Communications in Transportation Research最新文献

Public acceptance of driverless buses: An extended UTAUT2 model with anthropomorphic perception and empathy 公众对无人驾驶公交车的接受度：具有拟人化感知和同理心的扩展UTAUT2模型

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-03-11 DOI: 10.1016/j.commtr.2025.100167

Zijing He , Ying Yang , Yan Mu , Xiaobo Qu

引用次数: 0

Toward developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework 面向开发符合社会要求的自动驾驶汽车：进展、专家见解和概念框架

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.commtr.2025.100207

Yongqi Dong , Bart van Arem , Haneen Farah

{"title":"Toward developing socially compliant automated vehicles: Advances, expert insights, and a conceptual framework","authors":"Yongqi Dong , Bart van Arem , Haneen Farah","doi":"10.1016/j.commtr.2025.100207","DOIUrl":"10.1016/j.commtr.2025.100207","url":null,"abstract":"<div><div>By improving road safety, traffic efficiency, and overall mobility, automated vehicles (AVs) hold promise for revolutionizing transportation. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs’ compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing socially compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An informal expert interview was also conducted to discuss the literature review results and identify critical research gaps and expectations toward SCAVs. On the basis of the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated via an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the importance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100207"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PriorFusion: Unified integration of priors for robust road perception in autonomous driving PriorFusion：用于自动驾驶稳健道路感知的先验统一集成

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-11-18 DOI: 10.1016/j.commtr.2025.100229

Xuewei Tang , Mengmeng Yang , Tuopu Wen , Peijin Jia , Le Cui , Mingshan Luo , Kehua Sheng , Bo Zhang , Kun Jiang , Diange Yang

{"title":"PriorFusion: Unified integration of priors for robust road perception in autonomous driving","authors":"Xuewei Tang , Mengmeng Yang , Tuopu Wen , Peijin Jia , Le Cui , Mingshan Luo , Kehua Sheng , Bo Zhang , Kun Jiang , Diange Yang","doi":"10.1016/j.commtr.2025.100229","DOIUrl":"10.1016/j.commtr.2025.100229","url":null,"abstract":"<div><div>With the growing interest in autonomous driving, there is an increasing demand for accurate and reliable road perception technologies. In complex environments without high-definition map support, autonomous vehicles must independently interpret their surroundings to ensure safe and robust decision-making. However, these scenarios pose significant challenges due to the large number, complex geometries, and frequent occlusions of road elements. A key limitation of existing approaches lies in their insufficient exploitation of the structured priors inherently present in road elements, resulting in irregular, inaccurate predictions. To address this, we propose PriorFusion, a unified framework that effectively integrates semantic, geometric, and generative priors to enhance road element perception. We introduce an instance-aware attention mechanism guided by shape-prior features, then construct a data-driven shape template space that encodes low-dimensional representations of road elements, enabling clustering to generate anchor points as reference priors. We design a diffusion-based framework that leverages these prior anchors to generate accurate and complete predictions. Experiments on large-scale autonomous driving datasets demonstrate that our method significantly improves perception accuracy, particularly under challenging conditions. Visualization results further confirm that our approach produces more accurate, regular, and coherent predictions of road elements.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100229"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuel- and noise-minimal departure trajectory using deep reinforcement learning with aircraft dynamics and topography constraints 基于飞机动力学和地形约束的深度强化学习的燃油和噪声最小离场轨迹

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-03-12 DOI: 10.1016/j.commtr.2025.100165

Chris HC. Nguyen , James M. Shihua , Rhea P. Liem

{"title":"Fuel- and noise-minimal departure trajectory using deep reinforcement learning with aircraft dynamics and topography constraints","authors":"Chris HC. Nguyen , James M. Shihua , Rhea P. Liem","doi":"10.1016/j.commtr.2025.100165","DOIUrl":"10.1016/j.commtr.2025.100165","url":null,"abstract":"<div><div>Designing an optimal departure trajectory for an airport can minimize fuel emissions within the surrounding airspace and noise perceived by nearby populations, which brings positive sociological and economic implications in addition to environmental benefits. Yet, designing a trajectory that considers realistic operational constraints could be complex and, consequently, computationally expensive. Traditional trajectory optimization methods often simplify the problem to manage computational costs, which leads to compromised accuracy. To overcome this challenge, we propose a reinforcement learning (RL) approach that can satisfy multidisciplinary constraints by leveraging accurately modeled flight dynamics, high-fidelity population data, and topological data. This is achieved by establishing a comprehensive, physically-consistent simulated environment for the learning algorithm, while keeping the computational cost low. Instead of directly designing the trajectory itself, we train an RL agent to control the aircraft, whose trajectory is then considered as optimal. We model the RL problem as a continuous Markov decision process and employ the soft actor-critic architecture. By changing the relative importance of fuel consumption and noise in the optimization objective, we can obtain different optimum trajectories that are well-suited to the specific region of interest. Not surprisingly, a trade-off between fuel consumption and noise impact is observed in our results. This developed framework provides a more accurate and sophisticated approach for departure trajectory optimization, whose results are beneficial for future airspace design and can support sustainable aviation efforts.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100165"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600833","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}

引用次数: 0

A systematic review of machine learning-based microscopic traffic flow models and simulations 基于机器学习的微观交通流模型和模拟的系统综述

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-02-27 DOI: 10.1016/j.commtr.2025.100164

Davies Rowan , Haitao He , Fang Hui , Ali Yasir , Quddus Mohammed

{"title":"A systematic review of machine learning-based microscopic traffic flow models and simulations","authors":"Davies Rowan , Haitao He , Fang Hui , Ali Yasir , 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":"2025-12-01","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}

引用次数: 0

Machine learning-based real-time crash risk forecasting for pedestrians 基于机器学习的行人实时碰撞风险预测

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-11-25 DOI: 10.1016/j.commtr.2025.100224

Fizza Hussain , Yuefeng Li , Shimul Md Mazharul Haque

{"title":"Machine learning-based real-time crash risk forecasting for pedestrians","authors":"Fizza Hussain , Yuefeng Li , Shimul Md Mazharul Haque","doi":"10.1016/j.commtr.2025.100224","DOIUrl":"10.1016/j.commtr.2025.100224","url":null,"abstract":"<div><div>Recent developments in artificial intelligence (AI) have made significant improvements in understanding and enhancing pedestrian safety—a vulnerable road user group that receives less attention than motorized road users do. Specifically, AI-based video analytics have provided insight into facilitating real-time safety at signalized intersections. However, past studies have not fully realized the essence of real-time analysis, which underpins forecasting pedestrian collision likelihood by analyzing how past extreme events influence future risk over sequential intervals. To this end, we combine extreme value theory and machine learning models for real-time pedestrian collision risk forecasting. Traffic conflicts and their associated variables were identified from 288 h of video footage obtained from three signalized intersections in Queensland, Australia, via computer vision techniques, including YOLO and DeepSORT, to obtain the post encroachment time for vehicle‒pedestrian interactions. A Bayesian non-stationary peak over threshold (POT) is developed to obtain real-time pedestrian crash risk at the signal cycle level. The performance of the POT model is compared with observed crashes, and the results demonstrate the reasonable accuracy of the model. The estimated pedestrian crash risk at each signal cycle forms contiguous univariate time series data (which serve as ground truth), which are used as input to develop time series machine learning models (recurrent neural networks (RNNs) and long short-term memory (LSTM)). Both of these models forecast pedestrian crash risk, with the RNN model outperforming the competing model and demonstrating that pedestrian crash risk can be reliably estimated 30−33 min in advance.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100224"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach 城市道路网络交通状态分类的简化模型：两阶段回归方法

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-06-05 DOI: 10.1016/j.commtr.2025.100185

Wei Huang , Dalin Tang , Xin Qiao , Guojun Chen

{"title":"A parsimonious model for classifying the traffic state of urban road networks: A two-stage regression approach","authors":"Wei Huang , Dalin Tang , Xin Qiao , Guojun Chen","doi":"10.1016/j.commtr.2025.100185","DOIUrl":"10.1016/j.commtr.2025.100185","url":null,"abstract":"<div><div>An effective method of traffic state classification is crucial for managing urban traffic congestion. Existing methods usually assume a given number of state categories, which is not flexible if real applications are required to define different state levels. In this study, a parsimonious statistical model is derived and validated for classifying urban traffic states. The model is developed on the basis of a large-scale empirical travel speed dataset from five cities in China. First, a hybrid clustering method that integrates DBSCAN and natural breaks is used to derive traffic state classification under various numbers of state categories. The classification results are then compiled to conduct the subsequent regression analysis. Second, a two-stage regression approach is proposed to investigate the correlation between the number of state categories and the classification criteria (i.e., state thresholds that separate one state level from another). In the first stage, a significant linear relationship between the classification criteria of adjacent traffic states is derived (<span><math><mrow><mover><msup><mi>R</mi><mn>2</mn></msup><mo>¯</mo></mover></mrow></math></span> = 0.80, <em>P</em> < 0.001). In the second stage, a significant correlation between the slope, intercept, and number of state categories is derived (<span><math><mrow><mover><msup><mi>R</mi><mn>2</mn></msup><mo>¯</mo></mover></mrow></math></span> = 0.95, <em>P</em> < 0.001). On the basis of the two-stage regression analysis, a novel parsimonious statistical model is developed. Third, the developed model is evaluated with three performance indicators, namely, the mean squared error (MSE), mean absolute error (MAE), and mean relative error (MRE). The claffication accuracy is further validated via a case study on the speed data of Foshan Avenue North road. We suggest that the model can be used to assist flexible decision-making support with different levels of detail.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100185"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144220975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corrigendum to “Interaction dataset of autonomous vehicles with traffic lights and signs”[Communications. Transp. Res. 5 (2025) 100201] “自动驾驶车辆与交通信号灯和标志的交互数据集”的勘误表[通信]。透明。Res. 5 (2025) 100201]

IF 14.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.commtr.2025.100217

Zheng Li , Zhipeng Bao , Haoming Meng , Haotian Shi , Qianwen Li , Handong Yao , Xiaopeng Li

引用次数: 0

Evaluation of accessibility disparities in urban areas during disruptive events based on transit real data 基于交通实际数据的破坏性事件中城市地区可达性差异评价

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-01-15 DOI: 10.1016/j.commtr.2024.100160

Alessandro Nalin , Nir Fulman , Emily Charlotte Wilke , Christina Ludwig , Alexander Zipf , Claudio Lantieri , Valeria Vignali , Andrea Simone

{"title":"Evaluation of accessibility disparities in urban areas during disruptive events based on transit real data","authors":"Alessandro Nalin , Nir Fulman , Emily Charlotte Wilke , Christina Ludwig , Alexander Zipf , Claudio Lantieri , Valeria Vignali , 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":"2025-12-01","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}

引用次数: 0

LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models LC-LLM：大语言模型的可解释变道意图和轨迹预测

IF 12.5

Communications in Transportation Research Pub Date : 2025-12-01 Epub Date: 2025-04-01 DOI: 10.1016/j.commtr.2025.100170

Mingxing Peng , Xusen Guo , Xianda Chen , Kehua Chen , Meixin Zhu , Long Chen , Fei-Yue Wang

{"title":"LC-LLM: Explainable lane-change intention and trajectory predictions with Large Language Models","authors":"Mingxing Peng , Xusen Guo , Xianda Chen , Kehua Chen , Meixin Zhu , Long Chen , Fei-Yue Wang","doi":"10.1016/j.commtr.2025.100170","DOIUrl":"10.1016/j.commtr.2025.100170","url":null,"abstract":"<div><div>To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this study, we address these challenges by proposing a Lane Change-Large Language Model (LC-LLM), an explainable lane change prediction model that leverages the strong reasoning capabilities and self explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information as natural language prompts for LLMs and employing supervised fine-tuning to tailor LLMs specifically for lane change prediction task. Additionally, we finetune the Chain-of-Thought (CoT) reasoning to improve prediction transparency and reliability, and include explanatory requirements in the prompts during the inference stage. Therefore, our LC-LLM not only predicts lane change intentions and trajectories but also provides CoT reasoning and explanations for its predictions, enhancing its interpretability. Extensive experiments based on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can effectively encode comprehensive interaction information for understanding driving behavior.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100170"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738664","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}

引用次数: 0