Communications in Transportation Research最新文献

{"title":"Reassessing desired time headway as a measure of car-following capability: Definition, quantification, and associated factors","authors":"Shubham Parashar ,&nbsp;Zuduo Zheng ,&nbsp;Andry Rakotonirainy ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.commtr.2025.100169","DOIUrl":"10.1016/j.commtr.2025.100169","url":null,"abstract":"<div><div>The desired time headway is often used to incorporate human behavior in car-following (CF) models by treating it as a measure of driver capability in car-following interactions, which is latent and cannot be directly observed. However, the desired time headway is often assumed to be a constant value for a driver across all speed levels. This assumption can be unrealistic and unreliable. Studies indicate that the mean time headway during steady-state car-following interactions quantifies the desired time headway, but inconsistent conditions for steady-state interactions in the literature make such assessments challenging. This study aims to reassess the desired time headway as a metric of driver capability in car-following interactions. Specifically, it identifies steady-state car-following conditions for reliable desired time headway estimates via the NGSIM I80 dataset. The results show that using a sustenance window of 3.5 ​s with an acceleration threshold of ±0.75 ​m/s² and a relative speed of ±1.52 ​m/s reduces transient and sporadic time headway observations, which in turn improves the reliability of the desired time headway. The obtained conditions are applied to the car-following trajectories in a driving simulator experiment, designed to focus on the steady-state at two speed levels (85 and 40 ​km/h) in traditional environment (TE) and connected environment (CE). The results indicate that the desired time headway is significantly longer in high-speed car-following (85 ​km/h) than in low-speed car-following (40 ​km/h) in the TE and CE and that driving aids help maintain more consistent desired time headways. A comparison of the TE and CE in low-speed car-following shows that most drivers prioritize safety by increasing the desired time headway in the CE. However, in high-speed car-following, the mean desired time headway is not significantly different between the TE and the CE on an aggregate level. Furthermore, the study presents a generalized linear mixed model (GLMM) describing the desired time headway selection in different conditions, identifying age, gender, and crash involvement as significant variables other than the driving conditions.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100169"},"PeriodicalIF":12.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609140","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":"Driving under the sun: Future of solar buses in Hong Kong, China","authors":"Zhuowei Wang ,&nbsp;Yiyang Peng ,&nbsp;Hongxing Yang ,&nbsp;Anthony Chen","doi":"10.1016/j.commtr.2025.100168","DOIUrl":"10.1016/j.commtr.2025.100168","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100168"},"PeriodicalIF":12.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600832","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":"Public acceptance of driverless buses: An extended UTAUT2 model with anthropomorphic perception and empathy","authors":"Zijing He ,&nbsp;Ying Yang ,&nbsp;Yan Mu ,&nbsp;Xiaobo Qu","doi":"10.1016/j.commtr.2025.100167","DOIUrl":"10.1016/j.commtr.2025.100167","url":null,"abstract":"<div><div>The sustainable transportation strategy emphasizes the enormous potential of driverless buses and enables their gradual integration into society over the coming decade. Therefore, it is crucial to cultivate public acceptance of driverless buses. This study is based on the extended unified theory of acceptance and use of technology (UTAUT2) and empathy theory. The structural equation modeling (SEM) method was used to analyze valid survey responses from 852 participants residing in China. Both the UTAUT2 factors and the anthropomorphic perception components independently predicted the public acceptance of driverless buses. This study indicates that future campaigns promoting driverless buses should highlight not only their functional value but also their perceived socioemotional value. Considering users’ psychological characteristics (such as empathy and communal traits) can help improve the travel experience, accelerate the transition to emerging innovative technologies, and achieve the potential benefits of intelligent and sustainable transportation.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100167"},"PeriodicalIF":12.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592787","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 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":"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}