Automatic Freeway Incident Detection Based on Fundamental Diagrams of Traffic Flow

Abstract

Algorithms for automatic incident detection (AID) detect traffic incidents on the basis of traffic flow measurements. There are two important steps in an AID algorithm: traffic flow feature generation and incident decision making. In the past decade, the research on freeway AID algorithms has been focused on using artificial intelligence to optimize the decision making of AID algorithms. In this paper, the primary focus is on finding a new set of variables for the feature generation. The new variables, uncongested and congested regime shifts (URS and CRS), are generated by conducting coordinate transformation on loop-detected flow and occupancy measurements. A novel AID algorithm, the fundamental diagrams–based automatic incident detection (FD AID) algorithm, is then developed by implementing the incident-related traffic flow knowledge using those variables. Preliminary results show superior performance of the FD algorithm compared with legacy algorithms.