Please wait a minute...
浙江大学学报(工学版)  2019, Vol. 53 Issue (12): 2357-2364    DOI: 10.3785/j.issn.1008-973X.2019.12.013
计算机科学与人工智能     
基于门控循环单元的多因素感知短期游客人数预测模型
王敬昌1(),陈岭2,*(),余珊珊2,蒋晨书2,吴勇1
1. 浙江鸿程计算机系统有限公司,浙江 杭州 310009
2. 浙江大学 计算机科学与技术学院,浙江 杭州 310027
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
 全文: PDF(719 KB)   HTML
摘要:

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    
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.

Key words: short-term tourist number prediction    multi-factor perception    gated recurrent unit (GRU)    convolutional neural network (CNN)    context information
收稿日期: 2018-10-25 出版日期: 2019-12-17
CLC:  TP 391  
基金资助: 国家重点研发计划资助项目(2018YFB0505000)
通讯作者: 陈岭     E-mail: wangjc@zjhcsoft.com;lingchen@cs.zju.edu.cn
作者简介: 王敬昌(1977—),男,高级工程师,从事数据挖掘研究. orcid.org/0000-0003-0884-1472. E-mail: wangjc@zjhcsoft.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
王敬昌
陈岭
余珊珊
蒋晨书
吴勇

引用本文:

王敬昌,陈岭,余珊珊,蒋晨书,吴勇. 基于门控循环单元的多因素感知短期游客人数预测模型[J]. 浙江大学学报(工学版), 2019, 53(12): 2357-2364.

Jing-chang WANG,Ling CHEN,Shan-shan YU,Chen-shu JIANG,Yong WU. Multi-factor perceived short-term tourist number prediction model based on gated recurrent unit. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2357-2364.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.12.013        http://www.zjujournals.com/eng/CN/Y2019/V53/I12/2357

图 1  基于门控循环单元的多因素感知短期游客人数预测模型(MFPPM)框架图
数据集 时间序列 参数符号
景区闸机
数据
景区内游客人数 xtot
进入景区游客人数 xin
离开景区游客人数 xout
监控点车辆
数据
本地客车进入数 xttbin
本地客车离开数 xttbout
本地轿车进入数 xttcin
本地轿车离开数 xttcout
外地本省客车进入数 xtfbin
外地本省客车离开数 xtfbout
外地本省轿车进入数 xtfcin
外地本省轿车离开数 xtfcout
外省客车进入数 xffbin
外省客车离开数 xffbout
外省轿车进入数 xffcin
外省轿车离开数 xffcout
表 1  利用景区闸机数据和监控点车辆数据得到的时间序列
图 2  用于分时序分段策略的CNN结构
图 3  门控循环单元(GRU)示意图
网络层 超参
卷积层 Filters=3;Kernel_size=5;Strides=1;Activation=Relu
池化层 第1个池化层:Pool_size=3;第2个池化层:Pool_size=2
GRU Units=d
全连接层 Units=1;Activation=LeakyRelu(alpha=0.3)
表 2  MFPPM超参设置
评估指标 RMSE MAPE
m=420 193.63 150.16
m=480 190.98 121.08
m=540 193.92 123.65
m=600 197.67 132.82
表 3  MFPPM中参数m对结果的影响
评估指标 RMSE MAPE 评估指标 RMSE MAPE
d=50 195.73 136.54 d=70 190.98 121.08
d=60 192.93 123.56 d=80 197.35 129.70
表 4  MFPPM中输出维度对结果的影响
模型 RMSE MAPE
MFPPM-W/O-Weather 193.02 129. 96
MFPPM-W/O-Holiday 203.04 140.28
MFPPM-W/O-Vehicle 217.04 159.77
MFPPM 190.98 121.08
表 5  不同情境因素对短期游客人数预测结果的影响
模型 RMSE MAPE
SVR-multi 272.63 147.19
GRU-multi 268.76 141.16
CNNGRU-multi 250.49 139.02
MFPPM-only 225.05 130.67
MFPPM 190.98 121.08
表 6  不同短期游客人数预测模型的预测效果对比
1 CHU F L Forecasting tourism demand with ARMA-based methods[J]. Tourism Management, 2009, 30 (5): 740- 751
doi: 10.1016/j.tourman.2008.10.016
2 GUSTAVSSON P, NORDSTROM J The impact of seasonal unit roots and vector ARMA modelling on forecasting monthly tourism flows[J]. Tourism Economics, 2001, 7 (2): 117- 133
doi: 10.5367/000000001101297766
3 BOX G E P, PIERCE D A Distribution of residual autocorrelations in autoregressive-integrated moving average time series models[J]. Journal of the American Statistical Association, 1970, 65 (332): 1509- 1526
doi: 10.1080/01621459.1970.10481180
4 AHMED M S, COOK A R. Analysis of freeway traffic time-series data by using Box-Jenkins techniques [M]. [S. l.]: Transportation Research Board, 1979.
5 LIM C, MCALEER M Time series forecasts of international travel demand for Australia[J]. Tourism Management, 2002, 23 (4): 389- 396
doi: 10.1016/S0261-5177(01)00098-X
6 PAI P F, HONG W C An improved neural network model in forecasting arrivals[J]. Annals of Tourism Research, 2005, 32 (4): 1138- 1141
doi: 10.1016/j.annals.2005.01.002
7 JANACEK G Time series analysis forecasting and control[J]. Journal of Time Series Analysis, 2010, 31 (4): 303
8 WU C H, HO J M, LEE D T Travel-time prediction with support vector regression[J]. IEEE Transactions on Intelligent Transportation Systems, 2004, 5 (4): 276- 281
doi: 10.1109/TITS.2004.837813
9 HONG W C Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm[J]. Neurocomputing, 2011, 74 (12-13): 2096- 2107
doi: 10.1016/j.neucom.2010.12.032
10 CHEN K Y, WANG C H Using genetic algorithms to optimize support vector regression in tourism demand forecasting[J]. Journal of Outdoor Recreat Study, 2005, 18 (1): 47- 72
11 PAI P F, HONG W C, CHANG P T, et al The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study[J]. International Journal of Management, 2006, 23 (2): 375
12 CHEN K Y, WANG C H Support vector regression with genetic algorithms in forecasting tourism demand[J]. Tourism Management, 2007, 28 (1): 215- 226
doi: 10.1016/j.tourman.2005.12.018
13 CHEN K Y Combining linear and nonlinear model in forecasting tourism demand[J]. Expert Systems with Applications, 2011, 38 (8): 10368- 10376
doi: 10.1016/j.eswa.2011.02.049
14 CHEN R, LIANG C Y, HONG W C, et al Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm[J]. Applied Soft Computing, 2015, 26: 435- 443
doi: 10.1016/j.asoc.2014.10.022
15 HOCHREITER S, SCHMIDHUBER J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
doi: 10.1162/neco.1997.9.8.1735
16 CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [J]. arXiv preprint. arXiv: 1406.1078, 2014.
17 LI Y F, CAO H. Prediction for tourism flow based on LSTM neural network [C] // International Conference on Identification, Information and Knowledge in the Internet of Things. Qufu: ELSEVIER, 2017: 277-287.
18 FU R, ZHANG Z, LI L. Using LSTM and GRU neural network methods for traffic flow prediction [C] // 2016 31st Youth Academic Annual Conference of Chinese Association of Automation. Wuhan: IEEE, 2016: 324-328.
19 GUO J, WANG Z, CHEN H. On-line multi-step prediction of short term traffic flow based on GRU neural network [C] // Proceedings of the 2nd International Conference on Intelligent Information Processing. Bangkok: ACM, 2017: 11.
20 王文庆, 郭恒, 范启富. 基于CNN与GRU的航空发动机剩余寿命预测 [C] // 第37届中国控制会议. 武汉: 中国学术期刊电子出版社, 2018: 546-551.
WANG Wen-qing, GUO Heng, FAN Qi-fu. Predict remaining useful life of aerospace engine based on CNN and GRU [C] // Proceedings of the 37th Chinese Control Conference. Wuhan: China Academic Journal Electronic Publishing House, 2018: 546-551.
[1] 陈纬奇,王敬昌,陈岭,杨勇勤,吴勇. 基于深度神经网络的多因素感知终端换机预测模型[J]. 浙江大学学报(工学版), 2021, 55(1): 109-115.
[2] 陈巧红,陈翊,李文书,贾宇波. 多尺度SE-Xception服装图像分类[J]. 浙江大学学报(工学版), 2020, 54(9): 1727-1735.
[3] 张彦楠,黄小红,马严,丛群. 基于深度学习的录音文本分类方法[J]. 浙江大学学报(工学版), 2020, 54(7): 1264-1271.
[4] 杨萍,王丹,康子健,李童,付利华,余悦任. 基于模式识别和集成CNN-LSTM的阵发性房颤预测模型[J]. 浙江大学学报(工学版), 2020, 54(5): 1039-1048.
[5] 李红光,郭英,眭萍,齐子森. 基于时频特征的卷积神经网络跳频调制识别[J]. 浙江大学学报(工学版), 2020, 54(10): 1945-1954.
[6] 贾子钰,林友芳,张宏钧,王晶. 基于深度卷积神经网络的睡眠分期模型[J]. 浙江大学学报(工学版), 2020, 54(10): 1899-1905.
[7] 叶刚,李毅波,马逐曦,成杰. 基于ViBe的端到端铝带表面缺陷检测识别方法[J]. 浙江大学学报(工学版), 2020, 54(10): 1906-1914.
[8] 吕艳,张萌,姜吴昊,倪益华,钱小鸿. 采用卷积神经网络的老年人跌倒检测系统设计[J]. 浙江大学学报(工学版), 2019, 53(6): 1130-1138.
[9] 赫贵然,李奇,冯华君,徐之海,陈跃庭. 基于CNN特征提取的双焦相机连续数字变焦[J]. 浙江大学学报(工学版), 2019, 53(6): 1182-1189.
[10] 董月,冯华君,徐之海,陈跃庭,李奇. Attention Res-Unet: 一种高效阴影检测算法[J]. 浙江大学学报(工学版), 2019, 53(2): 373-381.
[11] 李浩,赵文杰,韩波. 基于滤波器裁剪的卷积神经网络加速算法[J]. 浙江大学学报(工学版), 2019, 53(10): 1994-2002.
[12] 张林, 程华, 房一泉. 基于卷积神经网络的链接表示及预测方法[J]. 浙江大学学报(工学版), 2018, 52(3): 552-559.
[13] 王卫星, 孙守迁, 李超, 唐智川. 基于卷积神经网络的脑电信号上肢运动意图识别[J]. 浙江大学学报(工学版), 2017, 51(7): 1381-1389.
[14] 唐有宝, 卜巍, 邬向前. 多层次MSER自然场景文本检测[J]. 浙江大学学报(工学版), 2016, 50(6): 1134-1140.