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

Destination choice in MATSim Utility maximizing approach

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

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

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

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

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

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

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

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

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

Web-based Survey Overview

Web-Survey – Google Maps & Street View

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

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