Short-term prediction for bike share systems’ travel time under the effects of weather conditions

In the last decade Bike share systems (BSS) have seen tremendous growth across the globe. The objective of this paper is to predict short-time and short-distance BSS trip duration throughout the day under weather conditions. Short-term prediction of BSS trip duration is important especially when the trip includes park and ride, and/or when it is coordinated with public transit. Updating BSS travel times frequently within the day (e.g., on a half-hourly basis) and in advance will help save time and money for both bike share users and system operators. In this paper, historical BSS trip travel time of a hundred capital bike share stations in Washington D.C. were modeled using several different modeling techniques: Stepwise Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), and ARIMA with exogenous variables (ARIMAX). The data was grouped into two datasets based on trip distances: the first group is for trips that take less than 0.5 mile, and the second group is for trips between 0.5 mile and 1 mile. The results show that temperature, fog, and distance between bike stations have significant effects on BSS travel time. Based on statistics of fit, Stepwise MLR model had a better performance and was chosen to predict travel times for the bike share system. A unique contribution of this paper is to provide a finer resolution prediction of BSS duration throughout the day under the effect of weather conditions. The results of this research are beneficial to bikers in pre-planning their trips, and to bike share system managers and operators in predicting travel times, determining bikes’ availability, re-allocating bikes, and relocating bike stations in the bike share network.