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Provincial Models in Gauteng, South Africa Keith Bloy
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Contents of Presentation Gauteng History of PWV Consortium Results of 3 models compared to counts Some other aspects from studies
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Gauteng Province 1.4 % of land area 19.7 % of population 38 % of GDP 37 % of motor vehicles
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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
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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
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PWV Consortium’s Models Projective Land Use Model (PLUM) SAPLUM used for land use projections
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1975 PWV Study 16 000 km 2 544 zones Planpac/Backpac Capacity restraint assignment
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1985 Update Increased to 23 900 km 2 589 zones UTPS suite of programs Equilibrium assignment
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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
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New Volume Delay Functions
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Gauteng Transportation Study Being developed at present Screen line counts in 2000 Reduced study area (18 100 km 2 ) 828 zones
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GTS Study Area
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Gauteng Transportation Study Screen line counts (2000) 80 stations
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Comparison: Modelled vs Counts Individual Stations StudyR2R2 InterceptSlope 1985 Study0.608225.241.093 Vectura0.638133.881.014 Vectura-new0.760206.070.949
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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
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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
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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
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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
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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
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Example Using bint(x) 12345 Total 1001001.0 200100 300100 400100 500100 60.0 5.00.0 5.0
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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
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Example Using MATINT 12345 Total 1000101.0 200100 301100 410100 500101 6 5.0
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MATINT vs bint Admittedly a contrived example Actual matrices: 588 by 588 matrices Bint: column totals out by ± 32 MATINT: out by ± 1
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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
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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
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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
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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
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Original distribution
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New Distribution
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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)
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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
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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
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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
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Acknowledgements Gautrans Vela VKE
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