Real-time regional in-transit vehicle counting method and system based on checkpoint vehicle passing data

Abstract

The invention provides a real-time regional in-transit vehicle counting method and system based on checkpoint vehicle passing data. The method comprises the steps of: S1, obtaining historical checkpoint vehicle passing data, and carrying out the preprocessing on the historical checkpoint vehicle passing data, and obtaining preprocessed historical checkpoint vehicle passing data; S2, carrying out statistical analysis on the preprocessed historical checkpoint passing vehicle data, calculating the probability that a motor vehicle still runs on a road within a preset time interval after being captured and recorded at a checkpoint, and obtaining the offline in-transit rate of each checkpoint; S3, processing today checkpoint vehicle passing data, and obtaining the last vehicle passing record of each motor vehicle in a period from a time point when the vehicle is located in a region to the current moment; and S4, calculating the in-transit quantity of the whole region at the current moment according to the last vehicle passing record of each motor vehicle and the offline in-transit rate data of the checkpoint.

Description

technical field [0001] The present invention relates to the field of urban intelligent transportation, in particular to a method and system for counting vehicles on the way in a real-time region based on checkpoint passing data, which is applicable to the evaluation of the real-time operation status of regional road traffic and provides data support and decision-making for urban management departments in accordance with. Background technique [0002] In the analysis of urban road traffic flow, how to calculate the number of motor vehicles driving on the road in real time is of great significance to the organization and management of urban traffic, road congestion control and residents' travel analysis. The difficulty of the problem lies in the real-time in-transit calculation of city-level areas, which poses great challenges in both spatial and temporal dimensions. After investigation, the existing calculation methods are as follows. [0003] Method 1 is based on basic sta...

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Problems solved by technology

[0003] Method 1, based on basic static data such as urban motor vehicle ownership and the traffic capacity of each road, plus the experience of experts, the defect of this scheme is that there will be errors in empirical estimates, and real-time calculations cannot be achieved

[0004] The second method is real-time estimation based on the GPS data of the floating car in the whole city. The main defect of this scheme is that the data coverage is not enough, and only local sampling can be used to estimate the overall

[0005] Method 3, based on urban satellite images for motor vehicle target recognition, the disadvantage of this solution is that the image algorithm is difficult to identify on satellite images, the recognition accuracy is not enough, and the occlusion problem cannot be solved, resulting in large errors

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