Modeling bike sharing system using built environment factors

This paper aims to present a 3modeling of bike sharing demand at station level in the city of Lyon. Robust linear regression models were used in order to predict the flows of each station. The data used in this project consists of over 6 million bike sharing trips recorded in 2011. The built environment variables used in the model are determined in a buffer zone of 300 meters around each bike sharing station. In order to estimate the bike sharing flow, we use the method of linear regression during the peak periods of a weekday. The results show that bike sharing is principally used for commuting purposes by long term subscribers while short term subscriber's trips purposes are more varied. The combination between bike sharing and train seems to be an important inter-modality. An interesting finding is that student is an important user of bike sharing. We found that there were different types of bikesharing usage which are influenced by socio-economic factors depending on the period within the day and type of subscribers. The present findings could be useful for others cities which want to adopt a bikesharing system and also for a better planning and operation of existing systems. Further, the solutions to encourage the use of bikesharing will be various depending on type of subscribers. The approach in this paper can be useful for estimating car-sharing demand. (C) 2015 The Authors. Published by Elsevier B.V.