Numerical Optimization for Efficient Design and Scheduling of Public Transport Network

Abstract

Efficient public transport systems are key to sustainable urban development, yet their planning and scheduling are often vulnerable to inefficiencies as urban complexities multiply. In this research, a rigorous numerical optimization approach to strategic design and scheduling of public transport systems is outlined. By integrating operations research concepts, graph theory, and transportation engineering, the paper formulates and applies a Mixed Integer Linear Programming (MILP) model that minimizes total travel time, maximizes coverage, and optimizes vehicle utilization. Using a real-world case study dataset of Berlin, Germany's urban network, we apply the model and compare it with traditional scheduling approaches. The results show considerable improvement in performance indicators like waiting time, vehicle turnaround, and passenger satisfaction index. The article provides a scalable model applicable to different urban contexts and gives policy implications for urban transit planning agencies.