An Incident Detection Algorithm Based On a Discrete State Propagation Model of Traffic Flow

Traffic prediction;Discrete state propagation;Intelligent transportation systems;Traffic operations;Incident detection

Guin, Angshuman ; Civil and Environmental Engineering

University:Georgia Institute of Technology

Department:Civil and Environmental Engineering

关键词: Traffic prediction;    Discrete state propagation;    Intelligent transportation systems;    Traffic operations;    Incident detection;   

Others  :  https://smartech.gatech.edu/bitstream/1853/5040/1/guin_angshuman_200407_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance.This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.