Traffic control in dynamic environments Irregular demands Arterial - - PowerPoint PPT Presentation

▶
traffic control in dynamic environments
SMART_READER_LITE
LIVE PREVIEW

Traffic control in dynamic environments Irregular demands Arterial - - PowerPoint PPT Presentation

Dec 12, 2023 201 likes •392 views

Organic Traffic Control (OTC 3 ) J. Branke, J. Hhner, C. Mller-Schloer, H. Prothmann, H. Schmeck, S. Tomforde SPP 1183 Organic Computing | Final colloquium Nrnberg | September 15/16, 2011 Shannon Kokoska William Warby Brett Weinstein

slide-1
SLIDE 1

Organic Traffic Control (OTC3)

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

SPP 1183 Organic Computing | Final colloquium Nürnberg | September 15/16, 2011

Brett Weinstein William Warby Shannon Kokoska

slide-2
SLIDE 2

Irregular demands

Traffic control in dynamic environments

Arterial road at Karlsruhe Reoccurring daily traffic demands

:

 Adaptive traffic lights  Self-organised coordination  At run-time!  Dynamic route guidance

2

slide-3
SLIDE 3

Agenda

Phase I – Adaptive traffic lights Observer/controller architecture Phase II – Self-organised coordination

  • Decentralised progressive signal systems
  • Hierarchical extensions

Phase III – Dynamic route guidance

  • Decentralised routing
  • Regional extensions

3

slide-4
SLIDE 4
  • 1. ADAPTIVE INTERSECTIONS

4

by William Warby

slide-5
SLIDE 5

Observer/controller (O/C) architecture

  • Two-levelled learning for safety-

and performance-critical systems

  • Cooperation with

Optimisation at run-time  Reduces delays  Avoids costly reassessments

Adaptive Intersections

State of the art: Traffic-actuation

  • Loss of adaptivity for high

traffic demands

  • Logic predefined at design

time

  • No optimisation at run-time

5 Vehicle arrivals

  • Min. duration
  • Max. duration

Signal control unit Observer

Signal plan optimisation Signal plan selection

Controller

LCS EA Simulator Prediction Data analysis Preprocessing

slide-6
SLIDE 6
  • 2. SELF-ORGANISED COORDINATION

6

by Brett Weinstein

slide-7
SLIDE 7

Uncoordinated signals

slide-8
SLIDE 8

Coordinated signals

Cycle time Offset Preconditions for coordination

  • 1. Select coordinated intersections
  • 2. Determine common cycle time
  • 3. Select signal plans and offsets
slide-9
SLIDE 9

Self-organised coordination

State of the art: Adaptive network control systems

  • Network-wide control loop
  • Local traffic-actuation

 High effort for communication  High susceptibility to failure  Not always cost-effective Self-organised coordination Distributed O/C components

  • Local communication
  • Local signal plan selection

 Reduction of stops

  • Optional: Regional Manager

(conflict resolution)

9

http://www.mobility.siemens.com

Regional Manager Observer Controller

slide-10
SLIDE 10
  • 3. DYNAMIC ROUTE GUIDANCE

10

by Shannon Kokoska

slide-11
SLIDE 11

Dynamic route guidance

Driver information O/C components

  • Estimate local delays
  • Derive recommended

routes using adapted Internet protocols – Distance Vector Routing – Link State Routing  Minimise travel times  Prevent congestions  Improve robustness wrt incidents

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

11

slide-12
SLIDE 12

Regional routing

Two types of routing components 1. Intra-region: DVR/LSR ( ) 2. Inter-region: Border gateway routing ( ) Advantages

  • Reduced routing table size

( fewer routing messages)

  • Tables become partly static

(destinations in other regions)

  • Reduced number of hops per

message (depends on topology)

12

slide-13
SLIDE 13

Test scenario

Network

  • 3 regions
  • 27 intersections ( )
  • 28 destinations ( )

Signalised intersections

  • Variable Message Signs (VMS)
  • O/C architecture
  • 4-phased signal plans

Traffic demand

  • 6000 veh/h (equally distributed

among destinations)

  • Low (12.5%) , Medium (37.5%),

and High (75%) compliance

13

…

slide-14
SLIDE 14

No incidents Reductions Compliance L | M | H Travel time 9% |17% | 20% Stops 3% | 8% | 10%

Simulation results

14

Compliance No routing L M H Travel time Stops

DVR Regional DVR

slide-15
SLIDE 15

Incidents Reductions Compliance L | M | H Travel time 6% | 23% | 27% Stops 3% | 13% | 15%

Simulation results

15

Compliance No routing L M H Travel time Stops

DVR Regional DVR

slide-16
SLIDE 16

16

SUMMARY

slide-17
SLIDE 17

Summary

Adaptive traffic lights

  • O/C architecture supporting

two-levelled learning

  • Optimisation of signal plans at

run-time

  • Reduced vehicular delays

Self-organised coordination

  • Decentralised or hierarchical

coordination mechanisms

  • Traffic-responsive progressive

signal systems

  • Reduced stops, fuel

consumption and emissions

Dynamic route guidance

  • On-line routing based on

current intersection delays

  • Adapted Internet routing

protocols – Link State Routing – Distance Vector Routing  Reduced travel times Optional: Regional Routing (BGP)

  • Reduced effort for computation

and communication

  • Reduced routing table size

17

slide-18
SLIDE 18

Selected publications

  • H. Prothmann, H. Schmeck, S. Tomforde, J. Lyda, J. Hähner, C. Müller-Schloer, and J. Branke. Decentralised route guidance in Organic Traffic Control. In

5th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2011), 2011. Accepted for publication.

  • S. Tomforde, H. Prothmann, J. Branke, J. Hähner, M. Mnif, C. Müller-Schloer, U. Richter, and H. Schmeck. Observation and control of organic systems. In
  • C. Müller-Schloer, H. Schmeck, and T. Ungerer, editors, Organic Computing – A Paradigm Shift for Complex Systems, chapter 4.1, pages 325–338.

Birkhäuser, 2011.

  • H. Prothmann, S. Tomforde, J. Branke, J. Hähner, C. Müller-Schloer, and H. Schmeck. Organic traffic control. In C. Müller-Schloer, H. Schmeck, and T.

Ungerer, editors, Organic Computing – A Paradigm Shift for Complex Systems, chapter 5.1, pages 431–446. Birkhäuser, 2011.

  • 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 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 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 5th International Conference on

Autonomic and Trusted Computing (ATC-08), volume 5060 of LNCS, pages 219-233. Springer, 2008. BEST PAPER AWARD

  • J. Branke, P. Goldate, and H. Prothmann. Actuated traffic signal optimization using evolutionary algorithms. In 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 2nd International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA 2006), pages 185-191. IEEE, 2006.

2008 - 2009 2006 - 2007 2010 - 2011

18