A framework for estimating bikeshare origin destination flows using a multiple discrete continuous system. (February 2021) - Kumar Dey, Bibhas; Anowar, Sabreena; Eluru, Naveen
Highlights: Two choice dimensions: (1) total station level demand and (2) distribution of bike flows from an origin station across the network are examined. A linear mixed model is used to estimate total station level demand. Multiple Discrete Continuous Extreme Value (MDCEV) model is used to analyze flow distribution across destination stations. Impact of distance can be compensated by additional bicycling infrastructure. Abstract: Given the burgeoning growth in bikeshare system installations and their growing adoption for trip making, it is important to develop modeling frameworks to understand bikeshare demand flows in the system. The current study examines two choice dimensions for capturing the system level bikeshare system demand: (1) total station level demand and (2) distribution of bike flows from an origin station across the network. A linear mixed model is used to estimate the first choice and Multiple Discrete Continuous Extreme Value (MDCEV) model is used to analyze the latter. The data is drawn from the New York City bikeshare system (CitiBike) for six months (January through June 2017). For our analysis, we examine demand and distribution patterns on a weekly basis controlling for a host of independent variables (trip, socio-demographics, bicycle infrastructure, land use and built environment, temporal and weather). Model validation exercise results revealed that the proposed model performs well for low demand destinations. A policy exercise evaluating destination choice behavior demonstrated how the impact of distance is compensated by additional bicycling infrastructure in the farther locations. The results from the study help bikesharing system planners and operators to better evaluate and improve bikeshare systems.