Planning for the future of transport with agent-based modelling: The role of on-demand mobility services

Abstract

Today's urgent need for climate change solutions makes it critical to push for more sustainable transport solutions in our cities. Autonomous vehicle technology combined with Mobility on-demand (MoD) services, also known as shared automated vehicles (SAVs), are being considered as a potential way of creating an efficient and sustainable transport system. In particular, ride pooling can combine the benefits of public transport and private vehicles. Agent-based simulation models are extensively used to simulate these systems in order to capture the dynamics between demand and supply. Thus, policies and operational decisions that could directly impact travellers can be modelled and tested to manage travel demand.

However, modelling such a system requires rich data and complex strategies that consider the interactions between behavioural parameters and model components that affect the system. This means that there is no perfect simulation approach that can provide a complete model of the real world. Therefore, this dissertation aims to develop and test strategies for effective simulation of on-demand mobility services and to define best practices for using such models, which in turn should enable the creation of useful models that are representative of the real world and valuable for policy planning and forecasting.