Precise Traffic Anomaly Region Detection Based on a Heat Diffusion Model

• Main Authors: Wei-Li Lai, 賴韋利
• Other Authors: Yu-Ling Hsueh
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/q8yj6x

碩士 === 國立中正大學 === 資訊工程研究所 === 103 === The heat diffusion model is constructed based on the thermal conduction in physics, which computes the progress of how heat is diffused from objects with higher temperature to those with lower temperature over time. Given several metal objects of different temperatures in contact with each other, the heat diffusion model can be used to predict the temperature of the objects after a specified period of time. Recently, researchers have utilized the heat diffusion model in different application domains, such as social networks, traffic systems, and statistical manifolds. When applying the concept of the heat diffusion model to the traffic systems for anomaly region detection, many challenging issues need to be overcome. In a traffic system, sensors are deployed distributively on a road network. As a result, the sensor data contain useful features (e.g., driving direction and speed limit) that can be used for anomaly detection. Thus, our objective is to improve the heat diffusion model over a weighted directed graph, where each vertex represents a sensor, and each edge represents the distance between two sensors. In our experiments, we use two measurements to compare our work with one existing work, including the difference between our estimation and actual traffic flow, and the precision rate of anomaly detection. The experimental results show that our estimation of traffic flow is closer to the actual sensor records than that of the existing work, and our detection obtains a higher precision rate for all experimental parameters, particularly when the system detection interval is set to a shorter period of time.