A New Mathematical Approach to Solve Bike Share System Station Imbalances Based On Portable Stations

Abstract

Bike sharing systems (BSSs) have proliferated recently due to their ability to mitigate increased traffic congestion and provide an affordable and healthy transportation mode to commuters in dense cities. Yet logistical challenges occur daily because of unbalanced spatial-temporal demand. This leads BSS stations to be either empty or full during the day, causing the system to be inefficient and unreliable for users. Two main approaches for balancing BSS stations are discussed extensively in the literature: using a fleet of trucks to move bikes between stations overnight or moving them during the day. Both approaches assume stations are fixed in terms of location and capacity, failing to accommodate the dynamic demand change over a day or on a weekday versus a weekend. In this paper, we propose a new rebalancing approach using portable stations that can either be standalone or an extension of existing BSS stations. We propose using two approaches—greedy and stable marriage simulation—as a proof-of-concept. The simulated BSS was calibrated using a dataset collected from 2013 to 2015 in the San Francisco Bay area. Results show that adding two portable stations to a 35- station network could lower missed bike pick-ups over 20%, leading to increased user satisfaction and reducing repositioning operations. In this paper, a mathematical model was developed to be integrated with simulation as future work.