A Stochastic Analysis Approach for the Station Capacities for the Bicycle Sharing System in three U.S. Metropolitan Cities

Abstract

A bicycle sharing system is a service in which bicycles are made available for shared use to individuals on a short‐term basis. Having a huge fleet of bicycles, it is essential for the bikeshare system operators to decide on capacity levels for each station to minimize the shortage durations. This study focused on the stochastic analyses to find optimum capacity levels and to minimize the number of lost customers based on different initial capacity levels in the most commonly used stations to decrease the durations of shortage. Using bikeshare trip data from three U.S. metropolitan cities (greater Boston area, New York City, and Chicago), analyses were conducted to find the peak hour period each day and the traffic between the most active stations. We determined the initial capacity levels for each station to satisfy the minimization of the time percentage for the following cases: either having totally empty station or having a station with full of bikes during the rush hours. Since we wanted to minimize the number of lost customers for the selected stations, we obtained results to reach the optimum initial capacity levels for each station.We found a decrease in the lost customers after the changes in the initial capacity levels of the stations. By changing the initial values of bikes in the station, we reduced the number of lost customers during the peak hours. Our modeling approach will help ensuring the most efficient usage of the stations and providing a convenient transportation option for the individuals.