Public bicycle renting forecasting method based on multi-source data fusion

A public bicycle, multi-source data technology, applied in the field of urban intelligent public transportation system

Inactive Publication Date: 2015-07-15
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiency of a single predictio

Method used

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  • Public bicycle renting forecasting method based on multi-source data fusion
  • Public bicycle renting forecasting method based on multi-source data fusion
  • Public bicycle renting forecasting method based on multi-source data fusion

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Embodiment Construction

[0092] specific implementation

[0093] The present invention will be further described below in conjunction with drawings and embodiments.

[0094] Such as figure 1 As shown, a public bicycle rental prediction method based on multi-source data fusion includes the following steps:

[0095] Step (1) Data collection and processing:

[0096] Read the user's car rental records stored in the public bicycle system (PBS), calculate the remaining number of public bicycles at each station at each time period, and establish a vector N date =[n 1 ,n 2 ,...,n j ,...,n M ], M is the total number of time periods; obtain historical weather conditions, temperature and holidays from the Internet, and establish an environmental attribute vector S={S 1 ,S 2 ,...,S i ,...,S L}, S i =[d,w,t], L is the number of samples in the data sample set; M, L, i and j are all positive integers, where j≤M, i≤L;

[0097] The user car rental record includes rental time, rental site, rental car stake,...

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Abstract

The invention discloses a public bicycle renting forecasting method based on multi-source data fusion. According to the method, historical data about public bicycle renting/returning records, weather, temperature, holidays, festivals and the like are cleaned and preprocessed, and training datasets are acquired; the datasets are classified with a clustering algorithm, and different renting modes of public bicycles are divided; the classified datasets are used to establish a Bayesian classifier used for forecasting the renting modes according to conditions of holidays, festivals, weather and air temperature of one day in the future; a self-adaptive particle swarm neural network model corresponding to each mode is trained for different modes of datasets respectively; finally, the renting mode of one day is forecasted by the aid of the Bayesian classifier, a corresponding particle swarm neural network model is selected to forecast the renting law of public bicycles. The forecasting accuracy is high, the operation speed is high, reference basis is provided for bicycle renting and returning by a user, the duration time of the unbalanced state of a public bicycle station is shortened, and the users' satisfaction is improved.

Description

technical field [0001] The invention belongs to the technical field of urban intelligent public transportation systems, and relates to a method for predicting public bicycle rental based on multi-source data fusion, in particular to a method for predicting the number of bicycle rentals at different time periods in each public bicycle station. Background technique [0002] As a part of urban public transportation, the public bicycle system has the advantages of no pollution and strong mobility, which can effectively relieve urban traffic pressure, reduce carbon dioxide emissions, and improve the urban environment. Due to the fluidity and tidal nature of citizens' travel, there will be problems with no bicycles to borrow and no vacancies to return during certain periods of time. Urban intelligent transportation system (Intelligent Transportation System, referred to as ITS) is to effectively integrate advanced information technology, communication technology, sensor technology,...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30
Inventor 林菲范为迪余日泰徐海涛
Owner HANGZHOU DIANZI UNIV
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