An artificial neural network model for incident detection on major arterial streets Conference

Zhu, X, Gan, A. (2008). An artificial neural network model for incident detection on major arterial streets . 2 744-755.

cited authors

  • Zhu, X; Gan, A

authors

abstract

  • This study attempts to develop an arterial incident detection model by applying an Artificial Neural Network (ANN) with simulation data. A section of the US-1 corridor in Miami- Dade County, Florida was selected as the study area and coded in the CORSIM microscopic simulation model. Two data sets were generated via CORSIM simulation for model development and assessment. Multiple ANN models were designed for various scenarios. The model performances were evaluated using the selected measures of effectiveness (MOE), including detection rate (DR) and false alarm rate (FAR). The results showed that the ANN models in general could detect arterial incidents with a high DR of 90-95% and an acceptable FAR of lower than 4%. The study also identified some preferred features in the design of ANN incident detection models for this application. These include the detector configuration scheme, the selection of model input features, and the employment of data from previous cycles.

publication date

  • December 1, 2008

International Standard Book Number (ISBN) 13

start page

  • 744

end page

  • 755

volume

  • 2