Exploring Quantum Computing for Public Transit Origin-Destination Matrix Estimation

This research explores the integration of quantum computing with statistical methods to enhance public transit origin–destination (OD) matrix estimation. Traditional OD estimation relies on automated fare collection (AFC) and automatic passenger counting (APC) data, which often present challenges due to incomplete coverage. Scaling techniques like iterative proportional fitting (IPF) help address these gaps, but their accuracy declines at low AFC penetration rates.
This study proposes leveraging a hierarchical Bayesian framework alongside quantum algorithms, Quadratic Unconstrained Binary Optimization (QUBO) for combinatorial optimization and the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, to improve OD matrix accuracy. These methods will incorporate APC-derived alighting probabilities and AFC trip-chaining data. The approach will be validated on the Sioux Falls and Calgary transit networks, assessing its robustness under varying data conditions.
This project, led by Dr. Saidi and Dr. Nassir, aligns with Canada’s strategic priorities in quantum technology and transit innovation. By enhancing OD estimation, it aims to improve transit planning, optimize operations, and contribute to more efficient urban mobility. As the intern, I will gain hands-on experience in quantum computing and transportation analytics, fostering collaboration between Canadian and Australian research institutions.

Faculty Supervisor:

Saeid Saidi

Student:

Partner:

The University of Melbourne

Discipline:

Engineering

Sector:

Education

University:

University of Calgary

Program: