Abstrakt
Relevant path planning using public transportation is limited by reliability of the transportation network. In some cases it turns out that we can plan paths with respect to expected delays and hereby improve reliability of the resulting path.
In our work we focus on prediction of the delays in public transportation systems. For this purpose we use data from vehicle tracking systems used by transit operators - known as the AVL data.
We compare statistic methods to methods of artificial intelligence using data from Prague trams tracking system. We discovered that in some cases the neural networks show better results than the statistic methods.
In contrast, sometimes even simple statistical methods give as good results as those provided by the neural networks.