Real-Time Traffic Congestion Mapping Mobile App Using Crowdsourced Data for Efficient Traffic Management

Traffic congestion is an increasing challenge in urban areas that is affecting travel times, fuel consumption, and air pollution. The traditional traffic monitoring solutions utilize fixed sensors and surveillance cameras, which are in many cases high cost and limited in their visibility range. Therefore, this study proposes a Real-Time Traffic Congestion Mapping Mobile App that will leverage crowdsourced data from the users to ease the movement of traffic and provide better route suggestions. The application collects real-time GPS data, user-reported incidents-hurdles like accidents, roadblocks, and traffic jams- and environmental conditions in real time to produce dynamic congestion maps. Predicting traffic jams and suggesting the optimal routes for commuters involves the use of machine learning algorithms coupled with big data analysis. Also, the data processing and storing take place through cloud computing, while the friendly graphical user interface provides access to traffic insights. Configuration-wise, this approach is advantageous because it scales up in the small space and is cheaper than traditional road- based observation schemes. By utilizing a human-powered, dis- tributed collection of data, the operation runs real-time adaptive traffic management. This application not only offers improved experience for users but at the same time provides urban planners and traffic authorities with the requisite arsenal to establish smart mobility solutions for sustainable urban development. Index Terms—Keywords Real-Time Traffic Monitoring, Traffic Congestion Mapping, Crowdsourced Data ,Smart City Traffic Management, Machine Learning in Transportation,GPS-Based Navigation ,Cloud Computing for Traffic Systems ,Big Data An- alytics in Transportation,Urban Mobility Optimization,Intelligent Transport Systems (ITS)