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Exploring Heuristics Underlying Pedestrian Shopping Decision Processes

Exploring Heuristics Underlying Pedestrian Shopping Decision Processes. An application of gene expression programming. Ph.D. candidate Wei Zhu Professor Harry Timmermans. Introduction. Modeling pedestrian behavior has concentrated on individual level

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Exploring Heuristics Underlying Pedestrian Shopping Decision Processes

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  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

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