This paper describes the development of neural network models for Automatic Incident Detection (AID) on arterials, using data derived from simulated loop detectors and probe vehicles. This study extends previous research by comparing the performance of various neural network architectures for data fusion and by providing a comparison of model performance for various probe vehicle penetration rates and detector configurations. A set of 108 incidents were simulated and data was collected from loop detectors and probe vehicles at two locations on the network for two detector configurations. This research demonstrated the feasibility of developing a neural network model for detection of incidents on arterials using loop and probe vehicle data. Where it is not possible to collect probe vehicle data, satisfactory performance can be obtained using loop detector data alone. Data collected from the standard detector configuration was most effective. If speed data is available at a location, its use is highly recommended. (a) For the covering entry of this conference, please see ITRD abstract no. E211903.
Samenvatting