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

在地理社交媒体数据中提取交通事件和人员流动模式以评估实时道路交通

• 批准号: RGPIN-2017-05950
• 负责人: Li, Songnian
• 金额: $ 2.99万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636893
• 关键词: Extracting; Traffic; Events; Human; Mobility

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.

大城市中心和新兴城市地区以及世界上其他地区在继续快速发展的同时面临着一系列复杂的挑战，例如交通拥堵。世界上许多大城市，包括加拿大的温哥华和多伦多等城市，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造成延误和驾驶员的健康问题，并产生额外的空气污染，可靠的交通信息是世界各地旅行者的关键。这项拟议的研究将开发新的交通事件检测方法，使用大量的社交媒体数据提供实时信息，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）可以很好地利用新一代的城市规划师，城市交通管理者和研究人员。