URBAN TRAFFIC MODELING AND SIMULATION
URBAN TRAFFIC MODELING AND SIMULATION

Summary/Abstract: The article describes the use of different methods of building both micro- and macro-models of urban traffic. Traffic at inter-sections can be modeled with a fixed time increment allowing microscopic traffic analysis at the intersection. Attention was drawn to the importance of event-based models, exemplified by solutions based on hybrid and colored Petri nets. One of the newer solutions is a model that uses agent-based technol-ogy to take account of the impact of all traffic participants in the city. The article also describes the use of neural networks in the construction and implementation of urban traffic models. Generative model of artificial neural networks can complement data not reachable in actual traffic measurement, deep learning can be used to posses data from video and impute of missing data. A combination of macroscopic intersection models con-structed from deep multilayer neural networks can be used to construct a traffic light control system in a network of streets and intersections

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