MONITORING FREE FLOW TRAFFIC USING VEHICULAR NETWORKS

Hadi Arbabiand Michele C. Weigle

Department Of Computer ScienceOld Dominion University

3rd IEEE Intelligent Vehicular Communications System Workshop (IVCS), January 2001, Las Vegas

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 2

Motivation

Real-time monitoring of traffic Accurate estimation of travel time and speed

Including accidents, work zones, and other potential causes of congestion

Fixed point sensors and detectors cannot estimate travel time and space mean speed

Trends toward probe vehicle-based systems Dynamic points of interest Augment current technologies

Effect of Market Penetration Rate

3

Content

INTRODUCTION Traffic Monitoring

Dynamic Traffic Monitoring (DTMon) Task Organizer Vehicles Virtual Strips

APPROACH Monitoring Traffic Data in Rural Areas

Highways Message Reception

Methods of Message Delivery

EVALUATION Free-Flow Traffic

SUMMARYHadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 4

Introduction

Monitoring Vehicle classification Count information

Flow rate Volume Density

Traffic speed Time mean speed (TMS) Space mean speed (SMS)

Travel time (TT)

Traffic Management Center (TMC)

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Technologies In Use

Fixed point sensor and detectors Inductive loop detectors (ILD) Acoustic sensors Microwave radar sensors Video cameras

Probe vehicle-based system Automatic vehicle location (AVL) Wireless location technology (WLT) Automatic vehicle identification (AVI)

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 6

Dynamic Traffic Monitoring (DTMon)

DTMon - A probe vehicle-based system using VANET and dynamically defined points of interest on the roads Task Organizers (TOs) Vehicles Virtual Strips (VS)

Imaginary lines or points

Methods of Message Delivery

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DTMon: Task Organizer & Virtual Strips

TO

Virtual

Strip

Virtual

Strip

Virtual Segment

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 8

Task Organizer (TO) Communicates with passing

vehicles Assigns measurement tasks Collects reports from the vehicles Organizes received measurements Informs upcoming traffic conditions

Multiple TOs Centralized

Aggregate information about the whole region

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Vehicles

Equipped GPS and DSRC communications device CPU and Required Applications

Record Speed GPS Position Travel Direction Timestamp Classification, Route Number, and …

Receive tasks from a TO Triggered at a specific time, speed, or location

Report Forwarded to the listed TOs Stored and carried to the next available TO

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Multiple TOsMultiple VS Multiple VS and Segments

Dynamically Defined Multiple TOs

A Sample Task From TO to Vehicles

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Message Reception (Analysis) Amount of Information Delivered to TO

Message Reception Rate (MRR) Information Reception Rate (IRR)

Analyze Various Traffic Characteristics Traffic Speed, Density, Flow Rate

Inter-Vehicle Spacing Equipped Vehicles

Market Penetration Rate (PR) Distance to TO Transmission Range

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 12

Message ReceptionB = inter-vehicle spacingp = penetration rateS = mean speedv = flow rateE = inter-vehicle spacing of equipped vehiclesR0 = transmission range d = distance to TOE[C] = expected inter-vehicle spacing

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 13

What Message Delivery Method?

180036005400veh/h

Flow Rate

Transm

ission R

ange

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 14

Methods of Message Delivery Regular Forwarding (RF) Dynamic Transmission Range (DTR) Store-and-Carry (SAC)

If Multiple TOs

Hybrid RF+SAC DTR+SAC

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 15

Evaluation

Compare Delivery Methods Penetration Rate (PR) Message Reception Rate (MRR)

Information Reception Rate (IRR) IRR ≈ MRR x PR

Message Delay Quality of Traffic data

Delivery Methods and Type of Data

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 16

Evaluation

Several experiments using VANET modules that we developed for the ns-3 simulator

•H. Arbabi, M. C. Weigle, "Highway Mobility and Vehicular Ad-Hoc Networks in ns-3," In Proc. of the Winter Simulation Conference. Baltimore, MD, December 2010•Highway Mobility for Vehicular Networks (Project and Google Code)• http://code.google.com/p/ns-3-highway-mobility/

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 17

Simulation Setup Bi-directional six-lane highway

TO1 is located at 1 km away TO5 is located at 5 km away (optional

secondary TO) Vehicles enter the highway with

Medium flow rate (average 1800 veh/h) Uniform Distribution

Desired speed 110±18 km/h (30±5 m/s) Normal Distribution

Free flow traffic with poor connectivity

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 18

Simulation Setup

10 runs, 30 min each, p {5%, 25%, 50%, 100%}

Major defined strips by TOs {VS1 , VS2 , VS5 , VS9}

Comparison Each method with the others Actual simulation (ground truth) data

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 19

Freception Higher Penetration = Higher RFFarther Distance= Lower RF

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 20

MRR

VS250%

Hybrid = Forwarding + Carrying = Full MRR

Higher Penetration = More Forwarding = Less Carrying

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 21

MRR

VS250%

Higher Distance = No Forwarding

Lower Distance = Higher Forwarding

Higher Distance Does Not Affect SAC or Hybrid

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 22

MRR and Traffic In Opposite Direction

Penetration Rate

RF, w/o opp

RF, w/opp

DTR w/o opp DTR,

w/opp

5% 0% 0% 1.1% 2.4%

50% 59% 72% 78% 96.7%20-25% 20-25%

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 23

Message DelayRF Delay Very Low

Hybrid Delay 1. Amount of Carried Messages2. TTMore ForwardingLess DelayMore SAC More Delay

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 24

Quality of Data

Good Estimate?

Sensors and Detectors AVL DTMon

Flow Rate and Density Yes No

See Next Table

TMS YesUnderestimat

e Yes

Travel Time Not Available Overestimate Yes

SMS Not AvailableUnderestimat

e Yes

Vehicle Classification Not Accurate Limited Yes

t-test Alpha = 0.05 (Confidence

> 95%)

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 25

Quality of Data

High Quality Estimation Conf. ≥ 95%

Traffic Density

orPenetration

Rate

Message Delivery Method

Flow Rate and Density High Any

Classification,TMS

Travel Time,or

SMS

LowSAC, RF+SAC,

or DTR+SAC

Medium or High Any

t-test Alpha = 0.05 Confidence

> 95%

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 26

Summary

DTMon can estimate good quality Travel Time and Speed

DTMon can estimate good quality flow rate and density in higher penetration rates

Hybrid message delivery improves information reception rate with cost of latency as an option for low penetration rates

DTMon can augment current technologies and monitoring systems

Hadi Arbabi and Michele C. Weigle {marbabi, mweigle}@cs.odu.edu 27

Questions?

Hadi Arbabi and Michele C. Weigle Department of Computer Science at

Old Dominion University Vehicular Networks, Sensor Networks, and

Internet Traffic Research http://oducs-networking.blogspot.com/ {marbabi, mweigle}@cs.odu.edu

This work was supported in part by the National Science Foundation under grant CNS-0721586.