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An Optimal Vehicle Number Prediction Method

A prediction method and vehicle number technology, applied in the field of machine learning, can solve problems such as high vehicle idling rate, vehicle demand deviation, slow user growth, etc., and achieve the effect of improving company benefits

Active Publication Date: 2020-12-18
北京首汽智行科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Due to fewer factors considered in manual forecasting, there is a certain deviation between the forecast results and the actual vehicle demand, which affects the company's benefits
When there are too many vehicles in some cities, the growth of users is slow, which will cause the idle rate of vehicles to be too high; when there are too few vehicles in some cities, the number of users will grow rapidly, which will cause the supply to exceed demand and the transportation capacity to be insufficient

Method used

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Embodiment Construction

[0017] The present invention will be specifically introduced below in conjunction with specific embodiments.

[0018] The optimal vehicle number prediction method provided by the embodiment of the present invention includes the following steps:

[0019] S101, within a set period of time, count the number of currently operating vehicles, the number of currently available vehicles, and the average use time of the current vehicles in the same city to obtain current vehicle use data in the city, and generate first vehicle use data.

[0020] S102, collecting the historical number of vehicles in operation, the number of historical available vehicles, the ratio of adding / subtracting vehicles, the number of currently available vehicles, the average usage time of historical operating vehicles, and the average usage time of current operating vehicles in each city to obtain the vehicle usage data of each city, Generate a car data set.

[0021] Among them, the average use time of operati...

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Abstract

An embodiment of the invention provides an optimal vehicle number prediction method, and relates to the technical field of machine learning. According to the method, a binary classification model anda transfer learning mode are adopted, so that the demand quantity of the vehicles in each city can be accurately predicted, and the company benefit is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a method for predicting the optimal number of vehicles. Background technique [0002] At present, the number of vehicles released by the shared car industry in each city is mainly based on market conditions to artificially predict the number of vehicles suitable for each city. The disadvantages of this method are: [0003] Due to fewer factors considered in manual forecasting, there is a certain deviation between the forecasted results and the actual vehicle demand, which affects the company's benefits. When there are too many vehicles in some cities, the growth of users is slow, which will cause the idle rate of vehicles to be too high; when there are too few vehicles in some cities, the number of users will grow rapidly, resulting in a shortage of supply and insufficient transportation capacity. Contents of the invention [0004] In view of the defects ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01
CPCG08G1/0104
Inventor 袁春雷
Owner 北京首汽智行科技有限公司