Real-time traffic condition judging method based on time window

Abstract

The invention discloses a real-time traffic condition judging method based on a time window. The method comprises the following steps of: performing data preprocessing; performing traffic state clustering initialization through a two-step clustering algorithm; updating the time window to obtain the traffic flow base data of a current time interval; and updating two-step clustering algorithm parameters and a clustering center, and judging the real-time traffic state of the current time interval. According to the invention, based on historical traffic flow base data, the traffic state clustering initialization is performed by a two-step clustering algorithm, and the optimal classification of the traffic state is obtained through primary selection of the optimal cluster number and determination of the optimal cluster number respectively; then, based on the initialization result, the real-time data is introduced, and the data is updated dynamically by use of a storage structure of the time window; and finally, the two-step clustering algorithm parameters and the clustering center are calculated and updated in real time, the optimal classification of the traffic state is re-determined, and the real-time traffic state of the current time interval is judged. The method makes up for the limitation of the conventional traffic state judging algorithm, and is of positive significance to promoting the construction of the urban intelligent transport system.

Description

technical field [0001] The invention relates to the application field of intelligent traffic, in particular to a real-time traffic condition discrimination method based on a time window. Background technique [0002] Real-time and accurate traffic status discrimination is one of the key technologies for establishing a comprehensive monitoring system for urban road traffic conditions and a real-time release and guidance system for traffic information. It is an important basis for managers to formulate congestion relief plans and evaluate road traffic performance. It is of great significance to improve road operation efficiency, optimize the distribution of traffic flow in road network space, reduce travel time, and reduce exhaust emissions. [0003] The identification of traffic state and the algorithm of traffic state discrimination constitute the technical system of traffic state discrimination. The identification of traffic state has experienced the evolution and developm...

AI Technical Summary

Problems solved by technology

[0004] 1. The traffic status is mostly divided by fixed thresholds, which cannot be adjusted according to the actual situation. There is a one-size-fits-all problem in the identification of traffic status of roads of different types and different functional levels;

[0005] 2. The relationship between traffic flow operating parameters (flow, speed, occupancy rate) is not considered comprehensively, and the discrimination results based on a single parameter are one-sided, with low accuracy and insufficient reliability;

[0006] 3. The traffic state discrimination algorithm contains many empirical components, and the discrimination rules are relatively fixed, and cannot be dynamically adjusted in a data-driven manner based on real-time data. The traffic state discrimination is not comprehensive enough. When the road traffic flow is affected by pedestrians, bicycles, weather, etc. When factors are affected, the misjudgment rate of the algorithm is high;

[0007] 4. The real-time performance of using clustering method to distinguish traffic status is not high. As time goes by, the clustering data set will continue to increase, which not only reduces the computing efficiency of the clustering algorithm, but also tends to increase the number of clusters and cluster centers. Stabilization, weakening the ability to respond to the time-varying nature of the traffic state

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