An activity-based model for district-level modal share analysis with electric vehicles

Abstract

This study introduces an activity-based modeling approach designed to support decision-makers in understanding the dynamics of private car trips and their potential transition to electric mobility. Despite the growing emphasis on sustainable mobility, there remains a gap in the analyses of electric vehicle (EV) penetration into district-level modal share, especially with respect to urban spatial and infrastructural heterogeneity. By leveraging daily activity-travel patterns data from different urban districts in the city of Budapest, Hungary, this research evaluates a range of scenarios across varying levels of EV penetration. The elaborated approach is used to evaluate modal share change objectives by linking individual trip characteristics, such as distance and CO₂ emissions, with district-level attributes, such as availability of charging infrastructure, average node degree, and average shortest path length. The results show that increased EV penetration in the modal share reduces CO₂ emissions across all districts, by up to 23% in some cases, while often increasing travel distances, particularly in regions with lower network density and charger availability. This study aims to provide valuable insights by offering a practical framework that integrates optimization and operation research techniques, incorporates empirical data from surveys and various policy documents, as well as embeds perspectives from transportation geography. Furthermore, the research is further strengthened by sensitivity analyses in the attempt to capture social and spatial heterogeneity in urban mobility electrification.