This paper reports on models developed from data collected using the PARKIT parking
choice simulator. PARKIT provided an experimental environment in which drivers’
choice of car parks, and of the routes chosen to reach them, could be observed and the
influence of different levels of parking-stock knowledge (derived from experience or from
information provided via roadside message signs) monitored. Separate models were
estimated for the drivers’ initial choice of car park and for their revision of that choice as
their journey progresses and they learn about actual conditions. The importance of price,
walking time and driving distance is confirmed but the addition of variables describing the
drivers’ choices on previous days, their expectations and their immediately preceding
route-choice, greatly improved the models’ explanatory power. It is noted that variables
such as these are not generally considered because they are rarely available to the
modeller. Different discrete choice model structures were found to be appropriate for
different decisions. Route choice was represented as an exit-choice model (whereby each
journey is treated as a sequence of decisions – one at each intersection encountered). The
paper discusses the incorporation of these choice models into a network assignment model
and concludes that much of the power of the choice models is lost if the network model is
not able to support use of information about travellers’ socio-economic characteristics and
knowledge of the network and about the detailed network topology