  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.
