Agent-Based Modeling for Sustainable Urban Passenger Vehicle Mobility: A Case of Tehran

Abstract

In response to escalating congestion and deteriorating air quality in urban centers worldwide, exacerbated by overburdened transportation systems, there is an urgent need for accurate traffic forecasting and effective sustainable urban development strategies. This study employs agent-based modeling through four distinctive scenarios for Tehran, I. R. Iran. A synthetic population is meticulously crafted using simulated annealing, enabling the emulation of daily commuting patterns. Results show that by bolstering cycling infrastructure and enhancing public transportation services, reliance on private cars is reduced up to 46%. The introduction of flexible working hours reduces the traffic volumes during peak traffic hours by 47% and significantly altering the daily distances traveled by personal cars, as evidenced by a 1:6 ratio in car volume increase between scenarios emphasizing flexible working hours and those with more conventional traffic patterns. The results provide powerful insights for decisionmakers to manage the traffic especially in high polluted air conditions.