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The parking search process (Fig. 1) assumes that motorists decide whether or not to stop searching after evaluating the current car park. This involves comparing the utility of the present car park with the utilities of other car parks in the choice set. After a detailed examination of numerous modelling approaches it was considered that an economic principle based on expected gain in utility could be adapted to represent the parking search process. That is, searching is assumed to continue until the expected gain is less than the cost of continuing to search. However, the conventional economic search modelling approach based on the expected gain in utility (Richardson, 1982) could not be directly transferred to represent the parking search process since a large number of its general assumptions are violated (Axhausen and Polak, 1990). Common specifications of this approach assume that decision makers are risk neutral, have an unlimited time budget, face constant search costs, have full recall and possess a perfect knowledge of the utility distribution. Various procedures were therefore developed for incorporating features of the parking search process into the overall concept of expected gain in utility. Since car parks may be only tempora- rily available, the rejection of a car park at any point in time means that it may not be available when and if the motorist decides to return to it at a later stage in the current trip. This general `lack of recall' relating to the availability of previously inspected car parks, results in the current alternative being the most appropriate basis of comparison in the calculation of the expected gain in utility when deciding whether or not to continue the search. This is in contrast to basic eco- nomic search modelling where the maximum of all the previous alternatives inspected so far is usually used as the basis for comparisons. It is difficult to make general assumptions relating to the distribution of utilities or its parameters in parking search. Perceptions of the utilities of car parks are largely based on previous experience and network knowledge (those in the choice set) and hence do not generally conform to common statistical distributions. The costs of continuing searching have been internalised by including the travel time to a car park within the disutility of each car park. This component is represented by the minimum in-vehicle travel time to reach the car park being considered from the vehicle's current location. The expected values of the stochastic cost components were used to represent individuals' perceptions. The disutility was converted into a utility using an additive inverse transformation combined with a scaling parameter. The net change in utility made by selecting another car park over the current one was estimated by comparing the utilities of respective car parks [eqn (2)]. |
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