Communications in Transportation Research最新文献

{"title":"A systematic review of machine learning-based microscopic traffic flow models and simulations","authors":"Davies Rowan ,&nbsp;Haitao He ,&nbsp;Fang Hui ,&nbsp;Ali Yasir ,&nbsp;Quddus Mohammed","doi":"10.1016/j.commtr.2025.100164","DOIUrl":"10.1016/j.commtr.2025.100164","url":null,"abstract":"<div><div>Microscopic traffic flow models and simulations are crucial for capturing vehicle interactions and analyzing traffic. They can provide critical insights for transport planning, management, and operation through scenario testing and optimization. With the growing availability of high-resolution data and rapid advancements in machine learning (ML) techniques, ML-based microscopic traffic flow models are emerging as promising alternatives to traditional physical models, offering improved accuracy and greater flexibility. Although many models have been developed, comprehensive studies that critically assess the strengths and weaknesses of these models and the overall ML-based approach are lacking. To fill this gap, this study presents a systematic review of ML-based microscopic traffic flow models and simulations, covering both car-following and lane-changing behaviors. This review identifies key areas for future research, including the development of methods to improve model transferability across different operational design domains, the need to capture both driver-specific and location-specific heterogeneity via benchmark datasets, and the incorporation of advanced ML techniques such as meta-learning, federated learning, and causal learning. Additionally, enhancing model interpretability, accounting for mesoscopic and macroscopic traffic impacts, incorporating physical constraints in model training, and developing ML models designed for autonomous vehicles are crucial for the practical adoption of ML-based microscopic models in traffic simulations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100164"},"PeriodicalIF":12.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510710","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":"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":"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}

{"title":"Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis","authors":"Fangjie Liu ,&nbsp;Muhammad Shafique ,&nbsp;Xiaowei Luo","doi":"10.1016/j.commtr.2024.100148","DOIUrl":"10.1016/j.commtr.2024.100148","url":null,"abstract":"<div><div>The transition toward battery electric vehicles (BEVs) is a critical element in the global shift toward sustainable transportation. This meta-analysis delves into the multifaceted factors influencing BEV performance, including environmental, technological, behavioral, and political-economic determinants. The purpose of this review is to systematically organize and assess how these factors impact BEV efficiency and sustainability across various operational scenarios, such as driving, charging, and decommissioning. By examining a wide range of literature, this study constructs a comprehensive framework that categorizes the primary components and performance metrics, revealing complex relationships and potential causal connections. The findings highlight that although technological advancements and regulatory frameworks are the predominant drivers of BEV performance, environmental conditions and user behaviors also play significant roles. The key emerging topics identified suggest further research avenues, particularly in optimizing battery technology and expanding policy support. Additionally, the analysis provides new and systematic insights compared with previous reviews, offering a clearer understanding of the determinants, their impacts, and the interactions between them. These insights are crucial for developing a transparent evaluation system for future research and policy formulation. This comprehensive synthesis not only aids in understanding the current landscape but also in directing future scholarly and practical endeavors in electric vehicle research.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100148"},"PeriodicalIF":12.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707036","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 machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics","authors":"Fizza Hussain ,&nbsp;Yuefeng Li ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.commtr.2024.100147","DOIUrl":"10.1016/j.commtr.2024.100147","url":null,"abstract":"<div><div>Traffic conflict techniques rely heavily on the proper identification of conflict extremes, which directly affects the prediction performance of extreme value models. Two sampling techniques, namely, block maxima and peak over threshold, form the core of these models. Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches, as their crash estimates are too imprecise, hindering their widespread practical use. Recently, anomaly detection techniques for sampling conflict extremes have been used, but their application has been limited to estimating crash frequency without considering the crash severity aspect. To address this research gap, this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level. In particular, modified time-to-collision (MTTC) and expected post-collision change in velocity (Delta-<em>V</em> or Δ<em>V</em>) have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level. Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane, Australia, using four days of data. Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques (isolation forest and minimum covariance determinant) were developed. The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters, including location, scale, and both location and scale parameters simultaneously. The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records, thereby demonstrating the viability of the proposed approach. A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model. Overall, the modeling framework presented in this study advances conflict-based safety assessment, where the severity dimension can be captured via bivariate hybrid models.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100147"},"PeriodicalIF":12.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661685","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":"Bridging the gap: Toward a holistic understanding of shared micromobility fleet development dynamics","authors":"Shahnaz N. Fuady ,&nbsp;Paul C. Pfaffenbichler ,&nbsp;Yusak O. Susilo","doi":"10.1016/j.commtr.2024.100149","DOIUrl":"10.1016/j.commtr.2024.100149","url":null,"abstract":"<div><div>Rapid urbanization and shifting demographics worldwide necessitate innovative urban transportation solutions. Shared micromobility systems, such as bicycle- and scooter-sharing programs, have emerged as promising alternatives to traditional urban mobility challenges. This study delves into the complexity of shared micromobility fleet development, focusing on the interplay between fleet size, user demand, regulatory frameworks, economic viability, and public engagement. By employing a system dynamics modeling approach that incorporates causal loop diagrams (CLDs) and stock and flow models (SFMs), we explore various policy scenarios to optimize micromobility management systems. Our findings reveal that financial incentives, such as fee reductions and government subsidies, significantly increase user adoption and profitability, whereas increased operational fees necessitate a delicate balance between cost management and service attractiveness. Sensitivity and uncertainty analyses highlight critical parameters for effective fleet management. This research offers actionable insights for policymakers and operators, promoting sustainable urban transport systems.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100149"},"PeriodicalIF":12.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661682","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}