By Mary Ebeling
A newly released study sponsored by CalTrans offers a thorough review and analysis of research and practice related to the limitations of existing travel forecasting models. The authors focus on limitations in forecasting induced vehicle travel generated by adding lane miles during a capacity expansion. When pointing out the problems related to modeling induced demand, the paper finds “an inconsistent lexicon in academic research and among practitioners, questions about research applicability, limitations in the sensitivity of travel forecasting models, and confusion about the appropriate use of induced vehicle travel elasticities from research.” Acknowledging that many practitioners may not have access to more advanced travel models capable of capturing induced demand, the authors recommend project staff in these cases rely on general elasticities while acknowledging the shortcomings in the analysis.
Existing research finds that VMT levels increase substantially with the addition of lane-miles, undermining the potential congestion relief of those projects. The authors acknowledge that the amount of induced vehicle travel may be additionally influenced by some of the following variables:
- Recent per capita VMT trends
- Demographic and social trends
- Disruptive technologies like Lyft and car sharing
- The size of the geographic area and the size of the network change. The influence of an individual project is relative to its proportion of the larger network.
- Other costs associated with driving, such as parking and fuel prices. Low fuel prices increase the induced travel effects of expanded capacity, as travel time becomes a greater share of travel costs in this situation.
- The starting level of congestion.
Despite potential complications from these additional factors, project planners and designers need to work to account for the known induced travel effects during the transportation project and planning analyses to improve decision making.
Mary Ebeling is a Transportation Policy Analyst at SSTI.