Roman Juránek, Jakub Špaňhel, Jakub Sochor, A. Herout, J. Novák
{"title":"Visual Analysis of Vehicle Trajectories for Determining Cross-Sectional Load Density","authors":"Roman Juránek, Jakub Špaňhel, Jakub Sochor, A. Herout, J. Novák","doi":"10.5507/TOTS.2019.002","DOIUrl":null,"url":null,"abstract":"The goal of this work was to analyze the behavior of drivers on third class roads with and without horizontal lane marking. The roads have low traffic volume, and therefore a conventional short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles by means of computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be difficult to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curve.","PeriodicalId":52273,"journal":{"name":"Transactions on Transport Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Transport Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5507/TOTS.2019.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
The goal of this work was to analyze the behavior of drivers on third class roads with and without horizontal lane marking. The roads have low traffic volume, and therefore a conventional short-term study would not be able to provide enough data. We used recording devices for long-term (weeks) recording of the traffic and designed a system for analyzing the trajectories of the vehicles by means of computer vision. We collected a dataset at 6 distinct locations, containing 1 010 hours of day-time video. In this dataset, we tracked over 12 000 cars and analyzed their trajectories. The results show that the selected approach is functional and provides information that would be difficult to mine otherwise. After application of the horizontal markings, the drivers slowed down and shifted slightly towards the outer side of the curve.