Abstract
As more people choose to travel by bicycle, transportation planners are beginning to recognize the need to rethink the way they evaluate and plan transportation facilities to meet local mobility needs. A modal shift towards bicycles motivates a change in transportation planning to accommodate more bicycles. However, the current methods to estimate bicycle volumes on a transportation network are limited. The purpose of this research is to address those limitations through the development of a two-stage bicycle origin–destination (O–D) matrix estimation process that would provide a different perspective on bicycle modeling. From the first stage, a primary O–D matrix is produced by a gravity model, and the second stage refines that primary matrix generated in the first stage using a Path Flow Estimator (PFE) to build the finalized O–D demand. After a detailed description of the methodology, the paper demonstrates the capability of the proposed model for a bicycle demand matrix estimation tool with a real network case study.
Highlights
Bicycling is increasing in many urban communities around the world due to the push for sustainable living and other factors [1,2,3]
Menghini et al [18] adopted a path-size logit (PSL)-based model on a pregenerated route, and Fagnant and Kockelman [19] developed a cyclist route choice model based on travel time and travel suitableness
To refine the primary bicycle O–D matrix with observed bicycle counts, we developed a sequential model consisting of route generation and O–D demand adjustment
Summary
Bicycling is increasing in many urban communities around the world due to the push for sustainable living and other factors [1,2,3]. Their review included the following: a comparison study of observed bicycle counts [8]; aggregate behavior studies [9,10]; bicycle sketch plan methods [11]; discrete choice models [12,13]; the regional travel demand model [14]; and latent demand score [9]. Most methodologies can only estimate or extrapolate the bicycle volumes on a network based on some bicycle counts in selected locations [19,20]. While these methodologies are certainly useful in bicycle modeling, they do not provide a complete picture of the bicycle transportation network
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