Alternative techniques to model route choice within traffic assignment models are compared. The performance of different models is dependent on thesize of the choice set and the correlation between routes. A method is presented of setting the scale of utility to reflect differences between link type, quality of information provided to drivers and traffic management measures. This reduces the task of parameter estimation and provides a systematic approach to model the effects of proposed traffic management measures on route choice. To account explicitly for correlation between routes,a Monte Carlo simulation of the Probit model was used as the reference against which the alternative route choice models were evaluated. To ensure practical application of the research, the tests were conducted using the national model of the Netherlands, comprising 4100 zones and over 200.000 links. Route sets of up to 6 routes were generated for a sample of originsand destinations. The results showed that the performance of each model depended on the characteristics of the route set available between each origin and destination, in particular, on the size of the choice set and the overlap between alternative routes. It is known that unless the same routechoice sets are used for calibration and application, the choice probabilities will be biased, making application of route choice models difficult for real-world networks. This problem can be overcome by introducing a parameter defining the ratio of variance to mean travel cost. Allowing this parameter to vary according to type of road, user or traffic management measures provides the basis for efficient estimation of route choice models accommodating a range of network conditions and traveller behaviour, including the effects of navigation systems and variable message signs. For the covering abstract see ITRD E145999
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