This paper presents a general framework for a modeling platform and a visualization tool for bicycle volume data of different quality and quantity. The modeling platform is aimed to estimate the annual average daily bicycle traffic (AADB) on links where bicycle volume data are collected during part of the year even if very limited data exist. The visualization tool, on the other hand, displays the estimated AADBs along with their associated quality indices on a digital network map so that it becomes available to both officials and end users. This paper describes the general structure of the model along with the estimation algorithms used in different stages. The assumptions associated with model development are discussed along with their implications. A case study is presented and is referred to as Vancouver Cycling Data Model. It was shown that the model could lead to a coverage ratio of more than 70% using an initial dataset that included only 5% of the total number of hourly volumes that are actually needed to calculate the AADBs. This demonstrates the efficiency of the model in expanding the estimation of AADB over the entire network using limited data. This research effort is one of only few existing studies that attempted to develop cycling data models that can be used as useful decision-making tools for planners and sustainable transportation experts.
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