Traffic congestion is a major issue in most big cities, resulting in longer travel time and increased greenhouse gas emission. Various factors can cause traffic congestion, and includes not only traffic events on roads (e.g., car accidents) but also urban events (e.g., football games, concerts, and festivals), where a large number of human activities happen in a certain place and at a certain time. The technology of connected vehicles (CV) has provided a crowd-souring platform enabling communication between vehicles and surrounding information share to be more timely and effective. Taking the advantage of that, in this paper we focus on navigation during urban events, and present an approach to find feasible routes avoiding traffic congestion caused by the different types of events. Using 12-month geo-tagged tweets, we create a human activity network to capture certain types of human activities across cities. Based on that, an event estimation algorithm is developed to find the possible events that would occur in the near future, and to estimate their probabilities. These detected events are represented in the form of obstacle polygons with timestamps, and are used by the routing algorithm to generate congestion avoidance routes. We apply our approach to the road network of Toronto, Ontario, Canada, and the experimental results show the capability of our approach in supporting routing during urban events.

中文翻译：

---

基于社交媒体的城市活动路线规划方法

交通拥堵是大多数大城市的主要问题，导致出行时间延长和温室气体排放增加。造成交通拥堵的因素有很多，不仅包括道路上的交通事件（如车祸），还包括城市事件（如足球比赛、音乐会、节日），大量人类活动发生在某个地方，在某个时间。车联网（CV）技术提供了一个众包平台，使车辆之间的沟通和周围信息共享更加及时有效。利用这一点，在本文中，我们专注于城市事件期间的导航，并提出了一种寻找可行路线的方法，以避免由不同类型的事件引起的交通拥堵。使用 12 个月带有地理标记的推文，我们创建了一个人类活动网络来捕捉跨城市的某些类型的人类活动。在此基础上，开发了一种事件估计算法来寻找在不久的将来可能发生的事件，并估计它们的概率。这些检测到的事件以带有时间戳的障碍多边形的形式表示，路由算法使用它们来生成拥塞避免路线。我们将我们的方法应用于加拿大安大略省多伦多的道路网络，实验结果表明我们的方法在支持城市事件期间的路线选择方面的能力。这些检测到的事件以带有时间戳的障碍多边形的形式表示，路由算法使用它们来生成拥塞避免路线。我们将我们的方法应用于加拿大安大略省多伦多的道路网络，实验结果表明我们的方法在支持城市事件期间的路线选择方面的能力。这些检测到的事件以带有时间戳的障碍多边形的形式表示，路由算法使用它们来生成拥塞避免路线。我们将我们的方法应用于加拿大安大略省多伦多的道路网络，实验结果表明我们的方法在支持城市事件期间的路线选择方面的能力。