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

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。 ** 拟议研究的短期目标将集中于三个主要研究项目，包括：1）模拟不同时空尺度的人类活动模式，以研究其与经常性和非经常性交通情况的相互作用; 2）研究和评估不同的交通和交通相关事件检测方法，以发展一种基于时空语义的检测方法;及3）评估及探讨社交媒体数据连同相关模型、工具及工作流程，如何透过发展一个协作式服务导向的开放平台，支援提供实时道路交通资讯。该平台将为研究人员、交通管理人员和决策者提供直接和及时的实时交通数据，这些数据主要来自各种社交网络、交通信息服务以及用于交通相关研究的合适的计算和分析模型和工具。我的研究计划的长期目标是开发一个实时的道路信息基础设施，可视化，发布和推近真实的时间交通信息的目标用户，并提供最佳的路由服务，但也能够预测实时交通拥堵考虑到非经常性的交通状况和长期交通考虑经常性的模式。这样的基础设施可以实现为基于网络的系统，该系统集成了云计算、网络服务和GIS，用于交通管理决策和交通规划。* 拟议的实验性开放平台，将模型、方法和工具作为服务实施，将建立强大的共享道路交通信息和服务的能力，并将为研究共享、HQP培训以及与加拿大政府机构和行业的合作开辟新的机会。

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. ******The short-term objectives of the proposed research will focus on three key research components that will: 1) model human activity patterns at various spatial and temporal scales to study their interactions with recurrent and non-recurrent traffic situations; 2) examine and evaluate different traffic and traffic-related event detection methods to develop a space-time-semantics based detection approach; and 3) assess and explore how social media data together with related models, tools and workflows can support the provision of real-time road traffic information by developing an collaborative service-oriented open platform. The platform will give researchers, traffic managers and decision makers direct and timely access to real-time traffic data, primarily extracted from various social networks, traffic information services, and suitable computational and analytical models and tools for traffic-related studies.******The long-term goal of my Research Program is to develop a real-time road information infrastructure that visualize, publish and push near real time traffic information to target users and provide optimal routing services, but also capable of predicting real-time traffic congestion taking into non-recurrent traffic situations and long-term traffic considering recurrent patterns. Such an infrastructure can be implemented as a web-based system that integrates cloud computing, web services and GIS for traffic management decision making and transportation planning. ***The proposed experimental open platform, with implementation of the models, methods and tools as services, will build strong capacity for sharing road traffic information and services and will open up new opportunities for research sharing, HQP training and collaborations with Canadian government agencies and industry.