intelligent infrastructure - urie bez
TRANSCRIPT
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CHANGING BEHAVIOURS
THROUGH USER INTERACTION
PRESENTED BY URIE BEZUIDENHOUT
DA VINCI TRANSPORT PLANNING
UNIVERSITY OF AUCKLAND
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SO HOW BAD IS CONGESTION?
It depends on how you define congestion and what you compare it to
$1billion per a?
Auckla nd Wel l ing t on Chr ist church
$702
m
$101
m
$77
m
$54
0
$21
0
$20
4
Congestion cost (2004)Total ($mpa) Person $pa)
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WHAT CAN WE DO ABOUT IT?
Now
Autonomous vehicles
Medium to Long termShort Term
Medium Term
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INFLUENCING DRIVER BEHAVIOUR
Now
Short Term
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TRAJECTORY PLOTS
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FLUSHING OUT QUEUES
Queued @ Red
QueuedPlatoon
Detector Zone
Free flow Platoon
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S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S13 S14 S15 S16 S17 S18 S19 S20 S21 S22 S23 S24 S25 S26 S270
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NEW Q-Predict Model Accuracy
Cycle Reference
Q -L
engt
h (v
eh)
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RESEARCH CASE STUDY – Auckland CBD
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EXPERIMENTAL EVIDENCESense traffic
Optimise signals
Drivers divert
Route flow
changes
Signal timing
changes
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OBJECTIVES
• Quantify theoretical maximum efficiency
• Signals can estimate queue
• Optimise OFFSET on cycle-by-cycle – queue minimisation
• Only selected corridors optimised
• Apply MUSIC - OPERA algorithm
• Drivers alter routes in response to signal changes
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RESULTS – NETWORK WIDE
Short term (Medium term)• Cyclical queues - 8% (-
28%)• Overcapacity queues - 4% (-31%)• Stop-start queues - 39% (-59%)• Travel time reduction - 5% (-20%)
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OPERA OPTIMISING BENEFITS
-25 % travel time
$ 400 Savings/person/year
$0.25 billion /yearTravel time savings for Auckland
500,000 trips in peak hr
11 min trip in peak hr
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• Competing signage
• Short block lengths
• Visually complex
• Queues
• Pedestrians and cyclists
DRIVER BEHAVIOUR
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DRIVER WORKLOAD
+ 6 s ~ 90 m
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TASK ANALYSIS - INTERSECTIONS
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FIXATION
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GAZE HEATMAP
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DYNAMIC HAZARDS
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DYNAMIC HAZARDS
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CALIBRATING DRIVERS
• Use queues to discourage movements• Flush out queues before arrival• Greenwave along alternative route• 4 - 6 weeks to get measurable benefits