论文标题
使用无监督的学习方法基于Twitter数据的流量事件描述，用于印度道路条件
Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions
论文作者
论文摘要
非旋转和不可预测的交通事件直接影响道路交通状况。需要动态监控和预测这些不可预测的事件，以改善道路网络管理。现有的传统方法（流程或速度研究）的问题在于，许多印度道路的覆盖范围是非常稀疏且可重现的方法，可以识别和描述这些事件。添加其他一些形式的数据对于解决此问题至关重要。这可能是实时速度监视数据，例如Google Maps，Waze等。或Twitter，Facebook等的社交数据。在本文中，无监督的学习模型用于执行有效的推文分类以增强印度流量数据。该模型使用单词插头来计算语义相似性，并达到94.7％的测试分数。
Non-recurrent and unpredictable traffic events directly influence road traffic conditions. There is a need for dynamic monitoring and prediction of these unpredictable events to improve road network management. The problem with the existing traditional methods (flow or speed studies) is that the coverage of many Indian roads is very sparse and reproducible methods to identify and describe the events are not available. Addition of some other form of data is essential to help with this problem. This could be real-time speed monitoring data like Google Maps, Waze, etc. or social data like Twitter, Facebook, etc. In this paper, an unsupervised learning model is used to perform effective tweet classification for enhancing Indian traffic data. The model uses word-embeddings to calculate semantic similarity and achieves a test score of 94.7%.
