Tourist Flow Prediction Based on GA-ACO-BP Neural Network Model

• Published in: Informatics (Basel) Vol. 12; no. 3; p. 89
• Main Authors: Yang, Xiang, Cheng, Yongliang, Dong, Minggang, Xie, Xiaolan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Accuracy; Algorithms; Ant colony optimization; ant colony optimization algorithm; Artificial intelligence; Back propagation networks; BP neural network; Datasets; Digital map services; Efficiency; genetic algorithm; Genetic algorithms; Mathematical optimization; Methods; Neural networks; Optimization algorithms; Performance evaluation; Pheromones; Prediction models; Real time; Resource allocation; Root-mean-square errors; Scenic areas; sustainability in tourism; Sustainable development; Time series; Tourism; Tourism industry; Tourist attractions; tourist flow prediction; Traffic flow; Travel industry; China
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics12030089

Tourist flow prediction plays a crucial role in enhancing the efficiency of scenic area management, optimizing resource allocation, and promoting the sustainable development of the tourism industry. To improve the accuracy and real-time performance of tourist flow prediction, we propose a BP model based on a hybrid genetic algorithm (GA) and ant colony optimization algorithm (ACO), called the GA-ACO-BP model. First, we comprehensively considered multiple key factors related to tourist flow, including historical tourist flow data (such as tourist flow from yesterday, the previous day, and the same period last year), holiday types, climate comfort, and search popularity index on online map platforms. Second, to address the tendency of the BP model to get easily stuck in local optima, we introduce the GA, which has excellent global search capabilities. Finally, to further improve local convergence speed, we further introduce the ACO algorithm. The experimental results based on tourist flow data from the Elephant Trunk Hill Scenic Area in Guilin indicate that the GA-AC*O-BP model achieves optimal values for key tourist flow prediction metrics such as MAPE, RMSE, MAE, and R2, compared to commonly used prediction models. These values are 4.09%, 426.34, 258.80, and 0.98795, respectively. Compared to the initial BP neural network, the improved GA-ACO-BP model reduced error metrics such as MAPE, RMSE, and MAE by 1.12%, 244.04, and 122.91, respectively, and increased the R2 metric by 1.85%.