Towards Proactive Social Learning Approach for Traffic Event Detection based on Arabic Tweets

Intelligent Transportation System (ITS) help drivers by showing the shortest routes and some driving information such as congestion, accident and roadwork. Twitter traffic detection systems depend on real time collection of traffic and road-related data where users share real time events that can help extract traffic status on different roads. However, the current twitter-based approaches are not applied on Arabic traffic related tweets, and do not take into consideration tweets about roads that do not have congestion. In this paper, we address the aforementioned limitations by proposing a new proactive social learning approach for (1) detecting traffic related tweets, (2) extracting location using local dictionary and Google Maps API, (3) determining traffic status using Support Vector Machine (SVM), and (4) extracting the cause by classifying them into three different categories using incremental learning. Our experimental results show that our approach can identify tweets referring to traffic with an accuracy of 98%, determine jam status from those tweets by an accuracy of 91.1%, and identify the cause of traffic-related events with an accuracy of 84.7% per class label.