This study evaluates the effectiveness of using detailed cellular network signalling data for travel time estimation and route classification. Here, the authors propose a processing pipeline for estimating travel times and route classification based on Cell ID and received signal strength (RSS) measurements from a cellular network. The pipeline combines cellular fingerprinting, particle filtering, integrity monitoring, and map matching based on a hidden Markov model (HMM). The method is evaluated using a dataset of 11,000 cellular RSS measurements with corresponding GPS locations for the city of Norrköping, Sweden. The basic fingerprinting method has a CEP-67 location accuracy of 111 m and both particle filtering and integrity monitoring improved the results: 79 and 38 m for particle filtering and particle filtering with integrity monitoring, respectively. The route classification method resulted in a precision of 0.83 and a recall of 0.92, which are clear improvements compared to basic map matching of fingerprinting estimates. This new type of noise-adaptive travel time sampling in combination with an HMM-based route classification shows promising results and can potentially support large-scale estimates of both route choice and travel times using detailed cellular network signalling data in urban areas.
Integrated tracking and route classification for travel time estimation based on cellular network signalling data
IET Intelligent Transport Systems ; 14 , 9 ; 1087-1096
01.09.2020
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
travel times , travel time estimation , traffic information systems , 000 cellular RSS measurements , route choice , hidden Markov models , CEP-67 location accuracy , integrity monitoring , basic map matching , road traffic , 3G mobile communication , particle filtering , corresponding GPS locations , 11 RSS measurements , fingerprinting estimates , Global Positioning System , size 79.0 m , telecommunication signalling , signal strength measurements , large-scale estimates , cellular network signalling data , HMM-based route classification , integrated tracking , basic fingerprinting method , size 111.0 m , Bayes methods , size 38.0 m , noise-adaptive travel time , cellular radio , route classification method , mobile computing , detailed cellular network , processing pipeline , cellular fingerprinting , pattern classification
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