A spatial-temporal data model for choosing optimal multimodal routes in urban areas

In this paper we present an intelligent transportation system which help user in planning a route in a city, enhanced by various factors (be as fast as possible, be as short as possible, to involve or not changing the means of transport, etc). The new elements that bring the system proposed in the paper are: a static model of route planning combined with update in real time of traffic data; the data are retained to form a history from where being extracted patterns; the user can request an estimate of an optimal route for a current or a future time; the system takes account of uncertainty and provides a probability based on current data so that the proposed route to be carried out according to the estimate; operators to update the traffic data and return traffic routes subject to optimization criteria and time constraints. The proposed system was tested on an object-relational database with relevant data for the city of Cluj-Napoca. The results were revealing the advantages of estimating a route with real time data vs. historical data. However in the implementation of such solutions attention should be given to data sources that supply system (our tests were done on simulation data).