On-line dynamic station redeployments in bike-sharing systems

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Manna, Carlo
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Springer International Publishing AG
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Bike-sharing has seen great development during recent years, both in Europe and globally. However, these systems are far from perfect. The uncertainty of the customer demand often leads to an unbalanced distribution of bicycles over the time and space (congestion and/or starvation), resulting both in a loss of customers and a poor customer experience. In order to improve those aspects, we propose a dynamic bike-sharing system, which combines the standard fixed base stations with movable stations (using trucks), which will able to be dynamically re-allocated according to the upcoming forecasted customer demand during the day in real-time. The purpose of this paper is to investigate whether using moveable stations in designing the bike-sharing system has a significant positive effect on the system performance. To that end, we contribute an on-line stochastic optimization formulation to address the redeployment of the moveable stations during the day, to better match the upcoming customer demand. Finally, we demonstrate the utility of our approach with numerical experiments using data provided by bike-sharing companies.
On-line combinatorial optimization , Uncertainty , Smart cities
Manna C. (2016) ‘On-line dynamic station redeployments in bike-sharing systems’, in Adorni G., Cagnoni S., Gori M. and Maratea M. (eds.) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016: XVth International Conference of the Italian Association for Artificial Intelligence, Genova, Italy, 29 November –1 December. Lecture Notes in Computer Science, Vol. 10037. Springer International Publishing AG. doi:10.1007/978-3-319-49130-1_2
© 2016, Springer International Publishing AG. The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-49130-1_2