Analyzing Transport Policies in Developing Countries with ABM
Kathleen Salazar-Serna, Lorena Cadavid, Carlos Franco
TL;DR
Addressing the challenge of understanding travel mode choice in developing countries, the paper develops an agent-based model to simulate commuter decisions among motorcycle, car, and public transit under policy interventions. The ABM encodes demographic heterogeneity, social influence via a scale-free network, a CONSUMAT-inspired decision process, and a fare-free public transit scenario in a Colombian city, evaluated over 10 years with 100 replications. The results show that fare-free public transit can increase public transit use, reduce private-vehicle growth, lower CO2 emissions by about $72$ tons, fewer accidents, and improve peak-hour speeds, highlighting the role of cost and social feedback in mode choice. The work contributes a first developing-country-specific ABM for urban mobility that includes motorcycles and provides a test-bed for policymakers to compare policy options before implementation.
Abstract
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.