Proactive Traffic Merging Strategies for Sensor-Enabled Cars VANET 2007, September, 2007 Ziyuan Wang, Lars Kulik and Kotagiri Ramamohanarao Department.

Proactive Traffic Merging Strategies for Sensor-Enabled Cars

VANET 2007, September, 2007

Ziyuan Wang, Lars Kulik and Kotagiri Ramamohanarao

Department of Computer Science and Software EngineeringThe University of Melbourne, Australia

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Outline

Introduction

Problem Statement

Progress So Far

Future Directions

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Traffic Congestion

Some facts on traffic congestion

Total amount of delay: 3.7 billion hours in 2003

Wasted fuel: 2.3 billion gallons lost

Congestion cost: $63 billion

Source: Texas Transportation Institute, 2005 Urban Mobility Report.

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40%

25%

10%

15%

5%5%

Bottlenecks Traffic Incidents

Work Zones Bad Weather

Special Events Poor Signal Timing

Major Causes of Congestion

Source: Federal Highway Administration. Traffic Congestion and Reliability: Linking Solutions to Problems - Executive Summary.

Bottlenecks:

•Intersections of on-ramps and main roads

•Blockage due to obstacles

“slinky type” effect

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Emergence of VANETs Sensor-Enabled Cars

Spatial information

Dedicated Short-Range Communications (DSRC) Vehicle-to-Vehicle (V2V) Vehicle-to-Roadside (V2R)

Vehicular Ad hoc Networks (VANETs) Safety: less accidents Efficiency: higher road utility

Position

Speed

Acceleration

Deceleration

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Problem Statement

Goal Optimize traffic throughput

How Proactive traffic merging algorithms Technology available: sensor-enabled cars + VANETs

Applications Intersections at the ramp and the main road of highways

(Highway merge assistant) Lane changing when there are obstacles on the way

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Existing Approaches

Traffic signal timing Fixed Traffic-responsive

Ramp metering

Real-time information

Automation Fully: Platoon (tightly grouped cars) Partial: Adaptive Cruise Control (ACC)

Limitations

Adaptive

Flexible

Robust

Traffic conditions are highly variable and unpredictable

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Contributions

Proposed proactive traffic merging algorithms that aim to use the current road facilities efficiently

Designed a controlled simulation environment intended to test various traffic merging strategies

Investigated what criteria are significant to evaluate the performance of traffic merging algorithms

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Proactive Merging Algorithm

Highway bottleneck

Regular strategy

Local decision Distance-based Velocity-based

AB

XY

AB

XY

AB

XYRegular

Proactive

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Outline of Our Algorithms

Strategy

Information Right of Way Assumption

Distance-based Position The car that is closest to the merging point

Velocity does not vary much

Velocity-based Position

Velocity

The car that arrives to the merging point first

Acceleration does not vary much

Comparisons of the proactive merging algorithms

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Outline of Our Algorithms

Sliding decision point

Adjust speed appropriately

Output

{c, d, x, e, y}

{c, x, d, y, e}

{x, c, d, y, e}

Input

{c, d, e}

{x, y}

Merging strategy

Distance

Velocity

Regular

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Evaluation Metrics

Delay The time to fill up the main road with a certain number of

cars from the ramp

Throughput The number of cars that complete merging over a period of

time

Flow The product of density and velocity

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Simulation

Intelligent Driver Model (IDM) Microscopic traffic model Safety distance

Parameter Value

Maximum velocity

Safe time headway

Maximum acceleration

Maximum deceleration

100 km/h

1.5 s

1m/s^2

3m/s^2

Merging pointDecision point

Exit ramp

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Experiments and ResultsExperiment settings Light Medium Heavy Unit

Main road

Ramp

5 10 15

3.6 -- 7.2

cars/km

cars/km

∞ ∞ ∞ ∞

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Experiments and Results

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Summary

Traffic merging strategies benefit from sensor-enabled cars

Proactive merging algorithm outperforms regular strategy in terms of throughput and delay

Achieved at the cost of slightly lower velocity

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Robustness of Algorithms

Human factors

Imperfect information Sensor accuracy

Unreliable communication medium Studies* show only 50-60% of cars in range will receive a

car’s broadcast

Penetration rates Initially, only a small number of sensor-enabled cars

* Source: J. Yin, T. EIBatt, and S. Habermas, Performance evaluation of safety applications over DSRC vehicular ad hoc networks, VANET 2004

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Higher Degree of Realism

Obstacles

Blocking

Traffic patterns

Different distributions

Multiple lanes

Lane-changing

Heterogeneity

Different types of vehicles

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Thank you!

Questions,

Suggestions,

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