Analysis of heterogeneity of transport flows

Abstract

The situation when two independent transport flows are passing through the same edge of a transport network with different speeds is quite common for modern cities. The precision of mobile GPS devices, which are widely used for traffic monitoring, does not allow to determine the characteristics of transport flows in this situation because of their proximity. It causes difficulties in the analysis of the traffic situation, which may result in significant errors of transport routing systems. We propose a method to get the average speed of transport flows from common GPS traffic data using machine learning techniques. To do that we introduce a formal statement for the flow separation problem, which makes it possible to divide the problem into two sequential parts: statistical and optimizational. We analyze the possible approaches to the solution of both, construct features space for the statistical part and determine the complexity of the optimizational one. The developed techniques were implemented and tested to be working on real traffic data.