Vadim Sokolov
Cited by
Cited by
Deep learning for short-term traffic flow prediction
N Polson, V Sokolov
Transportation Research Part C: Emerging Technologies 79, 1-17, 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
Deep learning: A Bayesian perspective
NG Polson, V Sokolov
Bayesian Analysis 12 (4), 1275-1304, 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
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
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
Coordinated platoon routing in a metropolitan network
J Larson, T Munson, V Sokolov
2016 Proceedings of the Seventh SIAM Workshop on Combinatorial Scientific …, 2016
Bayesian analysis of traffic flow on interstate I-55: The LWR model
N Polson, V Sokolov
Annals of Applied Statistics 9 (4), 1864-1888, 2015
A solution of the affine quadratic inverse eigenvalue problem
BN Datta, V Sokolov
Linear algebra and its applications 434 (7), 1745-1760, 2011
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
Quadratic inverse eigenvalue problems, active vibration control and model updating
BN Datta, V Sokolov
Applied and Computational Mathematics 8 (2), 170-191, 2009
Bayesian particle tracking of traffic flows
N Polson, V Sokolov
IEEE Transactions on Intelligent Transportation Systems 19 (2), 345-356, 2017
Como funciona o deep learning
MA Ponti, GBP Da Costa
arXiv preprint arXiv:1806.07908, 2018
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
A flexible framework for developing integrated models of transportation systems using an agent-based approach
V Sokolov, J Auld, M Hope
Procedia Computer Science 10, 854-859, 2012
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
Clusters of driving behavior from observational smartphone data
J Warren, J Lipkowitz, V Sokolov
IEEE Intelligent Transportation Systems Magazine 11 (3), 171-180, 2019
Vehicle energy management optimisation through digital maps and connectivity
D Karbowski, V Sokolov, A Rousseau
22nd ITS World Congress, 5-9, 2015
Discussion of ‘Deep learning for finance: deep portfolios’
V Sokolov
Applied Stochastic Models in Business and Industry 33 (1), 16-18, 2017
Bayesian regularization: From Tikhonov to horseshoe
NG Polson, V Sokolov
Wiley Interdisciplinary Reviews: Computational Statistics 11 (4), e1463, 2019
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