Development of Demand Estimation Methodology for On-street Shared Paths

Term Start:

July 1, 2025

Term End:

May 31, 2027

Budget:

$150,000

Keywords:

Active Mode Split Prediction, Bicycle and Pedestrian Demand Forecasting, LASSO Regression Modeling

Thrust Area(s):

Data Modeling and Analytic Tools, Understanding User Needs

University Lead:

The University of Texas at Austin

Researcher(s):

Randy Machemehl

This is a human behavioral study designed to develop methodology to predict demand for on-street bicycle, scooter, and pedestrian facilities.

The current design guidance for on-street bicycle, scooter, and pedestrian facilities does not include specific guidance for predicting numbers of bicycle, scooter, and pedestrian users. The available guidance for such facilities suggests designers should consider the number of users, and the modal split among bikers, scooters, and walkers but does not suggest a methodology.

This study’s objective is to develop a procedure for forecasting numbers of users and the modal split.

Procedural guidance for on-street paths contained in the literature and used by states’ DOTs and cities will be identified and evaluated. Data from the Texas Bicycle and Pedestrian Count Exchange, along with data available from city count programs, will be collected. The LASSO machine learning regression technique will be used to develop relationships between bicycle, scooter, and pedestrian counts, and a variety of predictors, including demographic and socioeconomic variables, will be used. An estimation procedure will be derived to apply the relationships to proposed bicycle, scooter, and pedestrian facilities. Estimated modal splits among bicycle, scooter, and walking users of proposed paths will be part of the methodology.

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