Network Reduction Based on Flow Performance

Abstract

Transportation networks in large cities, such as Bogotá, face significant challenges due to the dynamic nature of demand across various scenarios. Traditional approaches to network design typically focus on topological analyses, which may fail to capture the complex flow patterns that emerge under changing conditions. This research introduces a new approach to optimize urban transportation networks by integrating network flow theory with scenario based analysis. Initially, we solve the Minimum Cost Flow Problem (MCFP) for multiple demand scenarios within a fixed network. In a subsequent stage, we aim to redesign the network by maximizing the overall similarity to the flow solutions of each scenario. This is achieved by reconstructing the network using the original nodes and defining the edges and associated costs, as well as determining how much flow is sent through each edge to best match the MCFP for each scenario. By combining these methods, we seek to create a more adaptive and efficient network that responds to real-time fluctuations in demand, leading to improved service in densely populated urban areas