Public bicycle flow variation volume forecasting method based on heap model fusion

A technology of public bicycles and traffic changes, applied in character and pattern recognition, instruments, data processing applications, etc., can solve problems such as rental/returning difficulties, lack of predictability, and no bicycles, so as to achieve good prediction accuracy and avoid excessive The effect of fitting and improving accuracy
CN107045673AActive Publication Date: 2017-08-15HANGZHOU DIANZI UNIV

Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
HANGZHOU DIANZI UNIV
Publication Date
2017-08-15

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Abstract

The invention discloses a public bicycle flow variation volume forecasting method based on heap model fusion. The public bicycle flow variation volume forecasting method comprises the steps of: 1, adopting a method of fusing public bicycle rental record data and meteorological data for extracting features, and constructing eigenvectors from several perspectives of time, space, meteorology, history, clustering and the like; 2, adopting a distance similarity matrix combining geological positions and a rental relation, clustering by utilizing a clustering algorithm, and configuring clustering features into the eigenvectors; 3, dividing the eigenvectors into five groups according to feature types, training five basic models by utilizing a machine learning system based on a gradient boosting tree algorithm, training features by adopting a cross validation method, and training a heap model by taking results of the five groups of basic models as features. The public bicycle flow variation volume forecasting method based on heap model fusion ensures that a certain difference exists among the basic models, constructs the heap model by adopting the cross validation method finally, improves the accuracy degree of the model, has good forecasting precision, and has small errors.
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Description

technical field

[0001] The invention belongs to the fields of intelligent transportation systems and data mining, and relates to a method for predicting the flow variation of public bicycles based on stack model fusion. Background technique

[0002] In the face of deteriorating climate and environment, public bicycles, as a zero-pollution, zero-emission, low-carbon and environmentally friendly transportation method, must be vigorously promoted. Domestically, dozens of cities including Hangzhou, Shanghai, Beijing, Wuhan, and Nanjing have already operated public bicycle systems. On May 5, 2008, Hangzhou City began to operate the public bicycle system. The purpose is to solve the problem of "the last mile". The "bus-bicycle" method can reach the destination conveniently, thereby increasing the bus travel rate. However, after several years of practice, Hangzhou's public bicycle system has encountered some problems that need to be solved urgently. According to the satisfaction...

Claims

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