Pedestrian Flow Prediction with Business Events
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Lei Shi | Wenjun Jiang | Ming Liu | Chao Song | Jiqing Gu | Haigang Gong
[1] Ruslan Salakhutdinov,et al. Probabilistic Matrix Factorization , 2007, NIPS.
[2] Yu Zheng,et al. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.
[3] Rong Du,et al. Predicting activity attendance in event-based social networks: content, context and social influence , 2014, UbiComp.
[4] Simon Scheider,et al. Pedestrian flow prediction in extensive road networks using biased observational data , 2008, GIS '08.
[5] Yongjun Ma,et al. Short term prediction of crowd density using v-SVR , 2010, 2010 IEEE Youth Conference on Information, Computing and Telecommunications.
[6] Xuan Song,et al. CityMomentum: an online approach for crowd behavior prediction at a citywide level , 2015, UbiComp.
[7] Peter Widhalm,et al. Learning Major Pedestrian Flows in Crowded Scenes , 2010, 2010 20th International Conference on Pattern Recognition.
[8] Yannis Theodoridis,et al. Index-based Most Similar Trajectory Search , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[9] Huayu Li,et al. Point-of-Interest Recommender Systems: A Separate-Space Perspective , 2015, 2015 IEEE International Conference on Data Mining.
[10] Xiaogang Wang,et al. Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[11] Ambuj K. Singh,et al. FCCF: forecasting citywide crowd flows based on big data , 2016, SIGSPATIAL/GIS.
[12] Nobuo Sato,et al. Pedestrian-Flow Analysis System for Improving Layout of Exhibitions , 2015, SSTD.
[13] Ryosuke Shibasaki,et al. A mixed autoregressive hidden-markov-chain model applied to people's movements , 2012, SIGSPATIAL/GIS.
[14] Hirozumi Yamaguchi,et al. Getting urban pedestrian flow from simple observation: realistic mobility generation in wireless network simulation , 2005, MSWiM '05.
[15] C. Fookes,et al. A review of pedestrian group dynamics and methodologies in modelling pedestrian group behaviours , 2014 .
[16] Michel Bierlaire,et al. Scenario Analysis of Pedestrian Flow in Public Spaces , 2012 .
[17] Eric Hsueh-Chan Lu,et al. Personalized trip recommendation with multiple constraints by mining user check-in behaviors , 2012, SIGSPATIAL/GIS.
[18] Wenjun Jiang,et al. Brand purchase prediction based on time‐evolving user behaviors in e‐commerce , 2018, Concurr. Comput. Pract. Exp..
[19] Christopher Leckie,et al. Improving Personalized Trip Recommendation by Avoiding Crowds , 2016, CIKM.
[20] Hakan Ferhatosmanoglu,et al. Location Recommendations for New Businesses Using Check-in Data , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[21] Natalie Fridman,et al. Simulating Urban Pedestrian Crowds of Different Cultures , 2018, ACM Trans. Intell. Syst. Technol..
[22] Xuan Song,et al. Prediction of human emergency behavior and their mobility following large-scale disaster , 2014, KDD.
[23] Licia Capra,et al. Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.
[24] Jie Wu,et al. Forming Opinions via Trusted Friends: Time-Evolving Rating Prediction Using Fluid Dynamics , 2016, IEEE Transactions on Computers.