# Regional Traffic Event Detection Using Data Crowdsourcing
## 3 Proposed Regional Traffic Event Detection Technique  
### Structure of Proposed Technique  
Existing event detection methods that use social media data have a few problems. Firstly, detection accuracy decreases with insufficient data and when the ratio of posts un related to the event of interest is unbalanced. Secondly, geographical location information (geotags) on social media is limited. Therefore, accuracy can be poor in fields of application where location information is essential. Furthermore, when different data are combined and used, inconsistencies can occur. As the amount of data increases, accuracy can decrease with increased preprocessing complexity and including unnecessary information. In the case of sensor data, there is the possibility that malfunctioning devices can collect unreliable values.  
In this study, data collected via crowdsourcing are applied to a machine-learning model to perform event detection. The collected data are first preprocessed and the features are extracted. A machine learning model is trained based on the extracted features. The trained model later receives new data as input and judges whether the data are related to an event. The machine learning method uses a classification model. This model classifies the input data into predefined classes. It accomplishes this by learning the decision boundary, which is the standard by which the model makes decisions based on the training data. Later, when new data are entered as input, the model determines which class the data belongs to according to the decision boundary and outputs the classification results. In this paper, we present a machine learning method that preprocesses data collected via crowdsourcing and trains a classification model based on these data to perform event detection. This method improves the accuracy of event detection.  
Figure 1 shows the proposed technique's overall structure, which consists of three modules: Data Preprocessing, Event Type Extraction, and Event Place Extraction. Each module is necessary to increase the accuracy of event detection. The first module, the Data Preprocessing module, includes a Tokenizer, Remove Noise and Stopwords, and Noun Extraction stages. It refines the collected data by removing any noise and stopwords. Social data contain many grammatically incorrect expressions, which reduce event detection accuracy. The second module, the Event Type Extraction module, identifies events that the collected data represent. The Event Selection stage first selects traffic-related events; then, the Categorize Selected Event Types stage determines the traffic-event types of the selected events. The final module, the Event Place Extraction module, extracts the event locations represented in the data. Since traffic events occur in specific regions, the events' location information must be known. Therefore, keywords representing regions are identified at the Keyword Selection stage, and the administrative districts where the events occurred are extracted at the Return Administrative District stage. In this way, regional information can be obtained.  
Figure 1: Regional traffic event detection technique: overall structure and specific modules.