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