Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/MDM.2012.16 | MDM |
Keywords | Field | DocType |
optimisation,city ride application,mobility analysis,best pair,journey planner,journey advisor,spatio-temporal prediction system,biking travel time,journey advisor optimization,personal journey advisor application,city navigation,bike-sharing,predictive bike sharing journey advisor,bike station,available bike,predictive bike,navigation,dublin bike-sharing system,mobile bikers behavior,spatial interaction network,prediction system,dublin bike sharing system,interactive systems,mobile phone application,cityride,mobile computing,personal journey advisor,available bike-sharing system,real time systems,prediction algorithms,bayesian methods,market research,availability | Mobile computing,Computer science,Operations research,Spatial interaction,Mobility analysis,Travel time,Optimization problem,Market research,Prediction system,Bayesian probability | Conference |
ISBN | Citations | PageRank |
978-0-7695-4713-8 | 22 | 1.26 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji Won Yoon | 1 | 112 | 23.94 |
| Fabio Pinelli | 2 | 972 | 50.96 |
| Francesco Calabrese | 3 | 22 | 1.26 |