Road transportation is the backbone of modern economies despite costing annually millions of human deaths and injuries and trillions of dollars. Twitter is a powerful information source for transportation but major challenges in big data management and Twitter analytics need addressing. We propose Iktishaf, developed over Apache Spark, a big data tool for traffic-related event detection from Twitter data in Saudi Arabia. It uses three machine learning (ML) algorithms to build multiple classifiers to detect eight event types. The classifiers are validated using widely used criteria and against external sources. Iktishaf Stemmer improves text preprocessing, event detection and feature space. Using 2.5 million tweets, we detect events without prior knowledge including the KSA national day, a fire in Riyadh, rains in Makkah and Taif, and the inauguration of Al-Haramain train. We are not aware of any work, apart from ours, that uses big data technologies for event detection of road traffic events from tweets in Arabic. Iktishaf provides hybrid human-ML methods and is a prime example of bringing together AI theory, big data processing, and human cognition applied to a practical problem.

尽管每年造成数百万人死伤和数万亿美元的损失，但道路运输仍是现代经济的支柱。Twitter是强大的运输信息源，但需要解决大数据管理和Twitter分析方面的主要挑战。我们建议基于Apache Spark开发的Iktishaf，Apache Spark是一种大数据工具，用于从沙特阿拉伯的Twitter数据中检测与交通相关的事件。它使用三种机器学习（ML）算法来构建多个分类器，以检测八种事件类型。使用广泛使用的标准并针对外部来源对分类器进行了验证。Iktishaf Stemmer改进了文本预处理，事件检测和功能空间。通过250万条推文，我们可以检测到没有先验知识的事件，包括KSA国庆日，利雅得大火，麦加和塔伊夫的降雨，以及Al-Haramain火车的落成典礼。除了我们的工作，我们没有其他工作使用大数据技术从阿拉伯语的推文中检测道路交通事件。Iktishaf提供了混合的人类ML方法，并且是将AI理论，大数据处理和应用于实际问题的人类认知结合在一起的典型示例。