Minimization Traffic Congestion with Smart Traffic Light by Using Fuzzy Logic

Abstract

Traffic congestion has become a pervasive problem in the world including Malaysia for the past few years while present indication shows that it is expected to get worse. The phenomenon of being stuck in traffic congestion for an extension period also causes stress and tiredness, Due to this, there is no denying that this problem is significantly contributes to negative social, economic, and environmental impacts. This study aims to reduce traffic congestion at Jalan Raja Ashman Shah, Ipoh by implementing an intelligent fuzzy logic traffic light system. The system aims to reduce waiting times and improve traffic flow by automatically adjusting green light durations based on real-time traffic conditions. Data on the number of vehicles and queue lengths during peak hours were collected to compare congestion levels before and after the intervention. The results of this study demonstrate the effectiveness of deploying an intelligent traffic light system with fuzzy logic in minimizing traffic congestion and reducing waiting times. By dynamically adjusting green light duration based on real-time traffic conditions, the system optimized traffic flow and improved overall congestion. A comparison of congestion levels before and after the introduction showed a significant reduction in congestion and an improvement in traffic flow. The adaptability of the intelligent traffic light system makes better use of road capacity, reducing waiting times and minimizing congestion. As a result, we found a significant reduction in waiting time compared to the previous static system. Consequently, this study gives great impact to Ipoh citizens if the fuzzy logic method is implemented in traffic light system. Due to this, improving the transportation system and the quality of life for road users.