Festival and holiday scenic spot passenger flow prediction method based on machine learning

Abstract

The invention discloses a festival and holiday scenic spot passenger flow prediction method based on machine learning, and the method comprises the steps: taking the festival and holiday scenic spot passenger flow as a target variable, obtaining a feature variable, carrying out the data preprocessing, and storing the data in a database as sample data; randomly dividing the sample data into a training set and a test set; selecting a support vector regression algorithm as a training model, and training the training model by using the training set to obtain a festival and holiday scenic spot passenger flow prediction model; and performing model prediction by using the test set, evaluating the generalization ability of the model, and evaluating the performance of the model. According to the method, the artificial intelligence algorithm and tourist travel business characteristics in the tourism industry are effectively combined, the influence of various factors and real-time network data on the number of people in the scenic spot in a specific time period is counted by integrating previous data, and the prediction result is corrected by incorporating emergency factors, so that the scenic spot passenger flow condition in festivals and holidays is more accurately predicted.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a machine learning-based method for predicting passenger flow in scenic spots during holidays. Background technique [0002] The rise of holiday tourism has become a new growth point of my country's national economy. Holiday tourism has become an important social and economic phenomenon. Holiday passenger flow forecasting has gradually become one of the important tasks of tourism demand management. [0003] During holidays, a large number of tourists gather at the same place at the same time, which can easily cause problems such as overloading of scenic spots, traffic congestion, tight accommodation, reduced service quality, and increased safety hazards. Accurate holiday passenger flow forecasts can provide effective decision-making assistance for tourist attraction management departments , It also provides a direct basis for the surrounding hotels, highways, avia...

AI Technical Summary

Problems solved by technology

Due to the seasonality and non-linear characteristics of tourist flow during holidays, the traditional time series forecasting method has a good prediction effect on the tourist flow with linear characteristics, but it is often difficult to realize the complex non-linear tourist flow prediction; the linear regression method , gray prediction method and exponential smoothing method can predict the value of passenger flow to a certain extent by analyzing data, but because the algorithm is not effectively combined with the characteristics of tourist travel business in the tourism industry, the prediction results are often not accurate enough when faced with passenger flow samples during holidays

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