• Design and implement algorithms that dynamically allocate vehicles to meet time-varying passenger demand. • Formulate and solve optimization problems (e.g., MILP, QP, network flow) for vehicle routing, rebalancing, and resource allocation. • Develop heuristics or approximations for real-time decision-making when full optimization isn’t tractable. • Implement feedback-based strategies to maintain stable fleet utilization. • Build mathematical and simulation models representing vehicle movement, demand patterns, and station queues. Derive and maintain performance metrics (e.g., wait time, idle miles, fairness, stability). • Analyze fleet utilization, imbalance, and throughput to identify bottlenecks or inefficiencies. • Conduct what-if studies using simulated origin/destination demand matrices, travel times, and network layouts to stress-test load-balancing strategies.