Improving Customer Satisfaction
in Bike Sharing Systems through Dynamic Repositioning
Abstract
In bike sharing systems (BSSs), the uncoordinated
movements of customers using bikes lead to empty
or congested stations, which causes a significant
loss in customer demand. In order to reduce the
lost demand, a wide variety of existing research
has employed a fixed set of historical demand patterns to design efficient bike repositioning solutions. However, the progress remains slow in understanding the underlying uncertainties in demand
and designing proactive robust bike repositioning
solutions. To bridge this gap, we propose a dynamic bike repositioning approach based on a probabilistic satisficing method which uses the uncertain demand parameters that are learnt from historical data. We develop a novel and computationally
efficient mixed integer linear program for maximizing the probability of satisfying the uncertain demand so as to improve the overall customer satisfaction and efficiency of the system. Extensive experimental results from a simulation model built on
a real-world bike sharing data set demonstrate that
our approach is not only robust to uncertainties in
customer demand, but also outperforms the existing
state-of-the-art repositioning approaches in terms
of reducing the expected lost demand