Intelligent urban traffic

Pidgurska, A.

Intelligent urban traffic

Файли

Black_Sea_Science_Proc_2020_Pidgurska.pdf(5.21 MB)

Дата

2020

Автори

Pidgurska, A.

Анотація

This investigation aims to create the algorithm that allows to find optimal routes for vehicles in cities where traffic jams are an acute problem. It's necessary to consider that an urgency of the problem is increased day by day. The situation is exacerbated in connection with the constant increasing of the cars number on the city streets. In the meantime, streets and intersections throughput stay virtually unchanged. To solve the formulated problem, logistic software algorithms are used to find the best routes. In this respect the most acceptable for decision of formulated problem is A*-algorithm. Technically, the flows` control process of vehicles can be done by obtaining information from sensors that are mounted at city intersections. Based on the collected data, a real-time interactive interplay between the Traffic Management Center (TMS) and each vehicle is carried out. The TMS transmits to each driver voice commands regarding the route to the destination, specified by the driver, as in normal GPS navigation. However, the main difference consists in choice of a criterion for optimality – our program chooses as such criterion a time of trip (t-optimality criterion), but usual GPS navigator takes into consideration geometrical factor (g-optimality criterion). The t-optimality criterion is realized by introducing special dynamic scales for the edges of the multigraph that simulates the city's transportation network. Therefore, the main purpose of this investigation is to lay out t-optimal routes for all vehicles who had ordered such routes. As a result, it will lead to optimization of used transport arteries throughout the city and to a decrease congestion numbers, to synchronization of the vehicles` flows – traffic will move to a new quality level.

Опис

Pidgurska A. Intelligent urban traffic / A. Pidgurska ; supervisor P. Nikolyuk // Black Sea Science 2020: proc. of the Intern. Competition of Student Scientific Works / Odessa National Academy of Food Technologies; eds. B. Yegorov, M. Mardar [et al.]. – Odessa: ONAFT, 2020. – P. 340–353 : fig. – Ref.: 16 tit.