Improving Induced Travel Demand Forecasting for Different Road Types: A Research Synthesis and Meta-Analysis

Research Team: Keuntae Kim (lead) and Jamey Volker

UC Campus(es): UC Davis

Problem Statement: The current approach to estimating the environmental impacts of roadway expansions often underestimates the induced travel effect, leading to inaccurate forecasts of vehicle miles traveled and project outcomes like greenhouse gas emissions and traffic congestion. The Induced Travel Calculator, developed by researchers at the National Center for Sustainable Transportation, helps estimate induced vehicle miles traveled, but there is debate about which vehicle miles traveled elasticities to use for accurate forecasts across different contexts. Since Caltrans began requiring induced travel analysis for capacity-expanding projects in 2020, these elasticity estimates have become increasingly important.

Project Description: This research project seeks to enhance the understanding of induced vehicle miles traveled elasticity to help practitioners and policymakers more accurately account for the induced travel effect in both project-specific and policy-level decisions. Through a systematic literature review and meta-analysis, the study will synthesize findings from a broad range of induced travel research and employ meta-regression to calculate pooled elasticity estimates. This approach will facilitate the examination of variability in elasticity across different roadway types, including class 1 (interstate highways) and class 2 (freeways and expressways). By analyzing these variations, the meta-regression will help uncover key factors influencing differences in induced VMT elasticity, such as geographic location, traffic conditions, and project characteristics.

Status: In Progress

Budget: $36,986