| Computer Science and Artificial Intelligence |
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| Multi-factor perceived short-term tourist number prediction model based on gated recurrent unit |
Jing-chang WANG1( ),Ling CHEN2,*(),Shan-shan YU2,Chen-shu JIANG2,Yong WU1 |
1. Zhejiang Hongcheng Computer Systems Company Limited, Hangzhou 310009, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China |
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Abstract The proposed prediction model adopted a time series segmentation strategy. Firstly, each time series was split into segments and convolved separately by using convolutional neural network (CNN). Then, gated recurrent unit (GRU) was utilized to mine the time series information. Finally, relevant context information (weather conditions and holidays) of the prediction time was added to predict short-term tourist number. Experiments on the gate dataset and vehicle monitoring dataset of a scenic area show that multi-factor perceived short-term tourist number prediction model based on GRU considers multi context factors and the differences between multi time series. The root mean square error (RMSE) and mean absolute percent error (MAPE) of the proposed model are smaller than that of the traditional models, which reveals that the model can effectively reduce short-term tourist number prediction error.
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Received: 25 October 2018
Published: 17 December 2019
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Corresponding Authors:
Ling CHEN
E-mail: wangjc@zjhcsoft.com;lingchen@cs.zju.edu.cn
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基于门控循环单元的多因素感知短期游客人数预测模型
提出的预测模型采取分时序分段策略，使用卷积神经网络（CNN）提取景区多因素时序数据的特征，并对不同因素的时序数据赋予不同的权重，将结果送入门控循环单元（GRU）以挖掘其中的时序信息，结合预测时刻的情境信息（天气状况和节假日）预测短期景区内游客人数. 在某景区的闸机数据集和监控点车辆数据集上的实验结果表明：基于门控循环单元的多因素感知短期游客人数预测模型可以充分考虑多情境因素并对不同因素时序数据赋予不同的权重，均方根误差（RMSE）和平均绝对百分比误差（MAPE）均小于传统模型，能够有效降低短期游客人数预测误差。
关键词：
短期游客人数预测,
多因素感知,
门控循环单元（GRU）,
卷积神经网络（CNN）,
情境信息
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