Developing a large-scale agent-based model for greater Jakarta: evaluating distance-based road pricing with complex multi-modal choice integration

Agent-based models have become valuable tools in transport planning, particularly for evaluating mobility policies in complex urban contexts. The model simulates the daily activity-travel patterns of approximately 3 million agents (10% of the population in Greater Jakarta), incorporating socio-demographic attributes and activity chains from household travel surveys. The modeling process includes population synthesis, activity imputation, and calibration against observed mode share, trip distances, and activity distributions, resulting in a realistic representation of urban travel behavior. This study further develops policy scenarios and assesses the impact of distance-based electronic road pricing (ERP) for private cars and motorcycles. ERP scenarios are applied to eight major arterial roads in Jakarta, with tolls differentiated by travel distance and agent income using a Discrete Mode Choice (DMC) framework through Eqasim. Results show that ERP effectively reduces private vehicle use, particularly during the evening peak, where car traffic decreases by up to 19.95% and motorcycle traffic by 14.21% under the highest pricing scenario. However, the impact during the morning peak is more modest, reflecting lower travel flexibility. The results also show that the impact varies across socio-demographic groups, with stronger effects observed among low-income individuals, female users, working-age individuals, and employed persons.