{"title":"Variable time discretization for a time-dependent shortest path algorithm","authors":"Ye Tian, Y. Chiu, Yang Gao","doi":"10.1109/ITSC.2011.6082871","DOIUrl":null,"url":null,"abstract":"This paper introduces a variable time discretization strategy for a time-dependent A∗ shortest path algorithm. The strategy is aimed at determining the optimal memory allocation for time-dependent travel times data in order to achieve a desirable compromise between accuracy and memory usage. The proposed strategy is based on the dispersion index of the travel times/costs over the entire analysis period, as a result, producing different intervals for each link. The links with travel times that have a higher variance and a lower mean will need to have a shorter time discretization length due to greater fluctuation in travel times. The proposed strategy is implemented in the time-dependent A∗ algorithm and tested with a numerical experiment on a Tucson, AZ, traffic network. The results show that with the same amount of computer memory usage, the proposed variable time discretization strategy achieves much higher accuracy than that of uniform time discretization.","PeriodicalId":186596,"journal":{"name":"2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)","volume":"48 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2011.6082871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper introduces a variable time discretization strategy for a time-dependent A∗ shortest path algorithm. The strategy is aimed at determining the optimal memory allocation for time-dependent travel times data in order to achieve a desirable compromise between accuracy and memory usage. The proposed strategy is based on the dispersion index of the travel times/costs over the entire analysis period, as a result, producing different intervals for each link. The links with travel times that have a higher variance and a lower mean will need to have a shorter time discretization length due to greater fluctuation in travel times. The proposed strategy is implemented in the time-dependent A∗ algorithm and tested with a numerical experiment on a Tucson, AZ, traffic network. The results show that with the same amount of computer memory usage, the proposed variable time discretization strategy achieves much higher accuracy than that of uniform time discretization.