Indonesian Study Found Artificial Intelligence (AI) and Internet of Things (IoT)-Based Traffic Management System Resulted in a 15 Percent Improvement in Traffic Flow.
Field Tests of Dynamically Adjusted Traffic Signal Timings Using AI Algorithms in Jakarta Reduced Congestion During Peak Hours.
Jakarta
Smart Traffic Management System for Reducing Urban Congestion in Major Indonesian Cities Using IOT and AI Technologies
Summary Information
Urban traffic congestion is an increasing problem in Indonesian cities, impacting both economic productivity and quality of life. This study introduced the development of a smart traffic management system utilizing Internet of Things (IoT) sensors and artificial intelligence (AI) algorithms to analyze traffic patterns and optimize flow. The study used real-time traffic data and applied predictive analytics to adjust traffic signals dynamically and conducted field tests in Jakarta.
METHODOLOGY
With the goal of developing a smart traffic management system leveraging IoT and AI technologies, this study collected real-time traffic data, including vehicle counts, speeds, and congestion levels using IoT sensors installed at several high-traffic intersections in Jakarta. The data was analyzed with machine learning techniques to develop predictive traffic models and inform dynamic traffic signal adjustment. The system’s effectiveness was evaluated by comparing key performance indicators such as travel times, traffic flow, and user satisfaction, compared to two weeks of baseline measurements.
FINDINGS
- Traffic flow improved by 15 percent compared to the baseline measurements.
- Average travel times during peak hours decreased by approximately 10 minutes.
- Surveys conducted with commuters and local businesses found that 78 percent of respondents felt the smart traffic management systems had a positive impact on their daily commutes.
