[1]李伯涵,郭茂祖,赵玲玲.基于分割注意力机制残差网络的城市区域客流量预测[J].智能系统学报,2022,17(4):839-848.[doi:10.11992/tis.202202014]
LI Bohan,GUO Maozu,ZHAO Lingling.Passenger flow prediction in urban areas based on residual networks with split attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(4):839-848.[doi:10.11992/tis.202202014]
点击复制
LI Bohan,GUO Maozu,ZHAO Lingling.Passenger flow prediction in urban areas based on residual networks with split attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(4):839-848.[doi:10.11992/tis.202202014]
基于分割注意力机制残差网络的城市区域客流量预测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
17
期数:
2022年第4期
页码:
839-848
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2022-07-05
- Title:
- Passenger flow prediction in urban areas based on residual networks with split attention mechanism
- Keywords:
- passenger flow prediction; spatio-temporal data; deep learning; split-attention residual network; urban functional area; feature extraction; intelligent city; intelligent transportation
- 分类号:
- TP391
- 摘要:
- 客流量预测是城市交通资源和公共安全智能化管理的重要依据。为了综合考虑城市乘客人群流动自身的既有周期性、趋势性和突发性，以及与城市物理和社会空间的耦合关系，在时空残差网络的基础上，本文提出了基于深度时空数据的分割注意力机制残差网络的城市细粒度客流量预测模型。首先以不同时空间隔的区域客流量历史数据为基础，引入分割注意力机制模块，为各模态的数据分配不同的权重，动态捕捉更高相关性的抽象数据特征；在时空数据的基础上，引入城市功能区属性作为联合特征，结合节假日、气候等外部特征，形成deep&wide网络结构，有效记忆重要特征对客流量变化的贡献。基于北京出租车数据的区域客流量对比实验表明，相比于传统的深度时空残差网络和其他经典机器学习模型，引入了分割注意力机制和城市功能区特征的预测模型能够更好地提取数据多元化的特征，预测精度明显优于其他同类别方法。
- Abstract:
- Passenger flow prediction is an important basis for intelligent management of urban transportation resources and public safety. In order to comprehensively consider the existing periodicity, trend and suddenness of urban passenger crowd flow itself, as well as the coupling relationship with urban physical and social space, based on the spatio-temporal residual network, this paper proposes an urban fine-grained passenger flow prediction model based on the residual network of split-attention mechanism with deep spatio-temporal data. Firstly, based on the regional passenger flow history data of different spatio-temporal intervals, the segmented attention mechanism module is introduced to assign different weights to the data of each modality to dynamically capture the abstract data features of higher relevance; on the basis of spatio-temporal data, the city functional area attributes are introduced as joint features, which are combined with external features such as holidays and climate to form a deep&wide network structure to effectively remember the contribution of important features to passenger flow changes. The regional passenger flow comparison experiments based on Beijing cab data show that compared with the traditional deep spatio-temporal residual network and other classical machine learning models, [] the prediction model introducing segmented attention mechanism and urban functional area features can better extract the features of data diversity, and the prediction accuracy is significantly better than other methods of the same category.
- 参考文献/References:
-
[1] GONG Yongshun, LI Zhibin, ZHANG Jian, et al. Network-wide crowd flow prediction of Sydney trains via customized online non-negative matrix factorization[C]//CIKM’18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 1243?1252.
[2] MA Xiaolei, DAI Zhuang, HE Zhengbing, et al. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[J]. Sensors, 2017, 17(4): 818.
[3] SILVA R, KANG S M, AIROLDI E M. Predicting traffic volumes and estimating the effects of shocks in massive transportation systems[J]. Proceedings of the national academy of sciences of the United States of America, 2015, 112(18): 5643?5648.
[4] FLORIO L, MUSSONE L. Neural-network models for classification and forecasting of freeway traffic flow stability[J]. Control engineering practice, 1996, 4(2): 153–164.
[5] XU Yanyan, KONG Qingjie, KLETTE R, et al. Accurate and interpretable Bayesian MARS for traffic flow prediction[J]. IEEE transactions on intelligent transportation systems, 2014, 15(6): 2457–2469.
[6] CHEN Pota, CHEN Feng, QIAN Zhen. Road traffic congestion monitoring in social media with hinge-loss Markov random fields[C]//2014 IEEE International Conference on Data Mining. Shenzhen: IEEE, 2014: 80?89.
[7] ZHENG Yu, YI Xiuwen, LI Ming, et al. Forecasting fine-grained air quality based on big data[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2015: 2267?2276.
[8] ZHAO Yi, LI Jianbo, MIAO Xin, et al. Urban crowd flow forecasting based on cellular network[C]//ACM TURC ’19: Proceedings of the ACM Turing Celebration Conference - China. New York: ACM, 2019: 1?5.
[9] HE Yuxin, LI Lishuai, ZHU Xinting, et al. Multi-graph convolutional-recurrent neural network (MGC-RNN) for short-term forecasting of transit passenger flow[J]. IEEE transactions on intelligent transportation systems, PP(99): 1?20.
[10] WEI Yu, CHEN Muchen. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks[J]. Transportation research part C:emerging technologies, 2012, 21(1): 148–162.
[11] LI Yang, WANG Xudong, SUN Shuo, et al. Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks[J]. Transportation research part C:emerging technologies, 2017, 77: 306–328.
[12] FU Xiao, YU Guanyi, LIU Zhiyuan. Spatial-temporal convolutional model for urban crowd density prediction based on mobile-phone signaling data[J]. IEEE transactions on intelligent transportation systems, 2021, 13(2): 1–13.
[13] WANG Senzhang, MIAO Hao, LI Jiyue, et al. Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks[J]. IEEE transactions on intelligent transportation systems, 2022, 23(5): 4695–4705.
[14] MA Jiaman, CHAN J, RAJASEGARAR S, et al. Multi-attention 3D residual neural network for origin-destination crowd flow prediction[C]//2020 IEEE International Conference on Data Mining. Sorrento: IEEE, 2020: 1160?1165.
[15] EBRAHIMPOUR, WAN, CERVANTES, et al. Comparison of main approaches for extracting behavior features from crowd flow analysis[J]. ISPRS international journal of geo-information, 2019, 8(10): 440.
[16] SMITH B L, DEMETSKY M J. Traffic flow forecasting: comparison of modeling approaches[J]. Journal of transportation engineering, 1997, 123(4): 261–266.
[17] BOX G, JENKINS G, REINSEL G. Time series analysis: forecasting and control. rev. ed[J]. Journal of marketing research, 1977, 14(2): 269.
[18] SMITH B L, WILLIAMS B M, KEITH OSWALD R. Comparison of parametric and nonparametric models for traffic flow forecasting[J]. Transportation research part C:emerging technologies, 2002, 10(4): 303–321.
[19] ZHANG Junbo, ZHENG Yu, QI Dekang. Deep spatio-temporal residual networks for citywide crowd flows prediction[J]. Proceedings of the AAAI conference on artificial intelligence, 2017, 31(1): 1655–1661.
[20] SONG Xuan, ZHANG Quanshi, SEKIMOTO Y, et al. Prediction of human emergency behavior and their mobility following large-scale disaster[C]//KDD’14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM, 2014: 5?14.
[21] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770?778.
[22] CHANDRA S R, AL-DEEK H. Predictions of freeway traffic speeds and volumes using vector autoregressive models[J]. Journal of intelligent transportation systems, 2009, 13(2): 53–72.
[23] QIN Tianxiang, LIU Tong, WU Hexiang, et al. RESGCN: RESidual graph convolutional network based free dock prediction in bike sharing system[C]//2020 21st IEEE International Conference on Mobile Data Management. Versailles: IEEE, 2020: 210?217.
[24] YAO Huaxiu, WU Fei, KE Jintao, et al. Deep multi-view spatial-temporal network for taxi demand prediction[J]. Proceedings of the AAAI conference on artificial intelligence, 2018, 32(1).
[25] ZHANG HANG, WU CHONGRUO, ZHANG ZHONGYUE, et al. ResNeSt: split-attention networks[EB/OL]. (2020?04?30)[2022?02?20]. https://www.researchgate.net/publication/340805846_resnest_split-attention_networks.
[26] XIE Saining, GIRSHICK R, DOLLáR P, et al. Aggregated residual transformations for deep neural networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 5987?5995.
[27] YAMAMOTO M, SATO A, KAWADA S, et al. Incremental tracking of human actions from multiple views[C]//Proceedings of 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara: IEEE, 1998: 2?7.
[28] LECUN Y A, BOTTOU L, ORR G B, et al. Efficient BackProp[M]//Lecture Notes in Computer Science. Berlin: Springer Berlin Heidelberg, 2012: 9?48.
[29] LI Xiang, WANG Wenhai, HU Xiaolin, et al. Selective kernel networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 510?519.
[30] LI Yexin, ZHENG Yu, ZHANG Huichu, et al. Traffic prediction in a bike-sharing system[C]//SIGSPATIAL ’15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2015: 1?10.
[31] WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7794?7803.
[32] ZHANG Junbo, ZHENG Yu, QI Dekang. 北京出租车数据集[EB/OL].[2022?02?20]. https://gitee.com/arislee/taxi-bj.
[33] LI Bohan. ST-SANet 算法代码[EB/OL].[2022?02?20]. https://gitee.com/arislee/st-sanet_-code.git
[34] ZHANG Junbo, ZHENG Yu, QI Dekang, et al. DNN-based prediction model for spatio-temporal data[C]//SIGSPACIAL’16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2016: 1?4.
[35] ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50: 159–175.
[36] ZHANG G P, QI Min. Neural network forecasting for seasonal and trend time series[J]. European journal of operational research, 2005, 160(2): 501–514.
[37] ZAREMBA W, SUTSKEVER I, VINYALS O. Recurrent neural network regularization[EB/OL]. (2014?09?08)[2022?02?20]. https: //arxiv. org/abs/1409.2329.
[38] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735–1780.
[39] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL]. (2014?06?03)[2022?02?20]. https: //arxiv. org/abs/1406.1078.
[40] DOUGHERTY M S, COBBETT M R. Short-term inter-urban traffic forecasts using neural networks[J]. International journal of forecasting, 1997, 13(1): 21–31.
备注/Memo
收稿日期:2022-02-20。
基金项目:国家自然科学基金面上项目（61871020）；北京市属高校高水平创新团队建设计划项目（IDHT20190506）
作者简介:李伯涵，硕士研究生，主要研究方向为深度学习、智慧城市、时间序列数据;郭茂祖，教授，博士，博士生导师，北京建筑大学电气与信息工程学院院长，“建筑大数据智能处理方法研究”北京市重点实验室主任，中国人工智能学会机器学习专委会常委、中国建筑学会计算机性设计学术委员会常委、中国计算机学会生物信息学专委会副主任，主要研究方向为机器学习、智慧城市、计算生物学等。2019年以第一完成人获吴文俊人工智能自然科学二等奖。发表学术论文300余篇;赵玲玲，副教授，中国计算机学会生物信息学专委会委员，中国建筑学会计算性设计专委会委员，主要研究方向为机器学习、城市计算、生物信息学。主持和参与国家自然科学基金青年基金、面上项目、重点项目8项。发表学术论文40余篇。
通讯作者:赵玲玲. Email: zhaoll@hit.edu.cn
更新日期/Last Update:
1900-01-01