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Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data.

, , , and . IEEE Trans. Intelligent Transportation Systems, 16 (3): 1370-1379 (2015)

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Using Data From the Web to Predict Public Transport Arrivals Under Special Events Scenarios., , and . J. Intellig. Transport. Systems, 19 (3): 273-288 (2015)Place in Perspective: Extracting Online Information about Points of Interest., , , and . AmI, volume 6439 of Lecture Notes in Computer Science, page 61-72. Springer, (2010)Learning Supervised Topic Models for Classification and Regression from Crowds., , , and . CoRR, (2018)Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach., , and . Information Fusion, (2019)Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach., , and . CoRR, (2018)Can Topic Modelling benefit from Word Sense Information?, , , and . LREC, European Language Resources Association (ELRA), (2016)Robust Optimization for a Maritime Inventory Routing Problem., , , and . Transportation Science, 52 (3): 509-525 (2018)Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation., , and . CoRR, (2018)A Bayesian Additive Model for Understanding Public Transport Usage in Special Events., , , and . CoRR, (2018)Deep Learning from Crowds., and . AAAI, page 1611-1618. AAAI Press, (2018)