Multi-source fused data-based expressway traffic flow parameter correction method

Abstract

The invention discloses a multi-source fused data-based expressway traffic flow parameter correction method. The method includes the following steps that: (1) traffic flow parameter data and meteorological data are extracted from corresponding traffic flow detection equipment and meteorological inspection equipment, and time and space dimension matching is performed on the data; (2) missing data are screened; (3) modeling for restoration is performed on the data; (4) the data are restored; and (5) inverse normalization is performed on the data, and resultant data obtained after the inverse normalization are interposed into a corresponding position of a matrix X, so that a complete data matrix can be obtained. According to the method of the invention, data information including the information of data in the missing data is fully utilized, and the missing data are corrected; and influence on traffic flow caused by weather factors is considered, so that restoration accuracy can be improved. The proposed algorithm is simple and concise and can meet real-time processing requirements.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to an expressway traffic flow parameter correction method based on multi-source fusion data. Background technique [0002] With the continuous development of information technology, the traffic operation management center can not only obtain a large amount of traffic flow parameter data through various collection equipment, especially a large number of fixed detectors installed on the expressway, can detect traffic flow parameters in real time, including: traffic volume, Speed ​​and occupancy, and the ability to acquire large amounts of weather data. However, due to communication, power-on and other reasons, fixed detectors often have data loss problems, which brings great difficulties to subsequent traffic data mining. [0003] In the prior art, methods for repairing traffic flow parameters include various intelligent algorithms such as time series and neural ne...

AI Technical Summary

Problems solved by technology

However, these algorithms cannot fully mine data information, and mostly use a single data source, lack of consideration of the impact of weather and other factors on traffic flow, and cannot meet the development requirements of intelligent transportation systems

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