Smart Traffic Congection Prediction and Smart Lights Using Mobile Application
Prof.Nikita Chavan
Vaishnavi Daswadkar
Nisha Shinde
Komal Khonde
Computer Engineering
BSCOER,POLY.Narhe
ABSTRACT
Traffic congestion is a major problem in urban areas worldwide, leading to wasted time, increased fuel consumption, and environmental pollution. The Traffic Congestion Prediction and Smart Lights Mobile Application is designed to address these issues by predicting traffic conditions in real time and dynamically controlling traffic signals. The system integrates mobile application technology, IoT devices, and predictive algorithms to provide an efficient and user-friendly traffic management platform. Key features include vehicle registration, real-time traffic monitoring, congestion prediction, and smart traffic light control. By optimizing traffic flow and reducing delays, the system improves urban mobility and road safety. Future developments could include advanced machine learning algorithms, city-wide traffic integration, and emergency vehicle prioritization.
Traffic congestion has become a major challenge in modern urban areas, leading to increased travel time, fuel consumption, and environmental pollution. The Traffic Congestion Prediction and Smart Lights Mobile Application provides a practical solution by combining real-time traffic monitoring, predictive analysis, and adaptive traffic signal control. Using mobile technology and IoT-enabled devices, the system allows drivers to register vehicles, view live traffic updates, and receive congestion alerts, while traffic authorities can optimize signal timings based on predicted traffic density. The application classifies traffic into low, moderate, or high congestion levels and adjusts smart traffic lights dynamically to improve flow and reduce delays. By integrating predictive algorithms, real-time data collection, and user feedback, this system aims to enhance road safety, minimize travel time, and contribute to the development of smart city traffic management solutions.
improves traffic management efficiency, reduces travel time, minimizes fuel consumption, and supports smart city initiatives. The experimental results show that the proposed approach effectively reduces traffic congestion and enhances overall road utilization.
Keywords:- Traffic Congestion, Smart Traffic Lights, Mobile Application, Machine Learning, Real-Time Traffic Monitoring, Intelligent Transportation System
