Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization

Abstract

This paper presents the first results of an agent-based model aimed at designing feeder bus routes able to cover the gap between public transport coverage and ridership in weak demand areas. The optimized design of feeder bus routes has been approached as a Vehicle Routing Problem applied to passenger transport, using Ant Colony Optimization (ACO) to find the minimum cost paths within a road network. The methodology proposed has been applied to the case of Catania (Italy), where a metro line is being extended to the city centre to peripheral areas. A GIS approach has been used to build the road network, select all potential bus stops, and weight them via accessibility indicators, as a proxy of the potential transport demand. Then, the ACO algorithm has been developed and implemented in NetLogo, a multi-agent programming and modelling environment for simulating complex systems, in order to find an optimal set of feeder bus routes, where the terminal is a given metro station. These routes are chosen to maximize the potential demand of passengers while complying with the constraint of a desired travel time. Different scenarios have been analysed by comparing a set of key performance indicators based on service coverage and ridership. First results highlight the validity of the method to find suitable routes to cover the gap between conventional public transport and weak demand urban areas and provide useful suggestions for the operation and design of a feeder service.