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

大型城市中心和新兴城市地区，以及世界其他地区，在继续快速增长的同时，面临着一系列复杂的挑战，如交通拥堵。世界上许多大城市，包括加拿大的温哥华和多伦多，都是交通拥堵最严重的城市之一。随着日益严重的交通拥堵给驾车者造成延误和健康问题，并产生额外的空气污染，可靠的交通信息对世界各地的旅行者来说至关重要。这项拟议的研究将开发新的交通事件检测方法，利用大量社交媒体数据提供此类实时信息，并将建立额外的工具，将人类移动模式整合到短期交通拥堵预测中，这将有助于预测未来15-60分钟内旅行者的道路交通状况。本研究的结果（模型、方法和工具）可以为新一代城市规划者、城市交通管理者和研究人员提供很大的帮助。

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.