A Bicycle Quantity Prediction Method Based on Binary Gaussian Inhomogeneous Poisson Process

Abstract

The invention discloses a method for predicting the number of bicycles based on a binary Gaussian non-homogeneous Poisson process, which belongs to the technical field of data mining and prediction. According to the historical data of bicycle sites, the Poisson theory is used to establish a non-homogeneous Poisson model, and the environment is considered Influenced by parameters, the final prediction model is established, the time series is simulated and the number of bicycles is corrected. Only the historical bicycle usage data of the bicycle site can be used for prediction. Not only can the corresponding data acquisition be limited, the site The prediction of the number of bicycles can reduce the impact of weather and other non-real-time update data on the prediction results, and significantly improve the accuracy and timeliness of the number of bicycles at the site in a certain period of time in the future.

Description

technical field

[0001] The invention relates to the technical field of data mining prediction, in particular to a method for predicting the number of bicycles based on a binary Gaussian non-homogeneous Poisson process. Background technique

[0002] As a new type of transportation, shared bicycles have developed rapidly in recent years. In the process of use, the distribution of bicycles is often uneven. In order to facilitate the management of the bicycle system administrator, it is necessary to dynamically schedule the bicycles. To dispatch bicycles, it is necessary to predict the number of bicycles at the station in advance. Predicting the number of bicycles is also a challenging task as bicycle usage is affected by many uncertain factors.

[0003] In the existing technology, the commonly used method is to mine the factors that affect the use of bicycles, and then combine some data mining methods (such as regression model, decision tree, neural network, support vector ma...

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