旅游景区游客日流量智能预测方法仿真  

Simulation of intelligent prediction method for daily tourist flow in scenic spots

作　　者：董银军 宋乐[2] DONG Yin-jun;SONG Le(Yunnan Communications Investment&Construction Group Co.,Ltd.Yunnan Kunming 650091,China;Yunnan University,Yunnan Kunming 650091,China)

机构地区：[1]云南省交通投资建设集团有限公司,云南昆明650091 [2]云南大学,云南昆明650091

出　　处：《计算机仿真》2025年第2期453-456,481,共5页Computer Simulation

基　　金：云南省交通投资建设集团有限公司科技创新项目“大滇西旅游环线交旅融合发展路径与业态创新研究”(YCIC-YF-2021-18);云南省哲学社会科学规划项目“大滇西旅游环线线性旅游空间吸引力提升研究”(YB2022020)。

摘　　要：旅游景区日客流量预测需要大量的历史数据作为支撑,这些数据可能来源于多个渠道,存在大量穴余和噪声信息,增加了数据处理的复杂性。为了提高预测精度,以满足旅游景区管理和运营的实际需求,提出了改进BP神经网络的旅游景区日客流量预测算法。将特征递归消除法与随机森林算法结合,利用随机森林算法评估特征的重要性,然后递归地消除最不重要的特征,获取降维处理后的旅游景区日客流量分布特征数据。建立BP神经网络,并通过粒子群算法优化BP神经网络,确定粒子适应度与惯性权重,求得全局最优值,将降维后的数据输人至改进后的BP神经网络中,输出旅游景区日客流量预测结果。实验结果表明,所提算法能够精准预测旅游景区日客流量,确保游客得到良好的服务体验,避免资源浪费。The daily passenger flow forecast of tourist attractions needs a lot of historical data as support,which may come from multiple channels,and contain a lot of redundant and noise information,which increases the complexity of data processing.In order to improve the prediction accuracy and meet the actual needs of the management and operation of tourist attractions,an improved BP neural network algorithm for daily passenger flow prediction of tourist attractions is proposed.Combining the feature recursive elimination method with the random forest algorithm,the importance of features is evaluated by the random forest algorithm,and then the least important features are recursively eliminated to obtain the daily passenger flow distribution feature data of tourist attractions after dimensionality reduction.The BP neural network is established,and the particle swarm optimization is used to optimize the BP neural network,determine the particle fitness and inertia weight,and obtain the global optimal value.The data after dimensionality reduction is input into the improved BP neural network,and the daily passenger flow forecast results of tourist attractions are output.The experimental results show that the proposed algorithm can accurately predict the daily passenger flow of tourist attractions,ensure that tourists get a good service experience and avoid wasting resources.

关 键 词：改进BP神经网络 旅游景区 数据降维 日客流量预测 粒子群算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]