Inference Method and System of Traffic Congestion Diffusion Path

Abstract

The invention discloses a method for estimating traffic congestion diffusion path, which includes acquiring road network data, and constructing a road section Unicom relationship matrix according to the road network data; acquiring historical data, and obtaining each time of each road section corresponding to a day after road matching The historical average speed of the segment and the historical maximum speed of vehicles on each road segment; obtain real-time data, and after road matching and data completion based on the historical average speed, obtain the real-time average speed of vehicles on each road segment; according to the real-time average speed Obtain the road condition distribution matrix and the historical maximum speed, and obtain the congestion state matrix according to the road condition distribution matrix and the link connection matrix; obtain the local propagation matrix according to the congestion propagation judgment method and the congestion state matrix; calculate the global propagation path according to the local propagation path. The method for inferring traffic congestion diffusion path of the present invention can solve the problem of data sparsity, and fully understand traffic conditions according to the constructed global congestion propagation path.

Description

technical field [0001] The invention relates to the traffic field, in particular to a method and system for inferring traffic jam diffusion paths. Background technique [0002] With the rapid development of China's social economy and the continuous advancement of urbanization, the number of motor vehicles, especially private cars, has shown a blowout growth. However, the relatively slow development of traffic and road facilities has gradually failed to meet people's growing travel needs, and the problem of traffic congestion has become more serious. Traffic congestion not only seriously hinders the pace of urbanization, but also seriously hinders economic development and brings environmental pollution and other problems. In recent years, various solutions to traffic congestion have emerged in an endless stream, but their effects have been minimal. The main reason is that the existing solutions are based on where the congestion is, where it is managed, when it is congested, ...

AI Technical Summary

Problems solved by technology

However, in the actual road network, there is a very strong temporal-spatial correlation between roads, and the simple macro-traffic flow model is not suitable for the analysis of the traffic congestion diffusion path on the traffic road network in the whole city.

In addition, due to the large number of urban roads, it is difficult to collect the traffic status of all roads at each moment based on the existing data

Images