[1] |
ZHOU L, WU D, DONG Z J ,et al.When Collaboration Hugs Intelligence: Content Delivery over Ultra-Dense Networks[J]. IEEE Communications Magazine, 2017,55(12):91-95. DOI: 10.1109/mcom.2017.1700481
|
[2] |
ZHOU L, WU D, CHEN J X ,et al.Greening the Smart Cities: Energy-Efficient Massive Content Delivery via D2D Communications[J]. IEEE Transactions on Industrial Informatics, 2018,14(4):1626-1634. DOI: 10.1109/tii.2017.2784100
|
[3] |
WU D, WANG J L, HU R Q ,et al.Energy-Efficient Resource Sharing for Mobile Device-To-Device Multimedia Communications[J]. IEEE Transactions on Vehicular Technology, 2014,63(5):2093-2103. DOI: 10.1109/tvt.2014.2311580
|
[4] |
ZHAO T S, LIU Q, CHEN C W . QoE in Video Transmission: A User Experience-Driven Strategy[J]. IEEE Communications Surveys & Tutorials, 2017,19(1):285-302. DOI: 10.1109/comst.2016.2619982
|
[5] |
PAMULA T . Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks[J]. Archives of Transport, 2012,24(4):519-529. DOI: 10.2478/v10174-012-0032-2
|
[6] |
GUO F C, POLAK J W, KRISHNAN R. Comparison of Modelling Approaches for Short Term Traffic Prediction under Normal and Abnormal Conditions [C]//13th International IEEE Conference on Intelligent Transportation Systems. Funchal, Portugal, 2010: 1209-1214. DOI: 10.1109/ITSC.2010.5625291
|
[7] |
WILLIAMS N, ZANDER S, ARMITAGE G . A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification[J]. ACM SIGCOMM Computer Communication Review, 2006,36(5):5. DOI: 10.1145/1163593.1163596
|
[8] |
HU C C, YAN X H. Mining Traffic Flow Data Based on Fuzzy Clustering Method [C]//Fourth International Workshop on Advanced Computational Intelligence. Wuhan, China, 2011: 245-248. DOI: 10.1109/IWACI.2011.6160011
|
[9] |
HÖCHST J, BAUMGÄRTNER L, HOLLICK M, et al. Unsupervised Traffic Flow Classification Using a Neural Autoencoder [C]//IEEE 42nd Conference on Local Computer Networks (LCN). Singapore, Singapore, 2017: 523-526. DOI: 10.1109/LCN.2017.57
|
[10] |
JEONG Y S, JEONG M K, OMITAOMU O A . Weighted Dynamic Time Warping for Time Series Classification[J]. Pattern Recognition, 2011,44(9):2231-2240. DOI: 10.1016/j.patcog.2010.09.022
|
[11] |
CHEN T, GUESTRIN C,. XGBoost: A Scalable Tree Boosting System [C]//ACM International Conference on Knowledge Dicovery and Data Mining. San Francisco, USA, 2016: 785-794
|
[12] |
LI Z, LEI Q, XUE K Y, et al. A Novel BP Neural Network Model for Traffic Prediction of Next Generation Network [C]//Fifth International Conference on Natural Computation, Tianjin, China, 2009: 32-38. DOI: 10.1109/ICNC.2009.673
|
[13] |
YANG W, YANG D Y, ZHAO Y L, et al. Traffic Flow Prediction Based on Wavelet Transform and Radial Basis Function Network [C]//International Conference on Logistics Systems and Intelligent Management (ICLSIM). Harbin, China, 2010: 969-972. DOI: 10.1109/ICLSIM.2010.5461098
|
[14] |
ZAHARIA M, CHOWDHURY M, FRANKLIN M, et al. Spark: Cluster Computing with Working Sets [C]//2nd Usenix Conference on Hot Topicsin Cloud Computing. Boston, USA, 2010: 10-10
|
[15] |
DEAN J, GHEMAWAT S. MapReduce: Simplified Data Processing on Large Clusters [C]//6th Symposium on Operating Systems Design & Implementaion. San Francisco, USA, 2004: 137-149
|
[16] |
SAKOE H, CHIBA S . Dynamic Programming Algorithm Optimization for Spoken Word Recognition[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978,26(1):43-49. DOI: 10.1109/tassp.1978.1163055
|
[17] |
MA X L, DING C, LUAN S ,et al. Prioritizing Influential Factors for Freeway Incident Clearance Time Prediction Using the Gradient Boosting Decision Trees Method[J]. IEEE Transactions on Intelligent Transportation Systems, 2017,18(9):2303-2310. DOI: 10.1109/tits.2016.2635719
|