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

2. Destination choice in MATSim
Utility maximizing approach

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

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

5. The Probabilistic Approach
cs formation criteria
endogenous!
But: combinatorial complexity
Speed-ups e.g. → convergence to deterministic approch
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.“ 5

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

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

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

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

10. Providing a research (survey) tool
Allora, …
Methodologial
Empirical
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
Providing a research (survey) tool
Model estimation
MATSim model 10

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

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

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