proactive traffic merging strategies for sensor-enabled cars vanet 2007, september, 2007 ziyuan...
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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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