інтелектуальні транспортні системи

The article presents a personalized functional view of the ITS architecture concerning Information for users of urban transport systems using the example of ITS systems of the central city of a metropolis. A fundamental issue from the perspective of transport users is obtaining information about the full functional scope of transport systems.

In the article, modern approaches to the development of Intelligent Transportation Systems (ITS) aimed at optimizing urban traffic are analyzed. Special attention is paid to model-free reinforcement learning algorithms (Q-Learning and Deep Q-Learning) used for controlling traffic lights in dynamic road traffic conditions. Simulation results in the SUMO environment have proven that implementing such algorithms significantly reduces intersection queues and increases the capacity of the transportation network.

Intelligent Transport Systems in urban conditions is one of the solutions to reduce congestion of vehicles and the amount of harmful emissions. An important component of ITS is the assessment of the duration of a public transport trip. It is necessary to focus on the study of the duration of the bus (the duration of traffic between stops and the dwelling time). In this paper, the authors focused on determining the dependence of the duration of buses at stops depending on the demand of passengers.