Journal of Intelligent Transportation Systems最新文献_第2页

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

Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows 用于估算匝道和非匝道交通流量的数据驱动迁移学习框架

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2301696

Xiaobo Ma , Abolfazl Karimpour , Yao-Jan Wu

{"title":"Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows","authors":"Xiaobo Ma , Abolfazl Karimpour , Yao-Jan Wu","doi":"10.1080/15472450.2023.2301696","DOIUrl":"10.1080/15472450.2023.2301696","url":null,"abstract":"<div><div>To develop the most appropriate control strategy and monitor, maintain, and evaluate the traffic performance of the freeway weaving areas, state and local Departments of Transportation need to have access to traffic flows at each pair of on-ramp and off-ramp. However, ramp flows are not always readily available to transportation agencies, and little effort has been made to estimate these missing traffic flows in locations where no physical sensors are installed. To bridge this research gap, a data-driven framework is proposed that can accurately estimate the missing ramp flows by solely using data collected from loop detectors on freeway mainlines. The proposed framework employs a transfer learning model. The transfer learning model relaxes the assumption that the underlying data distributions of the source and target domains must be the same. Therefore, the proposed framework can guarantee high-accuracy estimation of on-ramp and off-ramp flows on freeways with different traffic patterns, distributions, and characteristics. Based on the experimental results, the flow estimation mean absolute errors range between 23.90 veh/h to 40.85 veh/h for on-ramps and 31.58 veh/h to 45.31 veh/h for off-ramps; the flow estimation root mean square errors range between 34.55 veh/h to 57.77 veh/h for on-ramps, and 41.75 veh/h to 58.80 veh/h for off-ramps. Further, the comparison analysis shows that the proposed framework outperforms other conventional machine learning models. The estimated ramp flows based on the proposed method can help transportation agencies to enhance the operations of their ramp control strategies for locations where physical sensors are not installed.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"29 1","pages":"Pages 67-80"},"PeriodicalIF":2.8,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139470605","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

Smart Mobility in Smart Cities: Emerging challenges, recent advances and future directions 智慧城市中的智慧交通：新挑战、最新进展和未来方向

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2025-01-02 DOI: 10.1080/15472450.2023.2245750

Soumia Goumiri , Saïd Yahiaoui , Soufiene Djahel

引用次数: 0

Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR 基于深度学习的行人轨迹预测和风险评估，使用路边激光雷达捕获的轨迹数据

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2209912

Shanglian Zhou , Hao Xu , Guohui Zhang , Tianwei Ma , Yin Yang

{"title":"Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR","authors":"Shanglian Zhou , Hao Xu , Guohui Zhang , Tianwei Ma , Yin Yang","doi":"10.1080/15472450.2023.2209912","DOIUrl":"10.1080/15472450.2023.2209912","url":null,"abstract":"<div><div>In recent years, rapid advancements in the Autonomous Vehicles (AVs) industry have greatly motivated the research and development in pedestrian trajectory prediction and risk assessment. One of the critical requirements for AVs is to predict the future trajectories of pedestrians and provide collision warnings in an accurate and prompt manner. Nevertheless, accurate prediction of pedestrian trajectories remains a technical challenge, mainly caused by the heterogeneity of pedestrian crossing behavior and uncertainties in vehicle-pedestrian interactions. This paper proposes a deep learning-based method for pedestrian trajectory prediction and risk assessment, using trajectory data extracted from roadside LiDAR data and corresponding signal phasing information at MLK and Georgia Avenue in Chattanooga, TN. Meanwhile, a set of criteria referred to as the risk factor is established to quantitatively evaluate the risk of the pedestrian crossing behavior, which also serves as a learnable feature. A Long Short-Term Memory (LSTM) network is proposed, which takes the following data as the input: the pedestrian trajectory data, signal phasing data, and risk factors from the past 10 steps. Meanwhile, the network predicts the pedestrian trajectory and risk factor at the future time step. In the experimental study, the root-mean-square errors between the predicted and ground truth <em>x</em> and <em>y</em> coordinates are 0.225 meters and 0.377 meters, respectively, and the F1 score value for the risk factor is 99.6%, demonstrating the efficacy of the proposed LSTM-based methodology on pedestrian trajectory prediction and risk assessment.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 6","pages":"Pages 793-805"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88124815","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

Inferring the number of vehicles between trajectory-observed vehicles 推断轨迹观测车辆之间的车辆数量

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-11-01 DOI: 10.1080/15472450.2023.2227940

Zhiyong Wen , Xiaoxiong Weng

{"title":"Inferring the number of vehicles between trajectory-observed vehicles","authors":"Zhiyong Wen , Xiaoxiong Weng","doi":"10.1080/15472450.2023.2227940","DOIUrl":"10.1080/15472450.2023.2227940","url":null,"abstract":"<div><div>Traffic perception is the foundation of intelligent roads, and how to accurately perceive traffic has become a central issue for researchers. With the application of Vehicle-to-Everything communication technology, vehicle IDs, locations, velocities, and accelerations can be obtained by the Roadside Unit (RSU), i.e., trajectory-observed vehicles for the road. Inferring the number of vehicles between trajectory-observed vehicles can make traffic perception more accurate, with which the traffic can be sensed on the whole road. Thus, in the case of mixed traffic flow, a Real-Time Prediction Model was proposed, which is a novel model containing four modules: prior experience of the space headway, linear distribution of velocity and acceleration, identification of traffic shockwave, and filter. The inferred result was calculated in real time. During the test, we used US-101 lane-1 data of the Next Generation Simulation dataset and trajectory-observed vehicles with stochastic distribution for 20% penetration. The length of the study area on the US-101 highway was approximately 2100 feet, which was similar to the communication area of a single RSU. During the evaluation of the model accuracy with the real-world datasets, the error of the inferred vehicle numbers in the study area could be limited to ±5 vehicles almost. Results show that it is feasible to infer the number of vehicles between trajectory-observed vehicles. The model compensates for the shortcomings of traditional models (based on inductive loop, camera, or radar), thus providing a novel method for the traffic perception of intelligent roads.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 6","pages":"Pages 816-829"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74968740","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