traffic signal control with connected and … lily...traffic signal control with connected and...
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TRAFFIC SIGNAL CONTROL WITH CONNECTED AND AUTONOMOUS VEHICLES IN THE TRAFFIC STREAM
Dr. Lily Elefteriadou Director, UFTI Professor of Civil Engineering
Co-PIs: Dr. Carl Crane (MAE), Dr. Sanjay Ranka (CISE)
FAV Conference, Tampa, Florida November 27, 2018
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Research Project Overview• Developing signal control strategies for autonomous,
connected, and conventional vehicles • Funding from NSF ($1.3M) and FDOT ($392K) • Developing simulation environment (VISSIM) • Timeframe: 2.5 years completed/ 4 years total • Planning field testing in Gainesville/UF as part of the I-
STREET testbed
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Project TeamElefteriadou, Lily PD/PIRanka, Sanjay Co-PICrane, Carl Co-PIRidgeway, Shannon Staff ScientistNeal, Patrick Graduate Student/MAEPourmerab, Mahmoud Graduate Student/CCEEmami, Patrick Graduate Student/CISEOmidvar, Aschkan Graduate Student/CCEMartin Gasulla, Maria Graduate Student/CCELetter, Clark Graduate Student/CCEEsposito, John Graduated, MS in MAEKim, Mincheul Graduated, MS in MAEKiriazes, Rebecca Lucic, Michael
Undergraduate Student/CCE Undergraduate Student/ISE
Bedros, Saad ISS Staff Swingen, Cory ISS Staff Michalopoulos, Panos ISS Staff
Website: http://avian.essie.ufl.edu
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Research Objectives• Develop novel optimization
algorithms for AV trajectories and signal control • Consider Connected Vehicles
(CV) and conventional vehicles and their effects on optimal trajectories and control • Develop simulation environment
for testing • Develop novel sensors and data
fusion algorithms to implement our algorithms in mixed traffic • Implement the algorithm at an
intersection in the field
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Optimal AV Trajectory Determination
• Optimization determines three/four component trajectories for AV • Need to have
destination, which affects speed
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Optimization for AV Only
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Optimization Horizon Scheme
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Comparisons – Balanced Demand
Delay reduced by 16.3% to 79.3% depending on demand: the higher the demand the higher the benefit
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Comparisons – Unbalanced Demand
Varying demands by approach do not affect the performance: total demand is more important
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Effects of Communication Range
1000 vph
1400 vph 1800 vph
DSRC DSRC
600 vph
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Considerations for Field Implementation
• Optimization interval vs. initial trajectories • Communication
range vs. optimization interval • Approach speed
vs. communication range
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Optimization for Mixed Traffic• Need to have conventional signalization • Need to account for conventional vehicle movement • Assumed Gipps car following for conventional vehicles
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• Higher AV % results in lower delays • Min. saturation headway significantly affects travel time • Communication range does not significantly affect delay • Higher flow rates result in lower effective greens, with frequent switching
between phases
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• IICS is most effective for higher flows and lower saturation headways • IICS results in higher average effective greens, since it prevents gap outs.
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Transition To Practice: Initial Testing• Initial testing in
Gainesville and TERL in Tallahassee
• DSRC communication established – one “suitcase” at UF and three more at FDOT/TERL
• Completed fusion for radar & DSRC, now adding video
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Next Steps• Transitioning code to Python to enhance speed and
prepare it for field implementation • Adjustments planned for consideration of
pedestrians and bicycles • Optimization will consider cycle failures • New optimization will interact with VISSIM • Developing fusion approach for multiple inputs
(radar, video, DSRC) to determine location/speed of conventional vehicles and pedestrians/bicyclists • Continuing field tests at Gainesville intersection
for radar, DSRC, video
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Questions?
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