About Hi, I'm Mehdi
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Who I am and What I do?
I am a transportation and AI engineer with expertise in modeling and optimizing smart mobility and transit systems. I create innovative, data-driven solutions to enhance mobility in urban and suburban environments, specializing in strategic planning, network design, operational optimization, and the application of emerging technologies. My work covers public transit, on-demand mobility, shared transportation, delivery logistics, and first-/last-mile connectivity. I employ simulation-based methods, advanced optimization algorithms, and AI techniques—including machine learning and reinforcement learning—to forecast demand, optimize service performance, and inform decision-making in complex mobility systems.
Let’s innovate together!
How I Got Here
I began my journey in Transportation Engineering by modelling mobility services and optimizing their operations. As a Transportation Engineer at Tarrahan Parseh Research Institute in Tehran, I contributed to a range of projects including master transportation studies, network planning, and operational analysis.
Seeking to deepen my expertise in smart mobility systems, I joined the Laboratory of Innovation in Transportation (LiTrans) in Toronto as a Research Assistant, where I worked on a wide range of projects aimed at improving the efficiency of emerging mobility services, including on-demand, shared mobility (e.g. ride-hailing, ride-sharing) and multimodal delivery systems. My work spanned system design, simulation, and optimization to tackle challenges such as developing algorithms for ride-matching and enhancing routing strategies.
Building on this experience, I joined BusPas Inc. in 2023, a transportation technology startup in Montreal, where I focused on developing innovative and AI-powered solutions for suburban mobility. My work included designing a hybrid transit service that integrates on-demand and fixed-route bus systems to improve efficiency and reduce costs. I explored the application of advanced data-driven methods, including machine learning (ML) and reinforcement learning (RL), to optimize service design and operations.
I am currently working on optimizing the hybrid transit service by integrating simulation with artificial intelligence techniques—such as deep learning and reinforcement learning—to further enhance system efficiency.
Beyond research and development, I am passionate about mentoring future mobility professionals and leveraging AI and data-driven methods to solve complex transportation challenges and deliver impactful, real-world improvements to how people and goods move.
My Expertise & Contributions
Simulation and modelling
- Methods: Macro-simulation, micro-simulation, agent-based simulation, simulation-based optimization, scenario modelling and forecasting
- Tools: PTV Visum, Vissim, SUMO
Optimization
- Methods: Exact/heuristic/metaheuristic methods (Linear programming, Mixed-integer linear programming, Greedy methods, Genetic algorithm, Simulated annealing), Dynamic & real-time optimization (algorithms for online decision-making in ride-matching, routing and vehicle relocation)
- Tools: Gurobi, MATLAB
AI and data-driven methods
- Methods: Reinforcement Learning (RL) & Deep RL, Machine Learning (ML), Markov Decision Processes
- Tools: Python (Pandas, Numpy, Scikit-Learn)
Mentorship & Collaboration
I actively mentor students, researchers, and professionals, guiding them in AI-driven mobility solutions, simulation-based analysis, and advanced research methodologies. Fostering an innovative and inclusive community in smart transportation is a core part of my work.