A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences

Altman, 1992, An introduction to kernel and nearest-neighbour nonparametric regression, The American Statistician, 46, 175 Baek, 1992 Bergstra, 2012, Random search for hyper-parameter optimization, Journal of Machine Learning Research, 13, 281 Bergstra, 2013, Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms Bertsimas, 2019, Travel time estimation in the age of big data, Operations Research, 67, 498 Buhmann, 2013 Chung, 2014, Empirical evaluation of gated recurrent neural networks on sequence modeling, ArXiv Collobert, 2008, A unified architecture for natural language processing: deep neural networks with multitask learning Connor, 1994, Recurrent neural networks and robust time series prediction, IEEE Transactions on Neural Networks, 5, 240, 10.1109/72.279188 Donahue, 2014, DeCAF: a deep convolutional activation feature for generic visual recognition Donovan, 2014 Granger, 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica: Journal of the Econometric Society, 37, 424, 10.2307/1912791 Hartigan, 1979, Algorithm AS 136: a k-means clustering algorithm, Journal of the Royal Statistical Society: Series C (Applied Statistics), 28, 100 Hiemstra, 1994, Testing for linear and nonlinear granger causality in the stock price-volume relation, The Journal of Finance, 49, 1639 Hochreiter, 1997, Long short-term memory, Neural Computation, 9, 1735, 10.1162/neco.1997.9.8.1735 Ke, 2017, Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach, Transportation Research Part C: Emerging Technologies, 85, 591, 10.1016/j.trc.2017.10.016 Koesdwiady, 2016, Improving traffic flow prediction with weather information in connected cars: a deep learning approach, IEEE Transactions on Vehicular Technology, 65, 9508, 10.1109/TVT.2016.2585575 Kumar, 2016, Ask me anything: dynamic memory networks for natural language processing Liu, 2017, A novel passenger flow prediction model using deep learning methods, Transportation Research Part C: Emerging Technologies, 84, 74, 10.1016/j.trc.2017.08.001 Lu, 2017, Exploring models and data for remote sensing image caption generation, IEEE Transactions on Geoscience and Remote Sensing, 56, 2183, 10.1109/TGRS.2017.2776321 Lu, 2017, Remote sensing scene classification by unsupervised representation learning, IEEE Transactions on Geoscience and Remote Sensing, 55, 5148, 10.1109/TGRS.2017.2702596 Ma, 2017, Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction, Sensors, 17, 818, 10.3390/s17040818 Matérn, 1960 Mockus, 1978, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, 2, 117 Polson, 2017, Deep learning for short-term traffic flow prediction, Transportation Research Part C: Emerging Technologies, 79, 1, 10.1016/j.trc.2017.02.024 Rasmussen, 2006 Said, 1984, Testing for unit roots in autoregressive-moving average models of unknown order, Biometrika, 71, 599, 10.1093/biomet/71.3.599 Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Networks, 61, 85, 10.1016/j.neunet.2014.09.003 Siripanpornchana, 2017, Travel-time prediction with deep learning Snoek, 2012, Practical Bayesian optimization of machine learning algorithms Srinivas, 2009, Gaussian process optimization in the bandit setting: no regret and experimental design, ArXiv Vapnik, 1963, Pattern recognition using generalized portrait method, Automation and Remote Control, 24, 774 Wang, 2017, DeepSD: supply-demand prediction for online car-hailing services using deep neural networks Wang, 2017, Traffic speed prediction and congestion source exploration: a deep learning method Wu, 2015, Modeling spatial-temporal clues in a hybrid deep learning framework for video classification Xu, 2017, Real-time prediction of taxi demand using recurrent neural networks, IEEE Transactions on Intelligent Transportation Systems, 19, 2572, 10.1109/TITS.2017.2755684 Yang, 2017, Optimized structure of the traffic flow forecasting model with a deep learning approach, IEEE Transactions on Neural Networks and Learning Systems, 28, 2371, 10.1109/TNNLS.2016.2574840 Yao, 2018, Deep multi-view spatial-temporal network for taxi demand prediction, ArXiv Zhang, 2017, Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns, Transportation Research Part C: Emerging Technologies, 85, 476, 10.1016/j.trc.2017.10.010 Zhang, 2019, A deep learning based multitask model for network-wide traffic speed predication, Neurocomputing Zhang, 2017, Deep spatio-temporal residual networks for citywide crowd flows prediction Zhou, 2018, Predicting multi-step citywide passenger demands using attention-based neural networks