Design and Implementation of Urban Traffic Flow Resource Data Management System Based on Deep Learning Algorithm

Abstract

At present, with the rapid development of the economy, the number of vehicles in the city has increased rapidly; the city has a more serious congestion problem, so this problem has become an urgent problem for urban traffic to solve. In order to improve the traffic situation of the city, it is necessary to analyze the traffic flow situation of the city deeply and establish the data management system of urban traffic flow resources. Only in this way can the traffic flow of the city be monitored in real time, and on the basis of which the traffic area of the city can be predicted accurately, which can greatly improve the problem of urban traffic congestion. Deep learning algorithm is presented in this paper, and on the basis of the urban transportation network in each part of the vehicle traffic conditions and comparison of data collection, and with the aid of stochastic gradient descent algorithm, the current urban resources data management system of traffic flow design support provide a certain amount of data and model for the system set up to provide enough support, promote the design and implementation of data management system. The establishment of this system can realize the in-depth analysis of urban traffic, improve the urban traffic situation and promote the good development of urban traffic order.