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Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowd sourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users’ traveling route based on live traffic conditions and historical data.

A Personalized Multi-Path and Multi-User Traffic Analysis and Visualization Tool