Deep learning network model for travel time prediction and method for establishing same

Abstract

The invention provides a travel time predicting method based on a network convergence and taking account of a traffic state time-space relation. The method obtains high-precision data by subjecting floating car data to refined processing. In particular, the process for subjecting the floating car data to refined processing ingeniously uses a convolutional neural network and a recurrent neural network which are mainly used in the field of artificial intelligence currently, fuses the networks, fully takes account of the time-space relation of a traffic state, deeply excavates a traffic state evolution rule, and ultimately achieves precise travel time prediction. Compared with various conventional travel time prediction models, the method fuses the time-space relations of the traffic state, fully excavates historical data characteristics, overcomes a difficulty that other methods cannot cope with the high time-varying characteristics and non-linear change characteristics of the traffic flow, does not need priori knowledge and parameter identification, and has good fault tolerance and robustness, high precision, and good stability.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic information processing, and in particular relates to a travel time prediction model and an establishment method thereof. Background technique [0002] As one of the important evaluation indicators of traffic status, travel time has become a key component of advanced travel service information systems and advanced road traffic management systems. Real-time and accurate travel time information release is of great importance for refined traffic management and improved travel services. Important theoretical research value and practical significance. [0003] In the existing research, there are already some methods for establishing travel time prediction models. In summary, they mainly include two types: parametric methods and non-parametric methods. Parametric methods include macroscopic traffic flow models, time series methods, and Kalman filter methods. The modeling is simple and the ac...

AI Technical Summary

Problems solved by technology

[0003] In the existing research, there are already some methods for establishing travel time prediction models. In summary, they mainly include two types: parametric methods and non-parametric methods. Parametric methods include macroscopic traffic flow models, time series methods, and Kalman filter methods. The modeling is simple and the accuracy is high, but it is greatly affected by random interference factors and has strong uncertainty; non-parametric methods include neural network method, support vector machine, K nearest neighbor method, etc., which do not require prior knowledge and parameter identification , has strong fault tolerance and robustness, but has certain requirements for the amount of historical data

In summary, the existing research mainly has the following two shortcomings: the existing methods are mostly based on fixed detector data, the coverage area is small, the data is missing, and the preparation is insufficient; the prediction model is greatly affected by random factors and cannot cope with the height of traffic flow. Difficulties in time-varying characteristics and nonlinear changing characteristics, poor robustness and accuracy

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