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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.

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Presentation on theme: "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."— Presentation transcript:

1 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,

2 Brazil: overview Brazil 2010 Population: Total milions Urban -159 milions (83.7%) 5,564 Municipalities 38 cities over 500,00 habitants 16 cities over 1 milion habitants

3 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

4 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

5 Study aim and scope Increasing motorization ratio (FD)  Number of Vehicles for 100 Inhabitants 25,9 42,8 Source: Denatran and IBGE Population 2,70 % a. a. Car fleet 7,14 % a. a.

6 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

7 Shortcomings (transport studies) Source: Denatran and IBGE (a) (b) “Obstacles” - impedance Same level of Global integration (Rn) = 3,13374

8 Shortcomings (transport studies) Source: Denatran and IBGE (a) (b) Metric length Same level of Global integration (Rn) = 3, Km 10 Km

9 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

10 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

11 FD Axial Map Source: MEDEIROS (2006)

12 Material and Methods RA Recanto das Emas Rn Rn Rn Length Wgt

13 Results

14 Configurational variablesPerformance variable Statistics R²P-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) ATT 38,9%0,0033 Mean depth with Local topological radius R3 ATT 3,8%0,4094 Mean depth with Local topological radius R3 (weighted by segment length) ATT 0,3%0,8060 Mean depth (100 meter radius) ATT 1,0%0,6801 Mean depth (500 meter radius) ATT 1,3%0,6339 Mean depth (1,000 meter radius) ATT 2,7%0,4848 Mean depth (5,000 meter radius) ATT 2,1%0,5402 Mean depth (10,000 meter radius) ATT 14,5%0,0978 Mean depth (50,000 meter radius) ATT 30,5%0,0115 Global Integration with topological radius Rn ATT 22,0%0,0370 Rn Global topo-geometric Integration (weighted by segment length) ATT 58,0%0,0001 Local Integration with radius R3 ATT 8,5%0,2128 R3 Local topo-geometric Integration (weighted by segment length) ATT 0,5%0,7664

15 Results Local Measures Not significant Configurational variablesPerformance variable Statistics R²P-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) ATT 38,9%0,0033 Mean depth with Local topological radius R3 ATT 3,8%0,4094 Mean depth with Local topological radius R3 (weighted by segment length) ATT 0,3%0,8060 Mean depth (100 meter radius) ATT 1,0%0,6801 Mean depth (500 meter radius) ATT 1,3%0,6339 Mean depth (1,000 meter radius) ATT 2,7%0,4848 Mean depth (5,000 meter radius) ATT 2,1%0,5402 Mean depth (10,000 meter radius) ATT 14,5%0,0978 Mean depth (50,000 meter radius) ATT 30,5%0,0115 Global Integration with topological radius Rn ATT 22,0%0,0370 Rn Global topo-geometric Integration (weighted by segment length) ATT 58,0%0,0001 Local Integration with radius R3 ATT 8,5%0,2128 R3 Local topo-geometric Integration (weighted by segment length) ATT 0,5%0,7664

16 Results Global Traditional Measures Configurational variablesPerformance variable Statistics R²P-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) ATT 38,9%0,0033 Mean depth with Local topological radius R3 ATT 3,8%0,4094 Mean depth with Local topological radius R3 (weighted by segment length) ATT 0,3%0,8060 Mean depth (100 meter radius) ATT 1,0%0,6801 Mean depth (500 meter radius) ATT 1,3%0,6339 Mean depth (1,000 meter radius) ATT 2,7%0,4848 Mean depth (5,000 meter radius) ATT 2,1%0,5402 Mean depth (10,000 meter radius) ATT 14,5%0,0978 Mean depth (50,000 meter radius) ATT 30,5%0,0115 Global Integration with topological radius Rn ATT 22,0%0,0370 Rn Global topo-geometric Integration (weighted by segment length) ATT 58,0%0,0001 Local Integration with radius R3 ATT 8,5%0,2128 R3 Local topo-geometric Integration (weighted by segment length) ATT 0,5%0,7664 Sig. < 4% e R² = 22%

17 Configurational variablesPerformance variable Statistics R²P-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) ATT 38,9%0,0033 Mean depth with Local topological radius R3 ATT 3,8%0,4094 Mean depth with Local topological radius R3 (weighted by segment length) ATT 0,3%0,8060 Mean depth (100 meter radius) ATT 1,0%0,6801 Mean depth (500 meter radius) ATT 1,3%0,6339 Mean depth (1,000 meter radius) ATT 2,7%0,4848 Mean depth (5,000 meter radius) ATT 2,1%0,5402 Mean depth (10,000 meter radius) ATT 14,5%0,0978 Mean depth (50,000 meter radius) ATT 30,5%0,0115 Global Integration with topological radius Rn ATT 22,0%0,0370 Rn Global topo-geometric Integration (weighted by segment length) ATT 58,0%0,0001 Local Integration with radius R3 ATT 8,5%0,2128 R3 Local topo-geometric Integration (weighted by segment length) ATT 0,5%0,7664 Melhor estatística quanto maior o Raio de ação Results Topo-geometric measures Improved results with larger radius

18 Configurational variablesPerformance variable Statistics R²P-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) ATT 38,9%0,0033 Mean depth with Local topological radius R3 ATT 3,8%0,4094 Mean depth with Local topological radius R3 (weighted by segment length) ATT 0,3%0,8060 Mean depth (100 meter radius) ATT 1,0%0,6801 Mean depth (500 meter radius) ATT 1,3%0,6339 Mean depth (1,000 meter radius) ATT 2,7%0,4848 Mean depth (5,000 meter radius) ATT 2,1%0,5402 Mean depth (10,000 meter radius) ATT 14,5%0,0978 Mean depth (50,000 meter radius) ATT 30,5%0,0115 Global Integration with topological radius Rn ATT 22,0%0,0370 Rn Global topo-geometric Integration (weighted by segment length) ATT 58,0%0,0001 Local Integration with radius R3 ATT 8,5%0,2128 R3 Local topo-geometric Integration (weighted by segment length) ATT 0,5%0,7664 Melhor estatística quanto maior o Raio de ação Results Topo-geometric measures Improved results with larger radius

19 Final Remarks Future Studies  Test other configurational measures  Replication in other metropolitan areas  Method: multivariate and/or multilevel analyses

20 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

21 Thank you. sss8, Santiago,


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