Simulation of Traffic Congestion as Complex Behaviour - Ed Manley

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Presented by Ed Manley at the Society of Cartographers 48th Annual Conference

Presented by Ed Manley at the Society of Cartographers 48th Annual Conference

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  • 1. Simulation of Traffic Congestion as Complex Behaviour Society of Cartographers Annual Conference – 3rd September 2012Ed ManleyDepartment of Civil, Environmental and Geomatic EngineeringUniversity College London
  • 2. Today’s TalkThe Complexity of Road Congestion • Behaviour and Complexity in the City • Agent-based Modelling of Choice Behaviours • Analysis of Taxi Driver Route Selection Data
  • 3. Urban ComplexityA Product of Human Behaviour • The function and nature of the city is defined by its the choices of its citizens • Choices influence how we interact • This accumulation of behaviours lead to the patterns of movement we see everyday • Understanding and modelling these patterns requires a fundamental understanding of human behaviour
  • 4. Urban ComplexityRoad Congestion • Road congestion is an excellent example of how human behaviour influences urban dynamics • People unilaterally pick their route and proceed towards their target, they remain reactive to problems • Competition for limited space at a given time results in emergence of congestion • Following shocks to the system, the influence of individual responses is of greatest significance
  • 5. Urban ComplexityUnderstanding Individual Movement • We examine the individual behaviours that contribute towards the formation and spread of congestion • How do drivers really choose a route? • What areas of the city do they know best? • How do they use information to aid them? • What is the heterogeneity in behaviour across the population? • These behaviours are incorporated within an agent- based model of the urban road system
  • 6. Agent-based ModellingFrom Micro to Macro • Agent-based Modelling allows us to link individual behaviour with the macroscopic evolution of the system • Individuals are represented distinctly, enabling incorporation of population heterogeneity • Individuals are autonomous and independent • Interactions between agents may lead to emergence of macroscopic phenomena
  • 7. Case StudyInvestigating the Influence of Behaviour • Aim to identify how different definitions of route selection behaviour alter resulting road network patterns • A range of individual route selection behaviours are incorporated into agent-based model Route Selection Spatial Knowledge Least Distance 500m Area Least Time 1000m Area Least Angular Around OD Locations Least Turns
  • 8. Agent BehaviourDesign Driver agents independently choose route through city
  • 9. Model Test AreaCentral LondonLocation: Central London All road links Road regulations and capacities integrated 30 minutes during AM peakAgents: ~15000 driver agents AM peak OD distribution from TfL Trip MatrixModel: Developed using Java + Repast Simphony 1.2 © OpenStreetMap 2012
  • 10. The Base Case
  • 11. Base Case Path: Shortest Distance Knowledge: Complete0 0.5 1 mile
  • 12. The Influence of Route Choice
  • 13. Least Time Path: Least Time Knowledge: Complete Faster, main routes Reduced on subsidiaries Stronger influence in West > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev.0 0.5 1 -0.5 to -1.5 Std. Dev. -1.5 to -2.5 Std. Dev. mile < -2.5 Std. Dev.
  • 14. Least Angular Path: Least Angular Knowledge: Complete Greater redistribution Towards straighter sections > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev.0 0.5 1 -0.5 to -1.5 Std. Dev. -1.5 to -2.5 Std. Dev. mile < -2.5 Std. Dev.
  • 15. Least TurnsPath: Least Turns (Distance Constrained) Knowledge: Complete Effect not as strong Influenced by distance But, highlights straighter sections > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev. 0 0.5 1 -0.5 to -1.5 Std. Dev. -1.5 to -2.5 Std. Dev. mile < -2.5 Std. Dev.
  • 16. The Influence of Spatial Knowledge
  • 17. Partial Knowledge Path: Shortest Distance Knowledge: Reduced to 500m Movement away from subsidiaries Greater reliance on main routes > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev.0 0.5 1 -0.5 to -1.5 Std. Dev. -1.5 to -2.5 Std. Dev. mile < -2.5 Std. Dev.
  • 18. Partial Knowledge Path: Shortest Distance Knowledge: Reduced to 1000m Less deviation from base case Reduction in use of subsidiaries Due to greater all around knowledge > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev.0 0.5 1 -0.5 to -1.5 Std. Dev. -1.5 to -2.5 Std. Dev. mile < -2.5 Std. Dev.
  • 19. Modelling CitiesThe Need for a Realistic Model of Behaviour • Models demonstrate strong importance of establishing a realistic representation of behaviour • Small changes in behaviour definition lead to big changes in city level patterns • Establishing this model of behaviour represents an important research goal • In respect to route choice, we have been analysing route trace data from minicab firm in London
  • 20. Route AnalysisPrivate Hire Cab Routes • Dataset of 700k processed routes through London from Addison Lee taxi company • Not Black Cab drivers, but will have generally better knowledge and may use navigation devices • Analysis compared each route against a range of optimal paths – here we will focus mainly on distance • This work still in its early stages…
  • 21. Taxi Driver DataTotal Flows
  • 22. Route AnalysisComparison to Alternatives – Averages Percentage Choice Alternative • For each whole route, Matched Least Distance 39.83 percentage of path Least Time 38.21 matched against range of Least Angular Deviation 27.37 alternatives Least Angular Deviation constrained by distance 33.06 Least Angular Deviation constrained by time 32.86 • Average match taken for Least turns constrained by distance 42.48 Least right turns constrained by distance 39.48 each alternative Lowest descriptor term score constrained by distance 41.52 Lowest descriptor term score constrained by time 38.24 Lowest descriptor term score constrained by angle 28.58 Maximise number of lanes constraining distance 38.97 No strong stand out Maximise number of lanes constraining time 35.20 artificial representation Maximise number of lanes constraining angle 25.47 of behaviour Least turns constrained by time 39.50 Least right turns constrained by time 38.45
  • 23. Route AnalysisComparison to Alternatives – Good Matches Percentage Choice Alternative • Count of paths where Achieving 75% Least Distance 13.1 alternative matches over Least Time 12.4 75% of real journey Least Angular Deviation 6.1 Least Angular Deviation constrained by distance 8.4 • Only journeys over 1km Least Angular Deviation constrained by time 8.8 in distance considered Least turns constrained by distance 16.1 Least right turns constrained by distance 12.6 Lowest descriptor term score constrained by distance 15.9 Lowest descriptor term score constrained by time 13.2 Poor performance Lowest descriptor term score constrained by angle 7.4 by each measure of Maximise number of lanes constraining distance 12.8 prediction Maximise number of lanes constraining time 10.7 Maximise number of lanes constraining angle 5.8 WHY? Least turns constrained by time 14.1 Least right turns constrained by time 12.7
  • 24. Route AnalysisSpatial Distribution • No complete routing algorithms provide an adequate representation of reality • This finding goes against assumptions within many conventional models of traffic simulation • So, which parts of these journeys are a good match against optimal routes? • We looked at deviations in route patterns across space, by direction of travel, against optimal distance journeys
  • 25. East to West London Journeys Difference in flows between 7576 actual and optimal distance routes > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev. -0.5 to -1.5 Std. Dev. Std. Dev. = 137.20 0.5 1 Mean = 4.1 -1.5 to -2.5 Std. Dev. Maximum = 1991 Minimum = -2365 < -2.5 Std. Dev. mile
  • 26. West to East London Journeys Difference in flows between 9850 actual and optimal distance routes > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev. -0.5 to -1.5 Std. Dev. Std. Dev. = 143.90 0.5 1 Mean = 4.5 -1.5 to -2.5 Std. Dev. Maximum = 1553 Minimum = -3018 < -2.5 Std. Dev. mile
  • 27. SE16 to W London Journeys Difference in flows between 522 actual and optimal distance routes > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev. -0.5 to -1.5 Std. Dev. Std. Dev. = 18.2 Mean = 1.3 -1.5 to -2.5 Std. Dev.0 0.5 1 Maximum = 130 Minimum = -176 < -2.5 Std. Dev. mile
  • 28. W to SE16 London Journeys Difference in flows between 704 actual and optimal distance routes > 2.5 Std. Dev. 1.5 to 2.5 Std. Dev. 0.5 to 1.5 Std. Dev. 0.5 to -0.5 Std. Dev. -0.5 to -1.5 Std. Dev. Std. Dev. = 27.4 Mean = 1.0 -1.5 to -2.5 Std. Dev.0 0.5 1 Maximum = 184 Minimum = -381 < -2.5 Std. Dev. mile
  • 29. Route AnalysisSpatial Distribution • Differences seem to indicate an attraction and repulsion of certain parts of the road network • Apparent preference for straight, longer sections, possibly with greater salience or perception of travel time • Route choice appears to not consist of a single route selection, but a phase-based process of selection • But does this mean distance plays no role at all? That doesn’t appear to be quite the case…
  • 30. Route AnalysisDistance Minimisation
  • 31. Route AnalysisChoice Heterogeneity • Indications are that route selection is a heuristic process, probably involving minimisation of distance and route complexity • There is also a heterogeneity in decision-making – Perhaps variation in knowledge? Location of decision? • Analysing collections of paths between discrete locations reveal that both of these factors may further contribute
  • 32. E14 to Kings Cross JourneysFlows of 521 routes between origin and destination 0 0.5 1 mile
  • 33. SE16 to W Journeys Flows of 522 routes between origin and destination0 0.5 1 mile
  • 34. W to SE16 Journeys Flows of 704 routes between origin and destination0 0.5 1 mile
  • 35. Route AnalysisDecision Points • Visualisations also allow us to identify locations of significant splits in flow - decision points • These areas of high activity are likely to be more salient in an individual’s mind, on which choices made • Decision points identified where inflow is split between more than one outflow route (10% minimum) • Could be used as foundation for decision making process within model
  • 36. E14 to Kings Cross JourneysDecision Points origin and destination Size indicates volume of traffic flow through point 0 0.5 1 mile
  • 37. ConclusionsSummary of Research • The definition of behaviour is clearly highly influential in determining global patterns of movement • Getting this representation right is key – requires full examination of population heterogeneity • Initial route analysis has highlighted some interesting trends with relation to established assumptions • Route choice appears to take place in phases • Minimisation of distance and route complexity, attraction to salient features appear important
  • 38. Thank you Ed Manley Edward.Manley.09@ucl.ac.ukBlog: http://UrbanMovements.posterous.com Project: http://standard.cege.ucl.ac.uk Twitter: @EdThink