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Provincial Models in Gauteng

Provincial Models in Gauteng. Keith Bloy. Contents of Presentation. Gauteng History of PWV Consortium Results of 3 models compared to counts Some other aspects from studies. Gauteng Province. 1.4 % of land area 19.7 % of population 38 % of GDP 37 % of motor vehicles. The PWV Consortium.

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Provincial Models in Gauteng

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  1. Provincial Models in Gauteng KeithBloy

  2. Contents of Presentation • Gauteng • History of PWV Consortium • Results of 3 models compared to counts • Some other aspects from studies

  3. Gauteng Province • 1.4 % of land area • 19.7 % of population • 38 % of GDP • 37 % of motor vehicles

  4. The PWV Consortium • High economic growth in 60s & 70s • TPA decided to plan a major road network • Framework required for orderly development • Local authorities planning own roads • Need to protect corridors for the long-term • Cannot study single routes in isolation

  5. PWV Consortium • PWV Consortium appointed in 1973 with Mr van Niekerk as the leader • 5 Consulting engineers, 2 Town and regional planners • High growth in last the 30 years has shown the wisdom of the founders of the Consortium

  6. PWV Consortium’s Models • Projective Land Use Model (PLUM) • SAPLUM used for land use projections • Transportation models

  7. 1975 PWV Study • 16 000 km2 • 544 zones • Planpac/Backpac • Capacity restraint assignment

  8. 1985 Update • Increased to 23 900 km2 • 589 zones • UTPS suite of programs • Equilibrium assignment

  9. Vectura Study (1991) • Greater emphasis on public transport • Originaly the same study area as 1985 • Later enlarged to 29 200 km2 and 632 zones • EMME/2 • Equilibrium assignment

  10. New Volume Delay Functions 1994

  11. Gauteng Transportation Study (GTS) • Being developed at present • Screen line counts in 2000 • Reduced study area (18 100 km2) • 828 zones

  12. GTS Study Area

  13. Gauteng Transportation Study • Screen line counts (2000) • 80 stations

  14. Comparison: Modelled vs CountsIndividual Stations (80 Stations)

  15. Comparison: Modelled vs CountsScreen Line Sections

  16. Comparison: Modelled vs Counts • Good agreement on screen line sections (generation & distribution models good) • New volume delay functions improved R2 • Results good considering changes since 1994

  17. Comparison of Trip Distribution Using UTPS & EMME/2 • UTPS – Program GM (integer values) • EMME/2 – 3 Dimensional Balancing (real values) • Before function bint(x) • Basic Program, MATINT

  18. Example Using bint(x)

  19. Example Using bint(x)

  20. Example Using bint(x)

  21. Example Using MATINT

  22. Example Using MATINT

  23. MATINT vs bint • Admittedly a contrived example • Actual matrices: • 588 by 588 matrices • bint: column totals out by ± 32 • MATINT: out by ± 1

  24. Trip Distribution with a Difference • Old political system restricted where people could live • A single distribution resulted in inaccuracies • Several sub-area distributions based on known factors

  25. Original distribution

  26. New Distribution

  27. Development of a Travel Time Matrix • Based on travel times from Vectura model • Ensure correspondence between nodes and links of Vectura and GTS models • Vectura matrix adjusted using macro and counts (560 directional counts) • 10 iterations of macro, R2 = 0.809 to 0.971 (y = 17.087 + 0.969x) • New matrix assigned to Vectura network • Link travel times assigned to a user field (ul3)

  28. Development of a Travel Time Matrix • Link travel times for road network from Vectura network imported into user field in GTS network (ul3) • Single trip matrix assigned to GTS network, and centroid connector travel times assigned to user field (ul3) • Volume delay functions set to user field (ul3) • Single trip matrix assigned and the resultant travel travel time matrix saved = travel time matrix

  29. Development of a Travel Time Matrix • Add terminal times (based on area type and local knowledge) • Add intra-zonal travel times (1/2 travel time to nearest adjacent zone)

  30. Validation of Travel Time Matrix (Measured Travel Times) Y = 1.47 + 0.89X R2 = 0.827

  31. Validation of Travel Time Matrix (Measured Travel Times) Y = -1.78 + 1.04X R2 = 0.956

  32. Calculate Costs of Congestion • Equilibrium assignment, calculate costs • Identify links with level of service E or F • Matrix capping using macro DEMADJ and volumes = 0.9 of capacity on selected links • Iteration: Equilibrium assignment, identify remaining links with LOS E or F, and repeat process

  33. Calculate Costs of Congestion a) Capped matrix assigned and costs calculated and subtracted from original costs: cost of congestion = R 6 400 billion per year b) Remainder matrix also assigned costs calculated using travel times from (a) and added to (a): cost of congestion = R 1035 billion

  34. Travel Time Surveys

  35. Maximum Range of Average Running Speeds for Different Numbers of Runs (km/h)

  36. Final Remarks • Thanks to Gautrans

  37. Comparison of Trip Distribution Using UTPS & EMME/2 • Equal time intervals of 3 minutes • Same number of trips in each interval, 10 one-minute intervals • As many one-minute intervals as possible (25) Three dimensional balancing

  38. Comparison of Trip Distribution Using UTPS & EMME/2

  39. Comparison of Trip Distribution Using UTPS & EMME/2

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