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A Traffic Chaos Reduction Approach for Emergency Scenarios Syed R. Rizvi † , Stephan Olariu † , Mona E. Rizvi ‡ , and  Michele C. Weigle †  † Department of Computer Science, Old Dominion University, Norfolk, VA  ‡  Department of Computer Science, Norfolk State University, Norfolk, VA NetCri’07 The First International Workshop on  Research Challenges in Next Generation Networks for  First Responders and Critical Infrastructures April 13th, 2007
Problems Addressed ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Our Approach ,[object Object],[object Object],[object Object],[object Object],[object Object]
Assumptions ,[object Object],[object Object],[object Object]
Building Blocks ,[object Object],[object Object],[object Object],[object Object]
ESV Route Traffic Chaos Reduction Approach  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clearing Time Computation ,[object Object],[object Object]
Report Selection Strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ESV Route Code Connecticut Ave,  Point P {px,py} Point R {rx,ry} Street  Code: 13 Street  Code: 14 Street  Code: 15 Street  Code: 12 Street Code: M Street Code: N Street Code: O Street Code: P Street Code: cnt Start {sx,sy} End {dx,dy} {(sx,sy)/cnt, (px,py)/O, (rx,ry)/15, (dx,dy)}
Evacuation Traffic Chaos Reduction Approach   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ESV Simulation Model ,[object Object],[object Object],[object Object],[object Object]
Simulation Parameters
Simulation Results ,[object Object],[object Object],[object Object],Vehicular Density (vehicles/km 2 ) Data Success Rate
Conclusion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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A Traffic Chaos Reduction Approach for Emergency Scenarios

  • 1. A Traffic Chaos Reduction Approach for Emergency Scenarios Syed R. Rizvi † , Stephan Olariu † , Mona E. Rizvi ‡ , and Michele C. Weigle † † Department of Computer Science, Old Dominion University, Norfolk, VA ‡ Department of Computer Science, Norfolk State University, Norfolk, VA NetCri’07 The First International Workshop on Research Challenges in Next Generation Networks for First Responders and Critical Infrastructures April 13th, 2007
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. ESV Route Code Connecticut Ave, Point P {px,py} Point R {rx,ry} Street Code: 13 Street Code: 14 Street Code: 15 Street Code: 12 Street Code: M Street Code: N Street Code: O Street Code: P Street Code: cnt Start {sx,sy} End {dx,dy} {(sx,sy)/cnt, (px,py)/O, (rx,ry)/15, (dx,dy)}
  • 10.
  • 11.
  • 13.
  • 14.
  • 15.

Editor's Notes

  1. The sudden appearance of an ESV en route to an emergency can be critically disruptive to nearby vehicles as individual drivers maneuver to get out of the way. Some drivers become confused and create conflicts that can cause ESV crashes or block lanes increasing response times. EMS personnel in the United States have an estimated fatality rate of 12.7 per 100,000 workers, more than twice the national average. † † B. Maguire, K. Hunting, G. Smith, N. Levick, “Occupational Fatalities in Emergency Medical Services: A Hidden Crisis”, Ann Emerg Med 2002; 40:625-32. Preliminary studies have shown that contraflow strategies can increase the outbound volume by about 70 percent † . † Southeast United States Hurricane Evacuation Traffic Study: Executive Summary [Draft]. Federal Emergency Management Agency, Washington, D.C., 2000. Fuel and other urgent supplies need to be positioned prior to employing contraflow, or efforts to supply aid to the stricken or threatened areas would slow evacuation efforts. The task is tremendously challenging for the supply trucks to reach their destinations as quickly as possible and with minimal interruptions. An evacuee would only like to know what shelters are currently available near his or her current location and would likely not be interested in knowing the status of all shelters in a city. Real-time filtered information about the availability and location of shelters, gas stations, etc. can be utilized by the evacuating motorists. This would avoid the wastage of effort, time and fuel in searching for useful resources.
  2. Vehicles are equipped with wireless communication devices Form an ad-hoc network no infrastructure needed Exchange traffic safety information
  3. Vehicles broadcast the most relevant report to its neighbors.
  4. 5 m/s = 11 mph 10 m/s = 22 mph 15 m/s = 33 mph
  5. For our VANET-based infrastructure less system, this density dependency helps in the emergency and evacuation scenarios for the ESVs operating in higher congestion levels that are otherwise at higher risk for involvement in accidents and unpredictable delays.