Data Fusion of Fixed Detector and Probe Vehicle Data for Incident Detection

An important feature of many advanced traveler information systems (ATIS) is real-time information about incidents on the street network. This paper describes a system for automatically detecting incidents for such an ATIS developed using artificial neural networks and statistical prediction methods. The system monitors traffic conditions using two types of data: inductive loop detectors (ILDs) and vehicle probes. For both neural network and statistical methods, incident detection is accomplished using two approaches: by processing traffic input data directly and by processing the output of specialized algorithms that detect incidents using information from each data source. Analysis data generated from a simulation of a typical suburban signalized major arterial street are used. Different model configurations are examined and tested to identify the input variables and methods that are the best predictors of incident occurrence. The neural network approaches consistently perform at least as well as the discriminant analysis models, especially when results are adjusted to avoid false alarms.