Journal of Intelligent Transportation Systems最新文献_第5页

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 Epub Date: 2023-12-08 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 Epub Date: 2023-12-15 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 Epub Date: 2024-01-06 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 Epub Date: 2024-01-03 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 Epub Date: 2023-08-08 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 Epub Date: 2023-05-02 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 Epub Date: 2023-09-01 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

How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method 空间特征如何影响城市轨道交通预测精度：基于深度学习的客流预测方法

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-11-01 Epub Date: 2023-11-06 DOI: 10.1080/15472450.2023.2279633

Shuang Li , Xiaoxi Liang , Meina Zheng , Junlan Chen , Ting Chen , Xiucheng Guo

{"title":"How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method","authors":"Shuang Li , Xiaoxi Liang , Meina Zheng , Junlan Chen , Ting Chen , Xiucheng Guo","doi":"10.1080/15472450.2023.2279633","DOIUrl":"10.1080/15472450.2023.2279633","url":null,"abstract":"<div><div>Urban rail transit is an integral part of public transit, and has been extensive built in China. Previous studies have proved that the spatial features are closely related to rail transit ridership, considering a fundamental role of short-term passenger flow forecast in the urban rail operation, it is meaningful to explore how these factors affect the prediction accuracy. This study aims to find a way to improve prediction accuracy by considering spatial features of stations based on deep learning. Therefore, a CNN-LSTM model capturing the spatial and temporal features was applied and Suzhou (China) was choosing as a case study to explore the influence of three spatial features, namely relative position, location, and land use, on the prediction accuracy. The predict model used can extract spatiotemporal features and accurately predict the citywide stations, and the results show that, for the relative position, the inbound and outbound flow prediction errors of transfer stations and middle stations are the lowest, respectively. As for locational features, the more distant the station is from the city center, the more accurate the results are. For stations where land use is dominated by work and living services, the predictions are more accurate. The error rate is higher for stations whose services are mainly tourism, transportation, and leisure services. This study’s results can help operators predict the short-term passenger flow of target stations based on different demands and optimize their services on this basis.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 6","pages":"Pages 1032-1043"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135036902","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

Adaptive green split optimization for traffic control with low penetration rate trajectory data 低穿透率轨迹数据下交通控制的自适应绿裂优化

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-11-01 Epub Date: 2023-08-29 DOI: 10.1080/15472450.2023.2227959

Zihao Wang , Roger Lloret-Batlle , Jianfeng Zheng , Henry X. Liu

{"title":"Adaptive green split optimization for traffic control with low penetration rate trajectory data","authors":"Zihao Wang , Roger Lloret-Batlle , Jianfeng Zheng , Henry X. Liu","doi":"10.1080/15472450.2023.2227959","DOIUrl":"10.1080/15472450.2023.2227959","url":null,"abstract":"<div><div>Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 6","pages":"Pages 830-845"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73973599","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

Adaptive graph convolutional network-based short-term passenger flow prediction for metro 基于自适应图卷积网络的地铁短期客流预测

IF 2.8 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-11-01 Epub Date: 2023-05-02 DOI: 10.1080/15472450.2023.2209913

Jianli Zhao , Rumeng Zhang , Qiuxia Sun , Jingshi Shi , Futong Zhuo , Qing Li

{"title":"Adaptive graph convolutional network-based short-term passenger flow prediction for metro","authors":"Jianli Zhao , Rumeng Zhang , Qiuxia Sun , Jingshi Shi , Futong Zhuo , Qing Li","doi":"10.1080/15472450.2023.2209913","DOIUrl":"10.1080/15472450.2023.2209913","url":null,"abstract":"<div><div>With the development and acceleration of urbanization, urban metro traffic is gradually growing up to a large network, and the structure of topology between stations becomes more complex, which makes it increasingly difficult to capture the spatial dependency. The vertical and horizontal interlacing of multiple lines makes the stations distributed topologically, and the traditional graph convolution is implemented on the adjacency matrix generated based on a priori knowledge, which cannot reflect the actual spatial dependence between stations. To address these problems, this paper proposes an adaptive graph convolutional network model (Adapt-GCN), which replaces the fixed adjacency matrix obtained from a priori knowledge in the traditional GCN with a trainable adaptive adjacency matrix. This can not only effectively adjust the weights of correlations between adjacent stations, but also adaptively capture the spatial dependencies between non-adjacent stations. This paper uses the Shanghai Metro dataset to verify the effectiveness of the model in improving prediction accuracy and reducing training time.</div></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 6","pages":"Pages 806-815"},"PeriodicalIF":2.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73297920","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