The Oregon Highway Plan’s (OHP) mobility policies guide various planning and programming activities of the Oregon Department of Transportation (ODOT). Among these activities are ODOT’s land use change review responsibilities under the Transportation Planning Rule, as adopted by the state’s Land Conservation and Development Commission. This paper examines supplemental transportation performance metrics beyond the volume-to-capacity (v/c) metric that currently supports OHP mobility policies. Selected supplemental metrics are empirically analyzed using a travel demand model calibrated for a Medford, Oregon study area, focusing on a specific large-scale development project.
Critics of the single facility-based v/c measure charge that it is focused too narrowly on operational objectives and, in many cases, adherence to this standard has undermined community economic development, compact growth, and non-auto mode share objectives. Numerous alternative performance measures have been suggested that would better capture these concerns; however, many of them are difficult to predict as an outcome of particular land use change proposal.
In this study, we considered performance metrics that could be derived from a network-based travel demand model. Specifically, we examine network-wide v/c, mode shares, trip lengths, vehicle miles/hours traveled, person miles/hours traveled, regional accessibility and local accessibility measures across no-build and build scenarios. The analysis illuminates how project impacts attenuate spatially at concentric distance rings from the project site, and how the outcomes differ under alternative growth scenarios: 2 to 5 times the project magnitude, zero-net growth (redistribution), and an alternative ex-urban location. We also discuss the limitations and ambiguities present of the mobility metrics as well as the limitations of modeling these impacts more generally. Finally, this study recommended two mobility metrics for further consideration based on their robustness across a range of sensitivity tests and assumptions.