Journal of Intelligent Transportation Systems最新文献

Exploring traffic breakdown with vehicle-level data 利用车辆级数据探索交通细分

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2023.2301710

Youngjun Han , Jinhak Lee

引用次数: 0

Reliability-based journey time prediction via two-stream deep learning with multi-source data 通过多源数据的双流深度学习进行基于可靠性的行程时间预测

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2023.2301707

Li Zhuang , Xinyue Wu , Andy H. F. Chow , Wei Ma , William H.K Lam , S. C. Wong

{"title":"Reliability-based journey time prediction via two-stream deep learning with multi-source data","authors":"Li Zhuang , Xinyue Wu , Andy H. F. Chow , Wei Ma , William H.K Lam , S. C. Wong","doi":"10.1080/15472450.2023.2301707","DOIUrl":"10.1080/15472450.2023.2301707","url":null,"abstract":"<div><div>This paper presents a distribution-free reliability-based prediction approach for estimating journey time intervals with multi-source data using a two-stream deep learning framework. The prediction framework consists of a long short-term memory (LSTM) module for extracting temporal features and a convolutional neural network (CNN) module for extracting spatial-temporal features from the heterogeneous data. The precision and reliability of the prediction are assessed respectively by the Mean Prediction Interval Width (MPIW) and Prediction Interval Coverage Probability (PICP) metrics. For computational effectiveness, a Gaussian approximation is adopted to formulate a smooth and differentiable loss function for training the prediction framework. The computational experiments are conducted based on a real-world Hong Kong corridor, where multi-source data including traffic and weather conditions are collected. The proposed framework shows significant improvements over existing methods in terms of both precision and reliability over a range of traffic and weather conditions. This study contributes to the development of reliability-based intelligent transportation systems with advanced deep learning techniques.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 134-152"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139463577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections 交叉口行人控制模式和信号配时计划合作优化的统一策略

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2024.2307026

Jiawen Wang , Yudi Zhang , Jing Zhao , Chunjian Shang , Xinwei Wang

{"title":"Unified strategy for cooperative optimization of pedestrian control patterns and signal timing plans at intersections","authors":"Jiawen Wang , Yudi Zhang , Jing Zhao , Chunjian Shang , Xinwei Wang","doi":"10.1080/15472450.2024.2307026","DOIUrl":"10.1080/15472450.2024.2307026","url":null,"abstract":"<div><div>Pedestrian traffic management and control at intersections is crucial for ensuring traffic safety and efficiency while promoting green transportation development. Numerous studies have been conducted on optimizing signal timing of various pedestrian control patterns, such as exclusive pedestrian phases (EPPs) and leading pedestrian intervals (LPIs). However, cooperative optimization of these patterns and the corresponding signal timing is lacking. Hence, this study proposes a unified strategy for the cooperative optimization of pedestrian control patterns and signal timing plans to improve the efficiency and safety of pedestrian–vehicle mixed traffic flow. The existing control patterns, such as EPPs, LPIs, and two-way crossing (TWC), are unified. The safety and efficiency costs are monetized, and the minimization of average costs per traffic participant is taken as the optimization objective. Additionally, decision variables for diagonal crossing at intersections and pedestrian–vehicle priority are introduced to achieve cooperative optimization of the pedestrian control patterns and signal timing plans. The proposed model parameters were calibrated and validated using a real-world case study, and the applicable boundaries of different pedestrian control patterns under different pedestrian and vehicle flow scenarios were identified based on cost difference analysis. The results indicate that the vehicle turn ratio, average vehicle carrying rate, and unit cost ratio dynamically change the applicable boundaries. On average, the proposed method reduced the cost by 2.62% compared with separately optimized EPPs, LPIs, and TWC across various scenarios.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 170-196"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139561759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles 自动驾驶车辆与普通车辆混合共用的最佳车道分配策略

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2024.2307027

Yangbeibei Ji , Jingwen Liu , Hanwan Jiang , Xinru Xing , Wurong Fu , Xueqing Lu

{"title":"Optimal lane allocation strategy for shared autonomous vehicles mixed with regular vehicles","authors":"Yangbeibei Ji , Jingwen Liu , Hanwan Jiang , Xinru Xing , Wurong Fu , Xueqing Lu","doi":"10.1080/15472450.2024.2307027","DOIUrl":"10.1080/15472450.2024.2307027","url":null,"abstract":"<div><div>Autonomous driving technology has the potential to alter the way we travel and is rapidly evolving. Sharing rides in autonomous vehicles may become a popular mode of public transportation in the future. One strategy to enhance operational efficiency is to deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs). This study investigated optimal allocation for dedicated AV lanes considering mixed SAVs and regular vehicles (RVs) flows. First, the standard bottleneck model for morning rush hour was extended to account for both ridesharing and automation, and user equilibrium (UE) solutions were obtained by assuming dedicated AV lanes co-exist with general-purpose (GP) lanes. The equilibrium capacity allocation for dedicated AV lanes was determined for multi-modal traffic scenarios. The study found that commuters may prefer to use SAVs because SAVs were more cost-effective and time-saving. Furthermore, analysis of the fixed payment for ridesharing in SAVs revealed that the system performance could be negatively impacted by high fixed payments. The study proposed an enumeration algorithm for developing lane strategies for discrete lane settings. It was found that the deployment of dedicated AV lanes should be matched to the penetration rate of SAVs and different management objectives, following a gradual rather than a radical process. Finally, a sensitivity analysis was performed considering the value of travel time for SAVs and the bottleneck capacity of a dedicated AV lane. This work provides a new solution to AV lane capacity allocation and offers theoretical support for managers to deploy dedicated AV lane allocation.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 197-217"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A cooperative longitudinal lane-changing distributions advisory for a freeway weaving segment 高速公路交织路段的合作式纵向变道分布提示

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2023.2301705

Meng Long , Edward Chung , David Sulejic , Nasser R. Sabar

{"title":"A cooperative longitudinal lane-changing distributions advisory for a freeway weaving segment","authors":"Meng Long , Edward Chung , David Sulejic , Nasser R. Sabar","doi":"10.1080/15472450.2023.2301705","DOIUrl":"10.1080/15472450.2023.2301705","url":null,"abstract":"<div><div>The lane-changing (LC) concentration problem in freeway weaving segments poses crash risks and reduces freeway efficiency. To address this issue, this paper proposes a cooperative longitudinal LC distribution (CLLCD) advisory for freeway weaving segments utilizing cooperative intelligent transport system technology. The weaving segment is divided into sections, and the CLLCD strategy distributes lane changes for each section using a general rule that allows easy calculation of each section’s CLLCD from the maximum permitted number of lane changes for different movements. A corresponding percentage of drivers in each section are then permitted to change lanes from the start of that section. The CLLCD strategy is evaluated for 27 scenarios with varying traffic demands. A sensitivity analysis is conducted to determine optimal parameters, and the performance of the proposed strategy is compared to other methods. This study also explores the working mechanism of the proposed approach using headway data and speed profiles. The effects of the section configurations and penetration rates of connected vehicles (CVs) are discussed. Simulation results show that this easy-to-apply strategy improves speed and delay as effectively as the heuristic algorithms-based strategy. The number of sections does not influence the CLLCD strategy’s performance when the maximum freeway-to-ramp lane changes per section and other parameters per 100 m remain constant. The delay in the weaving area decreases as the CV penetration rate increases; however, only marginal further improvements are observed when the penetration rate increases beyond 60%. This study provides a practical and effective solution to enhance weaving segments’ efficiency.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 119-133"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139463461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the duration of reduced driver performance during the automated driving takeover process 预测自动驾驶接管过程中驾驶员表现下降的持续时间

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-03-04 DOI: 10.1080/15472450.2024.2307029

Changshuai Wang , Chengcheng Xu , Chang Peng , Hao Tong , Weilin Ren , Yanli Jiao

{"title":"Predicting the duration of reduced driver performance during the automated driving takeover process","authors":"Changshuai Wang , Chengcheng Xu , Chang Peng , Hao Tong , Weilin Ren , Yanli Jiao","doi":"10.1080/15472450.2024.2307029","DOIUrl":"10.1080/15472450.2024.2307029","url":null,"abstract":"<div><div>This study carried out a simulator test to determine and predict the duration of reduced driver performance during the automated driving takeover process. Vehicle trajectory and driver behavior data were collected in critical and non-critical takeover scenarios. The earth mover’s distance was then adopted to identify the data with the optimal combination of indicators by comparing it to the reference data. The Gaussian mixture model was employed to classify the driving state as either stable or unstable, and the duration of reduced driver performance was derived for each participant based on these results. Subsequently, a generalized linear mixed model was developed to predict the duration of reduced driver performance and examine the impact of various factors on it. Results uncovered a recovery of the reduced driver performance state after drivers took over the automated vehicle. In the non-critical and critical takeover scenarios, the mean duration of reduced driver performance was 17.48 and 27.25 s, respectively. Additionally, the developed model demonstrated good overall prediction accuracy, with the duration of reduced driver performance showing a positive correlation with the lead vehicle’s speed, duration of automated driving, and takeover request lead time. Furthermore, timid drivers exhibited a longer recovery duration than aggressive drivers. These research findings offer valuable insights into understanding the recovery of reduced driver performance during the takeover process, serving as a theoretical foundation for designing safer automated driving systems.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 2","pages":"Pages 218-233"},"PeriodicalIF":2.8,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient data-driven method to construct dynamic service areas from large-scale taxi location data 从大规模出租车位置数据构建动态服务区的高效数据驱动方法

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2289123

Minh Hieu Nguyen , Soohyun Kim , Sung Bum Yun , Sangyoon Park , Joon Heo

{"title":"An efficient data-driven method to construct dynamic service areas from large-scale taxi location data","authors":"Minh Hieu Nguyen , Soohyun Kim , Sung Bum Yun , Sangyoon Park , Joon Heo","doi":"10.1080/15472450.2023.2289123","DOIUrl":"10.1080/15472450.2023.2289123","url":null,"abstract":"<div><div>Service area analysis is crucial for determining the accessibility of public facilities in smart cities. However, the acquisition of service areas using conventional approaches has been limited. First, investigating traffic flow is difficult, as this factor varies significantly over time and space. Second, obtaining service areas of mobile facilities/targets has remained a challenge owing to a lack of data and methods. To address these problems, this study proposes an efficient big-data-driven approach that utilizes large-scale taxi GPS location data collected over two years within Seoul City and distributed computation to obtain the average travel time values on fine-grained grid cells of 100 m × 100 m resolution. On-the-fly visualization methods were then established with an ability to construct isochrone maps of service areas in near-real-time. This enabled performing accurate service area analysis of mobile facilities/targets dynamically. The proposed solution can be effectively used in various applications, such as optimizing the ride-sharing services or the routes of autonomous electric vehicles in future smart cities, as demonstrated in this study.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 1","pages":"Pages 1-17"},"PeriodicalIF":2.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DAnoScenE: a driving anomaly scenario extraction framework for autonomous vehicles in urban streets DAnoScenE：城市街道自动驾驶车辆异常驾驶场景提取框架

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2291680

Yuening Hu , Dan Zhao , Ying Wang , Guangming Zhao

{"title":"DAnoScenE: a driving anomaly scenario extraction framework for autonomous vehicles in urban streets","authors":"Yuening Hu , Dan Zhao , Ying Wang , Guangming Zhao","doi":"10.1080/15472450.2023.2291680","DOIUrl":"10.1080/15472450.2023.2291680","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) hold great potential to improve traffic safety. However, urban streets present a dynamic environment where unforeseen and complex scenarios can arise. The establishment of a systematic framework to extract a variety of vehicle driving scenarios could empower AVs to learn from and effectively navigate various situations. This study introduces a driving anomaly scenario extraction (DAnoScenE) framework tailored for AVs operating in urban street settings. The Waymo Open Motion Dataset (WOMD) is used to showcase the framework’s capability to capture an extensive range of realistic driving anomaly scenarios. The central process involves the detection and labeling of driving anomalies. To avoid the erroneous detected and labeled driving anomalies arising from issues such as outliers and noise within vehicle track data, a two-step approach is introduced to analyze and rectify vehicle movement parameters in raw data. To comprehend these driving anomalies and their associated scenarios, manual labeling identifies causative factors of scenarios such as traffic signals and behaviors of other agents, forming three scenario groups: Signal Interaction, Agent Interaction, and Other. A multimodal model is developed to classify scenario groups, complemented by a segmentation process that further divides groups into specific scenarios based on simple conditions. The results show that recognition accuracy for driving anomaly scenario groups achieved 98.4%, and the scenario segmentation method achieved 100% accuracy by simple conditions. The proposed framework provides valuable support for the advancement of autonomous driving algorithms and comprehensive AV testing, with a specific emphasis on navigating abnormal driving environments.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 1","pages":"Pages 32-52"},"PeriodicalIF":2.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modified Gipps model: a collision-free car following model 改良吉普斯模型：无碰撞汽车跟随模型

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2289149

Dhwani Shah , Chris Lee , Yong Hoon Kim

引用次数: 0

Generative adversarial network for car following trajectory generation and anomaly detection 用于生成汽车行驶轨迹和异常检测的生成对抗网络

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2301691

Haotian Shi , Shuoxuan Dong , Yuankai Wu , Qinghui Nie , Yang Zhou , Bin Ran

{"title":"Generative adversarial network for car following trajectory generation and anomaly detection","authors":"Haotian Shi , Shuoxuan Dong , Yuankai Wu , Qinghui Nie , Yang Zhou , Bin Ran","doi":"10.1080/15472450.2023.2301691","DOIUrl":"10.1080/15472450.2023.2301691","url":null,"abstract":"<div><div>Car-following trajectory generation and anomaly detection are critical functions in the sensing module of an automated vehicle. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes ‘TrajGAN’, an unsupervised learning approach based on the Generative Adversarial Network (GAN) to exploit vehicle car following trajectory data for generation and anomaly detection. The proposed TrajGAN consists of two modules, an encoder-decoder Long Short-Term Memory (LSTM)-based generator and an LSTM-multilayer perceptron (MLP) based discriminator, whose former component is used to generate vehicular car following trajectories and the latter one is for trajectory anomaly detection. By letting these two modules game with each other in training, we can simultaneously achieve robust trajectory generators and anomaly detectors. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Simulation results indicate that the proposed approach is efficient in reproducing artificial trajectories and identifying anomalous driving behaviors.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 1","pages":"Pages 53-66"},"PeriodicalIF":2.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139766121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0