01556979

Component

In order to evaluate the impacts of bicycle infrastructure and policy, municipalities often seek to determine whether cycling demand has increased or decreased from one year to the next. They do so by analyzing bicycle demand data, such as bicycle counts from a permanent counter, or trip count estimates from an origin-destination travel diary survey. The problem with this approach is that bicycle demand is dependent on weather conditions. A particularly warm or cold season can mask or inflate inherent changes in cycling demand, and direct comparison of raw bicycle count data can produce misleading results. This paper seeks to address this challenge by proposing a framework through which bicycle demand data can be adjusted to account for weather conditions. A regression model is calibrated and used to estimate counts based on observed and average weather conditions. These estimated counts are used to calculate adjustment factors which reflect the extent to which weather increased or decreased cycling demand. In two case study applications, this framework is applied to data from permanent counters and from an origin-destination survey in the City of Ottawa.

This paper was sponsored by TRB committee ABJ35 Highway Traffic Monitoring.

01550057

15-5185

English

24p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Bicycling; Case studies; Data analysis; Origin and destination; Regression analysis; Traffic counts; Travel demand; Weather conditions

Ottawa (Canada)

Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-5185

TRIS, TRB, ATRI

Dec 30 2014 1:44PM