A. Keskinarkaus, Ekaterina Gilman, Lauri Lovén, S. Tamminen, M. Hippi, G. Xiong, F. Zhu, T. Seppanen, J. Riekki, S. Pirttikangas
{"title":"Revealing reliable information from taxi traces: from raw data to information discovery","authors":"A. Keskinarkaus, Ekaterina Gilman, Lauri Lovén, S. Tamminen, M. Hippi, G. Xiong, F. Zhu, T. Seppanen, J. Riekki, S. Pirttikangas","doi":"10.1109/icdew55742.2022.00011","DOIUrl":null,"url":null,"abstract":"In this paper we present procedures for processing raw data collected with moving vehicles and for fusing this data with digital map data. The goal is to have a better understanding of the city traffic via quantitative research on collected taxi data in relation to digital map properties. Map attributes are provided by Digiroad, which is a database of Finnish road and street network. We define methods to clean up data that has been collected with taxis equipped with on-board vehicle tracking devices from real customer service situations. Consequently, the driving behavior may be inconsistent and sensor data can be limited and contain errors. We explain procedures of preparing data; filtering the most obvious errors from the data set, map-matching moving object data, and fetching map attributes along the routes of the moving vehicles. The fetched properties, as well as other measurement data, are used for deriving statistics and illustrations to study driving behavior in downtown Oulu, Finland.","PeriodicalId":429378,"journal":{"name":"2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icdew55742.2022.00011","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
In this paper we present procedures for processing raw data collected with moving vehicles and for fusing this data with digital map data. The goal is to have a better understanding of the city traffic via quantitative research on collected taxi data in relation to digital map properties. Map attributes are provided by Digiroad, which is a database of Finnish road and street network. We define methods to clean up data that has been collected with taxis equipped with on-board vehicle tracking devices from real customer service situations. Consequently, the driving behavior may be inconsistent and sensor data can be limited and contain errors. We explain procedures of preparing data; filtering the most obvious errors from the data set, map-matching moving object data, and fetching map attributes along the routes of the moving vehicles. The fetched properties, as well as other measurement data, are used for deriving statistics and illustrations to study driving behavior in downtown Oulu, Finland.