Communications in Transportation Research最新文献

{"title":"On the stochastic fundamental diagram: A general micro-macroscopic traffic flow modeling framework","authors":"Xiaohui Zhang,&nbsp;Jie Sun,&nbsp;Jian Sun","doi":"10.1016/j.commtr.2025.100163","DOIUrl":"10.1016/j.commtr.2025.100163","url":null,"abstract":"<div><div>The stochastic fundamental diagram (SFD), which describes the stochasticity of the macroscopic relations of traffic flow, plays a crucial role in understanding the uncertainty of traffic flow evolution and developing robust traffic control strategies. Although many efforts have been made to reproduce the SFD via various methods, few studies have focused on the analytical modeling of the SFD, particularly linking the macroscopic relations with microscopic behaviors. This study fills this gap by proposing a general micro-macroscopic modeling approach, which uses probabilistic leader–follower behavior to derive the macroscopic relations of a platoon and is referred to as the leader–follower conditional distribution-based stochastic traffic modeling (LFCD-STM) framework. Specifically, we first define a conditional probability distribution of speed for the leader‒follower pair according to Brownian dynamics, which is proven to be a general representation of the longitudinal interaction and compatible with classical car-following models. As a result, we can describe the joint distribution of vehicle speeds of the platoon through Markov chain modeling and further derive the macroscopic relations (e.g., the mean flow‒density relation and its variance) under equilibrium conditions. On the basis of this general micro-macroscopic framework, we utilize the maximum entropy approach to theoretically derive the SFD model, in which we provide a specific conditional distribution for longitudinal interaction and thus solve the analytical functions of the mean and variance of FD. The performance of the maximum entropy-based SFD model is thoroughly validated with the NGSIM I-80, US-101 and HighD datasets. The high consistency between the theoretical results and empirical results demonstrates the soundness of the LFCD-STM framework and the maximum entropy-based SFD model. Finally, the proposed SFD model has practical implications for promoting smoother driving behaviors to suppress stochasticity and improve traffic flow.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100163"},"PeriodicalIF":12.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394682","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":"Drone to recharge electric vehicles: Operations, benefits, and challenges","authors":"Dongdong He ,&nbsp;Ying Yang ,&nbsp;Andrea Morichetta ,&nbsp;Jianjun Wu","doi":"10.1016/j.commtr.2025.100162","DOIUrl":"10.1016/j.commtr.2025.100162","url":null,"abstract":"<div><div>Electric vehicles (EVs) are a promising solution to reduce greenhouse gas emissions and foster sustainable urban transportation. However, the widespread adoption of EVs is hindered by range anxiety and the fear of running outnqt of battery before reaching a charging station. To address this challenge, we propose a novel drone-to-vehicle (D2V) charging system, which leverages drones as mobile charging units to provide on-the-go recharging services for EVs. This study explores the operational and technical aspects of the D2V system, including drone charging docks, order-dispatching strategies, and dynamic drone reallocation mechanisms. A key contribution is to introduce a concept of the adaptive route meetup location selection (ARMLS), which optimizes drone dispatch and pricing models based on real-time parameters such as distance, battery levels, and traffic conditions. Our analysis highlights the potential of D2V systems to alleviate range anxiety, enhance road network efficiency through dynamic traffic redistribution, and reduce carbon emissions by integrating renewable energy sources. The study suggests that implementing D2V services can significantly improve the reliability of EVs in critical situations while fostering broader EV adoption. Future work will focus on reinforcement learning-based optimization algorithms to further improve drone operations and address scalability challenges. The proposed D2V system represents a crucial step toward a sustainable and efficient urban mobility future.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100162"},"PeriodicalIF":12.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143402606","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":"Integrating spatial-temporal risk maps with candidate trajectory trees for explainable autonomous driving planning","authors":"Qiyuan Liu ,&nbsp;Jiawei Zhang ,&nbsp;Jingwei Ge ,&nbsp;Cheng Chang ,&nbsp;Zhiheng Li ,&nbsp;Shen Li ,&nbsp;Li Li","doi":"10.1016/j.commtr.2025.100161","DOIUrl":"10.1016/j.commtr.2025.100161","url":null,"abstract":"<div><div>With increasing public concern about autonomous vehicles, there is a growing demand for developing explainable autonomous driving planning technology. Traditional risk field methods use handcrafted potential field models to explain driving risks in a scenario. When explaining highly interactive scenarios, such prior knowledge-based methods still lack flexibility, leading to insufficient interpretability. In this study, we first propose the concept of a risk map that can be seen as a discrete, ego vehicle's view form of the risk field. We then design an explainable trajectory planning framework that integrates risk maps with the candidate trajectory tree generated by trajectory prediction models. We further filter safe candidate trajectories from the tree on the basis of their cumulative risks in the risk maps and then select the optimal trajectory to execute by balancing other driving objectives. The validation results in various real-world scenarios demonstrate that our method can generate understandable risk maps and explain the risk differences between trajectories. Open-loop experiments show our model's advantages in terms of safety and efficiency for the trajectory planning task. An analysis of runtime demonstrated its potential for real-world applications.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100161"},"PeriodicalIF":12.5,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161572","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":"Evaluation of accessibility disparities in urban areas during disruptive events based on transit real data","authors":"Alessandro Nalin ,&nbsp;Nir Fulman ,&nbsp;Emily Charlotte Wilke ,&nbsp;Christina Ludwig ,&nbsp;Alexander Zipf ,&nbsp;Claudio Lantieri ,&nbsp;Valeria Vignali ,&nbsp;Andrea Simone","doi":"10.1016/j.commtr.2024.100160","DOIUrl":"10.1016/j.commtr.2024.100160","url":null,"abstract":"<div><div>The main motivation of this paper is to emphasize the necessity of assessing the actual performance of public transportation (PT), rather than relying on schedules, when assessing accessibility and equity in the provision of PT services. Real conditions are reflected in datasets such as the outcomes of Automatic Vehicle Monitoring (AVM) systems, whereas schedules are usually provided as General Transit Feed Specification (GTFS). In light of the dissimilar characteristics of central and peripheral neighborhoods, it is crucial to consider the operational conditions that users encounter, particularly in the context of unexpected disruptions that alter regular service. By examining a real-world case study in Bologna, Italy, the research combines well-known measures and innovative methods and demonstrates notable variation in accessibility and equity in the provision of PT services when comparing results based on real-time data with those based on schedules. This work contributes to a more nuanced understanding of urban accessibility and highlights the need for public stakeholders and transport authorities to incorporate actual service conditions into their evaluations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100160"},"PeriodicalIF":12.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161573","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 oscillation mitigation with physics-enhanced residual learning (PERL)-based predictive control","authors":"Keke Long,&nbsp;Zhaohui Liang,&nbsp;Haotian Shi,&nbsp;Lei Shi,&nbsp;Sikai Chen,&nbsp;Xiaopeng Li","doi":"10.1016/j.commtr.2024.100154","DOIUrl":"10.1016/j.commtr.2024.100154","url":null,"abstract":"<div><div>Real-time vehicle prediction is crucial in autonomous driving technology, as it allows adjustments to be made in advance to the driver or the vehicle, enabling them to take smoother driving actions to avoid potential collisions. This study proposes a physics-enhanced residual learning (PERL)-based predictive control method to mitigate traffic oscillation in the mixed traffic environment of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). The introduced model includes a prediction model and a CAV controller. The prediction model is responsible for forecasting the future behavior of the preceding vehicle on the basis of the behavior of preceding vehicles. This PERL model combines physical information (i.e., traffic wave properties) with data-driven features extracted from deep learning techniques, thereby precisely predicting the behavior of the preceding vehicle, especially speed fluctuations, to allow sufficient time for the vehicle/driver to respond to these speed fluctuations. For the CAV controller, we employ a model predictive control (MPC) model that considers the dynamics of the CAV and its following vehicles, improving safety and comfort for the entire platoon. The proposed model is applied to an autonomous driving vehicle through vehicle-in-the-loop (ViL) and compared with real driving data and three benchmark models. The experimental results validate the proposed method in terms of damping traffic oscillation and enhancing the safety and fuel efficiency of the CAV and the following vehicles in mixed traffic in the presence of uncertain human-driven vehicle dynamics and actuator lag.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100154"},"PeriodicalIF":12.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743040","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":"Towards explainable traffic flow prediction with large language models","authors":"Xusen Guo ,&nbsp;Qiming Zhang ,&nbsp;Junyue Jiang ,&nbsp;Mingxing Peng ,&nbsp;Meixin Zhu ,&nbsp;Hao Frank Yang","doi":"10.1016/j.commtr.2024.100150","DOIUrl":"10.1016/j.commtr.2024.100150","url":null,"abstract":"<div><div>Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models. To tackle these challenges, we propose a traffic flow prediction model based on large language models (LLMs) to generate explainable traffic predictions, named xTP-LLM. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions. This study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100150"},"PeriodicalIF":12.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759132","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":"Bidirectional Q-learning for recycling path planning of used appliances under strong and weak constraints","authors":"Yang Qi ,&nbsp;Jinxin Cao ,&nbsp;Baijing Wu","doi":"10.1016/j.commtr.2024.100153","DOIUrl":"10.1016/j.commtr.2024.100153","url":null,"abstract":"<div><div>With the continuous innovation in household appliance technology and the improvement of living standards, the production of discarded household appliances has rapidly increased, making their recycling increasingly significant. Traditional path planning algorithms encounter difficulties in balancing efficiency and constraints in addressing the multi-objective, multi-constraint challenge posed by discarded household appliance recycling routes. To tackle this issue, this study introduces a bi-directional <em>Q</em>-learning-based path planning algorithm. By developing a bi-directional <em>Q</em>-learning mechanism and enhancing the initialization method of <em>Q</em>-learning, the algorithm aims to achieve efficient and effective optimization of discarded household appliance recycling routes. It implements bidirectional updates of the state-action value function from both the starting point and the target point. Additionally, a hierarchical reinforcement learning strategy and guided rewards are introduced to minimize blind exploration and expedite convergence. By decomposing complex recycling tasks into multiple sub-tasks and seeking paths with superior performance at each sub-task level, the initial exploratory blindness is reduced. To validate the efficacy of the proposed algorithm, gridbased modeling of real-world environments is utilized. Comparative experiments reveal significant improvements in iteration counts and path lengths, thereby validating its practical applicability in path planning for recycling initiatives.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100153"},"PeriodicalIF":12.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722280","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":"Multi-objective optimization model of autonomous minibus considering passenger arrival reliability and travel risk","authors":"Zhicheng Jin ,&nbsp;Haoyang Mao ,&nbsp;Di Chen ,&nbsp;Hao Li ,&nbsp;Huizhao Tu ,&nbsp;Ying Yang ,&nbsp;Maria Attard","doi":"10.1016/j.commtr.2024.100152","DOIUrl":"10.1016/j.commtr.2024.100152","url":null,"abstract":"<div><div>The advancement of self-driving technologies facilitates the emergence of autonomous minibuses (ABs) in public transportation, which could provide flexible, reliable, and safe mobility services. This study develops an AB routing and scheduling model considering each passenger’s arrival reliability and travel risk. Firstly, to guarantee each passenger’s arrival on time, the arrival reliability (a predetermined threshold of on-time arrival probability of <em>α</em> ​= ​0.9) is included in the constraints. Secondly, three objectives, including system costs, greenhouse gas (GHG) emissions, and travel risk, are optimized in the model. To assess the travel risk of ABs, an enhanced method based on kernel density estimation (KDE) is proposed. Thirdly, an advanced multi-objective adaptive large neighborhood search algorithm (MOALNS) is designed to find the Pareto optimal set. Finally, experiments are conducted in Shanghai to validate model performance. Results show that it can decrease GHG emissions (−2.12%) and risk (−9.47%), while only increasing costs by 2.02%. Furthermore, the proposed arrival reliability constraint can improve an average of 14.70% of passengers to meet their arrival reliability requirement (<em>α</em> ​= ​0.9).</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100152"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722278","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":"Modeling the clustering strength of connected autonomous vehicles and its impact on mixed traffic capacity","authors":"Peilin Zhao,&nbsp;Yiik Diew Wong,&nbsp;Feng Zhu","doi":"10.1016/j.commtr.2024.100151","DOIUrl":"10.1016/j.commtr.2024.100151","url":null,"abstract":"<div><div>In a mixed traffic environment consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HVs), platooning intensity serves as a critical metric, quantifying the strength of CAV clustering, with inherent ramifications for traffic flow efficiency. While various definitions of platooning intensity are found in existing literature, many fall short in effectively capturing the strength of CAV clustering in mixed traffic. To address the gap, this study models the vehicle stream of mixed traffic on the single-lane road as a binary sequence and proposes the autocorrelation-based platooning intensity (API) metric. Through theoretical analysis, the proposed API is shown to be an effective indicator for measuring the clustering strength of CAVs. The probability distribution of API through fisher transformation is also derived. This study then moves on to formulate the capacity of mixed traffic, taking into account CAV penetration rate, API, and stochastic headway. Numerical verification of the estimated mixed traffic capacity reveals a negligible error (less than 1%) compared to simulated capacity. Marginal analysis confirms the validity of related propositions, notably that stronger CAV clustering does not always improve traffic capacity due to headway stochasticity. The outcome of this study contributes to the understanding of CAV platooning intensity and offers valuable insights for advancing mixed traffic modeling and management.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100151"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722279","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":"Integrated operator and user-based rebalancing and recharging in dockless shared e-micromobility systems","authors":"Elnaz Emami,&nbsp;Mohsen Ramezani","doi":"10.1016/j.commtr.2024.100155","DOIUrl":"10.1016/j.commtr.2024.100155","url":null,"abstract":"<div><div>This study proposes a rebalancing method for a dockless e-micromobility sharing system, employing both trucks and users. Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology. In addition, some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing. The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics, such as location and charge level. The problem is formulated as a mixed binary problem, which minimizes operational costs and total unmet demand while maximizing the system's profit. To solve the optimization problem, a Branch and Bound method is employed. Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem. We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies. The results indicate that the proposed method enhances system performance and user travel times.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100155"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722281","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}