OPERATION MANUAL FOR DECISION ANALYSIS IN ASSESSING THE ECONOMIC VALUE OF INTELLIGENT TRANSPORTATION SYSTEMS (ITS) PROJECTS

In this document a framework for evaluating intelligent transportation systems (ITS) projects is developed. One of the central issues addressed is whether ITS projects are distinctly different from other more conventional transportation projects and thus the traditional decision methods such as benefit-cost analysis cannot be used. The answer is mixed. The decision models used in the past are still relevant, however, these have been applied in an environment in which there was a well developed data base. The models identified, selected, assembled and evaluated data to make judgements as to whether the proposed projects were good or less good public investments. With ITS there is not the history of data on either the cost or benefit (demand) side. Therefore, ITS projects are much more model oriented than data collection oriented. In effect the data or information to be used in the decision models applied to ITS project evaluation must be generated through the use of models including simulation models. A number of conclusions can be drawn from this study. First the evaluation framework provides basic guidelines for conducting a benefit-cost analysis. The lists of ITS benefits and costs are useful in helping evaluators identify the specific benefits and cost of a specific ITS project. While the cost estimation is relatively easy, the benefit estimations are difficult tasks. They require sophisticated assumptions and modeling techniques to provide inputs for the estimations. Difference assumptions and modeling techniques will result in different inputs for calculation of benefits. They can alter the outcomes of the evaluation. This implies that ITS project evaluators should be fully aware of these limitations. Great effort should be placed in making and disclosing the assumptions for estimations of benefits and costs. There is an urgent need for collecting data from ITS deployments and developing models that can be used to accurately predict demands and benefits of ITS applications. There are two fundamental conclusions from this work. First, agencies need to put in place an information system that is oriented to the collection of more business and economic data and not simply engineering data if they are to establish a database for evaluating ITS projects in the future. Second, there would appear to be great value in estimating some well constructed demand models associated with ITS transportation projects, as once the parameters contained in these models are estimated they can be used in simplifying the evaluation of potential ITS projects.