What factors influence bike share ridership? An investigation of Hamilton, Ontario's bike share hubs
Abstract Hamilton, Ontario's bike share system was launched officially on March 22, 2015. This study analyzes the effects of weather conditions, temporal variables, hub attributes (most of which are derived for 200 m buffers around hubs) and a one day lag on daily ridership at the bike share's hubs during its first year of operation. Two random intercept multilevel models are estimated one for daily trip departures, the other for daily trip arrivals. All weather (temperature, precipitation) and temporal variables (daylight hours, university terms, weekdays, holidays) are statistically significant in both models. Conversely, variables measuring transportation infrastructure in the vicinity of hubs, including the amount of bike lanes, are largely insignificant, suggesting that these features of the built environment have little to no influence on ridership. Proximity to important locations in the city (McMaster University, Hamilton's downtown) has a strong impact on ridership. Although population density was an important consideration when locating the hubs, population does not influence daily departures or arrivals. Employment in the vicinity of hubs, which serves as a surrogate for an area's activities or its attractiveness, does influence ridership, as is the case for the one day lag effect. While all of these variables are able to explain some of the differences in daily ridership activity between hubs, the random intercept models confirm that they do not explain all of it. In other words, there remain intrinsic differences between hubs that are not captured by the independent variables differences that influence ridership.
Travel Behaviour and Society