Provincial Models in Gauteng, South Africa 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 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 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 PWV Consortiums Models Projective Land Use Model (PLUM) SAPLUM used for land use projections 1975 PWV Study km zones Planpac/Backpac Capacity restraint assignment 1985 Update Increased to km zones UTPS suite of programs Equilibrium assignment Vectura Study (1991) Greater emphasis on public transport Originaly the same study area as 1985 Later enlarged to km 2 and 632 zones EMME/2 Equilibrium assignment New Volume Delay Functions Gauteng Transportation Study Being developed at present Screen line counts in 2000 Reduced study area ( km 2 ) 828 zones GTS Study Area Gauteng Transportation Study Screen line counts (2000) 80 stations Comparison: Modelled vs Counts Individual Stations StudyR2R2 InterceptSlope 1985 Study Vectura Vectura-new Comparison: Modelled vs Counts Screen Line Sections StudyR2R2 InterceptSlope 1975 Study Study Vectura Vectura-new 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 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 Example Using bint(x) Total Example Using bint(x) Total 0.4 0 Example Using bint(x) Total Example Using MATINT Total Example Using MATINT Total MATINT vs bint Admittedly a contrived example Actual matrices: 588 by 588 matrices Bint: column totals out by 32 MATINT: out by 1 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 Comparison of Trip Distribution Using UTPS & EMME/2 ModelAvg Tvl Time% Intrazonals UTPS EMME/2 (a) EMME/2 (b) EMME/2 (c) 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) 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 Original distribution New Distribution 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) 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 Travel Time Surveys Avg Range in Speed Minimum No of Runs for permitted errors 2km/h3.5km/h5km/h6.5km/h8km/h Maximum Range of Average Running Speeds for Different Numbers of Runs (km/h) Road Types Number of Runs Freeway: Peak Off-peak Multi-lane : Peak Divided Off-peak Two-lane : Peak Two-way Off-peak Acknowledgements Gautrans Vela VKE