Communications in Transportation Research最新文献

The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories 自动驾驶汽车的基本图：交通状态估计和车辆轨迹证据

IF 14.5

Communications in Transportation Research Pub Date : 2025-10-10 DOI: 10.1016/j.commtr.2025.100212

Michail A. Makridis , Shaimaa K. El-Baklish , Anastasios Kouvelas , Jorge A. Laval

{"title":"The fundamental diagram of autonomous vehicles: Traffic state estimation and evidence from vehicle trajectories","authors":"Michail A. Makridis , Shaimaa K. El-Baklish , Anastasios Kouvelas , 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":"2025-10-10","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}

引用次数: 0

Minimum-delay opportunity charging scheduling for electricbuses 电动公交车最小延迟机会充电调度

IF 14.5

Communications in Transportation Research Pub Date : 2025-10-03 DOI: 10.1016/j.commtr.2025.100209

Dan McCabe , Xuegang (Jeff) Ban , Balaźs Kulcsár

{"title":"Minimum-delay opportunity charging scheduling for electricbuses","authors":"Dan McCabe , Xuegang (Jeff) Ban , 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":"2025-10-03","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}

引用次数: 0

Flying cars and urban air mobility: Redefining cities in three dimensions 飞行汽车和城市空中交通：在三维空间重新定义城市

IF 14.5

Communications in Transportation Research Pub Date : 2025-10-01 DOI: 10.1016/j.commtr.2025.100213

Di Lv , Kai Wang , Shulu Chen , Xiaobo Qu

引用次数: 0

A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models 一种基于知识的深度学习范式，用于泛化和稳定性优化的汽车跟随模型

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-20 DOI: 10.1016/j.commtr.2025.100211

Chengming Wang , Dongyao Jia , Wei Wang , Dong Ngoduy , Bei Peng , Jianping Wang

{"title":"A knowledge-informed deep learning paradigm for generaliz-able and stability-optimized car-following models","authors":"Chengming Wang , Dongyao Jia , Wei Wang , Dong Ngoduy , Bei Peng , Jianping Wang","doi":"10.1016/j.commtr.2025.100211","DOIUrl":"10.1016/j.commtr.2025.100211","url":null,"abstract":"<div><div>Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific datasets limits generalization across diverse scenarios and reduces reliability in real-world deployment. In addition to behavioral fidelity, ensuring traffic stability is increasingly critical for the safe and efficient operation of autonomous vehicles (AVs), requiring CFMs that jointly address both objectives. However, existing models generally do not support a systematic integration of these goals. To bridge this gap, we propose a knowledge-informed deep learning (KIDL) paradigm that distills the generalization capabilities of pre-trained large language models (LLMs) into a lightweight and stability-aware neural architecture. LLMs are used to extract fundamental car-following knowledge beyond dataset-specific patterns, and this knowledge is transferred to a reliable, tractable, and computationally efficient model through knowledge distillation. KIDL also incorporates stability constraints directly into its training objective, ensuring that the resulting model not only emulates human-like behavior but also satisfies the local and string stability requirements essential for real-world AV deployment. We evaluate KIDL on the real-world NGSIM and HighD datasets, comparing its performance with representative physics-based, data-driven, and hybrid CFMs. Both empirical and theoretical results consistently demonstrate KIDL’s superior behavioral generalization and traffic flow stability, offering a robust and scalable solution for next-generation traffic systems.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100211"},"PeriodicalIF":14.5,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099058","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

Hierarchical Bayesian threshold excess model for real-time vehicle-based conflict prediction in dynamic traffic environ-ments 动态交通环境下基于车辆冲突实时预测的层次贝叶斯阈值过剩模型

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-18 DOI: 10.1016/j.commtr.2025.100210

Léah Camarcat , Yuxiang Feng , Nicolette Formosa , Mohammed Quddus

{"title":"Hierarchical Bayesian threshold excess model for real-time vehicle-based conflict prediction in dynamic traffic environ-ments","authors":"Léah Camarcat , Yuxiang Feng , Nicolette Formosa , Mohammed Quddus","doi":"10.1016/j.commtr.2025.100210","DOIUrl":"10.1016/j.commtr.2025.100210","url":null,"abstract":"<div><div>Vehicle-based collision risk assessment methods often exhibit a tradeoff between simplifying assumptions in physics-based models and the interpretability challenges of learning algorithms. To tackle this, methods based on Extreme Value Theory (EVT) have gained momentum in recent years, but there is a lack of studies employing EVT for vehicle-based applications. This paper proposes a new, context-aware conflict prediction algorithm using a hierarchical Bayesian threshold excess model. Contextual traffic data are integrated with vehicle sensor data to improve the robustness and accuracy of the model. The feasibility of real-time deployment is also examined by optimising computational efficiency, leveraging several implementations of the Hamiltonian Monte Carlo No-U-Turn Solver (NUTS). The results demonstrate that including traffic covariates improves the model goodness-of-fit by 4.80% in terms of Deviance Information Criterion, and generalisability with a decrease of 1.36% in mean absolute error. However, partially pooled models, while enhancing goodness-of-fit, result in a reduction of generalisation capabilities. Additionally, the No-U-Turn Sampler compiled in JAX demonstrated sufficient performance for both online training and inference, thus making this methodology a feasible solution for real-time deployment in vehicle-based applications.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100210"},"PeriodicalIF":14.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099055","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

Perception strategies in low-altitude transportation: Single aircraft autonomous system vs. aircraft-ground-cloud integration system 低空运输中的感知策略：单架飞机自主系统与飞机-地面-云集成系统

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-11 DOI: 10.1016/j.commtr.2025.100208

Yuhao Wang , Kai Wang , Jing Gong , Xiaobo Qu

引用次数: 0

Privacy-preserving personalized pricing and matching for ride hailing platforms 为网约车平台提供保护隐私的个性化定价和匹配

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-11 DOI: 10.1016/j.commtr.2025.100205

Bing Song, Sisi Jian

{"title":"Privacy-preserving personalized pricing and matching for ride hailing platforms","authors":"Bing Song, Sisi Jian","doi":"10.1016/j.commtr.2025.100205","DOIUrl":"10.1016/j.commtr.2025.100205","url":null,"abstract":"<div><div>This research addresses the growing concern of balancing personalized services with data privacy in the ride-hailing industry. While personalized pricing and matching strategies, fueled by travelers’ personal data, can optimize platform revenue, they also expose users and platforms to significant privacy risks. The correlation between personalized pricing, waiting times, and personal information might be exploited by third-party agents to infer sensitive user attributes, resulting in potential economic losses for the platform and severe consequences for users, including compromised privacy and potential discrimination. Existing privacy protection methods often fall short in providing robust and quantifiable guarantees. To overcome these limitations, this study introduces a privacy-preserving approach for personalized pricing and matching within ride-hailing platforms. The proposed approach leverages the bounded Laplace (BL) mechanism and parallel composition to inject noise into the order price and waiting time feedback provided to travelers. This study rigorously demonstrates that the proposed approach satisfies differential privacy. Furthermore, the proposed approach outperforms other classic privacy-preserving methods in terms of platform revenue. This superior performance is validated through extensive numerical experiments using realistic ride-hailing data.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100205"},"PeriodicalIF":14.5,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048123","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

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 : 2025-09-08 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":"2025-09-08","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

Safety assurance adaptive control for modular autonomous vehicles 模块化自动驾驶汽车的安全保障自适应控制

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-03 DOI: 10.1016/j.commtr.2025.100204

Chengyuan Ma, Hang Zhou, Peng Zhang, Ke Ma, Haotian Shi, Xiaopeng Li

{"title":"Safety assurance adaptive control for modular autonomous vehicles","authors":"Chengyuan Ma, Hang Zhou, Peng Zhang, Ke Ma, Haotian Shi, Xiaopeng Li","doi":"10.1016/j.commtr.2025.100204","DOIUrl":"10.1016/j.commtr.2025.100204","url":null,"abstract":"<div><div>Recent studies and industry developments indicate that modular autonomous vehicles (MAVs) have the potential to enhance transportation systems by offering vehicles with adjustable capacities en route. However, the practical realization of reliable control during docking/undocking operations remains a significant challenge, primarily due to safety concerns arising from the close proximity of MAVs. This study proposes a safety assurance adaptive model predictive control (SAAMPC) framework to achieve distributed docking/undocking operations for MAVs in uncertain environments. The SAAMPC framework integrates a model predictive control (MPC) controller for trajectory optimization, an adaptive module for dynamic adjustment of control parameters with disturbance, and an adaptive safety assurance module with longitudinal and lateral control barrier functions (CFB) to ensure safe operation during risky and uncertain conditions. The effectiveness of the proposed approach is validated through simulations in Simulink and field tests on a reduced-scale MAV platform. Experimental results validate that the SAAMPC framework successfully ensures smooth and safe vehicle following and robust execution of docking/undocking operations under uncertainties.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100204"},"PeriodicalIF":14.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932209","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

Cross-city transfer learning: Applications and challenges for smart cities and sustainable transportation 跨城市迁移学习：智慧城市和可持续交通的应用与挑战

IF 14.5

Communications in Transportation Research Pub Date : 2025-09-03 DOI: 10.1016/j.commtr.2025.100206

Ying Yang , Jiahao Zhan , Yang Liu , Qi Wang

引用次数: 0