Method for identifying outlier data in effective parking lot occupancy

Abstract

The invention discloses a method for identifying outlier data in effective parking lot occupancy. The method includes steps of 1), determining a time sequence of the effective parking lot occupancy of a parking place; 2), carrying out N-scale wavelet decomposition and reconstruction for the time sequence of the effective parking lot occupancy by a wavelet function to obtain N+1 reconstructed time sequences, and forming a data set D by the N+1 reconstructed time sequences; 3), computing weighted projection vectors of all data points in the data set D with N+1 dimensions to form a weighted data set DW; 4), computing a mean local outlier factor of the weighted data set DW by an outlier data mining algorithm based on density; and 5), judging outlier points on the basis of the mean outlier factor of the weighted data set DW. The outlier data of the effective parking lot occupancy are identified by the outlier data mining algorithm on the basis of wavelet analysis and local information entropy weighting according to the characteristics of periodicity and volatility of the time sequence of the effective parking lot occupancy, the misjudgment rate is reduced, and the reliability is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent information processing in an intelligent transportation system, and relates to a quality control method for traffic data collected by an intelligent transportation system. Background technique [0002] Whether there are vacant berths in the parking lot for parking is one of the most concerned issues when drivers choose to park. It is a key technology of the parking guidance information system to obtain the information of vacant parking spaces in the parking lot by using the information collection technology of parking spaces. Accurately collect the vacant berth information of the parking lot, which can be used as a reference for system users when choosing a parking lot. [0003] Due to sampling distortion, measurement error, equipment failure and other possible influencing factors, there are usually samples that do not follow the law of data fluctuations in the collected effective parking spa...

AI Technical Summary

Problems solved by technology

The conventional outlier detection mode generally mines outliers for a single data point mapped in a high-dimensional vector space, and determines the difference and the discrimination threshold by defining the distance between various data points. Such a method cannot consider the cycle of data in the time series volatility, and often misjudge the peaks and troughs of the data

At present, in the field of traffic engineering, outlier data is mainly identified based on statistical methods. This method is simple to calculate, but its application needs to clarify the distribution of data in advance, which is generally difficult to achieve, and the actual data often does not meet any ideal state. mathematical distribution

In addition, most outlier detection algorithms based on statistics are only suitable for mining univariate numerical data, and it is difficult to identify high-dimensional, periodic, and classified data.

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