AbstractThis study contributes to research and practice by demonstrating the use of a composite measure, a bikeability index, to facilitate the use of and improve the performance of direct demand models for bicycle traffic, especially when only limited observation is available. The city of Austin was selected as a case study to develop the model using bicycle volume from 44 intersections. Existing knowledge and data were leveraged to develop the bikeability index that encompasses multiple built environment features (bicycle route length, comfort, connectivity, destination density, and transit coverage) to quantify the bike-friendliness of the network. In addition to the index, the demand model contained five demographic and land use variables. Some of the variables provided unique insights into bike travel behavior within the city, such as the significant and positive influence of the presence of bike signals and bike-accessible bridges. Along with the improved scalability and transferability of the modeling approach, the results and discussion are expected to facilitate and/or guide informed strategies and educational programs to increase nonmotorized activity in Austin as well as other regions.

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