Tourism traffic demand prediction method, device and system based on Markov chain

Abstract

The invention provides a tourism traffic demand prediction method, device and system based on a Markov chain, and relates to the technical field of traffic model prediction. The tourism traffic demand prediction method based on the Markov chain comprises the following steps: determining an initial state matrix according to the number of initial tourists in each function zone of a tourism area; determining a tourist traffic transfer matrix according to the tourist transfer probability among the functional zones of the tourist district; and predicting the tourism traffic demand according to the initial state matrix and the tourism traffic transfer matrix. According to the technical scheme of the invention, the tourism traffic demand is predicted through the initial state matrix and the tourism traffic transfer matrix, and the state value of the next time period is predicted according to the state value of the previous time period and the transfer matrix, so that the tourist travel distribution can be calculated by simulating the tourist transfer process between tourist attractions. Compared with an existing four-stage method, the calculation accuracy of internal travel and the roaming traffic demand prediction accuracy are improved.

Description

technical field [0001] The present invention relates to the technical field of traffic model prediction, in particular, to a Markov chain-based tourism traffic demand prediction method, device and system. Background technique [0002] Tourism traffic refers to the whole process of traveling with the main purpose of tourism and leisure, including arrival and departure traffic outside the tourist area and roaming traffic in the tourist area. Arrival and departure traffic can use the "four-stage method" for demand forecasting, while travel between scenic spots emphasizes tourist routes and tour ranges, and attaches importance to the correlation between various trips. The "four-stage method" is based on the aggregate statistical method, and calculates the travel distribution between scenic spots according to the occurrence and attraction rate of each scenic spot. The travel correlation of internal communities is weak, and the prediction accuracy for roaming travel between scenic...

AI Technical Summary

Problems solved by technology

The "four-stage method" is based on the aggregate statistical method, and calculates the travel distribution between scenic spots according to the occurrence and attraction rate of each scenic spot. The travel correlation of internal communities is weak, and the prediction accuracy of roaming travel between scenic spots is low.

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