Motor vehicle clustering method based on travel time characteristic

Abstract

The invention relates to a motor vehicle clustering method based on the travel time characteristic. The motor vehicle clustering method based on the travel time characteristic comprises the steps that customs pass traffic information of vehicles is extracted from an intelligent distributed traffic customs pass database; the number of times of passing through a customs pass of each vehicle in each hour every day is counted, so that the time characteristic vectors of the vehicles are obtained; the number k of optimum clustering centers is determined according to the max-min distance criterion; the KMeans clustering algorithm is compiled according to the MapReduce algorithm, the number k of optimum clustering centers is substituted into the KMeans clustering algorithm, and clustering analysis is conducted on the time characteristic vectors of the vehicles by means of the KMeans clustering algorithm, so that a clustering result is obtained; vehicle behaviors are analyzed according to the clustering result. According to the motor vehicle clustering method based on the travel time characteristic, the behavior characteristics of the motor vehicles are effectively learnt by grouping the vehicles, the vehicles are clustered according to the travel rule, so that vehicle information is obtained rapidly, and a reference basis is provided for evaluating the volume of traffic of the motor vehicles in the local area scientifically and predicting the road traffic passing condition in the future; meanwhile, the reference basis is also provided for checking suspicious vehicles for the public security and traffic police department.

Description

technical field [0001] The invention relates to the technical field of urban traffic checkpoint management, in particular to a motor vehicle clustering method based on travel time characteristics. Background technique [0002] With the development of computer storage and sensor technology, traffic smart checkpoints have been widely used and play an important role in urban traffic management. The data collected by the bayonet is increasing day by day, how to make full use of the big data resources of the bayonet has become a hot research issue. [0003] The application of traditional checkpoints is mainly used for public security deployment and violation warnings. It can capture violations and automatically identify violation vehicles to improve work efficiency and avoid possible impacts on traffic caused by traffic police on-site law enforcement. In terms of big data applications at bayonets, there is no public bayonet application for motor vehicle classification based on t...

AI Technical Summary

Problems solved by technology

In terms of big data applications at bayonets, there is no public bayonet application for motor vehicle classification based on travel time characteristics, and it is impossible to quickly obtain vehicle information, and the data processing efficiency is low

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