Vadim Sokolov

Bio: Vadim Sokolov is an academic researcher from George Mason University. The author has contributed to research in topics: Deep learning & Stochastic gradient descent. The author has an hindex of 15, co-authored 60 publications receiving 1349 citations. Previous affiliations of Vadim Sokolov include Argonne National Laboratory & Northern Illinois University.

Deep learning for short-term traffic flow prediction

Abstract: We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.

POLARIS: Agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations

Abstract: This paper discusses the development of an agent-based modeling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents show the potential of the system.

Deep Learning: A Bayesian Perspective

Abstract: Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning Traditional high-dimensional data reduction techniques, such as principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are all shown to be shallow learners Their deep learning counterparts exploit multiple deep layers of data reduction which provide predictive performance gains Stochastic gradient descent (SGD) training optimisation and Dropout (DO) regularization provide estimation and variable selection Bayesian regularization is central to finding weights and connections in networks to optimize the predictive bias-variance trade-off To illustrate our methodology, we provide an analysis of international bookings on Airbnb Finally, we conclude with directions for future research

POLARIS: Agent-Based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations

Abstract: This paper discusses the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate standalone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a longstanding issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents,congestion and weather events, show the potential of the system.

Analysis of the effects of connected-automated vehicle technologies on travel demand

Abstract: Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single interse...

Subset Selection in Regression

Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

Abstract: Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL). Then, we outline a few important applications of UQ methods. Finally, we briefly highlight the fundamental research challenges faced by UQ methods and discuss the future research directions in this field.