Communications in Transportation Research最新文献

{"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":"2025-08-22","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":"Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios","authors":"Zhanwen Liu ,&nbsp;Yujing Sun ,&nbsp;Yang Wang ,&nbsp;Nan Yang ,&nbsp;Shengbo Eben Li ,&nbsp;Xiangmo Zhao","doi":"10.1016/j.commtr.2025.100202","DOIUrl":"10.1016/j.commtr.2025.100202","url":null,"abstract":"<div><div>The dynamic range limitation is intrinsic to conventional RGB cameras, which reduces global contrast and causes the loss of high-frequency details such as textures and edges in complex, dynamic traffic environments (e.g., nighttime driving or tunnel scenes). This deficiency hinders the extraction of discriminative features and degrades the performance of frame-based traffic object detection. To address this problem, we introduce a bio-inspired event camera integrated with an RGB camera to complement high dynamic range information, and propose a motion cue fusion network (MCFNet), an innovative fusion network that optimally achieves spatiotemporal alignment and develops an adaptive strategy for cross-modal feature fusion, to overcome performance degradation under challenging lighting conditions. Specifically, we design an event correction module (ECM) that temporally aligns asynchronous event streams with their corresponding image frames through optical-flow-based warping. The ECM is jointly optimized with the downstream object detection network to learn task-ware event representations. Subsequently, the event dynamic upsampling module (EDUM) enhances the spatial resolution of event frames to align its distribution with the structures of image pixels, achieving precise spatiotemporal alignment. Finally, the cross-modal mamba fusion module (CMM) employs adaptive feature fusion through a novel cross-modal interlaced scanning mechanism, effectively integrating complementary information for robust detection performance. Experiments conducted on the DSEC-Det and PKU-DAVIS-SOD datasets demonstrate that MCFNet significantly outperforms existing methods in various poor lighting and fast moving traffic scenarios. Notably, on the DSEC-Det dataset, MCFNet achieves a remarkable improvement, surpassing the best existing methods by 7.4% in mAP50 and 1.7% in mAP metrics, respectively.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100202"},"PeriodicalIF":14.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863598","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":"2025-08-11","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":"Enhanced trajectory reconstruction from sparse and noisy GPS data: A progressive chunked transformer approach","authors":"Yonghui Liu ,&nbsp;Qian Li ,&nbsp;Inhi Kim","doi":"10.1016/j.commtr.2025.100200","DOIUrl":"10.1016/j.commtr.2025.100200","url":null,"abstract":"<div><div>Trajectory reconstruction from sparse and noisy GPS data is critical for applications such as urban mobility analysis, transportation planning, and navigation systems. However, large sampling intervals and the typically long output sequences required to reconstruct coherent travel trajectories significantly increase computational complexity, particularly in the presence of noise. To address these challenges, we propose a progressive chunked transformer (ProChunkFormer), which is a deep learning method for trajectory reconstruction that employs self-attention mechanisms and chunked processing to balance efficiency with accuracy. ProChunkFormer first generates intermediate trajectories at a semi-high frequency from low-frequency sampled data, and then the remaining trajectory is divided into manageable blocks and reconstructed parallelly in the condition of the semi-high-frequency trajectory. By combining progressive reconstruction with chunk processing, ProChunkFormer not only mitigates the cumulative errors commonly observed in autoregressive models but also alleviates the rapid increase in complexity associated with reconstructing ultralong trajectories. Specifically, our approach achieves quadratic optimization in time and space for attention modules, with cubic time savings compared with autoregressive decoding. A case study using an open-source taxi trajectory dataset confirms the effectiveness of our approach. The performance of ProChunkFormer is comparable to that of autoregressive transformers while offering better running efficiency. It improves the accuracy, F1 score (F1), mean absolute error (MAE), and road network mean absolute error (MAE_RN) by 23.1%, 18.6%, 22.3%, and 25.1%, respectively, for trajectories with a long interval time of up to 240 ​s. Furthermore, we investigate incorporating heuristic information to guide trajectory reconstruction for each block. The experimental results indicate an improvement in both the overall performance and convergence speed of the model.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100200"},"PeriodicalIF":14.5,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757300","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":"CCDSReFormer: Traffic flow prediction with a criss-crossed dual-stream enhanced rectified transformer model","authors":"Zhiqi Shao ,&nbsp;Michael G.H. Bell ,&nbsp;Ze Wang ,&nbsp;D. Glenn Geers ,&nbsp;Xusheng Yao ,&nbsp;Junbin Gao","doi":"10.1016/j.commtr.2025.100189","DOIUrl":"10.1016/j.commtr.2025.100189","url":null,"abstract":"<div><div>Accurate, efficient, and rapid traffic forecasting is essential for intelligent transportation systems and plays a pivotal role in urban traffic planning, management, and control. While existing spatiotemporal transformer models have demonstrated effectiveness in traffic flow prediction, they face notable challenges in achieving a balance between computational efficiency and accuracy. Additionally, they often prioritize global trends over local time series information and treat spatial and temporal data separately, limiting their ability to capture complex spatiotemporal interactions. To overcome these limitations, we propose the criss-crossed dual-stream enhanced rectified transformer (CCDSReFormer). This model introduces a novel rectified linear self-attention (ReLSA) mechanism combined with enhanced convolution (EnCov) to reduce computational overhead and sharpen the local feature focus. Furthermore, our cross-learning strategy seamlessly integrates spatial and temporal data, improving the model's ability to capture intricate traffic dynamics. Extensive experiments on six real-world datasets show that CCDSReFormer outperforms existing models in both accuracy and efficiency. An ablation study further validates the contributions of each component, confirming the model's superior ability to forecast traffic flow accurately and efficiently.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100189"},"PeriodicalIF":12.5,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686069","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":"Advancing transportation research: interdisciplinary insights from emerging technologies and diverse perspectives","authors":"Mingyang Pei ,&nbsp;Zhuoyan Wei ,&nbsp;Xin Pei ,&nbsp;Yu Zhang ,&nbsp;Xiaokun Cara Wang ,&nbsp;Yang Liu ,&nbsp;Ronghui Liu","doi":"10.1016/j.commtr.2025.100199","DOIUrl":"10.1016/j.commtr.2025.100199","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100199"},"PeriodicalIF":12.5,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680778","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":"Compensation scheme and split delivery in a collaborative passenger-parcel transportation system","authors":"Yitong Yu,&nbsp;Kechen Ouyang,&nbsp;Qingyun Tian,&nbsp;David Z.W. Wang","doi":"10.1016/j.commtr.2025.100197","DOIUrl":"10.1016/j.commtr.2025.100197","url":null,"abstract":"<div><div>The emerging collaborative passenger-parcel transport (CPT) mode aims to address the significant imbalance between passenger and parcel transport demand for last-mile delivery. By enabling passengers and parcels to share a single vehicle’s capacity, CPT reduces resource underutilization during off-peak hours and alleviates traffic congestion during peak hours. However, the successful implementation of such systems is not guaranteed, as passengers may decline shared rides due to reduced service quality. Compensation mechanisms, which incentivize passengers’ acceptance, offer a promising solution to such an issue. However, the design of optimal compensation scheme has not yet been investigated in the existing literature of collaborative transport. To fill this gap, this study incorporates compensation-affected behavior into a typical routing problem of the CPT system, where the routing problem allows delivery requests to be split across multiple trips and permits multiple visits to each node. We formulate this problem as the compensation scheme design in split delivery vehicle routing problem with time windows for a collaborative passenger-parcel transport system (C-SDVRPTW-CPT). We solve it by developing a Surrogate-based Adaptive Large Neighborhood Search framework (SOT-ALNS). Numerical experiments validate the model and algorithm, demonstrating the fast convergence of the algorithm and the advantages of collaborative transport and compensation, which improves profit by 3%–10%.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100197"},"PeriodicalIF":12.5,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662810","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":"2025-07-18","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":"A hybrid centralized-decentralized traffic control framework for unmanned aerial vehicles in urban low-altitude airspace","authors":"Xiangdong Chen ,&nbsp;Shen Li ,&nbsp;Meng Li","doi":"10.1016/j.commtr.2025.100195","DOIUrl":"10.1016/j.commtr.2025.100195","url":null,"abstract":"<div><div>Urban air mobility (UAM) represents a transformative approach to alleviating ground-level congestion by transitioning from two-dimension (2D) to three-dimension (3D) transportation systems. Envisioned as a safe, sustainable, and efficient mode of urban transit, UAM leverages aerial space to reduce dependence on traditional road infrastructure while addressing traffic congestion challenges in urban mobility. However, the rapid growth in aerospace transportation demand, coupled with the complexity of managing large-scale unmanned aerial vehicle (UAV) operations in 3D airspace, challenges the effectiveness of traditional traffic management systems. To address these challenges, this study proposes a hybrid framework for UAV air traffic control that integrates centralized and decentralized approaches. A 3D air traffic network is modeled in low-altitude airspace, capturing detailed 2D and 3D conflict relationships. The concept of a “virtual flight container” (VFC) is introduced to regulate UAV space–time trajectories, ensuring conflict-free, low-delay operations while minimizing real-time computational requirements, especially in high demands. The problem is addressed using a bi-level optimization approach: The upper level focuses on solving the traffic assignment problem, considering airway capacity constraints, while the lower level designs space–time trajectories to ensure conflict-free operations and enhance traffic efficiency, thereby complementing the traffic control scheme. Numerical experiments validate the proposed framework, highlighting its effectiveness in improving traffic efficiency and network throughput. Key insights are provided regarding the role of network structure, the placement of take-off and landing points, and control parameters in optimizing UAM operations.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100195"},"PeriodicalIF":12.5,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654928","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":"Should autonomous vehicles be subsidized to reduce parking fees? A productivity perspective","authors":"Yao Li ,&nbsp;Ziyue Yang ,&nbsp;Tao Wang ,&nbsp;Shuxian Xu ,&nbsp;Jiancheng Long","doi":"10.1016/j.commtr.2025.100196","DOIUrl":"10.1016/j.commtr.2025.100196","url":null,"abstract":"<div><div>Governments often advocate for and implement policies to promote the development of new technologies, such as electric vehicles. Are these policies promoting new mobility modes applicable to autonomous vehicles (AVs)? In this study, we develop an economic model to capture residents' behaviors, including mode choice, location choice, and parking choice. Two parking choices (parking downtown or at home) for AV users are considered. We construct utility maximization models under a user equilibrium state to capture government planning and residents' choices. By deriving the first-order conditions of the model, we analyze the influence of AVs on urban characteristics. We emphasize how the parking subsidy affects AV users’ behavior, thereby influencing urban productivity. The results indicate that parking subsidies for AVs undermine urban productivity, whereas cash-out policies, such as providing subsidies for public transit, can effectively enhance urban productivity.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100196"},"PeriodicalIF":12.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633710","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}