Modelling of Inter-stop Minibus Taxi Movements: Using Machine Learning and Network Theory

Abstract

Minibus taxis provide affordable alternative transport for the majority of urban working population in Sub-Saharan Africa. Often, these taxis do not follow predefined routes in their endeavours to look for passengers. Frequently, they stop by roadsides to pick up passengers and sometimes go off the main route in an attempt to fill the taxi with passengers to make the trip profitable. In addition, the destinations are changed from time to time depending on the driver. This uncoordinated movement creates a web of confusion to would-be passengers. The key aspects that are not clear to the passengers include; where to get a taxi, the waiting time and the travel time to the destination. These conditions leave taxi passengers at a very big disadvantage. In this research, we applied the concepts of machine learning and network theory to model the movements of taxis between stops. The model can be used to compute the waiting times at the stops and the travel times to a specified destination. Twelve minibus taxis were tracked for 6 months. Density-based clustering was used to discover the formal and informal taxi stops, which were modelled into a flow network with the significant stops as nodes and the frequency of departures between nodes as edges representing the strength of connectivity. A data driven model was developed. From the model, we can predict the time a passenger will have to wait at a stop in order to get a taxi and the trip duration