The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure.
However, many modeling tools fall short of what is required for robust estimates of demand with respect to toll and managed lanes in two key areas:
- The value-of-time is often aggregate and not consistently defined throughout the model system, and
- The reliability of transport infrastructure is rarely taken into account.
This presentation describes an effort which implemented recommendations of the Strategic Highway Research Program C04 and L03\L04 tracks on pricing and reliability within a regional activity-based modeling system for the San Diego, California region.
The implemented SHRP recommendations include distributed travel time sensitivities across the synthetic population and special travel markets, continuous cost sensitivity based on income, and multiple value-of-time bins in highway skimming and assignment. The work also included research related to the analysis of travel time variability based upon a temporally disaggregate (1-minute interval) dataset of auto travel speeds.
RSG is a nationally recognized leader in the development and application of advanced travel demand forecasting models. Joel’s model development experience includes the development of activity-based travel demand models for some of the largest and most complex metropolitan regions in the United States and large-scale integrated models of land-use and transportation for state departments of transportation. He has also provided travel forecasting support on numerous successful transit New Starts projects and has been involved in a number of toll and revenue forecasts for public sector clients. In addition to project work, Joel enjoys teaching and has served as adjunct faculty to Portland State University School of Urban Affairs and Department of Civil Engineering, and is a certified instructor for the National Highway Institute.