| Deep learning for short-term traffic flow prediction N Polson, V Sokolov Transportation Research Part C: Emerging Technologies 79, 1-17, 2017 | 1083 | 2017 |
| POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations J Auld, M Hope, H Ley, V Sokolov, B Xu, K Zhang Transportation Research Part C: Emerging Technologies 64, 101-116, 2016 | 275 | 2016 |
| Deep learning: A Bayesian perspective NG Polson, V Sokolov | 138 | 2017 |
| Analysis of the effects of connected–automated vehicle technologies on travel demand J Auld, V Sokolov, TS Stephens Transportation Research Record 2625 (1), 1-8, 2017 | 129 | 2017 |
| Deep learning for spatio‐temporal modeling: dynamic traffic flows and high frequency trading MF Dixon, NG Polson, VO Sokolov Applied Stochastic Models in Business and Industry 35 (3), 788-807, 2019 | 87 | 2019 |
| Coordinated platoon routing in a metropolitan network J Larson, T Munson, V Sokolov 2016 Proceedings of the seventh SIAM workshop on combinatorial scientific …, 2016 | 65 | 2016 |
| Analyzing stochastic computer models: A review with opportunities E Baker, P Barbillon, A Fadikar, RB Gramacy, R Herbei, D Higdon, ... Statistical Science 37 (1), 64-89, 2022 | 50* | 2022 |
| An optimization technique for damped model updating with measured data satisfying quadratic orthogonality constraint BN Datta, S Deng, VO Sokolov, DR Sarkissian Mechanical Systems and Signal Processing 23 (6), 1759-1772, 2009 | 45 | 2009 |
| Clusters of driving behavior from observational smartphone data J Warren, J Lipkowitz, V Sokolov IEEE Intelligent Transportation Systems Magazine 11 (3), 171-180, 2019 | 44 | 2019 |
| FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data. H Chen, S Jajodia, J Liu, N Park, V Sokolov, VS Subrahmanian IJCAI, 2074-2080, 2019 | 41 | 2019 |
| Maximization of platoon formation through centralized routing and departure time coordination V Sokolov, J Larson, T Munson, J Auld, D Karbowski Transportation Research Record 2667 (1), 10-16, 2017 | 40 | 2017 |
| Bayesian analysis of traffic flow on interstate I-55: The LWR model N Polson, V Sokolov | 39 | 2015 |
| Bayesian particle tracking of traffic flows N Polson, V Sokolov IEEE Transactions on Intelligent Transportation Systems 19 (2), 345-356, 2017 | 38 | 2017 |
| Bayesian regularization: From Tikhonov to horseshoe NG Polson, V Sokolov Wiley Interdisciplinary Reviews: Computational Statistics 11 (4), e1463, 2019 | 34 | 2019 |
| A solution of the affine quadratic inverse eigenvalue problem BN Datta, V Sokolov Linear Algebra and its Applications 434 (7), 1745-1760, 2011 | 28 | 2011 |
| Deep Learning VOS Nicholas G. Polson Wiley StatsRef: Statistics Reference Online, 2019 | 27* | 2019 |
| Bayesian optimization for transportation simulators L Schultz, V Sokolov Procedia computer science 130, 973-978, 2018 | 25 | 2018 |
| Internet-based stated response survey for no-notice emergency evacuations J Auld, V Sokolov, A Fontes, R Bautista Transportation Letters 4 (1), 41-53, 2012 | 25 | 2012 |
| Quadratic inverse eigenvalue problems, active vibration control and model updating BN Datta, V Sokolov Applied and Computational Mathematics 8 (2), 170-191, 2009 | 24 | 2009 |
| GREET Model: The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model M Wang, R Sabbisetti, A Elgowainy, D Dieffenthaler, A Anjum, V Sokolov Chicago, USA: Argonne National Laboratory, 2014 | 23 | 2014 |
Nick PolsonProfessor of Econometrics and Statistics, University of ChicagoVerified email at chicagobooth.edu
