Dynamic Visualization of Volume of Traffic
1Jedlička, K.; 2Ježek, J.; 3Kepka, M.; 4Hájek, P.; 5Mildorf, T.; 6Kolovský, F.; 7Beran, D.
The freedom of movement, one of the fundamental human rights, can be affected not only by governments, other authorities or hostile forces. It could be limited by such a common phenomenon as traffic congestion. There are options how to minimise the delay caused by dense traffic in the age of knowledge society. Most of them are based on informing drivers of the current or predicted movement of vehicles in a transportation network. Movements of vehicles in a network is often described by a Volume of Traffic - a variable which represents the number of vehicles passing a network segment in a period of time. As the vehicles’ movement is a complex spatio-temporal phenomenon, the volume of traffic varies dynamically in both space and time. Moreover, it could be differentiated according to vehicle types (cars, trucks, cyclists, pedestrians etc.). There exist several comprehensive visualization techniques. Their use for dynamic visualization, live simulation and comprehensive analytical applications in the entire area of interest is often problematic. The main contribution of the paper is a description of relevant data processing and visualization techniques. These techniques were applied and tested in the frame of the European project OpenTransportNet (CIP-ICT-PSP-PB 620533). There are four pilot regions where these techniques are being applied including the city of Antwerp, Bermingham, Issy-Les-Moulineaux and the Liberec Region. The paper firstly analyses contemporary progressive methods for dynamic phenomena visualization and methods which are suitable for traffic flow management in the area of interest. The next section of the paper explains the nature of traffic volume as a variable; the way how it can be measured on key network segments and then calculated for the whole network. The following process of estimation of the traffic volume time variations including short and medium-term prediction is also outlined. The paper describes the change of geographic data structure on the example of traffic data. This change is necessary for visualization of spatio-temporal data in contemporary clients. Finally, the selected visualization method is implemented in an online client and then analysed from the perspective of large datasets visualization. The benefits of parallel computing are discussed.Full Text ()