A method for predicting online car-hailing orders based on multi-source data fusion

Abstract

The invention discloses a method for predicting online car-hailing order volume based on multi-source data fusion. A hierarchical forecasting model based on weighted average of proportion matrix is ​​proposed to forecast OD order quantity. A method based on the weighted average of the proportion matrix is ​​proposed to predict the proportion matrix of the future time slice, and its weight is determined according to the similarity measurement function of time, weather, and other characteristics. Therefore, the algorithm can effectively integrate these multi-source data. fusion. Finally, according to the corresponding value in the obtained ratio matrix, the total order volume of the city is distributed to obtain the order volume of each OD. The present invention uses the gradient boosting regression tree algorithm to predict the total order volume of the city in the future time slice, and then predicts the scale matrix of the future time slice in combination with the weighted average of the proportion matrix, and finally uses the PMWA algorithm to effectively carry out these multi-source data. Fusion, get the order quantity of each OD, effectively solve the "multi-line forecasting" problem, with high forecasting accuracy.

Description

technical field [0001] The invention relates to the field of intelligent transportation systems, in particular to a method for predicting online car-hailing orders based on multi-source data fusion. Background technique [0002] With the continuous development of social economy and urbanization, the demand for urban transportation is growing rapidly, and passengers can choose more travel routes. Online car-hailing has become the preferred way for most people to travel. However, the contradictions of traffic congestion, unreasonable road planning and inadequate infrastructure have made urban road traffic problems increasingly serious, and the problem of "difficulty in taking a taxi" has emerged. It is gratifying that with the development of informatization, computers, automatic control and artificial intelligence The continuous advancement of technology and the establishment of intelligent transportation systems can effectively solve the above problems. [0003] The method o...

AI Technical Summary

Problems solved by technology

At the same time, because the change law of each OD sequence data is different, and commonly used traffic forecasting methods such as ARIMA and LSTM, etc., need to determine different parameters or model structures according to the change law of different OD sequence data, so they can only solve " "Single-line forecast" problem, that is, to predict the OD order quantity based on a single OD sequence data

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