1. Provincial Models in Gauteng, South Africa Keith Bloy

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 km 2 544 zones Planpac/Backpac Capacity restraint assignment

10. 1985 Update Increased to 23 900 km 2 589 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 km 2 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 km 2 ) 828 zones

15. GTS Study Area

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

17. Comparison: Modelled vs Counts Individual Stations StudyR2R2 InterceptSlope 1985 Study0.608225.241.093 Vectura0.638133.881.014 Vectura-new0.760206.070.949

18. Comparison: Modelled vs Counts Screen Line Sections StudyR2R2 InterceptSlope 1975 Study0.905148701.095 1985 Study0.913- 362.411.450 Vectura0.937- 160.051.186 Vectura-new0.970- 455.031.263

19. Comparison: Modelled vs Counts Good agreement on screen line sections (generation & distribution models good) New volume delay functions improved R 2 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) 12345 Total 10.2 1.0 20.2 1.0 30.2 1.0 40.2 1.0 50.2 1.0 6 5.0

22. Example Using bint(x) 12345 Total 10 +0.2 0 +0.4 1 –0.4 0 –0.2 0 0.0 1.0 20.2 1.0 30.2 1.0 40.2 1.0 50.2 1.0 60.8 1.80.8 5.0

23. Example Using bint(x) 12345 Total 1001001.0 200100 300100 400100 500100 60.0 5.00.0 5.0

24. Example Using MATINT 12345 Total 10 +0.2 +0.2 0 +0.4 +0.2 0 +0.6 +0.2 1 -0.2 -0.8 0 00 +0.2 1.0 20 +0.2 +0.4 0 +0.4 +0.4 1 -0.4 -0.6 0 -0.2 -0.6 0 0.0 +0.4 1.0 30.2 1.0 40.2 1.0 50.2 1.0 60.8 1.6 0.85.0

25. Example Using MATINT 12345 Total 1000101.0 200100 301100 410100 500101 6 5.0

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 a)Equal time intervals of 3 minutes b)Same number of trips in each interval, 10 one-minute intervals c)As many one-minute intervals as possible (25) Three dimensional balancing

28. Comparison of Trip Distribution Using UTPS & EMME/2 ModelAvg Tvl Time% Intrazonals UTPS21.48.9 EMME/2 (a)21.98.6 EMME/2 (b)21.98.7 EMME/2 (c)21.48.9

29. Comparison of Trip Distribution Using UTPS & EMME/2 Difference in cell values EMME/2 (a) (%of total) EMME/2 (b) (%of total) EMME/2 (c) (%of total) 082.482.685.6 ± 195.796.098.7 ± 297.097.299.3 ± 397.697.899.6 ± 498.098.299.7 ± 598.398.599.8

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 a)Equilibrium assignment, calculate costs b)Identify links with level of service E or F c)Matrix capping using macro DEMADJ and volumes = 0.9 of capacity on selected links d)Equilibrium assignment, identify remaining links with LOS E or F, return to (c)

34. Calculate Costs of Congestion a)Capped matrix assigned and costs calculated and subtracted from original costs: cost of congestion = US$ 870 billion per year b)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 Avg Range in Speed Minimum No of Runs for permitted errors ±2km/h±3.5km/h±5km/h±6.5km/h±8km/h 5.043222 10.084332 15.0147533 20.0219654 25.02813865 30.038161076

36. Maximum Range of Average Running Speeds for Different Numbers of Runs (km/h) Road Types Number of Runs 1098765 Freeway: Peak101112131518 Off-peak10 1213 Multi-lane : Peak7789910 Divided Off-peak101113151721 Two-lane : Peak6678911 Two-way Off-peak101112141619

37. Acknowledgements Gautrans Vela VKE