A Microscopic Simulation Modelling of Vehicle Monitoring Using Kinematic Data Based on GPS and ITS Technologies

This paper presents an en-route anti-terrorism security system for commercial vehicle operations (CVO) using kinematic data based on Global Positioning Systems (GPS) and Intelligent Transportation Systems (ITS) technologies. The real-time information of the coordinate position and speed of the concerned vehicle as well as the speed of and the gap to the vehicle ahead was taken into account during the terrorism detection. Two typical cases studies, i.e. container trucks running through a freeway network and a bank-armored vehicle traveling across a metropolitan Central Business District (CBD) area, were conducted to test the performance of the proposed system. The designed terrorism scenarios included mixtures of hijack behaviors like deviation from the original route, forced slowdown/stop by the in-vehicle terrorists and forced slowdown/stop by the vehicle ahead. Commercial vehicle operations under such terrorism scenarios were simulated by a customized microscopic traffic simulation model using real networks with real OD data. Experiment results from the simulations show that the proposed system is capable of being efficient in detecting the strange behaviors of commercial vehicles involved in a possible terrorist attack.

[1]  J. Sullivan,et al.  EMERGENCY PREPAREDNESS FOR TRANSIT TERRORISM , 2000 .

[2]  B. Jenkins.  Protecting public surface transportation against terrorism and serious crime : an executive overview , 2001 .

[3]  Baher Abdulhai,et al.  A neuro-genetic-based universally transferable freeway incident detection framework , 1996 .

[4]  Yorgos J. Stephanedes,et al.  FREEWAY INCIDENT DETECTION THROUGH FILTERING , 1993 .

[5]  Dipti Srinivasan,et al.  DEVELOPMENT AND ADAPTATION OF CONSTRUCTIVE PROBABILISTIC NEURAL NETWORK IN FREEWAY INCIDENT DETECTION , 2002 .

[6]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

[7]  Arun Chatterjee.  AN OVERVIEW OF SECURITY ISSUES INVOLVING MARINE CONTAINERS AND PORTS , 2003 .

[8]  Michael J. Cassidy,et al.  Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents , 1994 .

[9]  Der-Horng Lee,et al.  Mobile Sensor and Sample-Based Algorithm for Freeway Incident Detection , 2002 .

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

[11]  R L Cheu,et al.  FREEWAY INCIDENT DETECTION USING KINEMATIC DATA FROM PROBE VEHICLES , 2002 .

[12]  E R Case,et al.  DEVELOPMENT OF FREEWAY INCIDENT-DETECTION ALGORITHMS BY USING PATTERN-RECOGNITION TECHNIQUES , 1979 .

[13]  Stephen G. Ritchie,et al.  Simulation Of Freeway Incident Detection Using Artificial Neural Networks , 1993 .

[14]  Camille N.Y. Fink,et al.  Antiterrorism Security and Surface Transportation Systems: Review of Case Studies and Current Tactics , 2003 .