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.


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