Understanding Decision Making Driver and Dynamics in Transportation Mode Choice: An agent-based modelling approach

Abstract

Passenger transportation is a significant contributor to global greenhouse gas emissions. Transportation modes (TMs) that individuals choose in daily life differ in their emissions, and a shift towards more sustainable TM usage on a societal scale is desirable. However, many people still utilize high-emission options, such as traveling by car. Here, we present an agent-based model simulating TM choices over time. We implement decision-making mechanisms based on psychological theory on influences of social norms, environmental affordances, and internal attitudes. A sensitivity analysis is applied to explore which interplay of mechanisms could facilitate a transfer towards lower-emission TM choices. Our results indicate that, while the car is the most dominant mode at the start of the model, the model stabilizes with public transportation and car usage counts being even. Less popular TMs such as biking and carsharing decrease in popularity. Sensitivity analysis on the impact of social influence, experience of crowdedness, and internal attitudes indicates that lower tolerance towards crowdedness is the most relevant factor in reducing car usage. Our findings implicate that frequent crowdedness perception might be a key factor in reducing car usage, which could be relevant for policy development around reducing the infrastructure capacities of undesirable TMs. We further conclude that for social norms to have a positive effect on TM usage, the TM requires an initial popularity. Increasing societal acceptance of less popular car alternatives might require complementary approaches in popularizing the TM before the effects of social norms can apply.