Exploring Heuristics Underlying Pedestrian Shopping Decision Processes

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Exploring Heuristics Underlying Pedestrian Shopping Decision Processes. An application of gene expression programming. Ph.D. candidateWei Zhu ProfessorHarry Timmermans. Introduction. Modeling pedestrian behavior has concentrated on individual level - PowerPoint PPT Presentation

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Exploring Heuristics Underlying Pedestrian Shopping Decision Processes An application of gene expression programming
Ph.D. candidate Wei Zhu
Introduction
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
TU/e
Random utility model
Question 1: Too simple
Only choice behavior is modeled, ignoring other mental activities such as information search, learning
Question 2: Too complex
Utility maximization is assumed
Heuristic model
Human rationality is bounded, bounded rationality theory
Searching information—Stopping search—Deciding by heuristics
Degree of appropriateness?
Difficulties in heuristic model
Bettman, 1979
The program--GEPAT
Gene expression programming (Candida Ferreira 2001) as the core estimation algorithm
Two features:
Model complex systems through organizing simple building blocks
TU/e
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
TU/e
Get simultaneous solutions
Only one function can be estimated
-b2+b+bd-c
Get simultaneous solutions
Parallel functions can be estimated simultaneously.
TU/e
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
TU/e
Parallel computing
Slave
Master
TU/e
Model comparison
Data: Wang Fujing Street, Beijing, China, 2004
Assumption: The pedestrian thought about whether to go home at every stop.
Observations: 2741
Reason for going home
Using substitute factors
Time estimation
Grid space
Walking speed 1 m/s
Multinomial logit model
Go home
Hard cut-off model
LCRT
HCRT
LCAT
HCAT
PNS
Soft cut-off model
Heterogeneity, taste variation
Hybrid model
When the decision is hard to be made, more complex rules are applied
TU/e
Model calibrations
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
TU/e
Future research
Compare models
Improve GEPAT