1. Problem and Context Survey Tool What the Future May Bring: Model Estimation Empirically Approaching Destination Choice Set Formation A. Horni, IVT, ETH Zürich

2. Destination choice in MATSim Utility maximizing approach

3. Robustness of Estimated Parameters Thesis Schuessler (2010): Route choice Pellegrini et al. (1997): Shopping destination choice Problem for operational model

4. The Deterministic Approach r observed rtrt cs real (t) if any! threshold r t  = f(r t ) cs formation criteria (exogenous) To date: specification of exogenous factors for destination choice set formation rather ad hoc and more like a proof of concept.

5. The Probabilistic Approach cs formation criteria Speed-ups e.g. → convergence to deterministic approch endogenous! But: combinatorial complexity Conclusion in the words of Pagliara and Timmermans (2010): „Even though the inclusion of latent stochastic thresholds and the simultaneous estimation of thresholds and utility functions represents an important step forward in discrete choice analysis, forecasting results still depend on the researchers’ specification of the choice set.“

6. Decision Horizon: e.g., Grocery Shopping meat for dinner vegetables for dinner dinner for cat → relevant choice between and … and not in cs immediately prior to choice! choice set immediately prior to choice context!

7. Decision Horizon – Generation of PS Habitual „decisions“/ Routine response behavior preferred set of stores → relevant for transport planning extensive decisions impulsive decisions non-compensatory decision behavior → rule-based learning process e.g. grocery shopping

8. Decision Horizon: Sets Involved in the Decision Process (a First Step) Unawareness set Awareness set = cs(t –  t) Inept set (-) (Inert set (0)) cs(t) Narayana and Markin 1975 Evoked set (+) (Inert set (0))

9. Purely Statistical Approach vs. Behavior-Based Approach Homo oeconomicus → universal choice set Thresholds where parameters stabilize Lacking research Computationally infeasible Not explicative inconsistent productive? Behavior-based criteria for cs formation Lacking research

10. Allora, … EmpiricalMethodologial Decison horizon Statistical vs. behavioral model Preferred set - characteristics - frequencies Sets involved in decision process Core area within STP Reasons for NOT visiting a store Trip chaining Model estimation MATSim model Providing a research (survey) tool

11. Survey „Tool“ Web-based Google street view Grocery shopping 300 stores, partly manually collected future: attributes of stores

12. Web-based Survey Overview

16. Web-Survey – Google Maps & Street View Street View

19. Model Estimation Observed choice Awareness set? Preferred set Choice set Requirements: 1. Easy to survey and generate in op. models 2. Actually plays a well defined role in decision process „New“ model

20. Concluding Remarks Empirical basis TTB for time-geography Input to discussion on decision horizon and extent of behavioral basis of discrete choice models (vs. purely statistical) Survey tool Pretest - Game-like traits appreciated → less fatigue - Dominance of closest Coop or Migros (not deliberated)