A Traffic Chaos Reduction A Traffic Chaos Reduction Approach for Emergency ScenariosApproach 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 AddressedProblems Addressed

Traffic chaos caused by presence of emergency vehicles– sirens– increased accidents for emergency

personnel

Traffic chaos caused by large-scale evacuations– resource availability– contraflow

Our ApproachOur Approach

Efficient chaos-reducing information dissemination approach– targeted towards first responders and evacuations– using a vehicular ad-hoc network (VANET)

Provide emergency vehicle path-clearing technique

Provide real-time resource availability information

AssumptionsAssumptions

All vehicles act as information servers relaying information for the VANET.

No location servers or access points on the roadside.

Every vehicle has a navigation system, which plans its route and knows its current location.

Building BlocksBuilding Blocks

Resources: emergency service vehicles (ESVs), gas stations, hospitals, shelters, etc.

Reports: information sent by resources

Dissemination: wireless broadcast

Selection strategies: based on spatial relevance

ESV Route Traffic Chaos Reduction ESV Route Traffic Chaos Reduction Approach Approach

The ESV periodically broadcasts a report containing:– unique ID of ESV

– type of ESV

– start and end points

– route code

– tentative average speed of ESV along route

– current ESV location and time

– timestamp of report sent by the ESV

Clearing Time ComputationClearing Time Computation

Each vehicle in the path computes the time to intersection with the ESV based on the average ESV speed, ESV location, and current time

Driver should give way to ESV between 30-60 seconds before intersection time

Report Selection StrategyReport Selection Strategy

The relevance of a resource report is calculated through a relevance function– time report was sent– distance from the ESV– speed of the ESV

Periodically, reports in a vehicle’s database are sorted according to relevance

Most relevant report is used for computing clearing time and is broadcast to neighbors

Connecticut A

ve,

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 C

ode: cnt

Start {sx,sy}

End {dx,dy}

{(sx,sy)/cnt, (px,py)/O, (rx,ry)/15, (dx,dy)}

ESV Route CodeESV Route Code

Evacuation Traffic Chaos Evacuation Traffic Chaos Reduction ApproachReduction Approach

Resources (gas stations, shelters, hospitals) periodically broadcast reports– type of resource– availability of resource– location– timestamp of report

Reports updated as availability changes

Information filtered for relevance according to vehicle’s location

ESV Simulation ModelESV Simulation Model

Written in– use of multiple threads for traffic generation,

automobiles and ESVs.

Mobility model– nodes move in piecewise linear fashion, following

city streets.

Simulation ParametersSimulation Parameters

Simulation ResultsSimulation Results

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5 25 45 65Vehicular Density (vehicles/sq. km.)

Data success rate– ratio of number of

vehicles on ESV route that receive the message in time to clear the path to the total number of vehicles on the ESV route

As expected, increasing the density of vehicles, improves the success rate

Vehicular Density (vehicles/km2)D

ata

Suc

cess

Rat

e

ConclusionConclusion

Traffic Chaos Reduction Approach for Emergency Scenarios– emergency service vehicle path-clearing– evacuation resource availability

Future Work– improve simulation/mobility model– investigate bandwidth usage– investigate security issues

Department of Computer Science

Old Dominion University

Norfolk, Virginia

VANET Research Group

http://www.cs.odu.edu/~vanet

Department of Computer Science

Norfolk State University

Norfolk, Virginia