Study on Traffic Flow Breakdown with Nonlinear Statistical Theory of Change-Point

This paper introduces a statistical method to analyze traffic flow breakdown. Based on traffic flow theory and combined with mean-value change-point model, the hypothesis testing of the existence of change-points and the local-comparison algorithm to search change-points are discussed. The method is calibrated with the data from the city of Southampton, UK. An example of the applications is also included to test the effectiveness of the method. The breakdown of traffic flow is directly related with the abnormal phenomena such as road traffic incidents. It results in the discontinuities of traffic and reduces the service level. The research of traffic flow breakdown is often narrowly confined to incident detection, and there are approximately 4 kinds of algorithms: pattern recognition algorithm, statistical inference algorithm, catastrophic theory and neural network algorithm. These methods have played important roles in their past applications, and each has its shortcomings. The statistical theory of change-point is a nonlinear theory to study the breakdown phenomenon in real world. This kind of method will not change with time or location, and can be applied to various road conditions. Moreover, there is not special requirement for the distributions of traffic parameters such as traffic volume, speed, and lane occupancy.