Extracting Traffic Events and Human Mobility Patterns in Geosocial Media Data for Assessing Real-time Road Traffic

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Large urban centers and emerging city regions, as well as others in the world, face a series of complex challenges such as traffic congestion as they continue to grow at a rapid pace. Many largest cities around the world, including Canadian cities such as Vancouver and Toronto, are known among top worst for traffic congestion. With increasing traffic congestion causing delays and health problems for motorists and producing extra air pollution, reliable traffic information is key for travelers all over the world. This proposed research will develop new traffic event detection methods for providing such real-time information using massive social media data, and also will build extra tools for integrating human mobility patterns into short-term traffic congestion prediction that will help predict traveler’s road traffic conditions in next 15-60 minutes ahead. The results (models, methods and tools) of this research can be of great use by a new generation of urban planners, city traffic managers and researchers.