Bus Network Optimisation with Machine Learning
Buses are a vital component of an urban environment, and shifting away from private cars towards public transport is essential in minimising our environmental impact and creating sustainable cities. The UN Sustainable Development Goal 11 seeks to “Make cities and human settlements inclusive, safe, resilient and sustainable”. Specifically, target 11.2 states that cities should expand public transport. Unfortunately, there is a trend away from public transportation and towards private cars, due to passenger dissatisfaction with the public transport networks. However, with increased urbanisation, enduring widespread use of private cars is unsustainable, and we must make bus transport an attractive option for passengers. Many factors influence a passenger’s transport choices, but convenient routing options and reliable service are frequently reported unmet needs. Unfortunately, there are physical and financial limits on the service provided, so it is crucial to optimise the resources available to provide the best possible service. The proposed research seeks to provide better scheduling and better route design by applying machine learning (ML) to several under-exploited areas in the bus transit domain. Researchers have demonstrated that ML can improve the efficiency of public transport, and the focus to date has been on the application of various ML algorithms. However, the results are often conflicting, and the experiments are usually conducted on a single bus route in a single city. We propose to examine a whole network of buses and also to attempt to validate the transferability of our experiments on unseen routes, ideally from an unseen bus network. We also plan to address the conceptual model of the bus network and how the network is structured before ML modelling, and how this conceptual model interacts with various ML algorithms. Chokepoints are a significant factor that makes bus transport less reliable. Chokepoints cause bus bunching, which has been shown to impact severely upon the passenger’s service. Analysis has demonstrated that chokepoints in bus networks are caused by physical constraints like signalised intersections or bridges and dynamic factors such as weather or school collection times. We propose to work with OpenStreetMaps data to analyse features that impact bus reliability and train ML algorithms that can predict optimum bus routing. By applying ML to the bus transport domain, we hope to add knowledge that will help optimise bus networks.