neural-geo-temporal approach to travel demand modelling

ANALYSIS OF THE EVOLUTION OF TRAVEL DEMAND IN ANALYSIS OF THE EVOLUTION OF TRAVEL DEMAND IN URBAN AREAS: A NEURAL-GEO-TEMPORAL MODELLING URBAN AREAS: A NEURAL-GEO-TEMPORAL MODELLING

APPROACHAPPROACH

Andre Dantas, University of Canterbury, Christchurch, New Zealand.Andre Dantas, University of Canterbury, Christchurch, New Zealand.Yaeko Yamashita, University of Brasilia, Brasilia, Brazil.Yaeko Yamashita, University of Brasilia, Brasilia, Brazil.Marcus Vinicius Lamar, Federal University of Parana, Curitiba, BrazilMarcus Vinicius Lamar, Federal University of Parana, Curitiba, Brazil

Department of Civil EngineeringDepartment of Civil EngineeringMaster in Transportation EngineeringMaster in Transportation Engineering

•IntroductionIntroduction•Neural Networks and GISNeural Networks and GIS•Theoretical conception of the NGTMTheoretical conception of the NGTM•Case StudyCase Study•ConclusionsConclusions

Outline of the presentationOutline of the presentation

Complex commuting patterns all over　 the

city.

t=1

t=2

t=n

Central displacements on foot;

Travel Demand

Long-motorized travel from suburbs to CBD;

Introduction - Urban changes and travel demandIntroduction - Urban changes and travel demand

Agglomerations in urban centers and

subcenters

Concentration of activities

Provision of variousservices and facilities

Attraction of high amount of daily commuting

TrafficCongestion

CarAccidents

Pollution

NO

ParkingSpace

CONSEQUENCES

LIFEQUALITY

Introduction - Urban changes and travel demandIntroduction - Urban changes and travel demand

Introduction - travel demand modellingIntroduction - travel demand modelling

Considerable amount of research effortsConsiderable amount of research efforts

Land useLand use ..........

Application of economic theoryApplication of economic theory

Urban development modelsUrban development models

To explain the configuration and evolution of urban structuresTo explain the configuration and evolution of urban structures

Integrated Land use-ransport modelsIntegrated Land use-ransport models

Incorporate the most important spatial processes of development Incorporate the most important spatial processes of development in conjunction with travel demand forecastingin conjunction with travel demand forecasting

Travel demandTravel demand


Travel demand models have been criticizedTravel demand models have been criticized

BUT...BUT...

Massive and costly data Massive and costly data requirementsrequirements

for application to real problems.for application to real problems.Non-incorporation of temporal dynamic and Non-incorporation of temporal dynamic and

realistic dimensions of urban realityrealistic dimensions of urban reality

Still based on the traditional four-Still based on the traditional four-step approachstep approach


DynamicDynamic modellingmodelling

Land useLand useTransport system Transport system

interactionsinteractions

Non-linearNon-linear modellingmodelling

Travel demand models - RESEARCH PERSPECTIVESTravel demand models - RESEARCH PERSPECTIVES


Exploring new modelling techniques!Exploring new modelling techniques!

Employing available Employing available technology!!technology!!

Using Geo-spatial data!!! Using Geo-spatial data!!!

How do we reach this new modelling frontier?How do we reach this new modelling frontier?BUT...BUT...


Is it technically feasible???Is it technically feasible???

BUT...BUT...

What’s available???What’s available???

Can WE really think in a different way?Can WE really think in a different way?

Neural

Geo

Temporal

Model (NGTM)

Introduction - NGTMIntroduction - NGTM

GIS

Neural Networks (NN)

Consider spatial-temporal evolution of urban areas

To incorporate temporal interactions between the transportation system and

land use patterns

Overcome limitations

of linear modelling

Neural

Geo

Temporal

Model (NGTM)

Introduction - NGTMIntroduction - NGTM

NN AND GISNN AND GIS

•What is GIS?A special type of Information System;

Database, personnel and Technology in a systemic and interactive manner;

Manipulation, storage, visualization of georeferenced data; and

SPATIAL ANALYSIS providing new and useful information for decision-making/planning activities.


SPATIALANALYSIS

Entry

Manipulate

Present

Storage

ModellingWhat if…?

ROUTINGBest way to...?

TRENDWhat has changed?

CONDITIONHow is it…?

LOCATIONWhat is at…?


•What is a NN?A non-linear extension of conventional spatial statistical models;

Model reached without a priori assumptions;

To compute a function that expresses the correlation between independent and dependent variables;

An “analogy” of human brain processing;


wk1

wk2

wkj

x1

x2

xj

ukInput Signals

yk (uk )

wkpxp

SummingFunction(adder)

LinkWeights

ActivationFunction

Output

)exp(11)(

kk u

u

The Back-Propagation Algorithm

i

ii wOywE

2)(

21)(

)()()1( )()(

)( tww

wEtw lijl

ij

lij

Cost (error) Function

produced outputby the net, dependingon the weight matrix

training valueoutput

Learning Rule

learn rate used = 0.01 puls term used = 0.9

1

0

2)(1 M

iii Oy

ME

1

0

1 P

EP

E

Pyx ik ,...,1,,

Training Set ofInput-Output pairs

Average Root Mean Square (AveRMS)


NN

Training Data set

Test Data set

Training Process

Modelling function

Pre–ProcessingData Set


at the ith zone

TransportationSystem

SpatialLocation

Land Use patterns

Population

Interactions(UI)

Trip Generation

(TG)f

NGTM - THEORETICAL CONCEPTIONNGTM - THEORETICAL CONCEPTION

Local Spatial Interactions

Surrounding Spatial Interactions

Global Spatial Interactions

TransportationSystem

LandUse

Zone i Zone i Zone i


t=1t=2

t=n

Local Spatial Interactions

Surrounding Spatial Interactions

Global Spatial Interactions


UIi(1)

TGi(1)

UIi(2)

TGi(2)

UIi(z)

TGi(3) TGi

(z) TGi(z+1)

NGTM - RECURSIVE MODELLINGNGTM - RECURSIVE MODELLING

zi

zi

zi

zi

Zi

POSLLUTSUI ,,,

Input Vector zone ith, time z

Transportation system

Land Use

Spatial location

Population

NGTM - REPRESENTING SPATIAL-TEMPORAL INTERACTIONSNGTM - REPRESENTING SPATIAL-TEMPORAL INTERACTIONS

INDEPENDENT VARIABLES

1ZiTG


DEPENDENT VARIABLES

Trip production - number of trips FROM zone i

Trip attraction - number of trips TO zone i

.....


zi

zii TGIUX ,

1z

ii TGY

InputLayer

...

TGiz+1

...HiddenLayer

OutputLayer

UIiz TGi

z

z-1

ContextLayer

...-1 -1

NGTM - APPLICATION OF AN ELMAN NNNGTM - APPLICATION OF AN ELMAN NN

•326,35 Km2

•2,2 million people (1998)

CASE STUDY - LOCATION AND CHARACTERISTICS OF NAGOYA CASE STUDY - LOCATION AND CHARACTERISTICS OF NAGOYA CITYCITY

CASE STUDY - LOCATION AND CHARACTERISTICS OF NAGOYA CASE STUDY - LOCATION AND CHARACTERISTICS OF NAGOYA CITYCITY

4

21

Data collection & gathering

t = 0t = 1

t = n

NN

Data updatet = n+1

3

GIS

CASE STUDY - INTEGRATION NN-GISCASE STUDY - INTEGRATION NN-GIS

GIS

1 2

3

I

V

Georeferencing

Visualization & analysis

SpatialPatterns

Database

II

Queries

III

ntntntnt TripsZ,.....,Y,Xf

Modelling FunctionIVSpatial

analysis

VIII

VI VII

CASE STUDY - INTEGRATION NN-GISCASE STUDY - INTEGRATION NN-GIS

Road Transportation system Railway Transportation system

CASE STUDY - MAIN FEATURES OF THE GIS DATABASECASE STUDY - MAIN FEATURES OF THE GIS DATABASE

Large scale commercial land use

Commerce 1991

Commerce 1981

Commerce 1971


Features (Spatial data) Attributes (Non-spatial data)

Point Line Area AT2 …… ATvAT1

TZStations

Bus StopsBus Lines

Railways

NH rampNHRoads

Land Use

•248 Traffic Zones (TZ)•Data from 3 different sources



BUT...BUT...3 years to obtain/construct the 3 years to obtain/construct the

DATABASEDATABASE

And people say...And people say...““You were lucky!!!”You were lucky!!!”

CASE STUDY - TEMPORAL EVOLUTIONCASE STUDY - TEMPORAL EVOLUTION

Pattern No. Pattern Description No. Cases No.Cases%1 PO 71=PO 81 PO 81>PO 91 57 22.98%

13 PO 71=PO 81 PO 91>PO 81 40 16.13%

11 PO 71=PO 81 PO 81>PO 91 PT 71<PT 81 PT 81=PT 91 12 4.84%

3 PO 71=PO 81 PO 81>PO 91 PT 71=PT 81 PT 81<PT 91 10 4.03%

21 PO 71=PO 81 PO 81>PO 91 RL 71<RL 81 RL 81=RL 91 8 3.23%

14 PO 71=PO 81 PO 91>PO 81 PT 71<PO 81 PT 81=PT 91 7 2.82%

41 PO 71=PO 81 PO 91>PO 81 RL 71<RL 81 RL 81=RL 91 7 2.82%

6 PO 71=PO 81 PO 81>PO 91 PT 71=PT 81 PT 81<PT 91 RL 71=RL 81 RL 81<RL 91 7 2.82%

9 PO 71=PO 81 PO 81>PO 91 PT 71<PT 81 PT 81<PT 91 7 2.82%

22 PO 71=PO 81 PO 81>PO 91 RL 71=RL 81 RL 81<RL 91 7 2.82%others 86 34.68%

61 patterns - TS/LU changes61 patterns - TS/LU changes


Pattern No. Pattern Description No.Cases No.Cases%3 TG 71<TG 81 TG 81>TG 91 155 62.50%

1 TG 71>TG 81 TG 81>TG 91 59 23.79%

4 TG 71<TG 81 TG 81<TG 91 21 8.47%

2 TG 71>TG 81 TG 81<TG 91 13 5.24%

NN

2

3

Training Data set

Test Data set

BTraining Process

C

Modelling function D


A

CASE STUDY - NN PROCESSINGCASE STUDY - NN PROCESSING

zi

zi

zi

zi

zi

POSLLUTSUI ,,,

zi

zi

zi

zi

NHRTPTTS ,,

zi

zi

zi RLPLCLz

iLU ,,

ii HDSDziSL ,

ZiGT Zonal Trip

ends

trips

(all modes)/hour

CASE STUDY - FORMATION OF INPUT AND OUTPUT VECTORSCASE STUDY - FORMATION OF INPUT AND OUTPUT VECTORS

NN

2

3

Training Data set

Test Data set

BTraining Process

C



A


Data set

496 vectors

Training / Testing

random selection

Testing vectors (124)

Training vectors (372)

Input Data normalization 1

minmaxmin ][][][][8.01.0][

UIUIUIUIIU tzi

zi

Output Data 1

minmaxmin8.01.0

AAAAA zi

zi


NN

2

3

Training Data set

Test Data set

BTraining Process

C



A


TestingTraining


NN

2

3

Training Data set

Test Data set

BTraining Process

C



A


UIi(1971)

Ai(1971)

UIi(1981)

Ai(1981) Ai

(1991)

CASE STUDY - NGTM TRAININGCASE STUDY - NGTM TRAINING

NN parameters: NN parameters: •learning rate=0.01learning rate=0.01•sigmoid activation sigmoid activation

functionfunction•MSE=0.000229MSE=0.000229

•36677 iterations36677 iterations•20 hours of 20 hours of

processing (Pentium processing (Pentium 200 MHz)200 MHz)

NN

2

3

Training Data set

Test Data set

BTraining Process

C



A


CASE STUDY - NGTM TESTINGCASE STUDY - NGTM TESTING

Average Average error=87.11error=87.11

Standard Standard deviation of deviation of

the the errors=128.errors=128.

3535

Average Average relative relative

error=23.8%error=23.8%

R2=0.93


Why???Why???

0

0.5

1

1.5

2

2.5

3

3.5

4

zones

Trip

s (x1

000)

-60

-40

-20

0

20

40

60

80

100

120

Rela

tive

Erro

r(%

)

GVi91 Yi91 Erro Relativo (%)TGi91 Relative Error (%)



AA9191 =3373; =3373; YY91 91

=3170 =3170 AA8181>>AA7171

AA8181>>AA9191

TSTS9191>>TSTS7171

Commercial LU upCommercial LU up

AA9191 =113; =113; YY91 91 = = 232232

21% PO increase21% PO increaseAA8181==126126

AA7171==5252

UIi(1991)

Ai(2001)

UIi(1971)

Ai(1971)

UIi(1981)

Ai(1981) Ai

(1991)

CASE STUDY - NGTM FORECASTINGCASE STUDY - NGTM FORECASTING

[-160;-50[ [-50;-25[ [-25;0[

[0;50[ [50;100[ [100;200[ 200

Relative variation 1991-2001 (%)•Maximum positive

variation =441%

•Maximum negative variation =-160%

•Average variation =44.81%


[0;500[ [500;1000[ [1000;2000[

[2000;3000[ [3000;5000[ 5000

Zonal trip ends 2001

•Max. =7831

•Min. =0

•Average=574.12

•Variation % (2001-1991)= 1.45


Achievements in the case study

NGTM’s contribution

Modelling function capable of computing a very complex reality with time variation;

Remarble barriers on constructing the GIS database for the multi-year period.

Future improvements

Incorporation of temporal dimension into travel demand modelling;

Non-linear approach based on NN; and

Incorporation of geo-spatial data expressing urban interactions using GIS.

Classified output & use of extensive temporal database

CONCLUSIONCONCLUSION

Andre Dantas, University of Canterbury, Christchurch, Andre Dantas, University of Canterbury, Christchurch, New Zealand.New Zealand.

SCHOOL OF ENGINEERINGSCHOOL OF ENGINEERINGCIVIL ENGINEERINGCIVIL ENGINEERING4F4FROOM 406ROOM [email protected]@CANTERBURY.AC.NZEXTENSION 6238EXTENSION 6238

Department of Civil EngineeringDepartment of Civil EngineeringMaster in Transportation EngineeringMaster in Transportation Engineering

THANK YOU!!

OBRIGADO!!