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Modelling Adaptive Signals Realistically Pete Sykes. 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 - PowerPoint PPT Presentation
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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
