the use of ss in urban transport analysis limits and potentials rafael h. m. pereira frederico r. b....

The use of SS in urban transport analysis limits and potentials

Rafael H. M. Pereira

Frederico R. B. de Holanda

Valério A. S. de Medeiros

Ana Paula B. G. Barros

Institute of Applied Economic Research

sss8, Santiago, 01-04-2012

Brazil: overview

Brazil 2010

Population:

Total - 192 milions

Urban -159 milions (83.7%)

5,564 Municipalities

38 cities over 500,00 habitants

16 cities over 1 milion habitants

Brazil: overview

Brasilia 2010

Population figures:

1.Pilot Plan = 209,855

2.Federal District = 2,570,160

3.Conurbation = 3,276,966

4.Direct influence area = 3,451,043

Study aim and scope

To explore the potentials and limits of applying SS to the analysis of urban configurations so as to provide urban environments with greater transportation efficiency.

Case study: Federal District (FD - Brazil) + its 19 administrative regions

Study aim and scope

Increasing motorization ratio (FD)

Number of Vehicles for 100 Inhabitants

25,9

10

15

20

25

30

35

40

45

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

42,8

Source: Denatran and IBGE

2000 - 2009

Population 2,70 % a. a.

Car fleet 7,14 % a. a.

Shortcomings (transport studies)

Macro-traffic structures (rail, metro) are not captured

Fails to consider some street features that greatly influence urban transportation performance

road capacity (number of lanes) Direction of traffic flows Pavement conditions Topographic variations “Obstacles” (impedance) – i.g. traffic lights, speed bumps, etc Metric length

ignores the global extension of the road system as a whole

Traditional syntax approach

Shortcomings (transport studies)

Source: Denatran and IBGE

(a) (b)

“Obstacles” - impedance

Same level of Global integration (Rn) = 3,13374

Shortcomings (transport studies)

Source: Denatran and IBGE

(a) (b)

Metric length

Same level of Global integration (Rn) = 3,13374

5 Km10 Km

Material and Methods

Linear regression (Ordinary Least Squares - OLS)*

Urban Configuration Urban Transport Performance

Configurational Variables:Average Travel Time spent on

urban trips

* few observations (20)

- Topological Integration (Rn, R3)

- Mean Depth (Rn, R3 step)

- Topo-geometric measures: Length Wgt and Metric step

Material and Methods

Origin-Destination Survey conducted in the Federal District (Brazil) in 2000 Information for every trip on a typical work day in 2000

Filter: car, utility vehicle and taxi

*Average travel time for the trips within each AR and the Federal District (1,000,198 trips)

20 axial/ segment maps

- Federal District (FD)

- 19 R.A.’s

FD Axial MapSource: MEDEIROS (2006)

Material and Methods

RA Recanto das Emas

RnRn

Rn Rn Length Wgt

Results

0

4

8

12

16

20

Total (

DF)

Lago

Sul

Taguat

inga

Gua

rá

Brasí

lia

São S

ebasti

ão

Lago

Norte

Samam

baia

Riacho

Fun

do

Santa

Mar

ia

Sobra

dinh

o

Cruze

iro

Plana

ltina

Núcleo

Ban

deirant

e

Ceilân

dia

Gam

a

Brazlâ

ndia

Recan

to d

as Em

as

Parano

á

Candan

golând

ia

Tempo (min) RN metric

Results

Configurational variables Performance variableStatistics

R² P-value

Mean depth with Global topological radius Rn Average Travel Time (ATT)21,8% 0,0380

Mean depth with Global topological radius Rn (weighted by segment length)

ATT38,9% 0,0033

Mean depth with Local topological radius R3 ATT 3,8% 0,4094Mean depth with Local topological radius R3 (weighted by segment length)

ATT0,3% 0,8060

Mean depth (100 meter radius) ATT 1,0% 0,6801Mean depth (500 meter radius) ATT 1,3% 0,6339Mean depth (1,000 meter radius) ATT 2,7% 0,4848Mean depth (5,000 meter radius) ATT 2,1% 0,5402Mean depth (10,000 meter radius) ATT 14,5% 0,0978Mean depth (50,000 meter radius) ATT 30,5% 0,0115Global Integration with topological radius Rn ATT 22,0% 0,0370

Rn Global topo-geometric Integration (weighted by segment length) ATT58,0% 0,0001

Local Integration with radius R3 ATT 8,5% 0,2128

R3 Local topo-geometric Integration (weighted by segment length) ATT0,5% 0,7664

Results

Local Measures

Not significantConfigurational variables Performance variable

Statistics

R² P-value

Mean depth with Global topological radius Rn Average Travel Time (ATT)21,8% 0,0380

Mean depth with Global topological radius Rn (weighted by segment length)

ATT38,9% 0,0033

Mean depth with Local topological radius R3 ATT 3,8% 0,4094Mean depth with Local topological radius R3 (weighted by segment length)

ATT0,3% 0,8060

Mean depth (100 meter radius) ATT 1,0% 0,6801Mean depth (500 meter radius) ATT 1,3% 0,6339Mean depth (1,000 meter radius) ATT 2,7% 0,4848Mean depth (5,000 meter radius) ATT 2,1% 0,5402Mean depth (10,000 meter radius) ATT 14,5% 0,0978Mean depth (50,000 meter radius) ATT 30,5% 0,0115Global Integration with topological radius Rn ATT 22,0% 0,0370

Rn Global topo-geometric Integration (weighted by segment length) ATT58,0% 0,0001

Local Integration with radius R3 ATT 8,5% 0,2128

R3 Local topo-geometric Integration (weighted by segment length) ATT0,5% 0,7664

Results

Global Traditional Measures

Configurational variables Performance variableStatistics

R² P-value

Mean depth with Global topological radius Rn Average Travel Time (ATT)21,8% 0,0380

Mean depth with Global topological radius Rn (weighted by segment length)

ATT38,9% 0,0033

Mean depth with Local topological radius R3 ATT 3,8% 0,4094Mean depth with Local topological radius R3 (weighted by segment length)

ATT0,3% 0,8060

Mean depth (100 meter radius) ATT 1,0% 0,6801Mean depth (500 meter radius) ATT 1,3% 0,6339Mean depth (1,000 meter radius) ATT 2,7% 0,4848Mean depth (5,000 meter radius) ATT 2,1% 0,5402Mean depth (10,000 meter radius) ATT 14,5% 0,0978Mean depth (50,000 meter radius) ATT 30,5% 0,0115Global Integration with topological radius Rn ATT 22,0% 0,0370

Rn Global topo-geometric Integration (weighted by segment length) ATT58,0% 0,0001

Local Integration with radius R3 ATT 8,5% 0,2128

R3 Local topo-geometric Integration (weighted by segment length) ATT0,5% 0,7664

Sig. < 4% e R² = 22%

Configurational variables Performance variableStatistics

R² P-value

Mean depth with Global topological radius Rn Average Travel Time (ATT)21,8% 0,0380

Mean depth with Global topological radius Rn (weighted by segment length)

ATT38,9% 0,0033

Mean depth with Local topological radius R3 ATT 3,8% 0,4094Mean depth with Local topological radius R3 (weighted by segment length)

ATT0,3% 0,8060

Mean depth (100 meter radius) ATT 1,0% 0,6801Mean depth (500 meter radius) ATT 1,3% 0,6339Mean depth (1,000 meter radius) ATT 2,7% 0,4848Mean depth (5,000 meter radius) ATT 2,1% 0,5402Mean depth (10,000 meter radius) ATT 14,5% 0,0978Mean depth (50,000 meter radius) ATT 30,5% 0,0115Global Integration with topological radius Rn ATT 22,0% 0,0370

Rn Global topo-geometric Integration (weighted by segment length) ATT58,0% 0,0001

Local Integration with radius R3 ATT 8,5% 0,2128

R3 Local topo-geometric Integration (weighted by segment length) ATT0,5% 0,7664

Melhor estatística quanto maior o Raio de ação

Results

Topo-geometric measures

Improved results with larger radius

Configurational variables Performance variableStatistics

R² P-value

Mean depth with Global topological radius Rn Average Travel Time (ATT)21,8% 0,0380

Mean depth with Global topological radius Rn (weighted by segment length)

ATT38,9% 0,0033

Mean depth with Local topological radius R3 ATT 3,8% 0,4094Mean depth with Local topological radius R3 (weighted by segment length)

ATT0,3% 0,8060

Mean depth (100 meter radius) ATT 1,0% 0,6801Mean depth (500 meter radius) ATT 1,3% 0,6339Mean depth (1,000 meter radius) ATT 2,7% 0,4848Mean depth (5,000 meter radius) ATT 2,1% 0,5402Mean depth (10,000 meter radius) ATT 14,5% 0,0978Mean depth (50,000 meter radius) ATT 30,5% 0,0115Global Integration with topological radius Rn ATT 22,0% 0,0370

Rn Global topo-geometric Integration (weighted by segment length) ATT58,0% 0,0001

Local Integration with radius R3 ATT 8,5% 0,2128

R3 Local topo-geometric Integration (weighted by segment length) ATT0,5% 0,7664

Melhor estatística quanto maior o Raio de ação

Results

Topo-geometric measures

Improved results with larger radius

Final Remarks

Future Studies

Test other configurational measures Replication in other metropolitan areas Method: multivariate and/or multilevel analyses

Final Remarks

Regarding urban transport performance,

results suggest that: Global characteristics (rather than local ) are important Traditional topological measures do not help much… Topo-geometric measures play important role

More integrated and compact road systems (in topological and geometrical terms) tend to provide a more efficient urban environment in terms of time spent in car trips

Less environmentally damaging in terms of energy use and pollutant emissions

Thank you.

Email

fredholanda44@gmail.com

rafael.pereira@ipea.gov.br

sss8, Santiago, 01-04-2012