<![CDATA[ Introduction & Objective: Traffic congestion and the high rate of vehicular accidents remain major challenges in developing cities such as Manado, Indonesia. The rapid increase in the number of vehicles, unmatched by adequate infrastructure, has resulted in longer waiting times at intersections, higher levels of air pollution, and an elevated risk of accidents. With technological advancements, smart traffic light systems have emerged as innovative solutions in many cities. However, their effectiveness in the context of developing cities like Manado still requires further investigation. Therefore, this study aims to evaluate the effectiveness of smart traffic light systems in improving traffic flow, reducing emissions, and decreasing the number of accidents in Manado City. Methodology: A mixed methods approach was employed, combining retrospective traffic accident data from hospitals and police records with simulation models embedded with IoT and AI technologies. The evaluation focused on key performance indicators, including waiting time, travel time, emissions, and accident probability. Key Results & Discussion: Simulation outcomes revealed reductions of up to 40% in waiting times, 25% in travel times, and 20% in emissions. Retrospective data confirmed accident clusters in high-risk intersections, particularly along Jalan A. A. Maramis. Comparative analysis with international benchmarks further demonstrated that the projected benefits in Manado are consistent with results achieved in cities that have adopted similar technologies. Nevertheless, successful implementation depends on supportive infrastructure, stakeholder collaboration, and adaptive policymaking. Conclusion & Implications: Smart traffic light systems hold significant potential as a strategic intervention to enhance road safety and traffic efficiency in Manado. The integration of localized accident data with predictive simulation models represents the main scientific contribution of this study, offering a replicable framework for other secondary cities in Indonesia. The findings also provide actionable insights for local governments to design smart mobility policies aligned with global smart city agendas.]]>
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