Communications in Transportation Research最新文献

{"title":"A causality-based explainable AI method for bus delay propagation analysis","authors":"Qi Zhang ,&nbsp;Zhenliang Ma ,&nbsp;Zhiyong Cui","doi":"10.1016/j.commtr.2025.100178","DOIUrl":"10.1016/j.commtr.2025.100178","url":null,"abstract":"<div><div>Public transportation networks are highly interconnected, where disruptions like traffic congestion propagate bus delays and impact performance. Identifying delay causes is crucial, yet most studies rely on correlation-based methods rather than causal analysis. Attribution methods like the Shapley value quantify factor contributions but often overlook causal dependencies, leading to potential bias. This study uses a causal discovery model to uncover causal relationships between bus delays and various factors (e.g., operational factors, calendar, and weather). Based on this causal graph, an explainable Artificial Intelligence (AI) method quantifies each factor's contribution to delays, focusing on how these contributions vary at different stops along a route. By integrating scheduled route data and real-time vehicle locations, we analyze factor contributions over time and space, exploring various scenarios along the route. Cross-validation is conducted by comparing the importance ranking of factors with the Seemingly Unrelated Regression Equations (SURE). Results show significant variations in factors contributing to delays along the route. Delays at upstream stops propagate downstream, indicating a cascading effect. Operational factors dominate, accounting for 50%–83% of delays. Notably, delays from the preceding two to three stops have a larger impact than just the immediately preceding one stop, and origin delays strongly affect the first half of the route.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100178"},"PeriodicalIF":12.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808766","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":"CASAformer: Congestion-aware sparse attention transformer for traffic speed prediction","authors":"Yifan Zhang ,&nbsp;Qishen Zhou ,&nbsp;Jianping Wang ,&nbsp;Anastasios Kouvelas ,&nbsp;Michail A. Makridis","doi":"10.1016/j.commtr.2025.100174","DOIUrl":"10.1016/j.commtr.2025.100174","url":null,"abstract":"<div><div>Accurate and efficient traffic speed prediction is crucial for improving roaDongguand safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Most of the State-Of-The-Art (SOTA) models unfortunately do not differentiate between the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100174"},"PeriodicalIF":12.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808765","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":"Counterfactual explanations for deep learning-based traffic forecasting","authors":"Rushan Wang ,&nbsp;Yanan Xin ,&nbsp;Yatao Zhang ,&nbsp;Fernando Perez-Cruz ,&nbsp;Martin Raubal","doi":"10.1016/j.commtr.2025.100176","DOIUrl":"10.1016/j.commtr.2025.100176","url":null,"abstract":"<div><div>Deep learning models are widely used in traffic forecasting and have achieved state-of-the-art prediction accuracy. However, their black-box nature presents challenges for interpretability and usability, particularly when predictions are significantly influenced by complex urban contextual features. This study aims to leverage an explainable artificial intelligence (AI) approach, counterfactual explanations, to enhance the explainability of deep learning-based traffic forecasting models and elucidate their relationships with various contextual features. We present a comprehensive framework that generates counterfactual explanations for traffic forecasting. The study first implements a graph convolutional network (GCN) to predict traffic speed based on historical traffic data and contextual variables. Counterfactual explanations are generated through a multi-objective optimization process, with four objectives, validity, proximity, sparsity, and plausibility, each emphasizing different aspects of optimization. We investigated the impact of contextual features on traffic speed prediction under varying spatial and temporal conditions. The scenario-driven counterfactual explanations integrate two types of user-defined constraints, directional and weighting constraints, to tailor the search for counterfactual explanations to specific use cases. These tailored explanations benefit machine learning practitioners who aim to understand the model's learning mechanisms and traffic domain experts who seek insights for necessity factors to alter traffic condition. The results showcase the effectiveness of counterfactual explanations in revealing traffic patterns learned by deep learning models and explaining the relationship between traffic prediction and contextual features, demonstrating its potential for interpreting black-box deep learning models.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100176"},"PeriodicalIF":12.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800692","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":"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":"2025-04-01","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":"FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation","authors":"Guanyu Lin ,&nbsp;Sean Qian ,&nbsp;Zulqarnain H. Khattak","doi":"10.1016/j.commtr.2025.100175","DOIUrl":"10.1016/j.commtr.2025.100175","url":null,"abstract":"<div><div>Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication and automation enables cooperative driving, it also introduces new cybersecurity threats. This study introduces a federated learning concept for autonomous and connected vehicles, known as the federated agents on vehicle platooning (FedAV) framework, which is designed to address the challenges of cyberattack simulations and anomaly detection in cooperative vehicle platooning systems. The federated learning approach was adopted because of its decentralized nature, which allows each vehicle to learn independently with the ability to overcome adversarial attacks. First, FedAV employs a mixed cyberattack simulation approach to capture complex attack patterns effectively. We tested the scalability of our approach against several attacks, including spoofing, message falsification, and replay attacks, as well as on anomalies, including short anomalies, noise anomalies, bias anomalies, and gradual shifts. In addition, our approach integrates federated learning for decentralized anomaly detection, ensuring data privacy and reducing communication overhead. The anomaly detection performance was enhanced by average and weighted aggregation strategies. Real-world scenarios from cooperative driving experiments and simulations validated the framework's effectiveness and demonstrated its potential to improve the safety, privacy, and efficiency of CDA.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100175"},"PeriodicalIF":12.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748613","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":"Sustainable operational strategies for mixed fleets: Integrating autonomous and human-driven taxis with heterogeneous energy types","authors":"Qinru Hu ,&nbsp;Beinuo Yang ,&nbsp;Keyang Zhang ,&nbsp;Jose Escribano Macias ,&nbsp;Xiqun (Michael) Chen ,&nbsp;Yanfeng Ouyang ,&nbsp;Simon Hu","doi":"10.1016/j.commtr.2025.100171","DOIUrl":"10.1016/j.commtr.2025.100171","url":null,"abstract":"<div><div>Taxi systems are transitioning into a complex integration of autonomous and human-driven vehicles powered by heterogeneous energy sources. Traditional operational strategies designed for homogeneous fleets fail to capture the unique dynamics and interactions present in mixed fleets. To address this gap, this study proposes a comprehensive modeling and simulation framework for the dynamic operation of mixed taxi fleets, including autonomous electric taxis (AETs), human-driven electric taxis, and human-driven gasoline taxis. The framework integrates centralized and decentralized control mechanisms to address the distinct characteristics of each taxi type. An integer linear programming model is developed to optimize taxi assignment and scheduling, with the objective of maximizing system profits by accounting for customer service revenues and energy and travel costs. An agent-based simulation platform is designed to model dynamic interactions among taxis, customers, and charging stations, offering continuous feedback on system performance. Real-world case studies reveal significant environmental, economic, and social benefits when incorporating operating costs into decision-making. Impact analyses demonstrate the competitiveness of AETs in passenger service due to lower operating costs and enhanced environmental efficiency, with reduced carbon emission intensity per kilometer and per request. This study provides valuable insights for taxi platforms and policymakers in formulating strategies that promote sustainable urban mobility during the ongoing transition period.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100171"},"PeriodicalIF":12.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748612","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":"Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency","authors":"Jiangbo Yu ,&nbsp;Jinhua Zhao ,&nbsp;Luis Miranda-Moreno ,&nbsp;Matthew Korp","doi":"10.1016/j.commtr.2025.100172","DOIUrl":"10.1016/j.commtr.2025.100172","url":null,"abstract":"<div><div>Surveys and interviews—structured, semi-structured, or unstructured—are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. For example, distributed questionnaires lack the ability to provide real-time guidance and request immediate clarifications. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by generative artificial intelligence (AI) technologies. However, considering decisions in transportation investments and policies often carry significant socioeconomic and environmental consequences, surveys and interviews face unique challenges in integrating AI agents. This issue underscors the need for a rigorous, explainable, and resource-efficient approach that enhances participant engagement and ensures privacy. This paper bridges this gap by introducing a modular approach accompanied by a parameterized process for designing and deploying AI agents for surveys and interviews, thereby supporting decision-makings in high-stakes contexts. We detail the system architecture, integrating engineered prompts, specialized knowledge bases, and customizable, goal-oriented conversational logic. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies: (1) travel preference surveys, highlighting conditional logic and multimodal (voice, text, and image generation) capabilities; (2) public opinion elicitation on a newly constructed, novel infrastructure project, showcasing question customization and multilingual (English and French) capabilities; and (3) expert consultations about the impact of technologies on future transportation systems, highlighting real-time, clarification request capabilities for open-ended questions, resilience in handling erratic inputs, and efficient transcript postprocessing. The results suggest that the AI agent increases completion rates and response quality. Furthermore, the modular approach demonstrates controllability, flexibility, and robustness while addressing key ethical, privacy, security, and token consumption concerns. We believe this work lays the foundation for next-generation surveys and interviews in transportation research.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100172"},"PeriodicalIF":12.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683927","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":"Fuel- and noise-minimal departure trajectory using deep reinforcement learning with aircraft dynamics and topography constraints","authors":"Chris HC. Nguyen ,&nbsp;James M. Shihua ,&nbsp;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":"2025-03-12","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}

{"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":"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}