A data-driven urban traffic flow pattern recognition and real-time prediction and early warning method

Abstract

The invention relates to a data-driven urban traffic velocity pattern recognition and real-time prediction and early warning method, comprising: processing the historical traffic velocity data of road sections, filling missing values, screening and correcting abnormal values; based on the processed historical traffic velocity data of road sections, Using the contour coefficient to determine the optimal number of clusters, using the improved K-means clustering algorithm to obtain the historical pattern of road traffic flow velocity under different date characteristics; using the real-time traffic flow data of the road section, using the Holt exponential smoothing prediction model to predict the future section of the road section Time-based traffic flow rate; based on the current date characteristics and the historical pattern of road section traffic velocity matching, calculate the lower half of the deviation between the road section traffic flow prediction results and the historical model, and give an early warning when the lower half of the deviation is greater than a certain threshold. Under the background of increasingly congested urban traffic, the present invention comprehensively utilizes historical traffic velocity data and real-time traffic velocity data to drive pattern analysis and real-time prediction of road section traffic flow velocity, realizes real-time early warning of road section traffic flow velocity, and provides for preventing and alleviating traffic congestion. Decision basis.

Description

technical field [0001] The invention provides a data-driven urban traffic flow pattern recognition and real-time prediction and early warning method, using the historical data set of traffic flow velocity in urban road sections, mining potential historical patterns of traffic flow velocity through clustering algorithm, and identifying the historical law of traffic flow velocity; Historical data and real-time data of flow velocity, training prediction model, real-time prediction, and real-time early warning of traffic flow velocity in combination with historical traffic flow pattern, belong to the technical field of short-term prediction and early warning of urban traffic flow. Background technique [0002] In the process of rapid urban development, urban traffic is an indispensable part. When the traffic flow of a certain road section exceeds the specified capacity of the road section, traffic congestion will occur on this road section [1] . Traffic congestion increases tr...

AI Technical Summary

Problems solved by technology

[0008] The technical solution of the present invention is to provide a data-driven urban traffic flow pattern recognition and real-time prediction in view of the serious problem of traffic congestion in urban road sections at present, and the existing real-time traffic situation analysis system still has the problem of poor data processing and congestion identification The early warning method effectively combines the historical data and real-time data of urban traffic flow, which has obvious advantages in realizing the traffic management of urban road sections

Images