Abstract
The well-defined concept of value of perfect information (VOPI) was used to assess the value of improving flow prediction and the relative value of improving components of the prediction system. The concept is introduced with a simplified example of choosing whether to build a highway segment in a corridor, and then the example is extended to a network and more realistic components are incorporated. The examples are worked through to illustrate the general approach, the types of results that could be obtained, and the issues that arise. The probability distributions of the attributes—cost, time, fuel consumption, and vehicle emissions—used to summarize uncertainty in prediction are important components when calculating VOPI. An approach to modeling these distributions and the flow distributions on which they are conditioned is presented. Unlike traditional approaches, the present one recognizes uncertainty in the model itself and not only in the inputs and parameters of the model. The VOPI of arc flows is calculated for the network example and is compared with the project costs. VOPI is used to indicate that the marginal value of developing an error-free traffic assignment model would be much greater than that of developing an error-free trip distribution model in the example. The research and implementation issues raised by the example are discussed.
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