An individual-based spatio-temporal travel demand mining method and its application in improving rebalancing for free-floating bike-sharing system

Abstract

After the rapid expansion in the early stage, many enterprises have closed down or withdrawn from the Free-floating bike sharing (FFBS) market, the remaining few giants are also generally at a loss at present. One main reason causing these is the serious FFBS imbalance between supply and demand. As there is no fixed docking station, the individual-based identification method such as trip-chain, which is used for identifying travel demand and behaviour in traditional station-based bike sharing (SBBS) cannot be used in FFBS. Therefore, the lack of methods to obtain in-depth demand makes it unable to achieve reasonable rebalancing. This study constructs an individual-based spatio-temporal travel demand mining methodology, which is the first disaggregate travel demand mining model that suitable for FFBS. The proposed methodology consists of three steps. A spatio-temporal trajectory clustering algorithm is first developed to obtain an individual’s frequent trajectory clusters, and then a sequential pattern mining algorithm is applied to users who have multiple spatio-temporal trajectory clusters to extract travel patterns and trajectory sequential relations in patterns. A point clustering method is used finally to identify spatial relationships among different trajectory clusters. Besides, a zone aggregating method is proposed that aggregated granularity could be flexible adjusted for zone demand imbalance analysis. Based on these, how to utilize identified frequent pattern trajectories to improve rebalancing is studied. The proposed methodology is applied to Beijing Mobike dataset, six frequent travel patterns are mined out and analyzed in detail. On this basis, imbalance and rebalancing analysis are carried out with the case study at last. Consequently, this research contributes a powerful tool to achieve accurate FFBS demand analysis and rebalancing.