Design of a Cultural Tourism Passenger Flow Prediction Model in the Yangtze River Delta Based on Regression Analysis

Xu, Jian‐Xin

doi:10.1155/2021/9913468

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…In this study, a cultural tourism passenger ow forecast framework is designed by taking into account a wide range of in uences. Experimentation has shown that this model can effectively estimate passenger ow in a real application, resulting in a large rise in the quantity of cultural tourist revenue [15]. Via geo-referenced data, they looked at the distribution of user check-ins across ten unique Shanghai neighborhoods.…”

Section: Related Workmentioning

confidence: 99%

Study on Influencing Factors of Tourist Flow in Scenic Spots Based on Big Data

Fu,

Yao,

Liu

et al. 2023

Preprint

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Tourism in China is a thriving industry. For the past few years, the growth of rural tourism was an important emphasis for many areas, which has had a positive impact on rural economies. Chinese people's attention to tourism cultures has shifted dramatically in the period of economic worldwide and the quick propagation of network data. Currently, popular tourism activities are now more attractive. Studying the allocation of tourism activities in terms of avoiding overpopulation and wasteful use of assets in tourist hotspots is a popular subject in the area of tourism. With the use of tourist activity information, this article expands a big data platform focused on spatial and temporal distribution (STD) features and presents a data mining technique founded on revamped genetic-coalesced convolutional neural network with fine-tuned long short-term memory (RG-CCNN-FLSTM) technique to forecast tourist activity in STD aspect. The distribution of tourist traffic can be successfully regulated; the goal of equitable distribution of tourism assets accomplished, and the establishment of intellectual tourism should be further boosted by providing the prediction results back to scenic spot workers in real-time. Using the simulation results, we may conclude that data mining is highly adaptable and accurate. The proposed technique here can lessen the negative effects induced by the unequal distribution of tourism activity and conceptual direction for a better-functioning tourism economy, according to this research. The findings of this article are presented in graphical forms by employing the Origin tool.

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Section: Related Workmentioning

confidence: 99%

Study on Influencing Factors of Tourist Flow in Scenic Spots Based on Big Data

Fu,

Yao,

Liu

et al. 2023

Preprint

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“…Although the GA-CNN-LSTM algorithm has higher accuracy in peak hours than other algorithms, the overall prediction accuracy for peak hours is still insufficient. Xu [12] designed a regression-analysis-based model for predicting cultural tourism flows in the Yangtze River Delta, with the competitiveness of flows as the core, and selected 28 indicators from four aspects: cultural tourism brand resources, cultural tourism support and protection, and urban tourism market income, to build an evaluation index system for the influencing factors of flows, and the designed model has a promising fit. Chen et al [13] combined residual networks with fully connected networks to provide an enhanced Quad-ResNet model for predicting the regional tourist flow of rural tourism.…”

Section: Introductionmentioning

confidence: 99%

Passenger Flow Prediction of Scenic Spots in Jilin Province Based on Convolutional Neural Network and Improved Quantile Regression Long Short-Term Memory Network

Qin

Yin

Dong

et al. 2022

IJGI

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Passenger flow is an important benchmark for measuring tourism benefits, and accurate tourism passenger flow prediction is of great significance to the government and related tourism enterprises and can promote the sustainable development of China’s tourism industry. For daily passenger flow time series data, a passenger flow forecasting method based on convolutional neural network (CNN) and improved quantile regression long short-term memory network (QRLSTM), denoted as CNN-IQRLSTM, is proposed with reconstructed correlation features and in the form of sliding windows as inputs. First, four discrete variables such as whether the day is a weekend and holiday are created by time; then, a sliding window of width 42 is used to pass the passenger flow data into the network sequentially; finally, the loss function of the sparse Laplacian improved QRLSTM is introduced for passenger flow prediction, and the point prediction and interval prediction results under different quartiles are obtained. The application of quantile regression captures the overall picture of the data, enhances the robustness, fit, predictive power and nonlinear processing capability of neural networks, and fills the gap between quantile regression and neural network methods in the field of passenger flow prediction. CNN can effectively handle complex input data, and the improved nonlinear QR model can provide passenger flow quantile prediction information. The method is applied to the tourism traffic prediction of four 5A scenic spots in Jilin Province, and the effectiveness of the method is verified. The results show that the method proposed in this paper fits best in point prediction and has higher prediction accuracy. The MAPE of the Changbai Mountain dataset was 0.07, the MAPE of the puppet palace museum dataset was 0.05, the fit of the Sculpture Park dataset reached 93%, and the fit of the net moon lake dataset was as high as 99%. Meanwhile, the interval prediction results show that the method has a larger interval coverage as well as a smaller interval average width, which improves the prediction efficiency. In 95% of the interval predictions, the interval coverage of Changbai Mountain data is 99% and the interval average width is 0.49. It is a good reference value for the management of different scenic spots.

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