By understanding how users decide on their commuting modes, it is possible to identify factors that can be influenced to change travel behavior and promote the adoption of more sustainable transportation modes. Agent-based modeling (ABM) is particularly valuable for this purpose, as it can represent complex systems like transportation and identify emerging collective behaviors resulting from the autonomous decisions of transport users interacting among them and with the environment (Kagho, Balac, and Axhausen 2020). These capabilities make ABM suitable for analyzing the impacts of transport policies (Wise, Crooks, and Batty 2017). However, the application of ABM in analyzing transport mode choices has been limited and studies have been conducted predominantly in developed countries (Cadavid and Salazar-Serna 2021; Salazar-Serna, Cadavid, Franco, and Carley 2023). The effectiveness of these findings may not extend seamlessly to developing regions due to different contextual policy needs and the distinct ways socioeconomic and cultural factors influence human behavior (Carley 1991; Salazar-Serna et al. 2023). Therefore, policies that have been successful in one setting might not achieve similar outcomes in another. Previous studies in transportation have identified various determinants affecting mode choice. These factors can be grouped into several categories: sociodemographic characteristics such as age, sex, occupation, and income level (Ashalatha et al. 2013); travel habits including distance traveled, travel time, origin-destination pairs, and trip purpose (Madhuwanthi et al. 2016); and attributes of the built environment like design, density, and capacity (Ewing and Cervero 2010). Additionally, attitudes and perceptions regarding transport modes, which cover aspects such as comfort, cost, security, safety, quality, and reliability, play a crucial role (Fu 2021).

Deciphering travel behavior and mode choices is a critical aspect of effective urban transportation system management, particularly in developing countries where unique socio-economic and cultural conditions complicate decision-making. Agent-based simulations offer a valuable tool for modeling transportation systems, enabling a nuanced understanding and policy impact evaluation. This work aims to shed light on the effects of transport policies and analyzes travel behavior by simulating agents making mode choices for their daily commutes. Agents gather information from the environment and their social network to assess the optimal transport option based on personal satisfaction criteria. Our findings, stemming from simulating a free-fare policy for public transit in a developing-country city, reveal a significant influence on decision-making, fostering public service use while positively influencing pollution levels, accident rates, and travel speed.

Agent-based simulations have been used in modeling transportation systems for traffic management and passenger flows. In this work, we hope to shed light on the complex factors that influence transportation mode decisions within developing countries, using Colombia as a case study. We model an ecosystem of human agents that decide at each time step on the mode of transportation they would take to work. Their decision is based on a combination of their personal satisfaction with the journey they had just taken, which is evaluated across a personal vector of needs, the information they crowdsource from their prevailing social network, and their personal uncertainty about the experience of trying a new transport solution. We simulate different network structures to analyze the social influence for different decision-makers. We find that in low/medium connected groups inquisitive people actively change modes cyclically over the years while imitators cluster rapidly and change less frequently.