Exploring Heuristics Underlying Pedestrian Shopping Decision Processes
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Transcript of Exploring Heuristics Underlying Pedestrian Shopping Decision Processes
TU/e
Eindhoven University of Technology
Exploring Heuristics Underlying Pedestrian
Shopping Decision Processes
An application of gene expression programming
Ph.D. candidate Wei Zhu
Professor Harry Timmermans
TU/e
Department of architecture, building & planning
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
TU/e
Department of architecture, building & planning
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?
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Department of architecture, building & planning
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?
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Department of architecture, building & planning
Difficulties in heuristic model Implicit mental activities
Test different models
Structurally more complicatedGet simultaneous solutions
Irregular function landscapeEffective, efficient numerical estimation algorithm
Bettman, 1979
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Department of architecture, building & planning
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
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Department of architecture, building & planning
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
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Department of architecture, building & planning
Get simultaneous solutions
The chromosome structure in GEP Only one function can be estimated
-b2+b+bd-c
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Department of architecture, building & planning
Get simultaneous solutions
The chromosome structure in GEPAT Parallel functions can be estimated
simultaneously.
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Department of architecture, building & planning
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
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Department of architecture, building & planning
Parallel computing
Message Passing Interface (MPI)
Distribute computation by chromosome or record
Master
Slave
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Department of architecture, building & planning
Model comparison
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?
Shall I go
home?
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Department of architecture, building & planning
Reason for going home
Which are difficult to observe
Using substitute factors
Relative time
Absolute time
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Department of architecture, building & planning
Time estimation
Estimate time based on spatial information
Grid space
Assumption Preference on types
of the street Walking speed 1 m/s
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Department of architecture, building & planning
Multinomial logit model Choice between shopping and going
home
ATRTVs ** 21
3hV
)exp()exp(
)exp(
hs
hh VV
VP
Go home
Shopping
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Department of architecture, building & planning
Hard cut-off model
Satisficing heuristic
Lower and higher cut-offs for RT and AT
LCRT
HCRT
LCAT HCAT
PNS
Go home
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Department of architecture, building & planning
Soft cut-off model
Heterogeneity, taste variation
LCMRT LCSDRT
HCMRT HCSDRT
LCMAT LCSDAT
HCMAT HCSDAT
PNS
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Department of architecture, building & planning
Hybrid model
When the decision is hard to be made, more complex rules are applied
0** 213 ATRT
)**(1 321 ATRTFPhNS
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Department of architecture, building & planning
Model calibrationsMNL Hard Cut-off Soft Cut-off Hybrid
P Value P Value P Value P Value
β1 -0.007 LCRT 29.797 LCMRT 132.048 LCMRT 0.000
β2 -0.008 - - LCSDRT 83.976 LCSDRT 327.290
β3 -10.501 HCRT 674.966 HCMRT 676.000 HCMRT 676.992
- - - - HCSDRT 0.010 HCSDRT 0.010
- - LCAT 809.840 LCMAT 927.851 LCMAT 916.544
- - - - LCSDAT 87.422 LCSDAT 85.820
- - HCAT 1313.169 HCMAT 1305.591 HCMAT 1377.659
- - - - HCSDAT 104.161 HCSDAT 230.719
- - PhNS 0.308 PhNS 0.752 β1 -0.047
- - - - - - β2 0.000
- - - - - - β3 -3.502
ML -1121.200 -1381.830 -1070.599 -1077.843
AIC 2248.400 2773.660 2159.199 2177.687
Sim 0.546 0.656 0.743 0.744
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Department of architecture, building & planning
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
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Department of architecture, building & planning
Future research
Model other behaviors, e.g., direction choice, store patronage, environmental learning
Compare models
Improve GEPAT