AI Agent–Driven Digital Twin Framework for Intelligent Simulation and Optimization of Demand Responsive Transit (DRT) Systems

This project aims to develop an optimal Agentic AI Framework for next-generation intelligent mobility systems. Rather than focusing on a single algorithm or application, the research seeks to establish a generalizable architecture that integrates advanced optimization methods with graph-based large language model orchestration. Demand-Responsive Transit (DRT) serves as the primary testbed due to its inherent operational complexity, but the framework is designed to support a broad range of urban mobility services.

The core contribution lies in unifying three methodological components:
(1) an agentic control layer that interprets natural-language inputs and coordinates multi-step reasoning using a LangGraph-based structure;
(2) an optimization layer that adapts routing, dispatching, and policy parameters through formal algorithmic models; and
(3) a digital-twin simulation layer that evaluates system behaviors within an interactive urban mobility environment.

By integrating linguistic reasoning with algorithmic optimization, the project addresses a key gap between human decision intent and computational execution in current mobility systems. The resulting framework offers a scalable and extensible foundation for analyzing complex transportation scenarios, supporting evidence-based planning, and advancing research on AI-driven urban mobility.

Faculty Supervisor:

Sukhjit Singh Sehra

Student:

Partner:

Gachon University

Discipline:

Computer science

Sector:

Artificial Intelligence; Transportation (excluding aerospace); Technology

University:

Wilfrid Laurier University

Program: