1 |
YULE G U. Why do we sometimes get nonsense-correlations between time-series? — a study in sampling and the nature of time-series[J]. Journal of the Royal Statistical Society, 1926, 89(1): 1-64. 10.2307/2341482
|
2 |
BOX G, JENKINS G M, REINSEL G C. Time Series Analysis: Forecasting and Control[M]. San Francisco: Holden-Day, 1976: 88-126.
|
3 |
SIMS C A. Macroeconomics and reality[J]. Econometrica, 1980,48(1): 1-48. 10.2307/1912017
|
4 |
LI X L, PAN G, WU Z H, et al. Prediction of urban human mobility using large-scale taxi traces and its applications[J]. Frontiers of Computer Science, 2012, 6(1): 111-121.
|
5 |
MOREIRA-MATIAS L, GAMA J, FERREIRA M, et al. Predicting taxi-passenger demand using streaming data[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3): 1393-1402. 10.1109/tits.2013.2262376
|
6 |
郭宪,沈吟东.基于梯度提升回归树的网约出租车需求预测[C]// 2018世界交通运输大会论文集.北京:人民交通出版社, 2018: 310-320.
|
|
GUO X, SHEN Y D. Prediction for e-hail taxi demands based on gradient boosting regression trees [C]// Proceedings of the 2018 World Transport Convention. Beijing: China Communications Press, 2018: 310-320.
|
7 |
SAADI I, WONG M, FAROOQ B, et al. An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service[EB/OL]. (2017-03-07) [2022-06-25]. .
|
8 |
KE J T, ZHENG H Y, YANG H, et al. Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach[J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 591-608. 10.1016/j.trc.2017.10.016
|
9 |
LeCUN Y, BOSER B, DENKER J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551. 10.1162/neco.1989.1.4.541
|
10 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. 10.1162/neco.1997.9.8.1735
|
11 |
黎景壮,温惠英,林龙,等.基于QPSO_RBF神经网络的网约车需求量预测模型[J].广西大学学报(自然科学版), 2018, 43(2): 700-709.
|
|
LI J Z, WEN H Y, LIN L, et al. Demand forecasting for online car-hailing services based on QPSO-RBF neural network[J]. Journal of Guangxi University (Natural Science Edition), 2018, 43(2): 700-709.
|
12 |
SUN J, FENG B, XU W B. Particle swarm optimization with particles having quantum behavior [C]// Proceedings of the 2004 Congress on Evolutionary Computation, Volume 1. Piscataway: IEEE, 2004: 325-331. 10.1109/cec.2004.1330875
|
13 |
MOODY J, DARKEN C J. Fast learning in networks of locally-tuned processing units[J]. Neural Computation, 1989, 1(2): 281-294. 10.1162/neco.1989.1.2.281
|
14 |
YAO H X, WU F, KE J T, et al. Deep multi-view spatial-temporal network for taxi demand prediction [C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence, Palo Alto, CA: AAAI Press, 2018: 2588-2595. 10.1609/aaai.v32i1.11836
|
15 |
贾瑶.基于时空特征分析的网约车短时需求预测研究[D].北京:北京交通大学, 2019: 31-41.
|
|
JIA Y. Short-term demand forecasting of network car based on spatio-temporal feature analysis[D]. Beijing: Beijing Jiaotong University, 2019: 31-41.
|
16 |
KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82(1): 35-45. 10.1115/1.3662552
|
17 |
WANG Y D, YIN H Z, CHEN H X, et al. Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling [C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 1227-1235. 10.1145/3292500.3330877
|
18 |
FENG S Y, KE J T, YANG H, et al. A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5704-5716. 10.1109/tits.2021.3056415
|
19 |
YU B, YIN H T, ZHU Z X. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting [C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California: ijcai.org, 2018: 3634-3640. 10.24963/ijcai.2018/505
|
20 |
ZHANG D P, XIAO F, SHEN M Y, et al. DNEAT: a novel dynamic node-edge attention network for origin-destination demand prediction[J]. Transportation Research Part C: Emerging Technologies, 2021, 122: No.102851. 10.1016/j.trc.2020.102851
|
21 |
GUO S N, LIN Y F, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting [C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 922-929. 10.1609/aaai.v33i01.3301922
|
22 |
BAI L, YAO L N, LI C, et al. Adaptive graph convolutional recurrent network for traffic forecasting [C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2020: 17804-17815. 10.1109/ijcnn52387.2021.9534063
|