# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 2 Intelligent Transportation Systems (ITS)
### Traffic Pattern Analysis  
With the data organized, the next step for an ITS is to learn the traffic patterns, understand traffic status, and make traffic predictions. There are two critical steps in most tasks for traffic pattern learning: (1) feature selection and (2) model design. Essentially, feature selection forms the original data space, and model design converts the original space to a new space that is learnable for classification, regression, clustering, or other tasks. On the one hand, traffic sensing is so important that, without original data property collected, it is almost impossible to make up a new space for pattern learning from poor original data space. On the other hand, once the traffic sensing is done with good design and quality, it is then necessary to focus on designing models to extract useful information for your tasks. Machine learning has been widely applied for a variety of traffic pattern learning tasks, such as driver and passenger classification using smart phone data [36], K-means clustering for truck bottleneck identification using GPS data [37], estimation of the number of bus passengers using deep learning [38], and faulty detection in vehicular cyber-physical systems [39]. A traditional group of studies is transportation mode recognition. Models are developed to recognize the mode of travelers, such as working, biking, running, and driving. This can be achieved by identifying travel features, such as speed, distance, and acceleration. [PERSON] and [PERSON] applied multiple traditional machine learning techniques for mode recognition using mobile phone data and found Random Forest (RF) and Support Vector Machine (SVM) to have the best performances [40]. [PERSON] et al. enhanced the mode recognition accuracy by designing a two-layer hierarchical classifier and extracting new frequency domain features [41]. Another work introduced an online sequential extreme learning machine (ELM), which focuses on transfer learning techniques for mode recognition. It was trained with both labeled and unlabeled data for better training efficiency and classification accuracy. Recently, deep learning models were also developed for mode recognition [42]. [PERSON] et al. developed a convolutional bidirectional Long Short-Term Memory (LSTM) model for transportation mode recognition. Feature extraction includes time domain and frequency domain features from the raw data [43]. Another representative group in data-driven pattern analysis is traffic accident detection. It is beneficial for transportation management agencies and travelers to have real-time information of traffic accidents regarding where it occurs and what the situation is. Otherwise, it may cause severe congestion and other issues besides the accident itself. This group of work often extracts features from traffic flow data, weather data, and so on to identify the traffic pattern change or differences around the accident location. [PERSON] et al. implemented eXtreme Gradient Booting (XGBoost) to detect the occurrence of accidents using real-time data, including traffic flow, road network, demographic, land use, and weather information. The Shapley Additive exPlanation (SHAP) is employed for interpretation of the results for the analysis of the importance of individual features [44]. They also led another study that showed the superiority of probabilistic neural networks for accident detection on freeways using imbalanced data. It revealed that the speed difference between the upstream and downstream of the accident was very significant [45]. In addition to traffic flow data, social media data is also shown to be effective for traffic accident detection. [PERSON] et al. employed the Deep Belief Network (DBN) and LSTM in the detection of traffic accidents using Twitter data in Northern Virginia and New York City. They found that nearly 66% of the accident-related tweets can be located by the accident log and over 80% can be linked to abnormal traffic data nearby [46].