Urban area road network traffic flow prediction method and system based on mixed deep learning model

Abstract

The invention discloses an urban area road network traffic flow prediction method and system based on a hybrid deep learning model, and the method comprises the steps: carrying out traffic flow statistics based on vehicle passing data of a checkpoint; performing spatial-temporal distribution characteristic analysis on the traffic flow data of the checkpoint, and performing characteristic extraction according to an analysis result to obtain spatial-temporal influence factors; according to the space-time influence factors, constructing and training a ConvLSTM and BiLSTM mixed deep learning model; performing synchronous prediction on the traffic flow of an urban regional road network, selecting a prediction loss function and an evaluation index, and performing visual expression on a result; calculating the traffic flow change degree through a linear time sequence prediction model Prophet, carrying out traffic state recognition, and achieving traffic state pre-judgment. According to the invention, a traffic management department can be helped to carry out dynamic management scheduling on urban roads, optimization management is carried out on an urban road network from the overall situation, a management strategy and a management scheme are formulated, and effective data support is provided for traffic managers and decision makers.

Description

technical field [0001] The invention belongs to the technical field of model calculation, and in particular relates to a method and system for predicting traffic flow in urban regional road networks based on a ConvLSTM and BiLSTM hybrid deep learning model. Background technique [0002] In large and medium-sized cities, because the growth rate of the number of motor vehicles is much higher than the construction progress of transportation facilities, the construction of urban transportation infrastructure cannot meet the ever-increasing traffic demand, resulting in an imbalance between supply and demand of urban transportation, and the contradiction is becoming more and more acute. Cause economic losses, casualties, ecological environment deterioration and other social problems, traffic congestion has become one of the important reasons hindering urban development. Accurately judging the traffic operation status based on real-time traffic information on the road network, and ...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: In order to overcome the problems of insufficient data feature analysis and only applicable to a single road condition scenario in the prior art, a method and system for predicting traffic flow in urban regional road networks based on the hybrid deep learning model of ConvLSTM and BiLSTM are provided. Realize the prediction of traffic congestion

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