Urban road congestion degree prediction method based on time sequence traffic events

Abstract

The invention relates to an urban road congestion degree prediction method based on time sequence traffic events. The method comprises the steps of: S1, acquiring historical traffic event data, real-time traffic event data and video monitoring data of an urban road section; S2, identifying traffic congestion forewarning events in the video data through the 3D CNN, and performing data space-time fusion according to the historical traffic events; S3, determining congestion degree classification labels, constructing a time sequence traffic congestion event data dictionary, and screening a training set, a verification set and a test set; S4, establishing an LSTM sequence data classification model, inputting the training set, and iteratively updating model parameters by utilizing a gradient descent method; S5, inputting the verification set into the model with updated parameters, optimizing and adjusting hyper-parameters, and selecting an optimal model; and S6, inputting the test set into the optimal training model, checking the effectiveness of the model, and carrying out road congestion prediction according to real-time traffic monitoring data. According to the method, a sequence dataclassification model is established by using LSTM, and the urban road congestion degree is predicted based on time sequence traffic events.

Description

technical field [0001] The invention belongs to the technical field of urban intelligent traffic management, and relates to a method for predicting urban road congestion degree based on time series traffic events. Background technique [0002] At present, in order to effectively alleviate traffic congestion, reduce traffic accidents, improve road network capacity, reduce energy consumption and reduce environmental pollution, many cities have built intelligent transportation systems (ITS), the key technology of which is to be able to monitor the future traffic conditions of urban roads. Effective forecasting provides decision-making basis for urban traffic control and road guidance systems. [0003] Quantitative forecasting technology of traffic flow data is often used at home and abroad for traffic congestion problems, such as constructing linear models such as ARIMA model and Kalman filter model based on statistical theory, or constructing nonlinear models such as decision ...

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Problems solved by technology

[0003] Quantitative forecasting technology of traffic flow data is often used at home and abroad for traffic congestion problems, such as constructing linear models such as ARIMA model and Kalman filter model based on statistical theory, or constructing nonlinear models such as decision tree models and support vector machine models based on machine learning algorithms. However, due to the complexity and uncertainty of traffic flow, the time-dependent correlation of each subsystem of intelligent transportation is not strong enough, resulting in the joint realization of intelligent transportation goals by the traffic control system and guidance system.

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