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

TU/e

Eindhoven University of Technology

Thank you

Wei Zhuw.zhu@tue.nl

Harry Timmermansh.j.p.timmermans@bwk.tue.nl