Using agent-based modelling for investigating modal shift: The case of university travel. (January 2020) - Faboya, Olusola T.; Ryan, Brendan; Figueredo, Grazziela P.; Siebers, Peer-Olaf
Highlights: Identify challenges to modal shift in travel mode choices. Develop strategies for behavioural changes. Assess the impact of various policy measures on travellers' behaviours. Demonstrate the benefits of modelling mode choices with an integrated framework. The use of ABM to better understand travellers' decision factors. Abstract: Travel mode choices are a result of several factors and how they affect individual travellers. This paper examines those factors influencing travellers' mode choices commuting to and from a university. Furthermore, we investigate how a shift to alternative modes can be stimulated within the current transport system environment of that university. From focus groups studies, seven measurable themes were identified as metrics for travellers' satisfaction. Descriptive data has been collected from 348 participants through questionnaires. The analysis of the questionnaires provided insights into the development of appropriate policies to stimulate travellers' mode shift. To allow for studying the impact of applying proposed interventions over time, we simulated the effects of those interventions on travellers' mode choice by using an agent-based social simulation approach. We employed a framework designed for modelling modal shift in the transport domain to build the simulation model, taking the themes into consideration. The outcomes of the study assisted in understanding how decision factors and their interconnections contribute to sub-populations of travellers' choice. In addition, our experiments helped in assessing the importance of interactions among travellers on their decision making. Such an understanding provides insight into those factors within the system that need to be considered when policymakers develop strategies for interventions for mode shift. The outcomes of the simulation experiments indicate that different policy interventions result in distinct travellers' mode adoption patterns and that interventions perform better when the right categories or groups of travellers are targeted. In addition, the intervention should focus on the right travellers' concerns and be applied in the right proportion. This social simulation study has also demonstrated how a theory-based framework can be used with survey data in numerical experiments to explore real-life scenarios for the development of actions to promote behavioural changes.