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Journal of Intelligent Transportation Systems最新文献

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Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities 欧洲城市最后一英里物流配送机器人自主操作的不确定性分析
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2324388
Clément Lemardelé, Miquel Estrada, Laia Pagès
{"title":"Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities","authors":"Clément Lemardelé, Miquel Estrada, Laia Pagès","doi":"10.1080/15472450.2024.2324388","DOIUrl":"https://doi.org/10.1080/15472450.2024.2324388","url":null,"abstract":"Although autonomous delivery robots (ADRs) are widely anticipated to significantly enhance the efficiency of last-mile logistics operations in dense urban environments in the coming years, their im...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"4 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614637","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
Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles 捕捉真正的边界框：利用无人飞行器提取车辆运动学数据
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2341395
Tian Mi, Dénes Takács, Henry Liu, Gábor Orosz
{"title":"Capturing the true bounding boxes: vehicle kinematic data extraction using unmanned aerial vehicles","authors":"Tian Mi, Dénes Takács, Henry Liu, Gábor Orosz","doi":"10.1080/15472450.2024.2341395","DOIUrl":"https://doi.org/10.1080/15472450.2024.2341395","url":null,"abstract":"This paper presents a methodology by which kinematic variables of road vehicles can be extracted from unmanned aerial vehicle (UAV) footage. The oriented bounding boxes of the vehicles are identifi...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"38 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629719","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
Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways 基于多头注意力的高速公路智能车辆变道决策和轨迹预测模型
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-04-18 DOI: 10.1080/15472450.2024.2341392
Junyu Cai, Haobin Jiang, Junyan Wang, Aoxue Li
{"title":"Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways","authors":"Junyu Cai, Haobin Jiang, Junyan Wang, Aoxue Li","doi":"10.1080/15472450.2024.2341392","DOIUrl":"https://doi.org/10.1080/15472450.2024.2341392","url":null,"abstract":"With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory ...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"304 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614430","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 framework of transportation mode detection for people with mobility disability 行动不便者交通模式检测框架
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-03-19 DOI: 10.1080/15472450.2024.2329901
Jiwoong Heo, Sungjin Hwang, Jucheol Moon, Jaehwan You, Hansung Kim, Jaehyuk Cha, Kwanguk (Kenny) Kim
{"title":"A framework of transportation mode detection for people with mobility disability","authors":"Jiwoong Heo, Sungjin Hwang, Jucheol Moon, Jaehwan You, Hansung Kim, Jaehyuk Cha, Kwanguk (Kenny) Kim","doi":"10.1080/15472450.2024.2329901","DOIUrl":"https://doi.org/10.1080/15472450.2024.2329901","url":null,"abstract":"Transportation mode detection (TMD) is an important computational technique that aids human life at the social and individual levels. However, previous studies on TMD were focused on people without...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"8 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202551","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
Robust learning control for autonomous vehicle with network delays and disturbances 具有网络延迟和干扰的自主车辆鲁棒学习控制
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-03-19 DOI: 10.1080/15472450.2024.2329912
Jing Wang, Engang Tian, Huaicheng Yan
{"title":"Robust learning control for autonomous vehicle with network delays and disturbances","authors":"Jing Wang, Engang Tian, Huaicheng Yan","doi":"10.1080/15472450.2024.2329912","DOIUrl":"https://doi.org/10.1080/15472450.2024.2329912","url":null,"abstract":"This paper deals with a robust learning nonlinear model predictive control (RL-NMPC) scheme under time-varying delays and disturbances. It is well known that the in-vehicle network has considerable...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"67 1 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140202696","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
Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach 车联网环境下的公交信号优先：深度强化学习方法
IF 3.6 3区 工程技术
Journal of Intelligent Transportation Systems Pub Date : 2024-02-29 DOI: 10.1080/15472450.2024.2324385
Tianjia Yang, Wei (David) Fan
{"title":"Transit Signal Priority under Connected Vehicle Environment: Deep Reinforcement Learning Approach","authors":"Tianjia Yang, Wei (David) Fan","doi":"10.1080/15472450.2024.2324385","DOIUrl":"https://doi.org/10.1080/15472450.2024.2324385","url":null,"abstract":"Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service and enhance transportation equity. Conventional TSP...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"30 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005429","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
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
{"title":"Fuzing multiple erroneous sensors to estimate travel time","authors":"Fatemeh Banani Ardecani, Ahmadreza Mahmoudzadeh, Mahmoud Mesbah","doi":"10.1080/15472450.2024.2315514","DOIUrl":"https://doi.org/10.1080/15472450.2024.2315514","url":null,"abstract":"Estimating accurate travel time information is one of the fundamental tasks in controlling city traffic. In general, fuzing multiple sensors can generate more accurate information to measure traffi...","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"30 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139765852","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
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
{"title":"Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections","authors":"Christos Spatharis ,&nbsp;Konstantinos Blekas","doi":"10.1080/15472450.2022.2109416","DOIUrl":"10.1080/15472450.2022.2109416","url":null,"abstract":"<div><p>In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents’ policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework.</p></div>","PeriodicalId":54792,"journal":{"name":"Journal of Intelligent Transportation Systems","volume":"28 1","pages":"Pages 103-119"},"PeriodicalIF":3.6,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82061008","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 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 ,&nbsp;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 ,&nbsp;Meilan Jiang ,&nbsp;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
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