# Regional Traffic Event Detection Using Data Crowdsourcing
## 2 Related Work  
Existing traffic event detection techniques have adopted methods that improve detection accuracy by combining different kinds of data or using social media in conjunction with machine learning. For example, the authors of [12] proposed an architecture that detects traffic events using social media and taxi GPS data to increase event detection accuracy. This technique combines different data to improve accuracy, i.e., social media data to distinguish traffic problems and GPS data to extract spatiotemporal information. It also uses density-based clustering to group related roads into single groups and analyze them. Taxi GPS data are used to determine the times and locations of traffic abnormalities, whereas social data are used to describe the causes of traffic abnormalities. However, the presented framework can only detect traffic events and not the precise causes of the events. Meanwhile, the authors of [13] proposed the Smart Traffic Management Platform (STMP), which integrates and analyzes sensors and social media to detect traffic events and increase event detection accuracy. STMP detects concept drift integrates heterogenous big data streams such as IoT, smart sensors, and social media to distinguish repeating and non-repeating traffic events. This information can monitor the spread of event influence, predict traffic flow, analyze commuter sentiment, and determine optimal traffic control. However, this system requires semantic information to process sensor and social media data. The authors of [14] proposed a detection method for several emergency situations, including traffic situations, using machine learning to identify events. This technique uses binary classification to select data indicating emergency situations from social data and multi-class classification to classify event types. In addition, a bidirectional long-short-term memory (BiLSTM) model is used to extract time and location information. Finally, grouping is performed based on similarity calculations of event type, time, and location. This technique recognizes different data points as a single event through grouping and understands how events change over time. However, it has performed poorly in traffic events where location is important because its similarity calculation formula uses the same weights for time and location. The researchers of [15] used machine learning to distinguish complaints regarding road irregularities and poor road conditions. This approach extracts places and people through Twitter based on entity name recognition. The latitude and longitude of each entity name are obtained through the OpenStreetMap API. In addition, they used machine learning to classify tweets into three categories: useful, normal, and not useful. Tweets classified as \"Normal\" are converted into useful tweets using techniques proposed by the researchers. However, this approach has exhibited poor performance in terms of precision and recall. In reference [16], the authors proposed a contextual word-embedding method that combines a convolutional neural network (CNN) model and a bidirectional encoder representation from a transformer (BERT) model to detect traffic events. This technique shows that a CNN model created for image processing can also be used for text processing. Furthermore, it shows that two or more models can be combined and used together instead of a single model. This technique has demonstrated optimal performance. However, it can be improved to extract the times and locations of traffic events to perform additional analyses. In reference [17], the researchers used Twitter to detect events influencing traffic congestion and proposed an automatic labeling access method that automatically assigns labels while considering the large volume of data. However, because the method was created based on an existing dictionary, it experiences errors when confronted with keywords not included in the dictionary.