An Intelligent Traffic Planning using Improved Phase Timing Optimization Algorithm

Traditional urban transportation planning techniques cannot effectively deal with public transportation prob-lems due to many factors, such as the increase in the number of urban vehicles, the current state of inefficient traffic management system, unequal resource occupation in terms of time and space, and limited resources. There is congestion among the residents, and commuting times are unpredictable. Traffic congestion raises the risk of accidents, hinders economic expansion, and raises emissions. Nowadays, many see traffic congestion as a significant danger to city living. The pain brought on by a rise in car traffic, a lack of infrastructure, and ineffective traffic control has exceeded what is reasonable. This research suggests a computer vision-based traffic control system to solve the issues mentioned above. Data on traffic is optimized by the use of Improved Phase Timing Optimization (IPTO). This study focuses on average energy consumption, average network performance, average message delivery, average latency, and average access. Virtual environments were used for the entire trial. This demonstrates that our method can contribute to increasing the effectiveness of in-car Internet in the future.