Conference paper

Towards Privacy-Neutral Travel Time Estimation from Mobile Phone Signalling Data

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these applications are mobile subscriber dwell times. They express how long users stay in the service range of a base station. In this paper, we want to evaluate whether dwell time distributions can serve as predictors for road travel times. To this end, we transform floating car data into synthetic dwell times that we use as weights in a graph-based model. The model predictions are evaluated using the floating car ground truth data. Additionally, we show a potential link between handover density and travel times. We conclude that dwell times are a promising predictor for travel times, and can serve as a valuable input for intelligent transportation systems.

Reference

  • [DOI] T. Derrmann, R. Frank, S. Faye, G. Castignani, and T. Engel, “Towards Privacy-Neutral Travel Time Estimation from Mobile Phone Signalling Data,” in 2016 IEEE International Smart Cities Conference (ISC2) (ISC2 2016), Trento, Italy, 2016.
    @inproceedings{TD-ISC2-16,
    Address = {Trento, Italy},
    Author = {Thierry Derrmann and Raphael Frank and S{\'e}bastien Faye and German Castignani and Thomas Engel},
    Booktitle = {2016 IEEE International Smart Cities Conference (ISC2) (ISC2 2016)},
    Doi = {10.1109/ISC2.2016.7580735},
    Keywords = {MAMBA},
    Month = sep,
    Title = {Towards {Privacy-Neutral} Travel Time Estimation from Mobile Phone Signalling Data},
    Year = 2016,
    Bdsk-Url-1 = {http://dx.doi.org/10.1109/ISC2.2016.7580735}}