Taxi passenger flow prediction method based on variance-covariance combination

A prediction method and combined prediction technology, applied in the field of intelligent transportation, can solve the problem that the prediction accuracy and stability cannot reach the taxi management and operation.

Pending Publication Date: 2021-09-07
GUANGDONG UNIV OF TECH
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Taxi passenger flow prediction is very important to taxi management and operation, but the existing taxi passenger flow prediction using supp

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Taxi passenger flow prediction method based on variance-covariance combination
  • Taxi passenger flow prediction method based on variance-covariance combination
  • Taxi passenger flow prediction method based on variance-covariance combination

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] The present invention will be further described below in conjunction with specific embodiment:

[0053] Such as figure 1 As shown, a taxi passenger flow prediction method based on variance-covariance combination, specifically includes the following steps:

[0054] S1. Predict the passenger flow of taxi demand on a certain day in the future through the radial basis function neural network (RBFNN) prediction method;

[0055] The radial basis function neural network used is a three-layer feedforward neural network, including an input layer, a nonlinear hidden layer and a linear output layer;

[0056] where the actual output of the output layer is given by:

[0057]

[0058] In the above formula (1), X is the input vector of the network, that is, the historical data of some days in the collected taxi demand passenger flow, and y s is the output of the sth network, that is, predicting the passenger flow of taxi demand in a certain day in the future through the radial bas...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a variance-covariance combination-based taxi passenger flow volume prediction method. The method comprises the steps of S1, predicting taxi demand passenger flow volume in a certain day in the future through a radial basis function neural network prediction method; s2, predicting the passenger flow volume required by the taxi in the same day in the future through a wavelet neural network prediction method; and S3, combining the prediction result obtained in the step S1 and the prediction result obtained in the step S2, and adopting a variance-covariance combined prediction method to obtain a final taxi passenger flow prediction result. According to the method, two combined prediction methods of the radial basis function neural network and the wavelet neural network are combined, the advantages of the two methods are complemented, the disadvantages are avoided, and thus the prediction precision and stability of the taxi passenger flow volume are greatly improved.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a taxi passenger flow prediction method based on variance-covariance combination. Background technique [0002] Taxi passenger flow prediction is very important to taxi management and operation, but the existing taxi passenger flow prediction using support vector machine regression and BP neural network, the prediction accuracy and stability are not as good as taxi management and operation. Require. Contents of the invention [0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a taxi passenger flow prediction method based on variance-covariance combination with high prediction accuracy and high stability. [0004] In order to achieve the above object, the technical scheme provided by the present invention is: [0005] A method for forecasting taxi passenger flow based on variance-covariance combination,...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q30/02G06N3/04G06N3/08
CPCG06Q10/04G06Q30/0201G06N3/08G06N3/045
Inventor 金雷杨大鹏王银银
Owner GUANGDONG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap