An Agent-Based Model framework to simulate active travel-focused transport policy scenarios

Abstract

The transport sector is a major contributor to climate change, which is considered the most pressing environmental challenge of our time. Consequently, many cities around the globe are introducing sustainable transport legislation to meet the 2015 Paris Agreement of limiting global warming to 2°C and aiming for 1.5°C. To achieve this, policies are sought that can change mobility patterns to reduce emissions rapidly. This could involve a portfolio of measures where a combination of changes to the built environment, human behaviours and financial incentives or penalties are considered. The work presented in this thesis encompasses efforts to develop and validate a model that simulates a digital representation of the transport mobility, applying Agent-Based Modelling (AgBM) techniques. This model simulates the spatio-temporal interactions of synthetic individuals in the study area during their daily routines, using different transport modes. This validated model is then used as the baseline scenario to simulate different mobility policies and test their efficiency in reducing the number of private and polluting vehicles on the roads in favour of active modes (i.e., walking and cycling) and, therefore, reducing greenhouse gas emissions. The developed model is described and demonstrated for the Tyne and Wear region, showing its transport mobility during a regular day in 2019 (a pre-pandemic scenario with ‘normal’ mobility behaviours) and the potential estimated results that could be obtained when different mobility policies modifying the characteristics of the built environment and/or human behaviours are applied. The methodologies followed use open access datasets and open-source tools, when possible, being feasible to replicate the results and adapt them to any other region in the country. This thesis aims to help in the development and understanding of the urban transport mobility applying AgBMs, where spatio-temporal and human socio-demographic characteristics are considered through the simulation of active transport modes.