TY - GEN
T1 - Cityride
T2 - 2012 IEEE 13th International Conference on Mobile Data Management, MDM 2012
AU - Yoon, Ji Won
AU - Pinelli, Fabio
AU - Calabrese, Francesco
PY - 2012
Y1 - 2012
N2 - In this paper, we present 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 underlying spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The City ride application was tested with real data from the Dublin bike-sharing system.
AB - In this paper, we present 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 underlying spatial interaction network among the bike stations, and takes into account the temporal patterns included in the data. The City ride application was tested with real data from the Dublin bike-sharing system.
KW - bike-sharing
KW - journey planner
KW - mobile phone application
KW - mobility analysis
UR - http://www.scopus.com/inward/record.url?scp=84870731703&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870731703&partnerID=8YFLogxK
U2 - 10.1109/MDM.2012.16
DO - 10.1109/MDM.2012.16
M3 - Conference contribution
AN - SCOPUS:84870731703
SN - 9780769547138
T3 - Proceedings - 2012 IEEE 13th International Conference on Mobile Data Management, MDM 2012
SP - 306
EP - 311
BT - Proceedings - 2012 IEEE 13th International Conference on Mobile Data Management, MDM 2012
Y2 - 23 July 2012 through 26 July 2012
ER -