Smarter Urban Dynamics - Cityride


We have developed a personal journey advisor application for helping people to navigate the city using the available bike-sharing system. For a given origin and destination, the application suggests the best pair of stations to be used to take and return a city-bike, in order to minimize the overall walking and biking travel time as well as maximizing the probability to find available bikes at the first station and returning slots at the second one. To solve the journey advisor optimization problem, we modeled real mobile bikers’ behavior in terms of travel time, and used the predicted availability at every bike station to choose the pair of stations which maximizes a measure of optimality . To develop the application, we built a spatio-temporal prediction system able to estimate the number of available bikes for each station in short and long term, outperforming already developed solutions. The prediction system is based on an underlining spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The Cityride application was tested with real data from the Dublin bike-sharing system.

Paper

Cityride: a predictive bike sharing journey advisor. Ji Won Yoon, Fabio Pinelli, Francesco Calabrese, IEEE International Conference on Mobile Data Management (MDM), 2012.