Journal of Intelligent Transportation Systems最新文献_第7页

An anti-disturbance lane-changing trajectory tracking control method combining extended Kalman filter and robust tube-based model predictive control 结合扩展卡尔曼滤波器和鲁棒管基模型预测控制的抗干扰变道轨迹跟踪控制方法

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-26 DOI: 10.1080/15472450.2024.2315136

Fangzhi Yin, Changyin Dong, Ye Li, Yujia Chen, Hao Wang

引用次数: 0

A spatiotemporal distribution identification method of vehicle weights on bridges by integrating traffic video and toll station data 整合交通视频和收费站数据的桥梁车辆重量时空分布识别方法

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-25 DOI: 10.1080/15472450.2024.2312810

Jianliang Zhang, Yuyao Cheng, Jian Zhang, Zhishen Wu

引用次数: 0

Fuzing multiple erroneous sensors to estimate travel time 引信多个错误传感器来估算旅行时间

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-13 DOI: 10.1080/15472450.2024.2315514

Fatemeh Banani Ardecani, Ahmadreza Mahmoudzadeh, Mahmoud Mesbah

引用次数: 0

Deep survival analysis model for incident clearance time prediction 用于事故清理时间预测的深度生存分析模型

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-12 DOI: 10.1080/15472450.2024.2315126

Eui-Jin Kim, Min-Ji Kang, Shin Hyoung Park

引用次数: 0

Reinforcement learning approach to develop variable speed limit strategy using vehicle data and simulations 利用车辆数据和模拟，采用强化学习方法制定变速限制策略

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-08 DOI: 10.1080/15472450.2024.2312808

Yunjong Kim, Kawon Kang, Nuri Park, Juneyoung Park, Cheol Oh

引用次数: 0

Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest 通过自适应隔离林实时检测城市地区短期交通中断的异常情况

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-02-08 DOI: 10.1080/15472450.2024.2312809

Jingqin Gao, Kaan Ozbay, Yu Hu

引用次数: 0

Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections 在设有无信号交叉路口的交通区域进行多代理强化学习以实现自动驾驶

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-01-02 DOI: 10.1080/15472450.2022.2109416

Christos Spatharis , Konstantinos Blekas

引用次数: 3

A ridesharing simulation model that considers dynamic supply-demand interactions 考虑动态供需互动的共享乘车模拟模型

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-01-02 DOI: 10.1080/15472450.2022.2098730

Rui Yao , Shlomo Bekhor

{"title":"A ridesharing simulation model that considers dynamic supply-demand interactions","authors":"Rui Yao , Shlomo Bekhor","doi":"10.1080/15472450.2022.2098730","DOIUrl":"10.1080/15472450.2022.2098730","url":null,"abstract":"<div><p>This paper presents a new ridesharing simulation model that accounts for dynamic driver supply and passenger demand, and complex interactions between drivers and passengers. The proposed simulation model explicitly considers driver and passenger acceptance/rejection on the matching options, and cancelation before/after being matched. New simulation events, procedures and modules have been developed to handle these realistic interactions. Ridesharing pricing bounds that result in high matching option accept rate are derived. The capabilities of the simulation model are illustrated using numerical experiments. The experiments confirm the importance of considering supply and demand interactions and provide new insights to ridesharing operations. Results show that higher prices are needed to attract drivers with short trip durations to participate in ridesharing, and larger matching window could have negative impacts on overall ridesharing success rate. Comparison results further illustrate that the proposed simulation model is able to replicate the predefined “true” success rate, in the cases that driver and passenger interactions occur.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 31-53"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84768756","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: 1

Glocal map-matching algorithm for high-frequency and large-scale GPS data 高频和大规模 GPS 数据的局部地图匹配算法

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-01-02 DOI: 10.1080/15472450.2022.2086805

Yuanfang Zhu , Meilan Jiang , Toshiyuki Yamamoto

{"title":"Glocal map-matching algorithm for high-frequency and large-scale GPS data","authors":"Yuanfang Zhu , Meilan Jiang , Toshiyuki Yamamoto","doi":"10.1080/15472450.2022.2086805","DOIUrl":"10.1080/15472450.2022.2086805","url":null,"abstract":"<div><p>The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 1-15"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90709378","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: 3

A robust machine learning structure for driving events recognition using smartphone motion sensors 利用智能手机运动传感器识别驾驶事件的稳健机器学习结构

IF 3.6 3区工程技术

Journal of Intelligent Transportation Systems Pub Date : 2024-01-02 DOI: 10.1080/15472450.2022.2101109

Mahdi Zarei Yazd , Iman Taheri Sarteshnizi , Amir Samimi , Majid Sarvi

{"title":"A robust machine learning structure for driving events recognition using smartphone motion sensors","authors":"Mahdi Zarei Yazd , Iman Taheri Sarteshnizi , Amir Samimi , Majid Sarvi","doi":"10.1080/15472450.2022.2101109","DOIUrl":"10.1080/15472450.2022.2101109","url":null,"abstract":"<div><p>Driving behavior monitoring by smartphone sensors is one of the most investigated approaches to ameliorate road safety. Various methods are adopted in the literature; however, to the best of our knowledge, their robustness to the prediction of new unseen data from different drivers and road conditions is not explored. In this paper, a two-phase Machine Learning (ML) method with taking advantage of high-pass, low-pass, and wavelet filters is developed to detect driving brakes and turns. In the first phase, accelerometer and gyroscope filtered time series are fed into Random Forest and Artificial Neural Network classifiers, and the suspicious intervals are extracted by a high recall. Following that, in the next phase, statistical features calculated based on the obtained intervals are used to determine the false and true positive events. To compare the predicted and real labels of the recorded events and calculate the accuracy, a method that covers the limitations of previous sliding windows is also employed. Real-world experimental result shows that the proposed method can predict new unseen datasets with average F1-scores of 71% in brake detection and 82% in turn detection which is comparable with previous works. Moreover, by sensitivity analysis of our proposed model, it is proven that implementing high-pass and low-pass filters can affect the accuracy for turn detection up to 30%.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 54-68"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79452237","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: 2