Communications in Transportation Research最新文献

{"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}

{"title":"Few-shot learning for novel object detection in autonomous driving","authors":"Yifan Zhuang ,&nbsp;Pei Liu ,&nbsp;Hao Yang ,&nbsp;Kai Zhang ,&nbsp;Yinhai Wang ,&nbsp;Ziyuan Pu","doi":"10.1016/j.commtr.2025.100194","DOIUrl":"10.1016/j.commtr.2025.100194","url":null,"abstract":"<div><div>Artificial intelligence and advanced sensing technologies have significantly advanced the intelligent transportation system and autonomous vehicles. Perception, a critical component, extracts real-time traffic information essential for various system functionalities, such as agent behavior prediction. However, the quality of information derived from perception greatly influences overall system performance. This study focuses on enhancing perception robustness in autonomous vehicles, particularly in detecting rare objects, which pose a challenge due to limited training samples. While deep learning-based vision methods have shown promising accuracy, they struggle with rare object detection. To address this, we propose a few-shot learning training strategy tailored for improved detection accuracy of rare or novel objects. Additionally, we design a one-stage object detector for efficient object detection in autonomous driving scenarios. Experiments on a self-driving dataset augmented with rare objects alongside the popular few-shot object detection (FSOD) benchmark, the pattern analysis, statical modeling, and computational learning PASCAL Visual Object Classes (PASCAL-VOC), demonstrate state-of-the-art accuracy in rare categories and superior inference speed compared to alternative algorithms. Furthermore, we investigate the impact of intra-class variance on detection accuracy, providing insights for data annotation in the preparation stage.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100194"},"PeriodicalIF":12.5,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban visual clusters and road transport fatalities: A global city-level image analysis","authors":"Zhuangyuan Fan,&nbsp;Becky P.Y. Loo","doi":"10.1016/j.commtr.2025.100193","DOIUrl":"10.1016/j.commtr.2025.100193","url":null,"abstract":"<div><div>Road traffic crashes are among the leading causes of death and injury worldwide. While urban planning and design are known to influence road safety, it is not clear how features of the built environment contribute to traffic fatalities. In this study, we analyze road fatality data from 106 cities across six continents via a combination of computer vision and unsupervised clustering on 26.8 million Google Street View images. We use deep learning tools to extract 25 features from the images. Among these features, 19 are relatively static built environment features, and 6 are dynamic usage-related features (such as pedestrians, cars, buses, and bikes). On the basis of the built environment features, we group the urban streetscapes into six distinct visual clusters. We then examine how these clusters are related to city-level road fatality rates when various control variables (e.g., population size, carbon emissions, income, road length, road safety policy, and continent) and dynamic features are combined. Our findings show that cities with Open Arterials streetscape (extensive road surface, open-sky views, and railings) tend to have higher road fatality rates. After accounting for differences in the built environment, cities with better public transit (proxied by buses detected) tend to have fewer traffic deaths—specifically, a 1% increase in bus presence is linked to a 0.35% decrease in fatalities per 100,000 people. This study demonstrates the power of using widely available street view imagery to uncover global disparities in urban design and their connection to road safety.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100193"},"PeriodicalIF":12.5,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535777","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":"SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data","authors":"Yutian Liu ,&nbsp;Chengfeng Jia ,&nbsp;Soora Rasouli ,&nbsp;Jian Gong ,&nbsp;Tao Feng ,&nbsp;Melvin Wong ,&nbsp;Tianjin Huang","doi":"10.1016/j.commtr.2025.100192","DOIUrl":"10.1016/j.commtr.2025.100192","url":null,"abstract":"<div><div>Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them vulnerable to perturbed environmental factors, like sensor malfunctions, data storage issues, and adverse weather conditions. To overcome the limitation, we propose SAFER-Predictor, a novel sparse adversarial training (Sparse AT) framework for enhancing the reliability of deep learning based spatiotemporal traffic prediction models. Sparse AT extends traditional adversarial training (AT) through a two-phase process: pre-training and fine-tuning. In the pre-training phase, the model is optimized to capture normal traffic patterns, enhancing predictive performance by understanding standard dynamics without external disruptions. In the fine-tuning phase, the focus shifts to strengthening robustness against corrupted inputs by employing an iterative min-max strategy during AT, optimizing performance for worst-case scenarios. Furthermore, we derive theoretical formulations that establish an upper bound on the model's prediction error following Sparse AT under certain noise levels. Experimental results indicate that incorporating Sparse AT into the representative traffic flow prediction models improves stability and ensures high accuracy under various perturbation scenarios.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100192"},"PeriodicalIF":12.5,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}