1. 12-1988: High-Resolution Destination Choice in Agent-Based Demand Models A. Horni K. Nagel K.W. Axhausen destinations persons  00  nn  10  ij i j

2. MATSim Shopping and Leisure Destination Choice destination choice MATSim utility function (absolute utilities) Multi-Agent Transport Simulation MATSim

3. 3 day plans fixed and discretionary activities travel time budget relatively small set of locations per iteration step time geography Hägerstrand Earlier MATSim Destination Choice Approach: Local Search Competition effects at destinations -> utility reduction +

4. 4 V +  implicit +  explicit MATSim and Heterogeneity why not only random coefficients? -> bipolar distance distribution due to iterative approach

5. 5 A Step Back Adding heterogeneity: conceptually easy, full compatibility with DCM framework But: technically tricky for large-scale application

6. 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)

7. Search Method: Local vs. Best Response

8. 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

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

10. Results: Synthetic Small-Scale Scenario

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

12. Next Steps utf estimation: running survey probabilistic choice set generation approaches (-> search space) variability analysis:  ij person i alternative j seed Random draws from DCM microsimulation results = random variables X -> microsimulations are a sampling tool estimation of parameters for X (=statistic) with random sampling