Evolutionary modeling of large-scale public transport networks

Abstract

A genetic algorithm to design efficient large-scale public transport networks is extended. It goes beyond existing approaches by incorporating a dynamic demand response towards both changes in the network and external disruptions. The algorithm is based on an agent-based (MATSim) simulation and tested for the city of Zurich. Compared to the existing public transport system, it proposes a sparser network with substantially higher frequencies. By doing so, the algorithm predicts a higher transit ridership at a lower level of subsidies, thus increasing the effectiveness of public transportation. Moreover, it reliably identifies corridors for potential capacity upgrades. The approach may help transport planners to assess their existing public transport networks and to plan public transport infrastructure for the era of automated vehicles.