1. ◊MATSim Destination Choice ◊Upscaling Small- to Large-Scale Models ◊Research Avenues How to Improve MATSim Destination Choice For Discretionary Activities? A. Horni, IVT, ETH Zürich

2. MATSim Shopping and Leisure Destination Choice destination choice MATSim utility function (absolute utilities)

3. 3 V +  implicit +  explicit why not only random coefficients? -> bipolar distance distribution due to iterative approach MATSim and Heterogeneity

4. 4 A Step Back adding heterogeneity: conceptually easy, full compatibility with DCM framework but: technically tricky for large-scale application

5. Repeated Draws: Quenched vs. Annealed Randomness fixed initial random seed freezing the generating order of  ij storing all  ij destinations persons  00  nn  10  ij i j person i alternative j store seed k i store seed k j regenerate  ij on the fly with random seed f(k i,k j ) one additional random number can destroy «quench» i,j ~ O(10 6 ) -> 4x10 12 Byte (4TByte)

6. Search Method: Local vs. Best Response

7. costs(destination(  max )) estimate by distance realized utilities pre-process once for every person  max –  t travel = 0 search space boundary d max = … distance to dest with  max Search Space Boundary

8. Search Space Optimum t departure t arrival Dijkstra forwards 1-nDijkstra backwards 1-n approximation probabilistic choice search space workhomeshopping

9. Results: 10% Zurich Scenario shopping leisure link volumes 70K agents 25min/iteration 100 iterations

10. here: preliminary, future study (IATBR) crux of matter in practice → study should be exemplified for specific microsimulation and data (MATSim and Swiss data) triumvirate of problems: I.data II.computational issues III.implementation/application issues -> research avenues Upscaling

11. 11 broad range of disciplines such as transport and urban planning, marketing and retailing science, economics, geography, psychology. attributes/choice determinants: examples: standard attributes: prices & incomes no comprehensive literature review yet I. Small-Scale Destination Choice Models

12. 12 person age, gender, mobility tools, occupancy, home loc (ha), work loc (municipality) act chain randomly assigned from microcensus destination location (ha), open times, rough type (h, w, s, l, e) validation counts → relatively limited set of attributes other microsims → similar but no comprehensive literature review yet available I. MATSim Attributes

13. main data sets, complete Switzerland: census of population (full survey) -> population / person attributes microcensus (person sample) -> demand business census (hectare) -> supply (infrastructure) miv counts (lane) -> validation only locally available (canton, municipality, city) pt schedules and lines (*) parking supply (*) green times (*) open times (*) (*) ZH scenario only I. Swiss Data Base

14. incomes vot activity / shop subtypes size categories (shop) education number of employees store price level store hours (complete CH) parking prices I. “Missing” Attributes in MATSim Destination Choice

15. I. MATSim Data Usage

16. huge destination choice sets, in particular routing very expensive (for evaluation of an alternative) microsimulation randomness/variability: no data → sophisticated rolling of a dice + correlations → model is not restrained → large variability → many runs required II: Computational Issues

17. agglomeration terms + correlated error terms 17 interaction effects at destinations (e.g., at parking lots) similar to space-time competition on roads Load category 1: 0 – 33 % 2: 33 - 66 % 3: 66 - 100 % 4: > 100% 10 % ZH Scenario: 60K agents reduces number of implausibly overloaded facilities III: Destination Choice Research Avenues

18. 18 destination choice equilibration e.g., approximate calculation of travel times err tot = w  err  + w  err  + w tt err tt if (w tt = small) -> tt can be approximated equilibrium concept is approximate itself III: Destination Choice Research Avenues

19. 19 further heterogeneity of agents and alternatives, in particular, prices, income, vot (household budget survey) finer activity classification available in microcensus and business census choice dimensions interrelation, e.g., mode – destination etc. parking 19 III: Destination Choice Research Avenues