Identifying Optimum Bike Station Initial Conditions using Markov Chain Modeling

Abstract

Bike sharing systems (BSSs) are being deployed in many cities because of their environmental, social, and health benefits. To maintain low rental costs, rebalancing costs must be kept minimal. In this paper, we use BSS data collected from the San Francisco Bay Area to build a Markov chain model for each bike station. The models are then used to simulate the BSS to determine the optimal station-specific initial number of bikes for a typical day to ensure that the probability of the station becoming empty or full is minimal and hence minimizing the rebalancing cost.

A GIS-MCDM Method for Ranking Potential Station Locations in the Expansion of Bike-Sharing Systems

“…Primarily, having an efficient system with suitable station locations (fixed stations in SBSSs [8], virtual stations in DBSSs [9], or virtual stations in a mix of both generations [10]) is a challenging task. One study [11] aimed to determine the optimal station number and distribution to enable more efficient systems. The authors considered public transport infrastructure, land use, and population density factors.…”

“…The first step of the proposed method is the identification of the various criteria that might influence the station location process. To support this, frequently used criteria can be determined based on literature reviews [8,11,[13][14][15]. Still, case-specific criteria may also be considered, e.g., valuable and influential location factors can be identified from existing transaction data.…”