Data Development for Implementing Integrated Land-use and Transportation Forecasting Models in Medium-sized Metropolitan Planning Organizations

Abstract

Integrated land-use and transportation forecasting models require large amounts of data to calibrate and estimate. Obtaining reliable datasets for these models can be one of the most cost-prohibitive and time-consuming stages of such an endeavor. The purpose of this paper is to present a case-study data development program that was able to successfully provide all of the needed data for the estimation and calibration of an integrated land-use and transportation forecasting model. The recently developed Cube Land model was implemented in the Montgomery (Alabama) Area Metropolitan Planning Organization with funding from the Alabama Department of Transportation. The data development program was fiscally and temporally constrained and replicates typical model development conditions in medium-sized metropolitan planning organizations. This case study presents findings demonstrating that in the US locally developed datasets combined with national data sources and ‘off-the-shelf’, relatively low-cost but high-quality, purchasable datasets can be obtained in a relatively short amount of time and are sufficient to estimate and calibrate an integrated land-use and transportation forecasting model.