Provincial Models in Gauteng, South Africa

1. Provincial Models in Gauteng,South Africa KeithBloy

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

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

6. 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 long-term • Cannot study single routes in isolation

7. 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 30 years has shown the wisdom of the founders of the Consortium

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

9. 1975 PWV Study • 16 000 km2 • 544 zones • Planpac/Backpac • Capacity restraint assignment

10. 1985 Update • Increased to 23 900 km2 • 589 zones • UTPS suite of programs • Equilibrium assignment

12. 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

13. New Volume Delay Functions

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

15. GTS Study Area

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

17. Comparison: Modelled vs CountsIndividual Stations

18. Comparison: Modelled vs CountsScreen Line Sections

19. 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

20. 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

21. Example Using bint(x)

22. Example Using bint(x)

23. Example Using bint(x)

24. Example Using MATINT

25. Example Using MATINT

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

27. 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

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

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

30. 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

31. Original distribution

32. New Distribution

33. 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 • Equilibrium assignment, identify remaining links with LOS E or F, return to (c)

34. Calculate Costs of Congestion • Capped matrix assigned and costs calculated and subtracted from original costs: cost of congestion = US$ 870 billion per year • Remainder matrix also assigned and costs calculated using travel times from (a) and added to (a): cost of congestion = US$ 140 billion per year

35. Travel Time Surveys

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

37. Acknowledgements • Gautrans • Vela VKE