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CH: Population Alternatives: • Artificial sample generation from Census marginals • Census • Private census with additional imputations (datapuls, Lucerne) plus household “formation” 36
CH: Mobility tools Approach: Data: • MNL of mobility tool packages (Beige) • Socio-demographics • Location types • Travel times to main centre by road and public transport • National travel survey (MZ 2000) 37
Page 1 and 2: Preferred citation style MATSim dev
Page 3 and 4: Overview • Structure and team •
Page 5 and 6: Current team Strategy: • Kai Nage
Page 7 and 8: Current team: Implementation (2/2)
Page 9 and 10: Task and solution methods 9
Page 11 and 12: What would we like to do ? Personal
Page 13 and 14: Degrees of freedom of activity sche
Page 15 and 16: What do we (generally) do ? “Scen
Page 17 and 18: MATSIM-T aims (1): Steady-state ver
Page 19 and 20: System architecture • XML standar
Page 21 and 22: Architecture: Data flow JAVA C++ 21
Page 23 and 24: Current task allocation: activity s
Page 25 and 26: Result of each iteration: Plan
Page 27 and 28: Utility function: Individual schedu
Page 29 and 30: 29 Utility function: Late arrival (
Page 31 and 32: Scheduling: Current planomat(s) Ver
Page 33 and 34: CH: Facilities for 140!000 hectares
Page 35: CH: Network Simplification: Results
Page 39 and 40: CH: Activity location choice Home:
Page 41 and 42: R: Hectare; M: Municipality CH: Kt.
Page 43 and 44: CH: Kanton Zürich 43
Page 45 and 46: CH: Kanton Zürich (planomat - Iter
Page 47 and 48: B/B: Population and demand • Gene
Page 49 and 50: B/B: Tolling in Berlin/Brandenburg
Page 51 and 52: B/B: total trip lengths with distan
Page 53 and 54: Current tasks: Functionality • Im
Page 55 and 56: Current tasks: Usability (Shareabil
Page 57 and 58: Acknowledgements (2/3) Router: Nico
Page 59 and 60: More information www.matsim.org www
Page 61 and 62: Speed-up Basic algorithm: A* Step 1
Page 63 and 64: Speed up of shortest-path calculati
Page 65 and 66: Approach Resolution physical (VISSI
Page 67 and 68: Q-event: Approach without gaps t Li
Page 69 and 70: Q-event: Implementation details •
Page 71 and 72: Q-event: Test setup • Road networ
Page 73 and 74: Q-event: Parallelisation
Page 75 and 76: Initial approach Method: • Plan e
Page 77 and 78: Impact of improved approach 77
Page 79 and 80: What should we do, when we do what
Page 81 and 82: What would we like to do ? Personal
Page 83 and 84: Architecture: Learning, search for
Page 85 and 86: CH: Mode choice - Observed share pu
Page 87 and 88:
CH: Facilities - Data sources Three
Page 89 and 90:
CH: Facilities - results 89
Page 91:
CH: Facilities - results 91
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