CHANGING BEHAVIOURS

THROUGH USER INTERACTION

PRESENTED BY URIE BEZUIDENHOUT

DA VINCI TRANSPORT PLANNING

UNIVERSITY OF AUCKLAND

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)

WHAT CAN WE DO ABOUT IT?

Now

Autonomous vehicles

Medium to Long termShort Term

Medium Term

INFLUENCING DRIVER BEHAVIOUR

Now

Short Term

TRAJECTORY PLOTS

FLUSHING OUT QUEUES

 

Queued @ Red 

QueuedPlatoon

Detector Zone 

Free flow Platoon

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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2

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NEW Q-Predict Model Accuracy

Cycle Reference

Q -L

engt

h (v

eh)

RESEARCH CASE STUDY – Auckland CBD

EXPERIMENTAL EVIDENCESense traffic

Optimise signals

Drivers divert

Route flow

changes

Signal timing

changes

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

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%)

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

• Competing signage

• Short block lengths

• Visually complex

• Queues

• Pedestrians and cyclists

DRIVER BEHAVIOUR

13

DRIVER WORKLOAD

+ 6 s ~ 90 m

14

TASK ANALYSIS - INTERSECTIONS

15

FIXATION

GAZE HEATMAP

DYNAMIC HAZARDS

DYNAMIC HAZARDS

CALIBRATING DRIVERS

• Use queues to discourage movements• Flush out queues before arrival• Greenwave along alternative route• 4 - 6 weeks to get measurable benefits