Customer Preference and Station Network in the London Bike-Share System

We study customer preference for the bike-share system in the city of London. We estimate a structural demand model on the station network to learn the preference parameters and use the estimated model to provide insights on the design and expansion of the bike-share system. We highlight the importance of network effects in understanding customer demand and evaluating expansion strategies of transportation networks. In the particular example of the London bike-share system, we find that allocating resources to some areas of the station network can be 10 times more beneficial than others in terms of system usage and that the currently implemented station density rule is far from optimal. We develop a new method to deal with the endogeneity problem of the choice set in estimating demand for network products. Our method can be applied to other settings in which the available set of products or services depends on demand. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.