Communications in Transportation Research最新文献

{"title":"Physical enhanced residual learning (PERL) framework for vehicle trajectory prediction","authors":"Keke Long,&nbsp;Zihao Sheng,&nbsp;Haotian Shi,&nbsp;Xiaopeng Li,&nbsp;Sikai Chen,&nbsp;Soyoung Ahn","doi":"10.1016/j.commtr.2025.100166","DOIUrl":"10.1016/j.commtr.2025.100166","url":null,"abstract":"<div><div>While physics models for predicting system states can reveal fundamental insights owing to their parsimonious structure, they may not always yield the most accurate predictions, particularly for complex systems. As an alternative, neural network (NN) models usually yield more accurate predictions; however, they lack interpretable physical insights. To articulate the advantages of both physics and NN models while circumventing their limitations, this study proposes a physics-enhanced residual learning (PERL) framework that adjusts a physics model prediction with a corrective residual predicted from a residual learning NN model. The integration of the physics model preserves interpretability and tremendously reduces the amount of training data compared with pure NN models. We apply PERL to a vehicle trajectory prediction problem with real-world trajectory data of both a human-driven vehicle (HV) and an autonomous vehicle (AV), using an adapted Newell car-following model as the physics model and four kinds of neural networks (Gated Recurrent Unit (GRU), Convolution long short-term memory (CLSTM), Variational Autoencoder (VAE), and the Informer model) as the residual learning model. We compare this PERL model with pure physics models, NN models, and other physics-informed neural network (PINN) models. The results reveal that PERL yields the best prediction when the training data are small. The PERL model converges quickly during training. Moreover, compared with the NN and PINN models, the PERL model requires fewer parameters to achieve similar predictive performance. A sensitivity analysis revealed that the PERL model consistently outperforms the physics models, NN models and PINN models with different physics and residual learning models given a small training dataset. Among these, the PERL model based on CLSTM achieved the most accurate predictions.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100166"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825944","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":"The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories","authors":"Michail A. Makridis ,&nbsp;Shaimaa K. El-Baklish ,&nbsp;Anastasios Kouvelas ,&nbsp;Jorge A. Laval","doi":"10.1016/j.commtr.2025.100212","DOIUrl":"10.1016/j.commtr.2025.100212","url":null,"abstract":"<div><div>The fundamental diagram (FD) is a key tool in traffic flow theory, describing the relationship between traffic flow and density at the link level. Traditionally, FD estimation relies on data from static sensors, although vehicle trajectory data provides an alternative approach. Driver heterogeneity strongly influences the shape and scatter of the FD and is crucial for traffic management. Autonomous vehicles (AVs), exhibiting distinct driving behavior from human drivers, are expected to alter the FD. However, limited observations of AVs in stationary conditions have constrained research in this area. This study addresses this gap by introducing the platoon fundamental diagram (PFD), a simple method to infer empirical FDs from platoon trajectory data. PFD derives pseudo-states from vehicle trajectories and aggregates them to capture consistent relationships between flow, density, and speed—without requiring stationary conditions or backward wave speed estimation. The results highlight the impact of AVs on traffic flow capacity, driver heterogeneity, and oscillation propagation. Comparative analysis with human-driven experiments provides additional insights. Furthermore, the PFD's potential as a practical tool for traffic state estimation in mixed traffic conditions is demonstrated through real-world applications using NGSIM and I–24 Motion datasets.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100212"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264828","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":"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":"2025-12-01","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":"MoTIF: An end-to-end multimodal road traffic scene understanding foundation model","authors":"Zihe Wang ,&nbsp;Haiyang Yu ,&nbsp;Changxin Chen ,&nbsp;Zhiyong Cui ,&nbsp;Yufeng Bi ,&nbsp;Yilong Ren ,&nbsp;Zijian Wang ,&nbsp;Delan Kong ,&nbsp;Jing Tian ,&nbsp;Shoutong Yuan ,&nbsp;Zhiqiang Li","doi":"10.1016/j.commtr.2025.100227","DOIUrl":"10.1016/j.commtr.2025.100227","url":null,"abstract":"<div><div>Video-based road intelligent detection constitutes a critical component in modern intelligent transportation systems, serving as a crucial role for comprehensive transportation planning and emergency traffic management. Current traffic scene perception methodologies relying on conventional deep learning architectures present inherent limitations, including heavy dependence on extensive manual annotations of specific traffic scenarios and predefined rule configurations. These approaches demonstrate constrained semantic representation capacity and limited generalizability across heterogeneous traffic scenarios. To address these challenges, this study proposes a novel end-to-end multimodal foundation model architecture that jointly generates dynamic traffic event detection outcomes and semantic-rich contextual descriptions. Through integration of low-rank adaptation (LoRA) and prompt fine-tuning as parameter-efficient fine-tuning strategies, we develop the multimodal road traffic scene understanding foundation model (MoTIF), which establishes cross-modal alignment between visual patterns and textual semantics. This framework demonstrates enhanced capability in extracting salient traffic targets and generating hierarchical scene representations, significantly improving automated detection efficiency in road video analytics. Notably, MoTIF exhibits contextual reasoning capabilities for implicit traffic event interpretation. Extensive evaluations on two real-world datasets encompassing urban road intersection scenarios in Tianjin and highway monitoring systems in Shandong Province reveal that MoTIF achieves superior performance metrics: 65.81 average score on multimodal scene understanding assessment and 83.33% event detection accuracy, outperforming mainstream benchmarks in both precision and computational efficiency. This research advances multimodal learning paradigms for intelligent transportation systems while providing practical insights for adaptive traffic management applications.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100227"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736818","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":"Optimal speed limit under multi-class user equilibrium: A prescriptive approach using mathematical programming","authors":"Xiao Lin ,&nbsp;Ludovic Leclercq ,&nbsp;Lóránt Tavasszy","doi":"10.1016/j.commtr.2025.100221","DOIUrl":"10.1016/j.commtr.2025.100221","url":null,"abstract":"<div><div>In practice, speed limits on road networks are often determined pragmatically, which can give suboptimal solutions for traffic performance and unfair results for the underlying user classes. This study presents an elegant approach to determine optimal speed limits on a traffic network with asymmetric user classes under congested conditions, that minimizes individual user travel time and does justice to differences in economic importance. Existing prescriptive approaches typically lack one or more of these features, cannot guarantee optimality or are difficult to solve. We formulate a new prescriptive method using mixed-integer quadratic programming. The model can be solved with well-established operation research approaches and commercial solvers such as Cplex or Gurobi. To demonstrate the approach, we apply it to a regional network in the Netherlands. The result shows a reduction of travel time of passenger cars by 6% and of trucks by 13%, with mild changes in speed limits compared to the base situation, of between −20% and +10%. The speed limit changes and impacts are in line with the relatively high economic importance of freight traffic. Also we find in this case that the speed limit changes are ordered by major routes through the network, which makes implementation relatively straightforward.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100221"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519371","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":"Minimum-delay opportunity charging scheduling for electricbuses","authors":"Dan McCabe ,&nbsp;Xuegang (Jeff) Ban ,&nbsp;Balaźs Kulcsár","doi":"10.1016/j.commtr.2025.100209","DOIUrl":"10.1016/j.commtr.2025.100209","url":null,"abstract":"<div><div>Transit agencies that operate battery-electric buses must carefully manage fast-charging infrastructure to extend daily bus range without degrading on-time performance. To support this need, we propose a mixed-integer linear programming model to schedule opportunity charging that minimizes the amount of departure delay in all trips served by electric buses. Our novel approach directly tracks queuing at chargers in order to set and propagate departure delays. Allowing but minimizing delays makes it possible to optimize performance when delays due to traffic conditions and charging needs are inevitable, in contrast with existing methods that require charging to occur during scheduled layover time. To solve the model, we develop two algorithms based on decomposition. The first is an exact solution method based on combinatorial Benders (CB) decomposition, which avoids directly enumerating the model’s logic-based “big M” constraints and their inevitable computational challenges. The second, inspired by the CB approach but more efficient, is a polynomial-time heuristic based on linear programming that we call Select–Sequence–Schedule (3S). Computational experiments on both a simple notional transit network and the real bus system of King County, Washington, USA demonstrate the performance of both methods. The 3S method appears particularly promising for creating good charging schedules quickly at real-world scale.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100209"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219485","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":"Towards fair lights: A multi-agent masked deep reinforcement learning for efficient corridor-level traffic signal control","authors":"Xiaocai Zhang,&nbsp;Lok Sang Chan,&nbsp;Neema Nassir,&nbsp;Majid Sarvi","doi":"10.1016/j.commtr.2025.100203","DOIUrl":"10.1016/j.commtr.2025.100203","url":null,"abstract":"<div><div>This study presents an adaptive traffic signal control (ATSC) method for managing multiple intersections at the corridor level by proposing a novel multi-agent masked deep reinforcement learning (DRL) framework. The method extends the hybrid soft-actor-critic architecture to optimize green light timings for intersections across a corridor network, fostering a balance between vehicle flow and pedestrian movements with an emphasis on humanism, fairness, and equality. By integrating an innovative phase mask mechanism, our model dynamically adapts to the fluctuating demand of different transportation modalities by discovering new states or actions that could avoid local optima and achieve higher rewards. We comprehensively test our method using five naturalistic traffic scenarios in Melbourne, Australia. The results demonstrate a significant improvement in reducing the number of impacted travellers compared to existing DRL and other baseline methods. Furthermore, the inclusion of the phase mask mechanism enhances our model's performance through ablation analyses. The proposed framework not only supports a fairer traffic signal system but also provides a scalable, adaptable solution for diverse urban traffic conditions. .</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100203"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826415","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":"A sustainable multi-objective framework for multi-phased, capacitated vertiport siting with land use integration","authors":"Hao Wu ,&nbsp;Shahriar Iqbal Zame ,&nbsp;Tao Guo ,&nbsp;Qing-Long Lu ,&nbsp;Constantinos Antoniou","doi":"10.1016/j.commtr.2025.100186","DOIUrl":"10.1016/j.commtr.2025.100186","url":null,"abstract":"<div><div>This research presents a multi-objective optimization framework for incremental siting of capacitated vertiports that integrates real land use data and aims to maximize generalized cost savings while minimizing infrastructure costs and emissions. The multi-phased siting framework uniquely facilitates the gradual evolution of Urban Air Mobility (UAM) operations from initial electric vertical takeoff and landing vehicles (eVTOLs) to more advanced modular flying vehicles (MFVs). This phased technological progression provides a practical pathway toward fully operational flying cars while ensuring feasible infrastructure adaptability across these transitions. Applied to the Munich metropolitan area, the framework demonstrates that multi-phased siting, particularly a 4-phased strategy, yielding about 1.315 × 10⁵ euros higher daily net profits. Specifically, compared to base single-phased approach, the 4-phased strategy delivers substantial marginal improvements across key metrics for an exemplary operating day: 1.3 × 10⁴ euros in generalized travel cost savings, 15 ​t in emissions reductions, and a 0.9% increase in UAM mode share. Beyond four phases, the benefits diminish relative to increased complexity. A full factorial analysis examining capacity constraints and infrastructure costs reveals that ignoring either factor leads to impractical outcomes-unconstrained capacity results in demand exceeding 60-fold capacity, while disregarding infrastructure costs generates negative net profits due to overinvestment. The analysis identifies an optimal infrastructure cost subsidy range of 20%–40%, balancing performance gains with economic sustainability. These findings enable integrated planning that effectively balances operational efficiency, system-wide environmental externalities, and economic viability through optimized cost allocation and phased investment strategies.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100186"},"PeriodicalIF":12.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312865","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":"Interaction dataset of autonomous vehicles with traffic lights and signs","authors":"Zheng Li ,&nbsp;Zhipeng Bao ,&nbsp;Haoming Meng ,&nbsp;Haotian Shi ,&nbsp;Qianwen Li ,&nbsp;Handong Yao ,&nbsp;Xiaopeng Li","doi":"10.1016/j.commtr.2025.100201","DOIUrl":"10.1016/j.commtr.2025.100201","url":null,"abstract":"<div><div>This study presents the development of a comprehensive dataset capturing interactions between autonomous vehicles (AVs) and traffic control devices, specifically traffic lights and stop signs. Derived from the Waymo Motion dataset, our work addresses a critical gap in the existing literature by providing real-world trajectory data on how AVs navigate these traffic control devices. We propose a methodology for identifying and extracting relevant interaction trajectory data from the Waymo Motion dataset, incorporating over 37,000 instances with traffic lights and 44,000 with stop signs. Our methodology includes defining rules to identify various interaction types, extracting trajectory data, and applying a wavelet-based denoising method to smooth the acceleration and speed profiles and eliminate anomalous values, thereby enhancing the trajectory quality. Quality assessment metrics indicate that trajectories obtained in this study have anomaly proportions in acceleration and jerk profiles reduced to near-zero levels across all interaction categories. By making this dataset publicly available, we aim to address the current gap in datasets containing AV interaction behaviors with traffic lights and signs. Based on the organized and published dataset, we can gain a more in-depth understanding of AVs’ behavior when interacting with traffic lights and signs. This will facilitate research on <span>AV</span> integration into existing transportation infrastructures and networks, supporting the development of more accurate behavioral models and simulation tools.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100201"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888779","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":"Efficient and stable ride-pooling through a multi-level coalition formation game","authors":"Yaotian Tan ,&nbsp;Shuyue Qian ,&nbsp;Aoyong Li ,&nbsp;Haiyang Yu ,&nbsp;Jie Gao","doi":"10.1016/j.commtr.2025.100220","DOIUrl":"10.1016/j.commtr.2025.100220","url":null,"abstract":"<div><div>Ride-pooling has the potential to offer a sustainable solution for urban mobility by reducing vehicle use and emissions through shared trips. However, its adoption remains limited due to poor matching performance. Many requests fail to form feasible pools, and even successful matches often involve long detours or minimal cost savings. These inefficiencies largely arise from fragmented market structures: most operators act independently, restricting matching to their own request pools and limiting the formation of beneficial coalitions. Aggregation platforms improve efficiency by integrating regional operators through unified dispatch systems, but raise concerns over long-term stability. Differences in operator cost structures and market shares may incentivize deviation, at the same time, passengers may reject assigned payments if more attractive alternatives exist. To address these challenges, we propose a multi-level coalition formation game that jointly models operator and passenger collaboration. At the upper level, operators play a non-cooperative game to decide coalition partners. At the lower level, passengers are grouped into shared trips through a cooperative game that ensures individually rational payments. The two layers are coupled via constraint propagation, forming a unified decision-making process. We evaluate our framework using real-world data from three Chinese regions—Chengdu, Haikou, and the Ningxia Hui Autonomous Region—chosen to reflect diverse urban and regional contexts. Compared to independent operations, our approach increases vehicle occupancy by 14%–28%, reduces total costs by 10%–15%, and shortens average travel distances by 4%–5%. The system maintains stable coalition structures with operator deviation rates below 6.81% and near-zero passenger deviation rates.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100220"},"PeriodicalIF":14.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571110","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}