We propose a data science framework that identifies streets that are candidates for adding new bike infrastructure, taking into account potential bike flow and preserving global connectivity. To that end, we identify potential bicycle trips by coupling mobile phone data and GPS traces from a smartphone application for bikers. Different from the latent demand concept derived from survey data, our potential demand are the trips that could be done by bicycle, we filter the trips extracted from phone data by a distance distribution. Using percolation theory, we identified links from the street network that form a giant component and thus a well-connected bicycle infrastructure using the potential demand flows per link as a threshold. We obtained satisfactory results, first when comparing the estimated potential demand with survey and sensors data of bicycle demand. Second, we obtained reasonable bike paths extensions, when comparing the proposed bike infrastructure of our method with the existing and planned infrastructure of the secretary of mobility from Bogota (SDM), our case study.
Publication:
Olmos L.E., Tadeo M.S., Vlachogiannis D., Alhasoun F., Espinet X., Ochoa C., Targa F. and González, M.C. A data science framework for planning the growth of bicycle infrastructures. Transportation Research Part C: Emerging Technologies, Volume 115, June 2020, 102640 [PDF]