Identification and Prioritization of Environmentally Beneficial Intelligent Transportation Technologies: Modeling Effort

In 1996, California Partners in Advanced Transit and Highways (PATH) commissioned a project team led by the Institute of Transportation Studies, University of California at Davis with the Claremont Graduate School to undertake a review of the environmental impacts of Intelligent Transportation Systems (ITS). The objectives of this project were to: 1) review previous qualitative and quantitative environmental assessments of ITS, from both field operational tests and modeling studies; 2) review the regulatory and policy contexts which encompass ITS; 3) develop a modeling framework suitable for assessing the short term (up to 10 years) environmental impacts of ITS; 4) identify those ITS technologies that have positive environmental effects; and 5) rank those technologies according to their energy and emission benefits. This evaluation of specific ITS technologies was to be performed within the context of legal and regulatory requirements, transport and environmental policy, State forecasts of vehicle miles of travel (VMT) and air quality, and broad transportation scenarios.The final phase of the project was the development of a model that would be capable of quantifying the short-term environmental impacts of ITS applications along a typical transportation corridor. The corridor chosen was a section of the SMART Corridor (Santa Monica Freeway (I-10) between I-405 and I-110). The model was built for the INTEGRATION V2.0 application, developed by Michel Van Aerde at Queen's University in Ontario, Canada (Van Aerde 1985; 1995). This report sets out the research effort relating to the final phase of this project. In particular, the model database is described with details of the modifications necessary to manipulate it into a form suitable for use with INTEGRATION V2.0. This discussion presents the difficulties and challenges faced, leading to the unfortunate conclusion of this project without obtaining useful quantitative results from the modeling exercise.