J. Tomic, Nemanja Gazivoda, M. Kušljević, P. Sovilj, Milan Šaš
{"title":"Travel Route Planning in Smart Cities","authors":"J. Tomic, Nemanja Gazivoda, M. Kušljević, P. Sovilj, Milan Šaš","doi":"10.1109/ZINC58345.2023.10174121","DOIUrl":null,"url":null,"abstract":"Travel route planning techniques for motor and electric vehicles are designed to find the optimal path from the start to the destination point on a roadmap. As traffic conditions can change very quickly during the journey, it is necessary to immediately update all changes in real time. For example, traffic congestion may occur in certain parts of the route due to an increased number of vehicles. The shortest path generally may not be in all cases the best path, as many conditions can affect the length of the journey. If we want to make our cities smart, the transportation system also has to be smart. In this paper, a practically implemented system for vehicle route planning in urban areas is presented, which works in real time and takes a large number of parameters of travel into account. Therefore, this system provides an estimation of travel time that is much more accurate than the standard auto router. In this paper, this estimation of travel time is practically proven.","PeriodicalId":383771,"journal":{"name":"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 Zooming Innovation in Consumer Technologies Conference (ZINC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ZINC58345.2023.10174121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Travel route planning techniques for motor and electric vehicles are designed to find the optimal path from the start to the destination point on a roadmap. As traffic conditions can change very quickly during the journey, it is necessary to immediately update all changes in real time. For example, traffic congestion may occur in certain parts of the route due to an increased number of vehicles. The shortest path generally may not be in all cases the best path, as many conditions can affect the length of the journey. If we want to make our cities smart, the transportation system also has to be smart. In this paper, a practically implemented system for vehicle route planning in urban areas is presented, which works in real time and takes a large number of parameters of travel into account. Therefore, this system provides an estimation of travel time that is much more accurate than the standard auto router. In this paper, this estimation of travel time is practically proven.