Research Overview

Introduction to Research

The focus of my research is in the development of efficient mathematical models and algorithms that are used in a wide variety of applications with a primary focus in transportation. Recent advances in the context of transportation modeling (e.g. accounting for uncertainties, air quality analysis and large scale data from social media data) mandate new paradigms and broadening of skills required to perform in this area. These skills include consolidating methodologies from a diverse array of domains such as network optimization, stochastic modeling, engineering economics, information technology, sound management approaches and many others. Such a multidisciplinary approach to transportation systems research appeals to me. In my group, we conduct systems related research by assimilating techniques from myriad research fields such as network science, information and sensor technology, stochastic optimization, game theory, real options and large scale data mining. In my research, I envision creating useful and implementable systems, and eventually deploying them in real world settings to characterize and evaluate their behavior and performance. To achieve this goal, I believe it is imperative to first critically understand the complexity involved in modeling these complex real world systems. Further, it is crucial to understand the inter-dependencies of transportation networks with other systems because transportation systems operate within the framework of many other systems. To this end, it is paramount to develop theory grounded on strong mathematical foundations. The insights gained from these theoretical frameworks can be used to build implementable models for real world problems by exploiting problem structures and data patterns. The research in my lab emphasizes this dual theme of strong theoretical founding and system building, with the former being the foundation for the latter.


My research focus in the next few years is in the following areas:


  1. Transportation Network Modeling


    2. We are conducting fundamental work in the area of dynamic traffic modeling to understand the notion of dynamic equilibrium and its computation for real traffic networks. Specific research in this area includes:


    • Dynamic Traffic Equilibrium Models with Departure Time Choice and Route Choice with Spatial Queuing Models,

    • Algorithms for Solving Dynamic Equilibrium Models,

    • Simulation Based Equilibrium Models,

    • Traffic Flow Modeling,

    • Network Design and its variants (Stochastic, Robust and Multiobjective),

    • Congestion Pricing,

    • Signal Control Optimization and

    • Stochastic Transportation Modeling for accurate traffic forecasts for various applications such as planning, large scale infrastructure project investments (PPP) and state wide planning



  2. Emergency Management Issues


    3. We are developing a new class of disaggregate models which holistically integrate behavioral issues in emergency management with large scale systems wide simulation tools. Specific problems include:


    • Hurricane Evacuation - Behavioral Modeling,

    • Hurricane Evacuation - Routing and Traffic Simulation,

    • Humanitarian Logistics Issues,

    • Network Resilience in Disasters and

    • Evacuation Modeling and Retrofit of Facilities in Earthquakes



  3. Freight Transportation


    4. We are developing models to understand the behavior of various agents in the freight market including carriers, shippers and receivers:


    • Statewide and MPO based Freight Planning Models using Commodity and Truck Flows,

    • Large scale simulation of urban freight logistics operations,

    • Routing Algorithms such as the Pick up and Delivery problems, Vehicle Routing, etc.,

    • Freight Network Design,

    • Interaction of carrier and shipper activities,

    • The use of auctions to understand carrier and shipper behavior,

    • Agent Based Modeling of Freight Markets and

    • Sustainability objectives in Freight Modeling

    • Freight Transportation Survey Example



  4. Intelligent Transportation Systems


    5. The use of large scale data for characterizing real-time traveler behavior and traffic networks. Specific problems include:


    • Complex Network Science Tools using Large Scale Disaggregate Data,

    • Fundamental techniques to understand mobility patterns using large scale data sets,

    • Vehicular Ad Hoc Networks (Connectivity and Information propagation issues),

    • Real-Time Optimization (Adaptive) of Traffic Signals,

    • Real-Time Traffic Simulation Models and

    • Novel signal control strategies using wireless enabled communication



  5. Energy, Environment, Sustainability in Transportation


    6. Specific problems include:


    • Market penetration of PHEVs,

    • Locating charging facilities for PHEVs,

    • Incorporating energy and environment objectives in traffic signal optimization,

    • Incorporating energy and environment objectives in real-time traffic guidance,

    • Minimizing GHG emissions in the transportation sector,

    • Modeling of cap and trade policies for GHG emissions pricing and

    • Network Design strategies accounting for energy and environment objectives