Exploring Heuristics Underlying Pedestrian Shopping Decision Processes

1. Exploring Heuristics Underlying Pedestrian Shopping Decision Processes An application of gene expression programming Ph.D. candidate Wei Zhu Professor Harry Timmermans

2. Introduction • Modeling pedestrian behavior has concentrated on individual level • Decision processes only receive scant attention • As the core of DDSS, are current models appropriate? • Introducing a modeling platform, GEPAT • Comparing models of “go home” decision

3. Random utility model • Discrete choice models have been dominantly used • Question 1: Too simple • Only choice behavior is modeled, ignoring other mental activities such as information search, learning • Question 2: Too complex • Perfect knowledge about choice options is assumed • Utility maximization is assumed • Degree of appropriateness?

4. Heuristic model • Simple decision rules • E.g., one-reason decision, EBA, LEX, satificing • Human rationality is bounded, bounded rationality theory • Searching information—Stopping search—Deciding by heuristics • Degree of appropriateness?

5. Difficulties in heuristic model • Implicit mental activities Test different models • Structurally more complicated Get simultaneous solutions • Irregular function landscape Effective, efficient numerical estimation algorithm Bettman, 1979

6. The program--GEPAT • Gene Expression Programming as an Adaptive Toolbox • Gene expression programming (Candida Ferreira 2001) as the core estimation algorithm • Two features: • Get simultaneous solutions for inter-related functions • Model complex systems through organizing simple building blocks

7. Genetic algorithm • GA is a computational algorithm analogous to the biological evolutionary process • It can search in a wide solutions space and find the good solution through exchanging information among solutions • It has been proven powerful for problems which are nonlinear, non-deterministic, hard to be optimized by analytical algorithms

8. Get simultaneous solutions • The chromosome structure in GEP • Only one function can be estimated -b2+b+bd-c

9. Get simultaneous solutions • The chromosome structure in GEPAT • Parallel functions can be estimated simultaneously.

10. Test different models • Facilitate testing different models through organizing building blocks--“processors” • Each processor is a simple information processing node (mental operator) in charge of a specific task

11. Master Parallel computing • Message Passing Interface (MPI) • Distribute computation by chromosome or record Slave

12. Model comparison Shall I go home? • Go home decision • Data: Wang Fujing Street, Beijing, China, 2004 • Assumption: The pedestrian thought about whether to go home at every stop. • Observations: 2741 Shall I go home? Shall I go home?

13. Reason for going home • Which are difficult to observe • Using substitute factors • Relative time • Absolute time

14. Time estimation • Estimate time based on spatial information • Grid space • Assumption • Preference on types of the street • Walking speed 1 m/s

15. Multinomial logit model • Choice between shopping and going home Go home Shopping

16. Hard cut-off model • Satisficing heuristic • Lower and higher cut-offs for RT and AT Go home LCRT HCRT PNS LCAT HCAT

17. Soft cut-off model • Heterogeneity, taste variation LCMRT LCSDRT HCMRT HCSDRT PNS LCMAT LCSDAT HCMAT HCSDAT

18. Hybrid model • When the decision is hard to be made, more complex rules are applied

19. Model calibrations

20. Discussion • The satisficing heuristic fits the data better than the utility-maximizing rule, suggesting bounded rational behavior of pedestrians • Introducing the soft cut-off model is appropriate and effective; pedestrian behavior is heterogeneous • Lower cut-offs, as the baseline of decision, are much more effective than high cut-offs in explaining data, suggesting that pedestrians rarely put themselves to the limit in practice

21. Future research • Model other behaviors, e.g., direction choice, store patronage, environmental learning • Compare models • Improve GEPAT

22. Thank you Wei Zhu w.zhu@tue.nl Harry Timmermans h.j.p.timmermans@bwk.tue.nl