Traffic Data Analysis of Tourist Attractions Based on Time Domain Partition Model

DOI

10.2991/978-94-6463-262-0_85How to use a DOI?

Keywords

Tourist attractions; Traffic conditions; Time domain division; Multi-dimensional model; Convolutional neural network

Abstract

The traffic data around the scenic spot and the daily average passenger flow trend data associated with the tourist attractions are closely related to the travel. Considering the multiple factors hindering travel, the traffic structure adjustment model around the scenic spot to meet the needs of tourists is put forward, which can also be used as a reference for the management and guidance of tourist attractions. As for solving the problem that the traditional linear regression statistical model has a large error in the parameters of a single function, this paper proposes a prediction model based on Fourier transform. Using the least squares method of time trajectory and the corresponding relationship of spatial trajectory fusion, it improves the matching mode of the convolutional network and establishes a time domain division model to ensure the smooth flow of tourism traffic. Through the integration of real-time data optimization calculation of the time trajectory and spatial adjacent nodes, it provides short-term traffic commuting trend prediction for tourism projects. The experimental results indicate that the prediction results of the proposed method have the best fitting degree with the actual data. The calculation accuracy is more than 8. 5% higher than that of the comparative method, which can more accurately predict the traffic trend around the scenic spot. It also provides a reference for intelligent traffic management as well as for other traffic management platforms.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shuang Che
AU  - Yan Chen
PY  - 2023
DA  - 2023/10/09
TI  - Traffic Data Analysis of Tourist Attractions Based on Time Domain Partition Model
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 820
EP  - 827
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_85
DO  - 10.2991/978-94-6463-262-0_85
ID  - Che2023
ER  -