“The Unintelligent Swarm”
Traffic oscillations in dynamic navigation
and how to circumvent them
Arne.Kesting@tomtom.com
Ma...
Who’s TomTom – What’s in Berlin?
2
About 90 developers for Navigation and Traffic Information Software
Live Traffic Service for Dynamic Navigation
3
GPS data gathered from probe vehicles for real-time traffic infomation
1. Co...
Feedback+Delay -> Routing Oscillations
4
Microscopic simulation of traffic dynamics and routing (worst-case scenario)
Rout...
Small percentages beneficial for Traffic System
5
Simulations for 0% and 20% of vehicles receiving link traveltimes for ro...
TomTom‘s Traffic Manifesto
6
Visionary claim when launching TomTom Traffic in 2010
In the future, when 10% of drivers use
...
What happens for large Equipment Levels?
7
Information leads to routing instabilites in worst-case scenario
100% guided
How to overcome Instability?
8
Load balancing strategies needed but system dynamics difficult to predict
• All capacities ...
McFadden (Nobel Prize
2000) and Ben-Akiva
How to overcome Instability?
9
Stochastic route choice model based on Discrete C...
System Dynamics with Stochastic Route Choice
10
Systematic variations of equipment percentage and uncertainty parameter
Summary & Discussion
11
Routing oscillations as collective phenomenon
• Experienced in real-world (e.g. radio broadcast)
•...
Thank you
Any questions?
Arne.Kesting@tomtom.com
www.akesting.de
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Wherecamp Navigation Conference 2015 - The unintelligent swarm

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Arne Kesting from TomTom

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Wherecamp Navigation Conference 2015 - The unintelligent swarm

  1. 1. “The Unintelligent Swarm” Traffic oscillations in dynamic navigation and how to circumvent them Arne.Kesting@tomtom.com Martin Treiber, TU Dresden
  2. 2. Who’s TomTom – What’s in Berlin? 2 About 90 developers for Navigation and Traffic Information Software
  3. 3. Live Traffic Service for Dynamic Navigation 3 GPS data gathered from probe vehicles for real-time traffic infomation 1. Collect GPS data from probes 2. Match data to map for path and speed estimations 3. Traffic state estimation (instantaneous speed) 4. Delivery to clients for dynamic routing Traffic Incidents Nov 25, 9AM Relative Speeds Nov 25, 9AM
  4. 4. Feedback+Delay -> Routing Oscillations 4 Microscopic simulation of traffic dynamics and routing (worst-case scenario) Route A1: 15 km Service Provider Model • Instantaneous speed estimation • Default speed on all links 108 km/h • Traveltime A1 ~ 8.3 min • Traveltime A2 ~ 8.9 min movsim.org | traffic-flow-dynamics.org Traffic Demand • 1800/h for 90 min • Drop to 0 later O D Route A2: 16 km Decision Point
  5. 5. Small percentages beneficial for Traffic System 5 Simulations for 0% and 20% of vehicles receiving link traveltimes for routing Time Inst. Traveltime Time Space Trajectories A1 A1 A2
  6. 6. TomTom‘s Traffic Manifesto 6 Visionary claim when launching TomTom Traffic in 2010 In the future, when 10% of drivers use TomTom's HD Traffic™ navigation system there will be what experts are calling a 'collective effect'. Essentially, our road networks will start to balance out and we will reduce traffic congestion for everyone.
  7. 7. What happens for large Equipment Levels? 7 Information leads to routing instabilites in worst-case scenario 100% guided
  8. 8. How to overcome Instability? 8 Load balancing strategies needed but system dynamics difficult to predict • All capacities in road network known • Ability to predict onset of congestion in advance • Server-side routing for individual route assignment • Only one provider serving all drivers Requirements for Load Balancing in Ideal World:
  9. 9. McFadden (Nobel Prize 2000) and Ben-Akiva How to overcome Instability? 9 Stochastic route choice model based on Discrete Choice Theory (LOGIT) No perfect information about traffic system => Incorporate uncertainty in route-choice decisions • Instantaneous traveltime as deterministic utility • Plus stochastic component for individual utility • Parameter: Uncertainty of utility, e.g. 2 minutes Probability of Route ChoiceInst. Travel Time
  10. 10. System Dynamics with Stochastic Route Choice 10 Systematic variations of equipment percentage and uncertainty parameter
  11. 11. Summary & Discussion 11 Routing oscillations as collective phenomenon • Experienced in real-world (e.g. radio broadcast) • Simulation of „worst-case“ scenario High percentage of vehicles sharing same traffic information • All providers measure the same speeds Load-balancing strategies needed for coordination • Prediction (capacities/routing) remains difficult • Stochastic component improves system dynamics
  12. 12. Thank you Any questions? Arne.Kesting@tomtom.com www.akesting.de

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