published journal article
book/book chapter
published journal article
published journal article
Phd Dissertation
Optimization-based approaches are presented for the design of environment-oriented road pricing and traffic rationing schemes, particularly with the objective of curbing human exposure to motor vehicle generated air pollutants. In addition, surrogate-based solution algorithms are developed to accelerate the search of good solutions for the problems considered. A toll design problem is proposed for selecting tolling locations and levels that minimize environmental inequality and human exposure to pollutants, subject to budget constraints and pollutant concentration constraints at receptor points. A mixed-integer variant of the metric stochastic response surface algorithm and a hybrid genetic algorithm-metric stochastic heuristic are presented to solve the mixed integer toll design problem. Numerical tests suggest that the proposed algorithms are promising solution methods for transportation network design problems. In addition, an optimization problem is presented for the design of cordon and area-based road pricing schemes subject to environmental constraints. Flexible problem formulations are considered which can be easily utilized with state-of-the-practice transportation planning models. A surrogate-based solution algorithm that utilizes a geometric representation of the charging area boundary is proposed to solve cordon and area pricing problems. Lastly, a bi-objective traffic rationing problem is considered where the planner attempts to maximize auto usage while minimizing pollutant exposure inequality, subject to constraints on the levels of greenhouse gas emissions and pollutant concentration levels. A surrogate-assisted differential evolution algorithm for multiobjective continuous optimization problems with constraints is proposed.
published journal article
published journal article
Phd Dissertation
This thesis is concerned with specifying and estimating multivariate models in discrete data settings. The models are applied to several empirical applications with an emphasis in banking and monetary history. The approaches presented here are of central importance in model evaluation, policy analysis, and prediction. The first chapter develops a framework for estimating multivariate treatment effect models in the presence of sample selection. The methodology deals with several important issues prevalent in program evaluation, including non-random treatment assignment, endogeneity, and discrete outcomes. The framework is applied to evaluate the effectiveness of bank recapitalization programs and their ability to resuscitate the financial system. This paper presents a novel bank-level data set and employs the new methodology to jointly model a bank’s decision to apply for assistance, the central bank’s decision to approve or decline the assistance, and the bank’s performance. The article offers practical estimation tools to unveil new answers to important regulatory and government intervention questions. The second chapter examines an important but often overlooked obstacle in multivariate discrete data models which is the proper specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes of interest. This paper highlights the use of existing Bayesian model comparison techniques to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes. The third chapter, which is joint work with Professor Ivan Jeliazkov, studies the formulation of the likelihood function for simultaneous equation models for discrete data. The approach rests on casting the required distribution as the invariant distribution of a suitably defined Markov chain. The derivation resolves puzzling paradoxes highlighted in earlier work, shows that such models are theoretically coherent, and offers simple and intuitive linkages to the better understood analysis of continuous outcomes. The new methodology is employed in two applications involving simultaneous equation models of (i) female labor supply and family financial stability, and (ii) the interactions between health and wealth.
research report
Parking infrastructure is suffering from congestion as the number of vehicles circulating in urban areas is growing and expansion is not a cost-effective solution. In parallel, developments in autonomous vehicle technology mean that driverless vehicles are predicted to be in circulation by the 2020s and makeup 40% of vehicle travel by the 2040s. Expected benefits of autonomous vehicle travel include reduced congestion through vehicle sharing and reduced walking distance for passengers who can be dropped off chauffeur-style by autonomous vehicles. However, empty vehicle cruising, or the case in which autonomous vehicles cannot efficiently locate parking and circle instead, can potentially increase congestion. Given that this new technology has the potential to exacerbate existing congestion issues, it is necessary to develop a solution for parking congestion integrated with autonomous vehicles. Our project addresses this issue by providing a full-stack solution including sensors to monitor occupancy, Fog systems to perform local data pre- processing, and SDR radios to communicate with autonomous vehicles.
published journal article
Vehicular ad hoc networks (VANETs) offer a promising way to prevent accidents, facilitate eco-friendly driving, and provide more accurate real-time traffic information. This article describes the three different communication pathways incorporated by VANETs and briefly outlines potential applications. While there are still communication problems to solve within these complex systems, concerns about privacy, liability, and security are the chief obstacles that prevent progress towards large-scale implementation.
