Guide to Calculating Mobility Management Benefits Victoria Transport Policy Institute
This table describes the travel impacts of common mobility management strategies.
Conventional traffic models models tend to be insensitive to many mobility management strategies, such as improvements in transit comfort and convenience, improved walking and cycling conditions, marketing programs, and changes in land use development patterns, and they often incorporate various biases favoring automobile transportation. The travel surveys they are based on tend to ignore or undercount nonmotorized travel and so undervalue nonmotorized transportation improvements for achieving transportation planning objectives (Stopher and Greaves 2007). Most conventional traffic models do not accurately account for the tendency of traffic to achieve equilibrium (congestion causes travelers to shift when, how and where they travel) and the effects of generated traffic that results from roadway capacity expansion. They tend to exaggerate the congestion problems that result if roadway capacity is not expanded and the benefits that result if roadway capacity is expanded. These models are not sensitive to the impacts many types of TDM strategies have on trip generation and traffic problems, and so undervalue TDM benefits.
Some models are particularly appropriate for evaluating mobility management strategies, such as the TRIMMS (Trip Reduction Impacts of Mobility Management Strategies) Model (www.nctr.usf.edu/abstracts/abs77704.htm), the CUTR_AVR Model (www.cutr.usf.edu/tdm/download.htm), the Business Benefits Calculator (BBC) (www.commuterchoice.gov) and the Commuter Choice Decision Support Tool (www.ops.fhwa.dot.gov/PrimerDSS/index.htm). DKS Associates (2003) illustrates an example of impact analysis on a specific corridor.
The travel impacts of a mobility management program can be predicted by extrapolating results from other similar programs (often referred to as comps). For example, the travel impacts and expected benefits of a commute trip reduction program can be predicted based on the mode shifts achieved at other worksites with similar geographic conditions, demographics, and management programs.