An urban people flow prediction method based on a Seq2Seq generative adversarial network

Abstract

The invention discloses an urban people flow prediction method based on a Seq2Seq generative adversarial network, and the method comprises the steps: abstracting the urban people flow data at different times into image frames, and representing the people flow through a thermodynamic diagram; Dividing the observation data into training data and labels according to time, and converting the problem into an image problem; The idea of WGAN generative adversarial network is generally adopted, a generator generates pedestrian flow in a certain period of time in the future on the basis of historical data by using a Seq2Seq method, and external factors such as weather are added at the same time; The discriminator uses a Waserstein distance to discriminate true and false data; In the training process, the generator and the discriminator are continuously optimized by combining the generative adversarial loss and back propagation. And finally, when the discriminator cannot discriminate the authenticity, the optimized generator is used for predicting the future urban pedestrian flow. According to the method provided by the invention, the generative adversarial network is used for carrying out urban people flow prediction for the first time, and the problems of fuzzy prediction and slow algorithm convergence are solved in combination with external environment factors.

Description

technical field [0001] The invention provides an urban crowd flow prediction method based on Seq2Seq generation confrontation network, which relates to the field of intelligent transportation, is mainly used for urban crowd flow prediction, and plays an important role in urban traffic planning, citizen travel and traffic risk reduction. Background technique [0002] With the acceleration of my country's urbanization process, the contradiction between the growing urban population and limited space resources has become increasingly serious, resulting in traffic congestion and becoming a major problem hindering urban development. Since the 1960s, countries around the world have conducted research on urban traffic planning and urban traffic control. However, with the continuous expansion of cities and the increasingly complex traffic conditions, it is no longer possible to rely on these two measures for effective traffic management. No matter how feasible, Intelligent Transporta...

AI Technical Summary

Problems solved by technology

By adopting the method disclosed in the present invention, the time-space correlation of the traffic data flow can be effectively used to realize the prediction of the flow of people in the whole city, solve the problem of fuzzy prediction, and can ensure higher prediction accuracy in different situations

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