justifying the viability of the proposed method. Overall, the contribution of the study is a practical solution based on commonly available fleet traffic data, suggested for practitioners in traffic planning and management.</p></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000210/pdfft?md5=1bc8bbd0053046bb6abaa216dd86984f&pid=1-s2.0-S2772424724000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142150243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption","authors":"","doi":"10.1016/j.commtr.2024.100136","DOIUrl":"10.1016/j.commtr.2024.100136","url":null,"abstract":"<div><p>Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.</p></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":null,"pages":null},"PeriodicalIF":12.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772424724000192/pdfft?md5=8c1c643c9833c0c4fb0e764b30666e7b&pid=1-s2.0-S2772424724000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}