A sinusoidal model for seasonal bicycle demand estimation

Abstract

As urban populations grow, there is a growing need for efficient and sustainable modes, such as bicycling. Unfortunately, the lack of bicycle demand data stands as a barrier to design, planning, and research efforts in bicycle transportation. Estimating bicycle demand is difficult not only due to limited count data, but to the fact that bicyclists are highly responsive to a multitude of factors, particularly seasonal weather. Current estimation methods capable of accurately adjusting for seasonal demand change often require substantial data for ongoing calibration. This makes it difficult or impossible to utilize those methods in locations with minimal continuous count data. This research aims to help mitigate this challenge by developing an estimation method using sinusoidal model to fit the typical pattern of seasonal bicycle demand. This sinusoidal model requires only a single calibration factor to adjust for scale of seasonal demand change and is capable of estimating monthly average daily bicycle counts (MADB) and average annual daily bicycle counts (AADB). This calibration factor can be established using a minimum of two short-term counts to represent the maximum and minimum monthly MADB in summer and winter. To develop the model, this research use data from bike-share systems in four cities and 47 permanent bicycle counters in six cities. Although this model is not suitable for locations with mild or atypical seasons, it successfully models MADB and serves as a useful alternative or supportive estimation method in locations where minimal demand data exist.