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The use of SS in urban transport analysis limits and potentials. sss8, Santiago, 01-04-2012. Rafael H. M. Pereira Frederico R. B. de Holanda Valério A. S. de Medeiros Ana Paula B. G. Barros. Institute of Applied Economic Research. Brazil: overview. Brazil 2010 Population:
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The use of SS in urban transport analysis limits and potentials sss8, Santiago, 01-04-2012 Rafael H. M. Pereira Frederico R. B. de Holanda Valério A. S. de Medeiros Ana Paula B. G. Barros Institute of Applied Economic Research
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: • PilotPlan = 209,855 • Federal District = 2,570,160 • Conurbation = 3,276,966 • Directinfluencearea = 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 2000 - 2009 Population 2,70 % a. a. Carfleet7,14 % a. a. Increasing motorization ratio (FD) • Number of Vehicles for 100 Inhabitants 42,8 25,9 Source: Denatran and IBGE
Shortcomings (transport studies) Traditional syntax approach • 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
Shortcomings (transport studies) “Obstacles” - impedance (a) (b) Same level of Global integration (Rn) = 3,13374 Source: Denatran and IBGE
Shortcomings (transport studies) Metric length (a) (b) 5 Km 10 Km Same level of Global integration (Rn) = 3,13374 Source: Denatran and IBGE
Material and Methods Linear regression (Ordinary Least Squares - OLS)* Urban Configuration Urban Transport Performance AverageTravel Time spentonurbantrips Configurational Variables: - TopologicalIntegration (Rn, R3) - MeanDepth (Rn, R3 step) - Topo-geometricmeasures: LengthWgtandMetricstep *fewobservations (20)
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/ segmentmaps - Federal District (FD) - 19 R.A.’s
FD Axial Map Source: MEDEIROS (2006)
Material and Methods • RA Recanto das Emas Rn RnLengthWgt
Results Local Measures Not significant
Results Global Traditional Measures Sig. < 4% e R² = 22%
Results Topo-geometric measures Improved results with larger radius Melhor estatística quanto maior o Raio de ação
Results Topo-geometric measures Improved results with larger radius Melhor estatística quanto maior o Raio de ação
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. sss8, Santiago, 01-04-2012 Email fredholanda44@gmail.com rafael.pereira@ipea.gov.br