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Modelling Adaptive Signals Realistically Pete Sykes

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

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Modelling Adaptive Signals Realistically Pete Sykes

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  1. Modelling Adaptive Signals RealisticallyPete Sykes

  2. 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

  3. 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

  4. 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.

  5. 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)

  6. 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

  7. Arrival rate: Profiles

  8. Arrival rate: Profiles

  9. Arrival rate: Scenarios Combined to form 36 scenarios to be tested

  10. Arrival rate: Results

  11. 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.

  12. 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

  13. Arrival patterns New Signals

  14. Results: Arrival Patterns • Signals create platoons in traffic arriving at junction under test. • MOVA reacts to the gaps between platoons.

  15. Results: Arrival Patterns • Compare journey times on approaches to junction after the signals

  16. 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.

  17. Questions? • For copies of • TEC paper • User group presentation • JCT Symposium paper • MSC dissertation • Pete.Sykes@sias.com • Paramics@sias.com

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