A state roadway incident e-detection and e-characterisation system

The purpose of this research was to develop a computer-based road incident detection and characterisation system. It was designed for incorporation and enhancement of an existing traffic monitoring system used by the South Carolina Department of Transportation (SCDOT) Based on the requirements, a fuzzy logic approach was selected and adapted as the algorithmic processor for the decision support system. A working prototype system was then constructed in the first phase of development. This paper focuses on the second phase, primarily the expansion and evaluation of the road incident characterisation and re-routing capabilities.

[1]  Mark Kojima,et al.  Human factors in traffic management centers: A literature review. Technical report, January 1998--March 1999 , 1999 .

[2]  Benjamin Coifman,et al.  Distributed surveillance on freeways emphasizing incident detection and verification , 2007 .

[3]  N Sobhi,et al.  HUMAN FACTORS RECOMMENDATIONS FOR TMC DESIGN , 1999 .

[4]  Fred L. Hall,et al.  Catastrophe theory and patterns in 30 second freeway traffic data, implications for incident detection , 1989 .

[5]  Bhagwant Persaud,et al.  CONGESTION IDENTIFICATION ASPECTS OF THE MCMASTER INCIDENT DETECTION ALGORITHM , 1990 .

[6]  Mary Leary,et al.  Public sector enterprise system implementation , 2007, Electron. Gov. an Int. J..

[7]  Jiuh-Biing Sheu,et al.  A fuzzy clustering-based approach to automatic freeway incident detection and characterization , 2002, Fuzzy Sets Syst..

[8]  Petter Gottschalk.  Information systems in police knowledge management , 2007, Electron. Gov. an Int. J..

[9]  Torin Monahan,et al.  “War Rooms” of the Street: Surveillance Practices in Transportation Control Centers , 2007 .

[10]  Amir Hossein Ghapanchi,et al.  A framework for e-government planning and implementation , 2008, Electron. Gov. an Int. J..

[11]  Billy M. Williams,et al.  Traffic Management Center Use of Incident Detection Algorithms: Findings of a Nationwide Survey , 2007, IEEE Transactions on Intelligent Transportation Systems.

[12]  Chi Xie,et al.  Use of Driver-Based Data for Incident Detection , 2002 .

[13]  Mahadeo Jaiswal,et al.  A conceptual framework for measuring public value of enterprise applications , 2009, Electron. Gov. an Int. J..

[14]  Milos Hauskrecht,et al.  Towards a Learning Trac Incident Detection System , 2006 .

[15]  Mehdi Sagheb-Tehrani,et al.  A model of successful factors towards e-government implementation , 2010, Electron. Gov. an Int. J..

[16]  Daniel J. Fonseca,et al.  Evacuation Decision Support System for Road Incident Detection and Characterization , 2009 .

[17]  Samuel C Tignor,et al.  FREEWAY INCIDENT-DETECTION ALGORITHMS BASED ON DECISION TREES WITH STATES , 1978 .

[18]  H M Al-Deek,et al.  FREEWAY INCIDENT DETECTION USING FUZZY ART (ADAPTIVE RESONANCE THEORY) , 1998 .

[19]  Carroll J Messer,et al.  Incident detection on urban freeways , 1974 .