Cityride: A Predictive Bike Sharing Journey Advisor

A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

“…However, even if most factors have been considered, there is still inherent flaw in using regression model to forecast station's demand: the traditional method takes traffic zone as forecast unit, but one traffic zone may contain several stations, which generates obstacles to forecast every station's demand precisely. To solve this problem, some scholars establish ARIMA [17] and modified ARIMA [18] to forecast station's demand; their results show these new methods have a good predictive accuracy. At the same time, Bayesian network has also been used to forecast the station's demand.…”

A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy Genetic-Particle Swarm Optimization algorithm

“…Ando et al [3] analyzed the possibility of extension and the necessary conditions for bicycle rental system in a local city of Japan. A journey advisor application was presented by Yoon et al [4] for serving travelers to navigate the city using the existing bike sharing system. Lathia et al [5] used data analysis and mining techniques to consider the e ects of the user-access policy modi cation on London's bicycle hire scheme.…”

Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system

“…Other studied data sets are from Chicago [73,80,98,99], Lyon [63,65,74,100], Boston [12,76,80,101], Barcelona [11,70,102], Hangzhou [15,16,103], Brisbane [61,83], Minneapolis [76,104], Vienna [105,106], Denver [76,84], Pisa [64,107], Dublin [14,108], Minnesota [84], Seville [102], Montreal [109] Helsinki [110], Vancouver [111], Nanjing [112], and Castellon [113]. In addition to BSS data, some of those studies also used weather data as a feature of their analyses.…”

“…They revealed imbalance states in bike distribution that can be understood in terms of system structure and activity dynamics. Several studies have analysed and proposed some methods to address the imbalance issue from different points of view, such as the optimization of fleet routing and the number of fleets [67], a proposal for giving incentives to users to rebalance [68], implementing imbalance prediction [17,27,69], and proposing journey advice for users [108,114,115]. Some of those studies will be reviewed in the next subsections.…”

“…Yoon et al [108] proposed a personal journey advisor application for BSS in Dublin. For a given origin and destination, their application suggests the best pair of stations to be used to pickup and return the bikes.…”