Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data
Published Online:11 Feb 2022https://doi.org/10.1287/isre.2021.1072
References
- (2016) Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inf. Syst. 17(2):i–xxxii.Google Scholar
- (2008) Similarity measures for categorical data: A comparative evaluation. Proc. SIAM Data Mining Conf., 243–254.Google Scholar
- (2016) RETAIN: Interpretable predictive model in healthcare using reverse time attention mechanism. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. NIPS (Barcelona, Spain). https://proceedings.neurips.cc/paper/2016/file/231141b34c82aa95e48810a9d1b33a79-Paper.pdf.Google Scholar
- (2021) An image is worth 16x16 words: Transformers for image recognition at scale. Proc. 9th Internat. Conf. Learning Representation.Google Scholar
- (2020) Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intelligent Transportation Systems 21(3):972–985.Crossref, Google Scholar
- (2015) Crowdsending based public transport information service in smart cities. IEEE Comm. Magazine 53(8):158–165.Crossref, Google Scholar
- (2014) Sequential framework for short-term passenger flow prediction at bus stop. Transportation Res. Rec. 2417:58–66.Crossref, Google Scholar
- (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782.Crossref, Google Scholar
- (2016) Deep Learning (MIT Press, Cambridge, MA).Google Scholar
- (2011) Assessing the cost of transfer inconvenience in public transport systems: A case study of the London Underground. Transportation Res. Part A Policy Practice 45(2):91–104.Crossref, Google Scholar
- (2003) Urban transport in developing countries. Transportation Rev. 23(2):197–216.Crossref, Google Scholar
- (2020) Smart city operations: Modeling challenges and opportunities. Manufacturing Service Oper. Management 22(1):203–213.Link, Google Scholar
- (2014) Deep neural network based load forecast. Comput. Modeling New Tech. 18(3):258–262.Google Scholar
- (1997) Long short-term memory. Neural Comput. 9(8):1735–1780.Crossref, Google Scholar
- (2012) ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Processing Systems 1–9.Google Scholar
- (2014) A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Lett. 42(1):11–24.Crossref, Google Scholar
- (1998) Efficient BackProp. Neural Networks: Tricks Trade 7700:9–48.Crossref, Google Scholar
- (2007) A generalized and efficient algorithm for estimating transit route ODs from passenger counts. Transportation Res., Part B: Methodological 41:114–125.Crossref, Google Scholar
- (2015) Learning entity and relation embeddings for knowledge graph completion. Proc. 29th AAAI Conf. Artificial Intelligence and Learning, 2181–2187.Google Scholar
- (2017) A novel passenger flow prediction model using deep learning methods. Transportation Res., Part C Emerging Tech. 84:74–91.Crossref, Google Scholar
- (2009) Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3):127–149.Crossref, Google Scholar
- (2015) Traffic flow prediction with big data: A deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2):865–873.Google Scholar
- (2013) Distributed representations of words and phrases and their compositionality. Adv. Neural Inform. Processing Systems 3111–3119.Google Scholar
- (2012) Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile. Transportation Res., Part C Emerging Tech. 24:9–18.Crossref, Google Scholar
- (1992) Advancement in the application of neural networks for short-term load forecasting. IEEE Trans. Power Systems 7(1):250–257.Crossref, Google Scholar
- (2014) Glove: Global vectors for word representation. Proc. Conf. Empirical Methods and Natural Language Processing, 1532–1543.Google Scholar
- (2015) Deep learning in neural networks: An overview. Neural Networks 61:85–117.Crossref, Google Scholar
- (2005) Early vs. late fusion in semantic video analysis. Pereira F, Burges CJC, Bottou L, Weinberger KQ, eds. Proc. ACM Internat. Conf. Multimed., Lake Tahoe, USA, 399–402.Google Scholar
- (2015) Training very deep networks. Adv. Neural Inform. Processing Systems 2377–2385.Google Scholar
- (2012) The impact of weather on bus ridership in Pierce County, Washington. J. Public Transportation 6(16):95–110.Crossref, Google Scholar
- (2006) A Bayesian network approach to traffic flow forecasting. IEEE Trans. Intelligent Transportation Systems 7(1):124–132.Crossref, Google Scholar
- (2014) Demand-driven timetable design for metro services. Transportation Res., Part C Emerging Tech. 46(September):284–299.Crossref, Google Scholar
- (2009) Factored conditional restricted Boltzmann machines for modeling motion style. Proc. 26th Annual Internat. Conf. on Machine Learning, 1–8.Google Scholar
- (2020) Applications of artificial intelligence and machine learning in smart cities. Comput. Comm. 154(February):313–323.Crossref, Google Scholar
- (2017) Attention is all you need. Adv. Neural Inform. Processing Systems 5998–6008.Google Scholar
- (1989) Phoneme recognition using time-delay neural networks. Acoustic Speech Signal Processing IEEE Trans. 37(3):328–339.Crossref, Google Scholar
- (2011) Bus passenger origin-destination estimation and related analyses using automated data collection systems. J. Public Transportation 14(4):131–150.Crossref, Google Scholar
- (2016) A big data approach for smart transportation management on bus network. Proc. IEEE Internat. Conf. on Smart Cities.Google Scholar
- (2021) Predicting bus passenger flow and prioritizing influential factors using multi-source data: Scaled stacking gradient boosting decision trees. IEEE Trans. Intelligent Transportation Systems 22(4):2510–2523.Crossref, Google Scholar
- (2015) Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discrete Dynamics Nature Society i:1–11.Google Scholar
- (2016) Exploring spatial-temporal patterns of urban human mobility hotspots. Sustainability 8(7):674–692.Google Scholar
- Yang X, Xue Q, Yang X, Yin H, Qu Y, Li X, Wu J (2021) A novel prediction model for the inbound passenger flow of urban rail transit. Inform. Sci. (NY) 566:347–363.Google Scholar
- (2018) Deep multi-view spatial-temporal network for taxi demand prediction. Proc. AAAI Conf. on Artificial Intelligence.Google Scholar
- (2013) Reconstructing individual mobility from smart card transactions: A space alignment approach. Proc. IEEE Internat. Conf. on Data Mining, 877–886.Google Scholar
- (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction. Singh S, Markovitch S, eds. AAAI (AAAI Press, San Francisco), 1655–1661.Google Scholar
- (2015) Heterogeneous feature selection with multi-modal deep neural networks and sparse group LASSO. IEEE Trans. Multimedia 17(11):1936–1948.Crossref, Google Scholar

