1. 1 Preferred citation style MATSim development team (ed.) (2007) MATSIM-T: Aims, approach and implementation, IVT, ETH Zürich, Zürich.

2. MATSIM-T: Aims, approach and implementation August 2007

3. 3 Overview Structure and team Task and solution methods MATSIM aims System architecture Examples Switzerland Berlin/Brandenburg Outlook and next steps Progress on shortest path calculations Traffic flow model Improving the convergence

4. 4 Structure Software: Open-source project under GNU public licence Coordination: Kai Nagel, TU Berlin Data: Public sources, where available Private sources, when needed or as occasion arises

5. 5 Current team Strategy: Kai Nagel, TU Berlin Kay Axhausen, ETH Zürich Fabrice Marchal, LET, Lyon Coordination of the implementation and project management: Michael Balmer, ETH Zürich Marcel Rieser, TU Berlin

6. 6 Current team: Implementation (1/2) Michael Balmer, ETH David Charypar, ETH Francesco Ciari, ETH Dominik Grether, TU Berlin Jeremy Hackney, ETH Andreas Horni, ETH Gregor Lämmel, TU Berlin Nicolas Lefebvre, ETH Michael Löchl, ETH

7. 7 Current team: Implementation (2/2) Fabrice Marchal, LET Konrad Meister, ETH Kai Nagel, TU Berlin Marcel Rieser, TU Berlin Nadine Schüssler, ETH David Strippgen, TU Berlin

8. 8 Current funding sources Basic research support for the chairs (competitive) ETH research fund Swiss National Fund German Research Society EU Framework funding VW Foundation Swiss Commission for Technology and Information (KTI) (datapuls, Lucerne)

9. 9 Task and solution methods

10. 10 What we would like to do ? Personal world Biography Projects Learning Personal worlds of others Social captial: stock of joint abilities, shared histories and commitments Household locations Social network geography Mobility tools

11. 11 What would we like to do ? Personal daily dynamics Activity repertoire (t)Activity repertoire (t+1)................ Activity calendar (t) Physiological needs Commitments Desires Pending activities Activity schedule (t) Mental map (t)Mental map (t+1)................ Scheduling Networks, Opportunities Rescheduling, Execution Updates, Innovations Unexecuted activities

12. 12 Understanding scheduling Budget constraints Capability constraints Generalised costs of the schedule Generalised cost of travel Generalised cost of activity participation Risk and comfort-adjusted weighted sums of time, expenditure and social content

13. 13 Degrees of freedom of activity scheduling Number and type of activities Sequence of activities Start and duration of activity Composition of the group undertaking the activity Location of the activity Connection between sequential locations Location of access and egress from the mean of transport Vehicle/means of transport Route/service Group travelling together

14. 14 Understanding supply Slot: A path in the time-space environment, which allows moving or activity performance Regulated slots (e.g. table in a restaurant, reserved seat in a theatre, gate position of a plane, green light at a junction) Emergent slots (e.g. trajectory of a car on a motorway, players in a pub-soccer tournament) Waiting time ~ Reserve capacity = Capacity – Demand for slots

15. 15 What do we (generally) do ? Competition for slots on networks and in facilities k(t,r,j) i,n q i ≡ (t,r,j) i,n β i,t, r,j,k Population “Scenario” Activity scheduling Mental map Demand q are the i th movements of person p from the current location at time t on route (connection) r to location j. The resulting generalised costs k are used to adjust the schedules and to change the capacities C and prices P of facilities f

16. 16 Classification criteria Steady state (equilibrium) ? Aggregate demands ? Complete and perfect knowledge ? Optimised schedules ? Degrees of freedom and detail of scheduling Modelling of capacity restrictions (movement, activities) ?

17. 17 MATSIM-T aims (1): Steady-state version Steady state within 12 hours on a small multi-CPU machine 7.5 mio agents, parcels, navigation networks Shared time-of-day dependent generalised costs of travel and activity participation Optimised schedules Continuous time resolution; space: parcels; social networks Queuing for slots for movement and activities

18. 18 MATSIM-T aims (2): Path-dependent version Path-dependent development; precise estimates within 12h on a small multi-CPU machine Large scale scenario Agent-specific, learned time-of-day dependent generalised cost of travel and activity participation Optimised schedules at multiple decision points Continuous time resolution; space: parcels; social networks Queuing for slots for movement and activities

19. 19 System architecture XML standards Data handling tools (aggregation/disaggregation) Data base Iteration handling and control of convergence Models

20. 20 Architecture: JAVA - packages

21. 21 Architecture: Data flow JAVA C++

22. 22 Current task allocation: Initial demand generation Number and type of activities Sequence of activities Start and duration of activity Composition of the group undertaking the activity Location of the activity Connection between sequential locations Location of access and egress from the mean of transport Vehicle/means of transport Route/service Group travelling together

23. 23 Current task allocation: activity scheduling Number and type of activities Sequence of activities Start and duration of activity Composition of the group undertaking the activity Location of the activity Connection between sequential locations Location of access and egress from the mean of transport Vehicle/means of transport Route/service Group travelling together

24. 24 Current ability: competition for slots Shortest paths: A* dynamic algorythm Movement: Queue-based simulation of car traffic No representation of cycling, walking, public transport vehicles Activities No competition for facilities yet

25. 25 Result of each iteration: Plan 1900 1899 1897 1899 1848 1925 1924 1923 1922 1068 1067 1136 1137 1921 1922 1923 1925 1848 1899

26. 26 Scheduling and its utility function

27. 27 Utility function: Individual schedules

28. 28 Utility function: Travel

29. 29 Utility function: Late arrival

30. 30 Utility function: Activity performance

31. 31 Scheduling: Current planomat(s) Version 1: GA optimiser of durations and starting times Retains time-of-day profile of generalised costs Version 2: CMA-ES (Covariance Matrix Adaptation Evolutionary strategy) of durations and starting times Retains time-of-day profile of generalised costs

32. 32 Examples (scenarios): CH: Switzerland and subsets Base tests for Kanton Zürich B/B: Berlin/Brandenburg Tolling case studies

33. 33 CH: Facilities for 140’000 hectares

34. 34 CH: Comparisons at the aggregate levels

35. 35 CH: Network Simplification: Remove unconnected links and subnetworks Remove nodes between unchanging link types Cut off dead ends Results: Navteq (882’120 links) (25% loss of links/nodes) Teleatlas (1’288’757 links) (currently not in use)

36. 36 CH: Population Alternatives: Artificial sample generation from Census marginals Census Private census with additional imputations (datapuls, Lucerne) plus household “formation”

37. 37 CH: Mobility tools Approach: MNL of mobility tool packages (Beige) Socio-demographics Location types Travel times to main centre by road and public transport Data: National travel survey (MZ 2000)

38. 38 CH: Structure of the chains, activity durations Data MZ 2000 MZ 2005 Approach: Selection via conditional probability distribution from Chains * Person types frequencies ca. 50 activity chains for MZ 2000 (>95% of all cases) Specified with activity durations given by MZ 2000  xxxx different activity chains (weighted)

39. 39 CH: Activity location choice Home: Random location in hectare Work/school Random allocation from census commuter matrices Disaggregation to facilities inside municipality Other locations “Neighbourhood search” from given home/work/school locations (a variation of Gravity Model)

40. 40 CH: Mode choice Approach: Fixed mode choice at the tour level as a function of Driving licence Mobility tool ownership Distance Age * season ticket Current set of chains include no subtours Data: MZ 2005

41. 41 CH: Kt. Zürich – Basic test R: Hectare; M: Municipality

42. 42 CH: Kt. Zürich

43. 43 CH: Kanton Zürich

44. 44 CH: Kanton Zürich (planomat - Iteration 0)

45. 45 CH: Kanton Zürich (planomat - Iteration 400)

46. 46 B/B: Network Source: “Senatsverwaltung für Stadtentwicklung Berlin” 11596 nodes 27770 links

47. 47 B/B: Population and demand Generated from “Kutter-Nachfragemodell für Berlin”, only commuters (work, education) in Berlin/Brandenburg, only car-trips simulated, 10% sample: 160’171 agents, each with 2 trips Facilities = traffic analysis zones

48. 48 B/B: Tolling in Berlin/Brandenburg Scenario: As described above Aims: Impacts of distance and area tolls

49. 49 B/B: Tolling in Berlin/Brandenburg

50. 50 B/B: Trip length within toll area with distance tolls

51. 51 B/B: total trip lengths with distance tolls

52. 52 Outlook

53. 53 Current tasks: Functionality Improving the realism of the scenario (e.g. opening hours, parameter distributions) Validation of the current scenarios Switzerland scenario in 12h to steady state Functional expansion the planomat (mode choice, destination choice) Parameter estimation for planomat Interface to UrbanSim

54. 54 Future tasks: Functionality Integration of social network data structures Explicit social network choices Addition of supply agents (car sharing, demand responsive transport, retail location, parking pricing, road pricing) (Traffic control)

55. 55 Current tasks: Usability (Shareability) Analysis tools Anonymous test data sets Improved documentation Web and sourceforge maintenance User support, including visits to ETH/TU Berlin

56. 56 Acknowledgements (1/3) System architecture:Michael Balmer System integration:Michael Balmer, Marcel Rieser System management:Michael Balmer, Marcel Rieser Facilities:Konrad Meister Mobility tools:Francesco Ciari Tour mode choice: Francesco Ciari Counter infrastructure:Andreas Horni KML – interfaces:Konrad Meister, Dominik Grether

57. 57 Acknowledgements (2/3) Router:Nicolas Lefebvre Traffic flow model:David Charypar Planomat:Konrad Meister, David Charypar Supply agents:Michael Löchl, Francesco Ciari Social networks:Jeremy Hackney

58. 58 Acknowledgements (3/3) Past contributors: Michael Bernard, ETH Sigrun Beige, ETH Ulrike Beuck, TU-Berlin Dr. Nurhan Cetin (ETH) Phillip Fröhlich, Modus, Zürich (ETH) Dr. Christian Gloor (ETH) Dr. Bryan Raney, HP America (ETH) Marc Schmitt (ETH) Arnd Vogel, PTV (TU Berlin)

59. 59 More information www.matsim.org www.vsp.tu-berlin.de www.ivt.ethz.ch/vpl/publications/reports

60. 60 Progress on shortest – path calculation Possibilities: Bounding boxes: Find out whether certain nodes can at all be on a shortest path and if not, do not consider them. Multi-level approach: Add shortcuts to the network where possible to bypass several edges at a time when routing. Bi-directed search: Start routing at the end and at the start node at the same time. Goal-directed search: Change the way the nodes are ranked such that nodes which are less likely to be on the shortest path are also less likely to be visited. The most popular algorithm that uses this technique is A*.

61. 61 Speed-up Basic algorithm: A* Step 1: Improve data structure for list of candidate nodes (7% reduction) Step 2: Improve handling of the “visited flag” (~ length of route) Step 3: Detect dead ends (1/4 of nodes in the NavTeq network) (50% reduction) Step 4: Use euclidian-distance to destination to rank candidates (50-80% reduction) Step 5: Use intermediate landmarks (80-90% reduction)

62. 62 Landmark selection Divide network into sectors containing an equal number of nodes For each sector, choose a node that is farthest away from the center Check that the landmarks are not too close to each other. If so, narrow one sector and choose a new landmark within the sector

63. 63 Speed up of shortest-path calculations Free flow conditions: 97-99% reductions Loaded conditions: 95% reductions on navigation networks

64. 64 Traffic flow model Competition for slots on networks and in facilities k(t,r,j) i,n q i ≡ (t,r,j) i,n β i,t, r,j,k Population “Scenario” Activity scheduling Mental map

65. 65 Approach Speed Resolution physical (VISSIM) CA (TRANSIMS) meso (METROPOLIS) macro (VISUM) Q (Cetin) Q event (MATSIM) parallel Q event (MATSIM)

66. 66 Parallel q-event driven simulation with gaps Approach Fundamental diagram (on a ring motorway) Domain decomposition Test

67. Q-event: Approach without gaps t Link 1Link 2 v free

68. Q-event: Approach with gaps t Link 1Link 2 v gap

69. 69 Q-event: Implementation details Squeezing to avoid grid-lock Inflow capacity = 110% of outflow capacity (1800 veh/h* lanes) Vehicles are served in order of arrival at the junctions C++ with binary data interface to MATSim-T

70. 70 Q-event: Fundamental diagram

71. Q-event: Test setup Road network of the federal states of Germany Berlin and Brandenburg 11.6k nodes, 27.7k links 7.05M person days Average number of trips per agent: 2.02 Average length of a trip: 17.5 links Total: 249M road segments to be traveled 77 min on a single dual-core CPU (1.6GH; 256 GB RAM)

72. 72 Q-event: Integrated domain decomposition

73. Q-event: Parallelisation

74. 74 Improving the convergence

75. 75 Initial approach Method: Plan everybody at Iteration 1 Replan for a fixed share Convergence: On mean performance

76. 76 Improved approach Method: Plan for everyone at Iteration 1 Replan for a predetermined, but decreasing share over the number of iterations Measurement: Aggregates

77. 77 Impact of improved approach

78. 78 Support graphs and further ideas

79. 79 What should we do, when we do what we do ? Competition for slots on networks and in facilities Activity scheduling k(t,r,j) i,n q i ≡ (t,r,j) i,n Parameter calibration β i,t, r,j,k Observed schedules and generalised costs

80. 80 What would we like to do ? Competition for slots on networks and in facilities Activity scheduling (private, commercial) k(t,r,j) i,n q ≡ (t,r,j) i,n Mental map Locating, sizing and pricing of slots C f (t); P f (t) k(t,r,j) i,n

81. 81 What would we like to do ? Personal long-term dynamics (Life) goals (t)(Life) goals (t+1)................ Projects (t) [committments] Definition of „Self“ Desires Pending projects Project sequence (t) Personal world (t)Personal world (t+1)................ Planning, Negotiation Markets and networks Replanning, Execution Updates, Innovations, Reflection Unexecuted projects

82. 82 Architecture: Initial data preparation

83. 83 Architecture: Learning, search for consistency

84. 84 CH: Car availability (Census)

85. 85 CH: Mode choice – Observed share public transport

86. 86 Scheduling: Once and future Planomat Number and type of activities out of an agenda Sequence of activities Start and duration of activity Composition of the household group undertaking the activity Location of the activity Connection between sequential locations Location of access and egress from the mean of transport Vehicle/means of transport Route/service Group travelling together

87. 87 CH: Facilities – Data sources Three tables of Enterprise Cernsus: NOGADescriptionFirmsFT-equivalents Code 10-45 (sector 2)6200 19Leather and shoe production20-9 20Chemical industry350-249 19.10Leather productionyes 19.20Leather goodsno 19.30Shoe productionno

88. 88 CH: Facilities – current allocation Read census of employment for each hectare Generate the required number of facilities Add activity work Set number of work place to minimum of class Distribute remainder proportionally to class size Add activity of use Add standard opening hours Randomly distribute on the hectare and attach to nearest link

89. 89 CH: Facilities - results

90. 90 CH: Facilities - results

91. 91 CH: Facilities - results