Continuous and Reusable Data Management Strategies for Large Scale Integrated Transportation and Land Use Modeling: A Focus on Data Quality Indicators

摘要

Since the 1990s, federal legislation as well as local and state political environments has changed the landscape for metropolitan planning of land use and transportation. There is an urgent need for improved models that address the interdependencies between land use and transportation, and considerable new work is underway to develop such models by MPOs. However, these models require the integration of massive amounts of land use and socio-economic data. The current state of practice for preparing and managing data for integrated modeling is ad-hoc and messy. Such practice is increasingly challenged, as data sources increasingly update more frequently and come with various quality issues. This paper reports the progress of an ongoing project that aims to address this challenge. The project focuses on bringing interdisciplinary methods to make the best use of available data in land use and transportation domain. It identifies strategies for changing the paradigm of data management from episodic and costly to continuous and efficient. We recommend continuous and reusable strategies for managing data in this new paradigm, and pay special attention to monitoring quality of the data through automated data quality indicators as data from various sources are being integrated on a continuous basis. This paper primarily focuses on a key piece of the framework, the data quality indicators, and demonstrates their application with data from Oregon.