Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

By R. Silva, S. M. Kang, Edoardo M. Airoldi. 2015

104 citations recorded by the source corpus.

How researchers see this paper

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Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “Both datasets contain about one month of location data, and have been used often in ubiquitous computing [5, 29] and location privacy [22, 27] research.”
Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020, background): “Some other researchers aim to predict travel speed and trafﬁc volume on the road [1], [25].”
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “Some other researchers aim to predict travel speed and traffic volume on the road [25, 26, 27].”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “, applications geared to improve urban planning [4], study the effect of “shocks” on transport [44], predict events [22], detect traffic anomalies [32], generate real-time traffic statistics [1], etc.”
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016, background): “Some other researchers aim to predict travel speed and trafﬁc volume on the road (Abadi, Ra-jabioun, and Ioannou 2015; Silva, Kang, and Airoldi 2015; Xu et al. 2014).”