ANALYSIS OF THE EVOLUTION OF TRAVEL DEMAND IN URBAN ANALYSIS OF THE EVOLUTION OF TRAVEL DEMAND IN URBAN AREAS: A NEURAL-GEO-TEMPORAL MODELLING APPROACH AREAS: A NEURAL-GEO-TEMPORAL MODELLING APPROACH e Dantas, University of Canterbury, Christchurch, New Zealand. e Dantas, University of Canterbury, Christchurch, New Zealand. o Yamashita, University of Brasilia, Brasilia, Brazil. o Yamashita, University of Brasilia, Brasilia, Brazil. us Vinicius Lamar, Federal University of Parana, Curitiba, Brazil us Vinicius Lamar, Federal University of Parana, Curitiba, Brazil Department of Civil Engineering Department of Civil Engineering Master in Transportation Engineering Master in Transportation Engineering
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Neural-Geo-Temporal approach to travel demand modelling
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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
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
CASE STUDY - NGTM TESTINGCASE STUDY - NGTM TESTING
CASE STUDY - NGTM TESTINGCASE STUDY - NGTM TESTING
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%
CASE STUDY - NGTM FORECASTINGCASE STUDY - NGTM FORECASTING
[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
CASE STUDY - NGTM FORECASTINGCASE STUDY - NGTM FORECASTING
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