I am passionate about designing and modeling innovative, efficient, and sustainable mobility solutions for passengers and freight. I finished my PhD in Transportation Systems Analysis & Planning at Northwestern University, focusing on network design, optimization, and data analytics.
I am also a chartered civil engineer in the United Kingdom with experience in large-scale project management.
I joined Uber Autonomous Mobility & Delivery in San Francisco in 2024, pushing forward optimization and service planning of shared autonomous vehicles.
This paper presents an optimization framework for the joint multimodal transit frequency and shared autonomous vehicle (SAV) fleet size optimization, a problem variant of the transit network frequency setting problem (TNFSP) that explicitly considers mode choice behavior and route selection. To address the non-linear non-convex optimization problem, we develop a hybrid solution approach that combines metaheuristics (particle swarm optimization, PSO) with local nonlinear programming (NLP) improvement, incorporating approximation models for SAV waiting time, multimodal route choice, and mode choice. Applied to the Chicago metropolitan area, our method achieves a 33.3% increase in transit ridership.
@inproceedings{ng_joint_caspt_2025,address={Kyoto, Japan},title={Joint {Optimization} of {Multimodal} {Transit} {Frequency} and {Shared} {Autonomous} {Vehicle} {Fleet} {Size} with {Hybrid} {Metaheuristic} and {Nonlinear} {Programming}},doi={10.48550/arXiv.2412.19401},urldate={2025-03-10},booktitle={16th {International} {Conference} on {Advanced} {Systems} in {Public} {Transport} ({CASPT2025}) and {TransitData} 2025},author={Ng, Max T. M. and Mahmassani, Hani S. and Tong, Draco and Verbas, Omer and Cokyasar, Taner},year={2025},keywords={Computer Science - Systems and Control, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control},}
2024
Redesigning large-scale multimodal transit networks with shared autonomous mobility services
This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin–destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.
@article{ng_redesigning_2023,title={Redesigning large-scale multimodal transit networks with shared autonomous mobility services},issn={0968-090X},doi={10.1016/j.trc.2024.104575},urldate={2024-03-27},journal={Transportation Research Part C: Emerging Technologies},author={Ng, Max T. M. and Mahmassani, Hani S. and Verbas, Ömer and Cokyasar, Taner and Engelhardt, Roman},month=mar,year={2024},keywords={Autonomous vehicles, Multimodal, Network optimization, Shared Autonomous Mobility Services (SAMS), Transit network design},pages={104575},}
Semi-on-Demand Hybrid Transit Route Design with Shared Autonomous Mobility Services
This study examines the route design of a semi-on-demand hybrid route directional service in the public transit network, offering on-demand flexible route service in low-density areas and fixed route service in higher-density areas with Shared Autonomous Mobility Service (SAMS). The study develops analytically tractable cost expressions that capture access, waiting, and riding costs for users, and distance-based operating and time-based vehicle costs for operators. Two formulations are presented for strategic and tactical decisions in flexible route portion, fleet size, headway, and vehicle size optimization, enabling the determination of route types between fixed, hybrid, and flexible routes based on demand, cost, and operational parameters. The practical applications and benefits of semi-on-demand feeders are demonstrated with numerical examples and a large-scale case study in the Chicago metropolitan area. Findings reveal scenarios in which flexible route portions serving passengers located further away reduce total costs, particularly user costs. Lower operating costs in lower-demand areas favor more flexible routes, whereas higher demand densities favor more traditional line-based operations. On two studied lines, a current cost forecast favors smaller vehicles with flexible routes, but operating constraints and higher operating costs would favor bigger vehicles with hybrid routes. The study provides an analytical tool to design SAMS as directional services and transit feeders, and tractable continuous approximation formulations for future research in transit network design.
@inproceedings{ng_semi--demand_2024,address={Washington, D.C.},title={Semi-on-{Demand} {Hybrid} {Transit} {Route} {Design} with {Shared} {Autonomous} {Mobility} {Services}},booktitle={103rd {Transportation} {Research} {Board} {Annual} {Meeting}},author={Ng, Max T. M. and Dandl, Florian and Mahmassani, Hani S. and Bogenberger, Klaus},year={2024},keywords={Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control},}
