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Citation |
Brand D. Transp. Res. Rec. 1993; 1408: 1-7. |
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Copyright |
(Copyright © 1993, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing) |
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DOI |
unavailable |
PMID |
unavailable |
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Abstract |
A framework of linked cause and effect relationships (models) is derived for use in intelligent vehicle highway systems (IVHS) project and operational test evaluation. IVHS has the potential for greatly increased mobility, measured in travel opportunities and benefits, as well as considerable potential for improved transportation system operation. The framework avoids serious underestimation of the mobility and other user benefits from IVHS and allows the estimation of the effects of IVHS on aggregate volumes of travel and levels of congestion. The mobility benefits of IVHS must be measured at the level of the individual tripmaker, not on the basis of aggregate measures of flow volumes and travel times on the network. This means that the air pollution, safety, fuel consumption, and other flow volume-related impacts of IVHS do not vary in a straightforward way with the sum of the individual user benefits from IVHS. Therefore, the causal model chain for predicting IVHS impacts will vary from the conventional "planning model". The various predictive models required to evaluate IVHS improvements, including the formulation of model inputs, are described. The evaluation framework is intended to help guide the evaluation and selection of IVHS projects on the basis of their site-specific benefits and costs, rather than the desired results. Although the latter is entirely acceptable for planning a research program whose payoff cannot be known in advance, it is necessary to proceed to the next step of carefully evaluating operational field tests and advancing IVHS into its production mode. The causal framework makes it possible to anticipate the important consequences of IVHS and therefore carry out benefit-cost analyses of new investments as well as collect the appropriate data for planning and evaluating operational field tests. Language: en |