Online car-hailing target order prediction method based on CNN-LSTM

Abstract

The invention relates to the technical field of order data processing, in particular to an online car-hailing target order prediction method based on CNN-LSTM. The method comprises the following steps: 1, fragmenting a preset region into a plurality of sub-regions; 2, collecting original order data of each subarea in a preset area; 3, obtaining target order data based on the original order data, wherein the target order data comprises the total order quantity, the average order price, the POI characteristics, the weather characteristics and the time characteristics of the same region in the same time period; 4, predicting order quantity data of each region in the next time period based on the CNN-LSTM model: inputting order total quantity data, POI features, weather features and time features in the target order data into the CNN-LSTM model to obtain order quantity prediction data of each region in the next time period; and 5, establishing a regional PVD model to obtain a value thermodynamic diagram of each sub-region. According to the invention, the target order data can be predicted comprehensively and accurately.

Description

Technical field [0001] The present invention relates to the field of order data processing, and more particularly to a webmark-based target order prediction method based on CNN-LSTM. Background technique [0002] In recent years, with the rapid development of China's economy and the continuous improvement of urban scale, residents' demand for daily traffic travel is increasing. The webmark has become an important part of the intelligent transportation system. The webmark has a variety of travel methods such as fast-car, windmills, etc., and simultaneously give passengers more choice spaces in terms of taxation time and demand models, which greatly meets the daily travel needs of residents. However, net approximation companies have also faced a series of issues such as: Target order quantity prediction difficulties, and the vehicle scheduling is difficult. These issues have seriously hinder the profitability of webmark trucks, especially the target order quantity prediction, has b...

AI Technical Summary

Problems solved by technology

However, online car-hailing travel companies are also facing a series of problems, such as: difficulty in forecasting target order volume, and difficulty in optimizing vehicle scheduling

These problems have seriously hindered the profitability of online car-hailing travel companies, especially the problem of target order forecasting has become a bottleneck restricting the development of travel companies in recent years, bringing enormous pressure and challenges to the daily management and operation of online car-hailing travel companies , and online car-hailing drivers usually have the problem of long empty driving mileage and long distance from the boarding place. This is caused by the inaccurate prediction of target order volume and insufficient vehicle scheduling. Therefore, it is urgent to design an accurate prediction of target order data. Methods

Images