Geneology
Polaris Project
Goole Scholar

Papers

• Deep Learning: A Bayesian Perspective (2017)
  N. Polson and V Sokolov. Bayesian Analysis (under review)

• Discussion of ‘Deep learning for finance: deepportfolios’ (2017)
  V. Sokolov. Applied Stochastic Models in Business and Industry.

• Platoon formation maximization through centralized routing and departure time coordination (2017)
  V. Sokolov, J. Larson, T. Munson, J. Auld, D. Karbowski. Transportaiton Research Recorsds.

• Analysis of the impacts of CAV technologies on travel demand (2017)
  J. Auld, V. Sokolov, T. Stephens. Transportation Research Records.

• Deep Learning Predictors for Traffic Flows (2016)
  N. Polson and V. Sokolov

• Bayesian Particle Tracking of Traffic Flows (2016)
  N. Polson and V. Sokolov.

• Assessing The Energy Impact Of Traffic Management and Vehicle Hybridization (2016)
  V. Sokolov, J. Auld, D. Karbowski and N. Kim, International Journal of Complexity in Applied Science and Technology

• POLARIS: Agent-Based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations (2016)
  J. Auld, M. Hope, H. Ley, V. Sokolov, B. Xu and K. Zhang. Transportation Research Part C, 64 101–116.

• Bayesian Analysis of Traffic Flow on Interstate I-55: The LWR Model (2015)
  N. Polson and V. Sokolov. The Annals of Applied Statistics, Volume 9, Number 4, 1864-1888.

• A flexible framework for developing integrated models of transportation systems using an agent-based approach (2012)
  V.Sokolov, J.Auld, and M.Hope. Procedia Computer Science, 10:854-859.

• Internet-based stated response survey for no-notice emergency evacuations (2012)
  J. Auld, V. Sokolov, A. Fontes, and R. Bautista. Transportation Letters: The International Journal of Transportation Research, 4(1):41-53.

• A solution of the affine quadratic inverse eigenvalue problem (2011)
  B.N. Datta and V.Sokolov. Linear Algebra and Its Applications, 434(7):1745-1760.

• Fuzzy rule-based approach for evacuation trip demand modeling (2010)
  Y.S. Park, E.Manli, M.Hope, V.Sokolov, and H.Ley. TRB Annual Meeting, 2010.

• An optimization technique for damped model updating with measured data satisfying quadratic orthogonality constraint (2009)
  B. Datta, S. Deng, V. Sokolov, and D. Sarkissian. Mechanical Systems and Signal Processing, 23(6):1759-1772.

• Quadratic inverse eigenvalue problems, active vibration control and model updating (2009)
  B.N. Datta and V.Sokolov. Appl. Comput. Math, 8(2):170-191.

• Quadratic Inverse Eigenvalue Problems: Theory, Methods, And Applications (2008)
  V. Sokolov. PhD thesis, Northern Illinois University.

• Special preconditioners for solving non-symmetric linear equation systems with dominant skew-symmetric part (2006)
  L.Krukier, O.A. Lapshina, and V.O. Sokolov. International Journal of Numerical Analysis and Modeling, 3(1):115-124.

Some Conference Proceedings

• Coordinated Platoon Routing in a Metropolitan Network (2016)
  J. Larson, T. Munson, V. Sokolov. SIAM Workshop on Combinatorial Scientific Computing.

• A Real World Evaluation of the Effects of Predictive Real-Time Traffic Signal Information (2016)
  D.Etherington, A. Rousseau, V. Sokolov, C. Schmid. ITS America Meeting.

• Addressing Some Issues of Map-Matching for Large-Scale, High-Frequency GPS Data Sets (2016)
  Q. Luo, J. Auld and V. Sokolov. TRB Annual Meeting.

• A Disaggregate Model System for Assessing the Energy Impact of Transportation at the Regional Level(2016)
  J. Auld, D. Karbowski, V. Sokolov and N. Kim. TRB Annual Meeting.

• Investigation of eigenvalue distribution of a matrix arising from a central difference approximation of the two dimensional convection diffusion problem (2003)
  V. Sokolov. Proceedings of the Conference on Numerical methods for solving linear and non-linear boundary problems, Kazan’, Russia.

• Investigation of eigenvalue distribution of transition operators of iterative methods for solution of strongly non-symmetric systems (2003)
  L. Krukier and V. Sokolov. Proceedings of the workshop on contemporary problems in mathematical modeling, Durso, Russia.

Software

• POLARIS: Designer. Developer. Transportation systems simulations framework (C++)

• GREET: Designer. Lead Developer. An implementation of The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) Model. (C#, .NET, SQLite). more then 800 unique users within first year of release, 2013

• MATCOM: Contributor. Distributed on CD with Numerical Linear Algebra and Applications, Second Edition book By Biswa Nath Datta, SIAM. (MATLAB)

• TRANSIMS: Contributor. An agent-based forecast software for modeling regional transport systems. (C++); 22,295 total downloads since 2006

• Advanced Numerical Methods II: Sole Developer. Package for solving large scale control problems. (Mathematica); an experimental library that was not published

Research Coverage

• Argonne wins grant to help transit agencies cope with emergencies (Chicago Tribune)

• Argonne will research how transportation systems should respond to natural hazards (WBEZ)

• What Happens When Developers, Scientists and Super-Computers Connect on Urban Design (Next City)

• UM wins $2.7M grant to study driverless cars (The Detroit News)

• U-M teams with Argonne, Idaho national labs to study potential energy savings of connected vehicles (Michicgan News)

• ARGONNE, FERMILAB, MARINE BIOLOGICAL LABORATORY AND THE UNIVERSITY OF CHICAGO DISCOVERIES THAT CHANGED THE WORLD (University of Chicago News)

• Argonne to study emergency response of Chicago transit (Chicago Sun Times)

• Designing future cities (phys.org)

• Using a Real Life SimCity to Design a Massive Development (Curbed Chicago)