The Problem of Data Mining in Modelling Traffic Flows in a Megapolis

Abstract

The article discusses the problems of using data mining in a transport model as a digital platform for analysing data on traffic flows in a megapolis, and prerequisites for creation in future of single data banks and an integrated environment for interaction of models of different levels as clusters of the digital economy, which will consider all modes of transport to assess transport demand and develop projects for organizing traffic in a megapolis.The objective of the work is to study the processes of obtaining quantitative characteristics of objects of transport modelling when creating a single electronic environment by calculating the derived parameters of the transport network of a megapolis. Quantitative spatial characteristics of an object are associated with calculating the distance from a city centre and a main street and are determined using geographic information systems entailing consequent problem of data unification and efficient data storage.As part of achieving that objective, it is shown that it is necessary to create a preprocessing and validation procedure for all primary transport data, since data sources have different formats and spatial interpolation of tracking data. For this, it is recommended to use various methods of data analysis based on GIS technologies, digital terrain modelling, topology of the road network and other objects of the transport network of a megapolis. Besides, the use of intelligent data should be preceded by formatting and grouping the source data in real time. The most common errors arise at the stage of the iterative process for obtaining quantitative characteristics of objects of transport modelling and building the optimal route in terms of travel time along a certain transport network.The existing trends of urban growth require global digitalization of all transport infrastructure objects, considering changes in the functions of the transport environment and in intensity of traffic flows. Theis entails further development and implementation of new information technologies for data processing using neural networks and other digital technologies.