As urban populations increase, there is a growing need for eﬃcient and sustainable transportation modes, such as bicycling. Unfortunately, the lack of bicycle demand data is a substantial barrier to eﬀorts in designing, planning, and researching bicycle transportation. Estimating bicycle demand is especially diﬃcult not only due to limited count data, but due to the fact that bicyclists are highly responsive to a multitude of factors, particularly seasonal weather conditions. Current bicycle demand estimation methods are increasingly improving and are capable of accurately adjusting for seasonal change in demand. However, these methods often require substantial data for each calibration, which is often diﬃcult or impossible in locations with partial or minimal continuous count data. This research aims to help mitigate this challenge by developing an estimation method which uses a sinusoidal model to ﬁt the typical pattern of seasonal bicycle demand expected in in many locations. This sinusoidal model utilizes a single calibration factor to adjust for scale of seasonal demand change and is capable of estimating monthly average daily bicycle counts (ADB) 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 monthly ADB in summer and minimum monthly ADB in winter, or ideally with continuous counts. The calibration factor can then be applied to other locations that are expected to have similar seasonal patterns, even if they have diﬀerent overall counts. To develop the model this research uses data from bike-share systems in four cities and permanent bicycle counters in six cities. Ultimately, this model functions as an alternative, or supportive, estimation method which allows for researchers and transportation agencies to approximate expected demand in locations that suﬀer from minimal seasonal bicycle demand data.
By: Nicholas Fourniera,∗, Eleni Christofaa, Michael A. Knodler Jr. ; UMass Amherst