1. Preferred citation style Horni A. (2013) MATSim Issues … suitable for a car trip discussion, Group seminar VPL, IVT, Zurich, September 2013.

2. MATSim Issues … Andreas Horni IVT ETH Zürich September 2013 execution replanning scoring controler Basic procedure Equilibrium-based and rule- based simulations Modeling Horizon and Temporal Variability Disaggregation UTF Estimation … suitable for a car trip discussion

3. Basic Procedure instantiation microsimulation (model) Output input feedback U max (day chains) population situation (e.g. season, weather) situation (e.g. season, weather) choice model generalized costs census travel surveys infrastructure data estimatione.g., network constraints, opening hours e.g., socio- demographcis network load simulation constraints

4. Basic Procedure microsimulation (model) choice model network load simulation (usually non-linear) system of equations fixed point problem (== UE)  hh hh 

5. Basic Procedure Numerics: Root finding problem ↔ fixed point problem (ans: x = 3 or -1 )

6. 1.x 0 = 4 2.x 1 = 3.31662 3.x 2 = 3.10375 4.x 3 = 3.03439 5.x 4 = 3.01144 6.x 5 = 3.00381 1.x 0 = 4 2.x 1 = 1.5 3.x 2 = -6 4.x 3 = -0.375 5.x 4 = -1.263158 6.x 5 = -0.919355 7.x 6 = -1.02762 8.x 7 = -0.990876 9.x 8 = -1.00305 1.x 0 = 4 2.x 1 = 6.5 3.x 2 = 19.625 4.x 3 = 191.070 convergence slow convergence divergence

7. Basic Procedure instantiation microsimulation (model) Output input Feedback U max (day chains) population situation (e.g. season, weather) situation (e.g. season, weather) choice model generalized costs Census travel surveys infrastructure data estimatione.g., network constraints, opening hours e.g., socio- demographcis network load simulation constraints

8. Evolutionary algorithm optimized plans Initial plans scoring replanning execution agent 1..n optimized plans initial plans scoring replanning execution MATSim agent 0 interaction species 1..n optimized population initial population recombination mutation survivor selection parent selection parents offsprings fitness evaluation fitness evaluation species 0 optimized population initial population recombination mutation survivor selection parent selection parents offsprings fitness evaluation fitness evaluation interaction Co- planomat, dc.br share →Charypar ?

9. Equilibrium-based vs. Rule-based Models t0t0 t1t1 t0t0 t1t1 Transition process Equilibrium models Needs to be efficient but not behaviorally sound Characteristics need to be defined (not under-determined) Computational process models Both need to be behaviorally sound Resonable but essentially does not matter boundary conditions accurate (chains) Equilibration process q0q0 q1q1 t0t0 t1t1 t0t0 t1t1 Simulated period s1s1 s0s0 Non-iterative Iterative Useful for longitudinal models ? warmstart

10. Modeling Horizon and Temporal Variability ?

11. avg(  0 x 0, …,  n x n ) +  0 (b) (a) (c) f(.) input model output  0 x 0 +  0 f(.) f (  0 x 0 +  0 ) … f(.) f (… )  n x n +  n f(.) f (  n x n +  n )  0 x 0 +  0, …,  n x n +  n F(.) F (  0 x 0 +  0, …,  n x n +  n ) averaging … f(.) f (avg(  0 x 0, …,  n x n ) +  0 ) … avg(  0 x 0, …,  n x n ) +  n f(.) f (avg(  0 x 0, …,  n x n ) +  n ) averaging endogenous correlations results = avg

12. Modeling Horizon and Temporal Variability (b) Project Suprice (a)MATSim standard (c) weekplans MonSun execution replanning scoring controler execution replanning scoring controler execution replanning scoring controler Wed replanning scoring execution ?

13. Disaggregation ABCD A B C D e.g., freight, cross-border traffic census travel surveys h h agent 0 h h agent 1 h h agent n population FAFA FBFB agent n+1 disaggregation FAFA FCFC FBFB agent n+2 disaggregate assignment correlations in plans + side effects w s s ? improve

14. Utility Function Estimation

15. Balmer 2005

16. Utility Function Estimation

21. Utility Function Estimation – Example (Home-Work-Home) Non-linear U dur Relative vs. absolute utilities Comprehensive model estimation due to e.g., activity dropping ? Balmer 2005