exploring heuristics underlying pedestrian shopping decision processes
DESCRIPTION
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 PresentationTRANSCRIPT
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?
TU/e
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?
TU/e
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
TU/e
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
TU/e
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
TU/e
Department of architecture, building & planning
Get simultaneous solutions
The chromosome structure in GEP Only one function can be estimated
-b2+b+bd-c
TU/e
Department of architecture, building & planning
Get simultaneous solutions
The chromosome structure in GEPAT Parallel functions can be estimated
simultaneously.
TU/e
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
TU/e
Department of architecture, building & planning
Parallel computing
Message Passing Interface (MPI)
Distribute computation by chromosome or record
Master
Slave
TU/e
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?
TU/e
Department of architecture, building & planning
Reason for going home
Which are difficult to observe
Using substitute factors
Relative time
Absolute time
TU/e
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
TU/e
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
TU/e
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
TU/e
Department of architecture, building & planning
Soft cut-off model
Heterogeneity, taste variation
LCMRT LCSDRT
HCMRT HCSDRT
LCMAT LCSDAT
HCMAT HCSDAT
PNS
TU/e
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
TU/e
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
TU/e
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
TU/e
Department of architecture, building & planning
Future research
Model other behaviors, e.g., direction choice, store patronage, environmental learning
Compare models
Improve GEPAT