A traffic trajectory prediction method based on high-dimensional road network and recurrent neural network

Abstract

The invention provides a traffic track prediction method based on a high-dimensional road network and a circulating neural network, and belongs to the field of intelligent traffic analysis. The methodcomprises the steps of acquiring a data source, extracting relevant attributes, and screening the track data set according to a vehicle speed threshold value; carrying out secondary screening on track data through a neighbor rule to obtain complete formatted track data; establishing a road network model, extracting the track data set through a time window, obtaining a target bayonet context relation, embedding target bayonet codes into a high-dimensional space through an embedding algorithm, and mapping the two-dimensional plane road network to a high-order spatial road network, wherein complex topological relation is not contained between the bayonets, and the character similarity between the bayonets in the track data can be measured by using the high-dimensional similarity; using a bi-directional cycle neural network for carrying out bidirectional learning and prediction on a track matrix, and carrying out learning and prediction on the track data by combining the forward information and the backward information. The prediction efficiency is improved.

Description

technical field [0001] The invention belongs to the field of intelligent traffic analysis, and relates to vehicle track prediction, in particular to predicting a possible future path based on the path of a user at the latest moment. Background technique [0002] In recent years, the number of vehicles in cities has increased dramatically, resulting in obstacles and development bottlenecks in urban traffic, hindering the deepening of urbanization. With the development of sensors and the Internet, people can obtain more and more public travel data by using roadside video surveillance equipment, car navigation, GPS, smart phones and other equipment. By analyzing and mining these data and extracting urban public travel patterns, it can provide users with personalized travel services, avoid traffic congestion, and provide a reference for traffic control and urban planning. Among them, the prediction technology of the future trajectory of vehicles is an important application poin...

AI Technical Summary

Problems solved by technology

However, in the above methods, the road network prediction model based on Euclidean distance cannot reflect the connection relationship between intersections, and it is often necessary to extract foothold information through feature extraction, trajectory clustering and other methods to obtain unified trajectory data; while the linear prediction model based on There is a zero-probability problem in the trajectory prediction of , and the effect is not good for trajectories without records

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