Method for predicting online car-hailing order quantity based on multi-source data fusion

Abstract

The invention discloses a method for predicting online car-hailing order quantity based on multi-source data fusion. A hierarchical prediction model is proposed based on proportional matrix weighted average to predict the OD order quantity. A proportional matrix weighted average mode is proposed to predict a proportional matrix of a future time slice, and the weight of the proportional matrix is determined according to a similarity measurement function of time, weather and other characteristics, so that an algorithm can effectively fuse the multi-source data. Finally, the total urban order quantity is allocated according to the corresponding value in the obtained proportion matrix to obtain the order quantity of each OD. According to the invention, the total urban order quantity of a future time slice is predicted by using a gradient lifting regression tree algorithm. A gradient lifting regression tree algorithm is adopted to predict city total order quantity of the future time slice,and then proportional matrix of the future time slice is predicted in combination with a proportional matrix weighted average mode. Finally, multi-source data is effectively fused through a PMWA algorithm to obtain the order quantity of each OD. The problem of multi-line prediction is effectively solved, and prediction precision is high.

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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