Communications in Transportation Research最新文献

{"title":"Quantitative assessment of mid-air collision probability in urban air mobility: A safety barrier-based framework for integrated operations","authors":"Jinpeng Zhang ,&nbsp;Yan Xu ,&nbsp;Kaiquan Cai ,&nbsp;Victor Gordo ,&nbsp;Gokhan Inalhan","doi":"10.1016/j.commtr.2025.100230","DOIUrl":"10.1016/j.commtr.2025.100230","url":null,"abstract":"<div><div>Risk assessment is a key issue when handling the increasing use of unmanned aircraft systems (UASs), especially in integrated operational urban airspace. This study proposes a method to systematically quantify the mid-air collision (MAC) risk for different operation types in urban air mobility (UAM). Specifically, a novel assessment framework is formulated that includes three timelines to track risk evolution for integrated operations (i.e., cooperative, non-cooperative, and manned aircraft) and four safety barriers (i.e., procedural, strategic, tactical, and collision avoidance mitigation) to prevent a conflict ending in MAC. Under the framework, analytical models are established considering trajectory uncertainty, latency time, and detection and avoidance (DAA) system risk ratio to calculate the failure probability of each barrier, and thus an overall MAC probability. Result-oriented, process-oriented and comprehensive Monte Carlo simulations are constructed to validate the proposed models and the MAC assessment timeline, followed by demonstrating four operational scenarios in real-world environment to illustrate the assessment process of the method. Results show that the simulation probability curves closely match the theoretical predictions. The cooperative UAS contributes the highest MAC risk in our designed integrated environment, with strategic mitigation failures accounting for the largest proportion, and thus effective strategic trajectory planning is crucial for maintaining the safety of integrated operations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100230"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683860","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}

{"title":"Urban rail transit resilience under different operation schemes: A percolation-based approach","authors":"Tianlei Zhu ,&nbsp;Xin Yang ,&nbsp;Yun Wei ,&nbsp;Anthony Chen ,&nbsp;Jianjun Wu","doi":"10.1016/j.commtr.2025.100177","DOIUrl":"10.1016/j.commtr.2025.100177","url":null,"abstract":"<div><div>To assess the resilience of urban rail transit (URT) systems under various operational conditions accurately and enhance their operation, this study develops a percolation model for nonfree flow transportation networks on the basis of percolation theory, which integrates multisource information and operational characteristics. Our model accounts for the state evolution of different hierarchical structures within the network and identifies nonlinear features. Specifically, we observed significant percolation transitions in the URT network, with distinct differences in critical percolation thresholds at different times, leading to multistate behavior. Network bottlenecks spatially shift with network phase transitions, exhibiting power-law frequency characteristics. On the basis of the full-day resilience assessment results, we analyzed the impact of different operational schemes on network resilience during the morning peak, the period with the lowest resilience. The results demonstrate that our resilience analysis framework effectively evaluates URT network resilience, providing theoretical support for enhancing operational management efficiency and accident prevention measures.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100177"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738665","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":"Perception strategies in low-altitude transportation: Single aircraft autonomous system vs. aircraft-ground-cloud integration system","authors":"Yuhao Wang ,&nbsp;Kai Wang ,&nbsp;Jing Gong ,&nbsp;Xiaobo Qu","doi":"10.1016/j.commtr.2025.100208","DOIUrl":"10.1016/j.commtr.2025.100208","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100208"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048260","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}

{"title":"FollowGen: A scaled noise conditional diffusion model for car-following trajectory prediction","authors":"Junwei You ,&nbsp;Rui Gan ,&nbsp;Weizhe Tang ,&nbsp;Zilin Huang ,&nbsp;Jiaxi Liu ,&nbsp;Zhuoyu Jiang ,&nbsp;Haotian Shi ,&nbsp;Keshu Wu ,&nbsp;Keke Long ,&nbsp;Sicheng Fu ,&nbsp;Sikai Chen ,&nbsp;Bin Ran","doi":"10.1016/j.commtr.2025.100215","DOIUrl":"10.1016/j.commtr.2025.100215","url":null,"abstract":"<div><div>Vehicle trajectory prediction is critical for advancing autonomous driving and advanced driver assistance systems (ADASs). Deep learning-based approaches, especially those using transformer-based and generative models, have significantly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions. However, they often overlook detailed car-following behaviors and the inter-vehicle interactions essential for real-world driving, particularly in fully autonomous or mixed traffic scenarios. Moreover, existing generative approaches in trajectory prediction are inefficient at conditioning predictions on relevant constraints. To address these issues, this study proposes FollowGen, a novel scaled noise conditional diffusion model for car-following trajectory prediction. FollowGen incorporates detailed inter-vehicular interactions and car-following dynamics within a generative framework, enhancing both the accuracy and realism of the predicted trajectories. The model uses a novel pipeline to capture historical vehicle behaviors. It leverages a noise scaling conditioning strategy to scale the noise with encoded historical features within the forward diffusion process to ensure history-constrained noise transformation. A cross-attention-based transformer architecture is employed in the reverse process to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimental results in various real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100215"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319546","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}

{"title":"Domain-enhanced dual-branch model for efficient and interpretable accident anticipation","authors":"Yanchen Guan ,&nbsp;Haicheng Liao ,&nbsp;Chengyue Wang ,&nbsp;Bonan Wang ,&nbsp;Jiaxun Zhang ,&nbsp;Jia Hu ,&nbsp;Zhenning Li","doi":"10.1016/j.commtr.2025.100214","DOIUrl":"10.1016/j.commtr.2025.100214","url":null,"abstract":"<div><div>Developing precise and computationally efficient traffic accident anticipation system is crucial for contemporary autonomous driving technologies, enabling timely intervention and loss prevention. In this study, we propose an accident anticipation framework employing a dual-branch architecture that effectively integrates visual information from dashcam videos with structured textual data derived from accident reports. Furthermore, we introduce a feature aggregation method that facilitates seamless integration of multimodal inputs through large models (GPT-4o, Long-CLIP), complemented by targeted prompt engineering strategies to produce actionable feedback and standardized accident archives. Comprehensive evaluations conducted on benchmark datasets (Dashcam Accidents Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D)) validate the superior predictive accuracy, enhanced responsiveness, reduced computational overhead, and improved interpretability of our approach, thus establishing a new benchmark for state-of-the-art performance in traffic accident anticipation.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100214"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319539","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}

{"title":"Strategic roles of female scholars in steering transportation research agendas","authors":"Mingyang Pei ,&nbsp;Zisen Lin ,&nbsp;Xiao Fu ,&nbsp;Xin Pei","doi":"10.1016/j.commtr.2025.100198","DOIUrl":"10.1016/j.commtr.2025.100198","url":null,"abstract":"<div><div>In recent years, female scientists have contributed to advancements in the transportation sector through technological innovation and unique perspectives, playing pivotal roles across various domains of the field. This study analyzes 54,511 publications from 20 Science Citation Index (SCI) Q1 transportation journals (2014–2024), encompassing over 100,000 scholars, to advance the understanding of the status of female scientists in transportation academia. Female authors constitute only 22.91% of first authors and 20.86% of corresponding authors, revealing persistent underrepresentation despite incremental progress in mixed-gender collaborations. This study uses a mixed-methods framework that includes data mining, the mean normalized log-transformed citation score (MNLCS), probabilistic gender identification, keyword co-occurrence, and clustering analysis to investigate macrolevel trends and longitudinally compare four collaboration modes. The key findings include that (1) mixed-gender teams exhibit significant growth, with MNLCS exceeding single-gender teams by 0.048–0.067, and (2) female-led collaborations exhibit a stronger tendency to drive sustained exploration in research fields. These findings support gender-equality policies and guide early-career scholars in collaboration strategies and frontier tracking, promoting inclusive development in transportation research.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100198"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654927","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}

{"title":"Interpretable machine learning for traffic congestion prediction: Unveiling the impact of different COVID-19 periods","authors":"Dan Zhu ,&nbsp;ChiSin Ng ,&nbsp;Litian Xie ,&nbsp;Yang Liu","doi":"10.1016/j.commtr.2025.100226","DOIUrl":"10.1016/j.commtr.2025.100226","url":null,"abstract":"<div><div>Traffic congestion prediction plays a crucial role in mitigating congestion. However, the COVID-19 pandemic and associated government control measures have significantly altered urban travel behavior, increasing the complexity of traffic congestion prediction. This study aims to predict traffic congestion in Alameda County in the San Francisco Bay Area, USA, during the prelockdown, lockdown, and postlockdown periods. We incorporate three external categories of data, i.e., weather conditions, seasonality factors, and COVID-19-related variables, and use recursive feature elimination with cross-validation to identify important features across different periods and avoid potential overfitting. On this basis, multiple advanced machine learning (ML) models, including support vector regression (SVR), multiple linear regression (MLR), recurrent neural network (RNN), and long short-term memory (LSTM) networks, are trained and optimized through extensive experimentation and parameter tuning. Since LSTM has more hyperparameters and is more sensitive to tuning than the other ML methods used, we employ an adaptive parameter selection approach to optimize its hyperparameters, enhancing model accuracy and efficiency, rather than manually tuning parameters for SVR and RNN. These models are evaluated via the normalized root mean square error. The results indicate that the bidirectional LSTM (Bi-LSTM) consistently outperforms the other models across all COVID-19 periods. This superior performance can be attributed to the Bi-LSTM's bidirectional architecture, which effectively captures temporal dependencies by analyzing data both forward and backward in time. To address the limited interpretability of ML methods and provide valuable insights, we apply the integrated gradient (IG) technique to interpret the best-performing and differentiable Bi-LSTM predictions. Our analysis revealed that new COVID-19 cases had a negative influence on traffic congestion during the lockdown and postlockdown periods. The observed reduction in traffic can be explained by heightened public risk awareness, voluntary reductions in travel, and compliance with government-imposed mobility restrictions. We also apply SHapley Additive exPlanations to SVR, given that IG is not applicable to this model. The results indicate that in the postpandemic period, people have become more cautious—high new hospitalization discourages travel, reducing traffic congestion, whereas high fuel prices do not deter a shift toward private vehicle use, leading to increased congestion.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100226"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617647","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}

{"title":"Cross-city transfer learning: Applications and challenges for smart cities and sustainable transportation","authors":"Ying Yang ,&nbsp;Jiahao Zhan ,&nbsp;Yang Liu ,&nbsp;Qi Wang","doi":"10.1016/j.commtr.2025.100206","DOIUrl":"10.1016/j.commtr.2025.100206","url":null,"abstract":"<div><div>Cross-city transfer learning (CCTL) has emerged as a crucial approach for managing the growing complexity of urban data and addressing the challenges posed by rapid urbanization. This paper provides a comprehensive review of recent advances in CCTL, with a focus on its applications in urban computing tasks, including prediction, detection, and deployment. We examine the role of CCTL in facilitating policy adaptation and influencing behavioral change. Specifically, we provide a systematic overview of widely used datasets, including traffic sensor data, GPS trajectory data, online social network data, and map data. Furthermore, we conduct an in-depth analysis of methods and evaluation metrics employed across different CCTL-based urban computing tasks. Finally, we emphasize the potential of cross-city policy transfer in promoting low-carbon and sustainable urban development. This review aims to serve as a reference for future urban development research and promote the practical implementation of CCTLs.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100206"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932208","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}

{"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":"2025-12-01","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":"SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data","authors":"Yutian Liu ,&nbsp;Chengfeng Jia ,&nbsp;Soora Rasouli ,&nbsp;Jian Gong ,&nbsp;Tao Feng ,&nbsp;Melvin Wong ,&nbsp;Tianjin Huang","doi":"10.1016/j.commtr.2025.100192","DOIUrl":"10.1016/j.commtr.2025.100192","url":null,"abstract":"<div><div>Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them vulnerable to perturbed environmental factors, like sensor malfunctions, data storage issues, and adverse weather conditions. To overcome the limitation, we propose SAFER-Predictor, a novel sparse adversarial training (Sparse AT) framework for enhancing the reliability of deep learning based spatiotemporal traffic prediction models. Sparse AT extends traditional adversarial training (AT) through a two-phase process: pre-training and fine-tuning. In the pre-training phase, the model is optimized to capture normal traffic patterns, enhancing predictive performance by understanding standard dynamics without external disruptions. In the fine-tuning phase, the focus shifts to strengthening robustness against corrupted inputs by employing an iterative min-max strategy during AT, optimizing performance for worst-case scenarios. Furthermore, we derive theoretical formulations that establish an upper bound on the model's prediction error following Sparse AT under certain noise levels. Experimental results indicate that incorporating Sparse AT into the representative traffic flow prediction models improves stability and ensures high accuracy under various perturbation scenarios.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100192"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481778","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}