Resumen
Nowadays plenty of navigation and route guidance methodologies are available, based on real-time traffic information collected from technologies that have not been originally developed to measure road traffic parameters, for example, cellular and GPS data of users? mobile phones and their current travel demands. Traditional traffic estimation methodologies, such as the four-step model, are not frequently employed. This way, reliable traffic data can only be obtained for those areas where there are enough users. In our paper, we present a route guidance methodology that combines current transportation demands with the results of the traditional four-step model. The predicted traffic state of the network is calculated for every fifteen minutes of the day by using a dynamic assignment with predefined static demand matrices as a first assignment. When travelers use the route suggestion system, their demands are collected in an actual demand matrix for the same time interval. This matrix is then combined with the original static demand matrix for this period and then allocated to the network as a second assignment. The real-time traffic disruptions on the network are also taken into account. Users will be provided with route suggestions based on the combination of the results of the two assignments. The methodology is tested with real static demand and network data of Budapest, using an integrated multimodal transport model maintained by the BKK Centre for Budapest Transport. The actual demands are simulated, modelling various traffic situations.