{"title":"Tracking vehicle trajectories by local dynamic time warping of mobile phone signal strengths and its potential in travel-time estimation","authors":"Charith D. Chitraranjan, A. Perera, A. Denton","doi":"10.1109/PERCOMW.2015.7134079","DOIUrl":null,"url":null,"abstract":"Tracking vehicles has many applications, especially in traffic engineering, including estimation of travel time/speed, traffic density, and Origin-Destination matrices. In this paper, we propose local alignment of mobile phone signal strength measurements to track the movement of vehicles, and demonstrate its application to travel-time estimation for a road segment. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping (LDTW) to align the signal strength trace of a phone carried in a vehicle, to a reference trace that we had collected for the relevant road segment. The signal strength trace from a mobile phone includes the strength of the signals received from the serving cell and six neighbor cells that form a multivariate time series. We perform the alignments on these multi-dimensional time series as they provide better location specificity than the univariate time series of the strongest cell, used in existing alignment-based methods. Experiments on drive test data show that our LDTW-based algorithm yields a lower positioning error with respect to ground truth (GPS traces), than comparison methods. Application of LDTW on real world call traces, made available to us by a mobile service provider, produced travel-time estimates with an average error of 11% and significant correlation with respect to travel-times computed through manual number plate recognition of vehicles.","PeriodicalId":180959,"journal":{"name":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PERCOMW.2015.7134079","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Tracking vehicles has many applications, especially in traffic engineering, including estimation of travel time/speed, traffic density, and Origin-Destination matrices. In this paper, we propose local alignment of mobile phone signal strength measurements to track the movement of vehicles, and demonstrate its application to travel-time estimation for a road segment. We use local alignment instead of the traditionally used global alignment to allow for vehicles changing roads. More specifically, we use local dynamic time warping (LDTW) to align the signal strength trace of a phone carried in a vehicle, to a reference trace that we had collected for the relevant road segment. The signal strength trace from a mobile phone includes the strength of the signals received from the serving cell and six neighbor cells that form a multivariate time series. We perform the alignments on these multi-dimensional time series as they provide better location specificity than the univariate time series of the strongest cell, used in existing alignment-based methods. Experiments on drive test data show that our LDTW-based algorithm yields a lower positioning error with respect to ground truth (GPS traces), than comparison methods. Application of LDTW on real world call traces, made available to us by a mobile service provider, produced travel-time estimates with an average error of 11% and significant correlation with respect to travel-times computed through manual number plate recognition of vehicles.