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Project overview Phases I & II Adaptive learning node - - PowerPoint PPT Presentation

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Organic Traffic Control (OTC 3 ) J. Branke, J. Hhner, C. Mller-Schloer, H. Prothmann, H. Schmeck, S. Tomforde SPP 1183 Organic Computing | 11 th colloquium Munich | October 7 th /8 th , 2010 by Sharon Drummond Project overview Phases I

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SLIDE 1

Organic Traffic Control (OTC3)

  • J. Branke, J. Hähner, C. Müller-Schloer, H. Prothmann, H. Schmeck, S. Tomforde

SPP 1183 Organic Computing | 11th colloquium Munich | October 7th/8th, 2010

by Sharon Drummond

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SLIDE 2

Project overview

Phases I & II

Adaptive learning node controller Collaborative control of traffic signals in urban road networks

  • Decentralised coordination
  • Hierarchical coordination

Regional Manager Observer Controller

SuOC Signal plan optimisation

Simulator EA

Signal plan selection

Observer LCS

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SLIDE 3

Project overview

Phase III

Route guidance and driver information

  • Communicating traffic lights
  • Variable Message Signs
  • Decentralised routing

 Minimise travel times  Prevent congestions  Improve robustness wrt incidents

Karte (c) OpenStreetMap (und) Mitwirkende, CC-BY-SA

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SLIDE 4

Agenda

1. Decentralised routing – Distance vector routing – Link state routing 2. Test scenarios and results 3. Observer/controller refinements 4. Summary and outlook

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SLIDE 5
  • 1. DECENTRALISED ROUTING

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by Shannon Kokoska

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SLIDE 6

Destination Next hop Delay

Routing components

  • Located at intersections
  • Estimate turning delays (based on

current flow / green time)

  • Manage routing tables (for

incoming sections)

  • Communicate routing data

– Distance vectors – Link states

6

25s 18s

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SLIDE 7

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Distance vector routing (DVR)

Routing components

  • Process local destinations ( )
  • Compute turning delays
  • Create routing entries
  • Advertise destinations

and distances

  • Receive routing data

– Add new routes to advertised destinations – Update distances if advertised distance is shorter

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A … …

  • Dest. Next Delay

A S1 20 S1 A | S1 |18 S2 S3 S5 S4 A | S3 | 36 A | S2 | 32 A | S4 | 46 20 18 14 10 A | S5 | 44

… …

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SLIDE 8

Link state routing (LST)

Routing components

  • Determine turning delays
  • Communicate delay changes

– Link state advertisement – Network flooding

  • Create weighted network

graph from received link states

  • Compute shortest paths using

Dijkstra’s algorithm

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S1 S2 Link Delay S2  S1 20 …

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SLIDE 9

Routing in road networks

Compared to Internets

  • Road networks limited in size
  • Intersections

Separate queues / routing tables for incoming links

  • Turnings

– Capacity influenced by traffic lights – Non-linear cost relations

  • Separate networks for road

traffic and routing traffic

  • Routing protocols differ in

– Computational effort – Communication cost  More important for Internets

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SLIDE 10

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  • 2. TEST SCENARIOS AND RESULTS
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SLIDE 11

Experimental evaluation

Scenario

Manhattan network

  • 25 intersections ( )
  • 20 destinations ( )
  • 120 road segments ( )

Signalised intersections

  • Variable Message Signs (VMS)
  • 4-phased signal plans
  • Organic traffic lights

Traffic demand 3800-7600 veh/h (equally distributed among destinations)

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…

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SLIDE 12
  • 75% compliance rate
  • Undersaturated

demand

  • No incidents

Reductions

DVR | LST Travel time 5.0% | 3.4% Stops 3.1% | 2.6% Fuel/CO2 5.8% | 4.6%

Experimental evaluation

Test case I

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No routing DVR LST Travel time Stops

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SLIDE 13
  • 75% compliance rate
  • Highly saturated

demand

  • No incidents

Reductions

DVR | LST Travel time > 50% Stops > 30% Fuel/CO2 > 35%

Experimental evaluation

Test case II

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No routing DVR LST Travel time Stops

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SLIDE 14
  • 75% compliance rate
  • Undersaturated

demand

  • 3 blocked roads

Reductions

DVR | LST Travel time 32% Stops 12% Fuel/CO2 19%

Experimental evaluation

Test case III

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1. 2. 3. 1. 2. 3.

No routing Routing No routing DVR LST Travel time Stops

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SLIDE 15

In cooperation with

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  • 3. OBSERVER/CONTROLLER REFINEMENTS

by Ryan Wick

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SLIDE 16

Observer/controller refinements

Level 1: On-line learning XCS-RC

  • Improved discovery component
  • Rule generalisation by inference

 Improved learning rate  Reduced population size

Cross-project publication

  • N. Fredivianus, H. Prothmann, and H. Schmeck. XCS Revisited: A

Novel Discovery Component for XCS. Accepted for 8th Int. Conf. on Simulated Evolution And Learning, 2010.

  • Presented at SPP-miniworkshop on Learning Classifier Systems

Level 2: Evolutionary optimisation Handling of noisy, simulation-based fitness estimations, e.g.:

  • Simulated duration vs. estimation

quality

  • Distribution of simulation time

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MP20

  • Perf. XCS

Popsize XCS

  • Perf. XCS-RC

Popsize XCS-RC

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SLIDE 17

Organic Network Control

Reliable broadcast protocol for MANETs

  • Increasing network load
  • Dynamic environments:

Static protocol configuration works well on average

  • Better performance due to:

– On-line adaptation – Context-aware protocol settings

SuOC Parameter optimisation

Network simulator (NS-2 / Omnet++) EA

Parameter selection

Observer LCS

Network protocol instance

Cross-project publication

  • S. Tomforde, E. Cakar, and J. Hähner. Dynamic control of network

protocols – A new vision for future self-organising networks. In Proc. of the 6th Int. Conf. on Informatics in Control, Automation and Robotics – Intelligent Control Systems and Optimization, pages 285-290, 2009.

Parameters

  • Delays
  • Buffer sizes
  • Interval lengths
  • Counter

Fitness: +8%

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SLIDE 18

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  • 4. SUMMARY AND OUTLOOK
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SLIDE 19

Summary and outlook

Decentralised routing

  • Extension of organic traffic lights

– Variable Message Signs – No car-to-x communication

  • Adapted Internet protocols

– Local communication – Local traffic data

  • Routing improves robustness

– Highly saturated demands – Road works – Accidents – … Outlook 1. Regional routing  Reduce communication effort and computational cost 2. Hierarchical routing

  • Network-wide traffic prediction
  • Incorporate external goals
  • System vs. user optimum

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SLIDE 20

Selected publications

  • S. Tomforde, H. Prothmann, J. Branke, J. Hähner, C. Müller-Schloer, and H. Schmeck. Possibilities and limitations of

decentralised traffic control systems. In IEEE World Congress on Computational Intelligence, pages 3298-3306. IEEE, 2010.

  • H. Prothmann, J. Branke, H. Schmeck, S. Tomforde, F. Rochner, J. Hähner, and C. Müller-Schloer. Organic traffic light

control for urban road networks. International Journal of Autonomous and Adaptive Communications Systems, 2(3):203- 225, 2009.

  • H. Prothmann and H. Schmeck. Evolutionary algorithms for traffic signal optimisation: A survey. In Proc. of mobil.TUM

2009 - International Scientific Conference on Mobility and Transport, 2009.

  • S. Tomforde, H. Prothmann, F. Rochner, J. Branke, J. Hähner, C. Müller-Schloer, and H. Schmeck. Decentralised

progressive signal systems for organic traffic control. In Proc. of the 2nd IEEE International Conference on Self-Adaption and Self-Organization (SASO 2008), pages 413-422. IEEE, 2008.

  • H. Prothmann, F. Rochner, S. Tomforde, J. Branke, C. Müller-Schloer, and H. Schmeck. Organic control of traffic lights. In
  • Proc. of the 5th International Conference on Autonomic and Trusted Computing (ATC-08), volume 5060 of LNCS, pages

219-233. Springer, 2008. ATC08 BEST PAPER AWARD

  • J. Branke, P. Goldate, and H. Prothmann. Actuated traffic signal optimization using evolutionary algorithms. In Proc. of the

6th European Congress on Intelligent Transport Systems and Services (ITS07), 2007.

  • F. Rochner, H. Prothmann, J. Branke, C. Müller-Schloer, and H. Schmeck. An organic architecture for traffic light
  • controllers. In Informatik 2006 – Informatik für Menschen, volume P-93 of LNI, pages 120-127. Köllen Verlag, 2006.
  • J. Branke, M. Mnif, C. Müller-Schloer, H. Prothmann, U. Richter, F. Rochner, and H. Schmeck. Organic Computing –

Addressing complexity by controlled self-organization. In Post-Conference Proce. of the 2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2006), pages 185-191. IEEE, 2006.

2008 - 2009 2006 - 2007 2010

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SLIDE 21

Summary and outlook

Decentralised routing

  • Extension of organic traffic lights

– Variable Message Signs – No car-to-x communication

  • Adapted Internet protocols

– Local communication – Local traffic data

  • Routing improves robustness

– Highly saturated demands – Road works – Accidents – … Outlook 1. Regional routing  Reduce communication effort and computational cost 2. Hierarchical routing

  • Network-wide traffic prediction
  • Incorporate external goals
  • System vs. user optimum

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