Abstract
Accurate tourist flow forecasting is an important issue in tourist destinations management. Given the influence of various factors on varying degrees, tourist flow with strong nonlinear characteristics is difficult to forecast accurately. In this study, a deep learning method, namely, Gated Recurrent Unit (GRU) is used for the first time for tourist flow forecasting. GRU captures long-term dependencies efficiently. However, GRU’s ability to pay attention to the characteristics of sub-windows within different related factors is insufficient. Therefore, this study proposes an improved attention mechanism with a horizontal weighting method based on related factors importance. This improved attention mechanism is introduced to the encoding–decoding framework and combined with GRU. A competitive random search is also used to generate the optimal parameter combination at the attention layer. In addition, we validate the application of web search index and climate comfort in prediction. This study utilizes the tourist flow of the famous Huangshan Scenic Area in China as the research subject. Experimental results show that compared with other basic models, the proposed Improved Attention-based Gated Recurrent Unit (IA-GRU) model that includes web search index and climate comfort has better prediction abilities that can provide a more reliable basis for tourist destinations management.
Highlights
Since the 2000s, the tourism industry in China has significantly increased given the rapid development of the Chinese economy
The proposed IA-Gated Recurrent Unit (GRU) model is compared with some basic models, such as Back Propagation Neural Network (BPNN), long short-time memory neural network (LSTM), GRU, Attention-LSTM (A-LSTM), and Attention-GRU (A-GRU)
This study proposes Improved Attention-based Gated Recurrent Unit (IA-GRU) model trained with competitive random search (CRS) for accurate tourist flow forecasting
Summary
Since the 2000s, the tourism industry in China has significantly increased given the rapid development of the Chinese economy. The number of inbound and domestic tourists in China is increasing annually, and the tourism industry is developing rapidly [1]. During the peak months, the surge in the number of tourists has brought a series of problems to tourist destinations management, including unreasonable allocation of resources in tourist attractions and congestion of tourists. Accurate tourist flow forecasting is essential for tourist destination management. Daily tourist flow presents a complicated nonlinear characteristic because of the effects of various factors in varying degrees. Accurate tourist flow forecasting remains a difficult task, it has attracted attention in the literature. Developing a new forecasting technique is necessary to obtain a satisfactorily accurate level
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