Welcome to the Transportation Modeling Lab (TML) at North Carolina Agricultural and Technical State University.

Lab Vision

Harnessing emerging technologies and mobility services to create safe, sustainable, equitable, and resilient transportation solutions that empower communities and improve the movement of people and goods.

Core Research Areas

The Transportation Modeling Lab integrates intelligent transportation systems (ITS) and emerging future mobility services in traffic operations and transportation planning models for the long-term future. Our areas of interest include:

  1. Modeling equity and sustainability in design and delivery of future transportation systems
  2. Developing efficient computational algorithms to solve current transportation and logistics problems with innovative integration of foundational transportation principles
  3. Transportation planning and operations in the era of real-time information and mobility-as-a-service (such as express lanes, parking search, real-time logistics, and ridesharing)
  4. Interpretable, multiobjective stochastic control of intelligent transportation services and autonomous systems using data-driven algorithms
  5. Smart cities and integrated infrastructure models using networks theory

Keywords: network modeling, congestion pricing, equity and accessibility, reinforcement learning, adaptive routing, and shared mobility.

The graphic below highlights the research focus at the TML lab in yellow ellipses within the context of transportation and the integrated infrastructure framework.

Overview

For those unfamiliar, the Manheim-Florian-Gaudry framework, detailed in Manheim (1980) and Florian and Gaudry (1980), and refined in Chow (2018), models transportation systems through the interaction between an activity system (A) and a transportation system (T). It employs a demand procedure (D) to derive transportation needs from the activity system and a performance procedure (P) to evaluate the transportation system’s response. This framework achieves market equilibrium (X) between the supply (S) and the demand (D) resulting in a set of system states (F), including operator decisions, user flows, and link costs, at a short-term operational level. Long-term adjustments, influenced by factors like changes in locations (L), travel times, or resource needs, affect demand and supply and require feedback for resource allocation and design changes. Environmental factors (E), such as climate and urban density, further distinguish system performances across different cities.