Urban road vehicle running speed forecasting method based on road network characteristics

Abstract

The invention belongs to the field of intelligent traffic, and relates to an urban road vehicle running speed forecasting method based on road network characteristics. The urban road vehicle running speed forecasting method can be applied to forecasting the vehicle running speed on an urban road during a period of time, a model is improved on the basis of a k-nearest neighbor nonparametric regression method, relations between road sections in a road network are considered, and a matched time series state vector is enlarged into a multi-dimensional space-time state matrix. A sample database is built through collected historical data, real-time data are collected for serving as a template to be matched with a sample, and the vehicle speed of a next time series of a target road section in the obtained neighbor sample serves as the forecasted vehicle speed. The gaussian function is used in the model two times for setting weights for the state matrix and forecasting results in an integrated mode so that the forecasting accuracy can be improved. The model provided in the urban road vehicle running speed forecasting method has the advantages that the data of the small road network with the road section to be detected as a center serve as the state matrix; compared with a prior forecasting model which only gives consideration to data of a current forecasted road section, the model provided in the urban road vehicle running speed forecasting method is higher in accuracy of multi-step forecasting; in addition, the method can offset real-time data or can be used for forecasting under the condition that a road section speed detector breaks down.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, can be applied to predict the vehicle speed on urban roads within a certain period of time, and can supplement real-time data in the case of road section speed detector failure. Background technique [0002] The continuous development of the city makes the traffic network structure and traffic operation status more and more complex, so the resulting traffic congestion and other problems affect the normal operation of the city. In order to respond to traffic problems faster, the prediction of traffic operation status has become a near A research that has emerged in the past two decades, it uses scientific statistical processing and data analysis methods to predict future traffic conditions through the study of current traffic network state parameters, compared with historical databases. Traffic forecasting can be divided into short-term traffic forecasting, medium-term traffic forecasting...

AI Technical Summary

Problems solved by technology

However, the past predictions are often single-step predictions, and the predictions only consider the data of the road section itself, failing to take into account the impact of the continuity of traffic flow between roads in the road network

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