Modelling Adaptive Signals Realistically Pete Sykes

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Modelling Adaptive Signals Realistically

Pete Sykes

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Modelling Adaptive Signals Realistically

Adaptive signal systems – What do they adapt to?

Variability in arrival rate

Rise and fall in flows arriving at junctions

Variability in arrival pattern

Non uniform gaps between individuals, platoons.

Detail of stop line arrivals is needed to complement detail of H.I.L simulation

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Modelling …

Q: How much difference does this actually make?

A: Use a model to investigate how MOVA performance varies with:

- Arrival rate

- Arrival pattern

Napier University Summer project.

Presented at JCT Signals Symposium Nottingham Sept 2007

Paper to be published in TEC Jan 2008

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Modelling H.I.L Realistically

Not a new question:

ENGELBRECHT , R.J., POE, C.M. (1999) Development of a Distributed Hardware-In-The-Loop Simulation System for Transportation Networks. Paper presented at the 1999 Annual Meeting of the Transport Research Board. [Available from http://ttiresearch.tamu.edu/r-engelbrecht/trb/1999/TRB99RJE.pdf ]

FOX, K. AND CLARK, S. (1998) Evaluating the benefits of a responsive UTC system using micro-simulation. EPSRC Research Project. [Available from: http://www.its.leeds.ac.uk/projects/flows/utsgkaf.pdf]

LI, H., ZHANG, L., GARTNER, N.H. (2006) Comparative Evaluation of three Adaptive Control Strategies: OPAC, TACOS and FLC. Paper presented at the 2006 Annual Meeting of the Transport Research Board. TRB 2006 Annual Meeting CD-ROM.

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Part 1: Arrival rate

Demand varies during the peak

Causes:

School Run

Commuting

Sports / Entertainment events

Religious events

Effects:

Flow variation

Different troughs and peaks on different arms

Medium time scale ( 5 min)

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Modelling: Arrival rate

Very Simple T junction, moderately saturated.

Fixed time optimisation using hourly flows

Use PCMOVA for adaptive signals

M.O.E was time in queues to the signals

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Arrival rate: Profiles

VARIABILITY (STD DEV)

0.005

0.010

0.015

0.020

0.025

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Arrival rate: Profiles

CORRELATION

1

0.7

0.2

0

-0.2

-0.7

-1

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Arrival rate: Scenarios

Combined to form 36 scenarios to be tested

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Arrival rate: Results

0.005

0.010

0.015

0.020

0.025

10.7

0.20

-0.2-0.7

-1

68

70

72

74

76

78

80

secs

std dev

corr

MOVA

0.005

0.01

0.015

0.02

0.025

10.7

0.20

-0.2-0.7

-1

84

86

88

90

92

94

96

98

100

102

104

secs

std dev

corr

FIXED TIME

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Arrival rate: Results

Note:

Hourly demand is the same.

Only the demand profile has changed.

How much performance improvement will adaptive signals give?

It depends on the detail.

0.005

0.010

0.015

0.020

0.025

10.7

0.20

-0.2-0.7

-1

68

70

72

74

76

78

80

secs

std dev

corr

MOVA

0.005

0.01

0.015

0.02

0.025

10.7

0.20

-0.2-0.7

-1

84

86

88

90

92

94

96

98

100

102

104

secs

std dev

corr

FIXED TIME

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Part 2: Arrival patterns

Causes:

Upstream junction

Signalised or not / Pedestrian crossings

Opposed movement turning off the main carriageway

Platoon formation

Overtaking opportunities / HGV density and speed

Bus stops and bus stop layout

Motorway slips

Shockwaves affecting arrival frequencies

Effect:

Varying gaps between vehicles

Time scale of seconds

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Arrival patterns

New Signals

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Results: Arrival Patterns

Signals create platoons in traffic arriving at junction under test.

MOVA reacts to the gaps between platoons.

Headway

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5 15 25 35 45 55 65 75 85 95M

ore

Frequency

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Results: Arrival Patterns

Compare journey times on approaches to junction after the signals

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Conclusion

Detail of stop line arrivals is needed to complement detail of H.I.L simulation

UK Signals expert stated at Simulation User Group that he’s watching us!

Further analysis to be done, and some comparison with real detailed data.

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Questions?

For copies of

TEC paper

User group presentation

JCT Symposium paper

MSC dissertation

Pete.Sykes@sias.com

Paramics@sias.com