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2nd ASIAN EMME/2 USERS CONFERENCE HONG KONG

2nd ASIAN EMME/2 USERS CONFERENCE HONG KONG. NOVEMBER 2000. Using Emme/2 to assess the Impact of and influence the restructuring of the apartheid city. City of Durban. Authors:. Logan Moodley City of Durban. Dave McFarlane VKE Engineers. Contents. 1. Introduction 2. Background

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2nd ASIAN EMME/2 USERS CONFERENCE HONG KONG

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  1. 2nd ASIAN EMME/2 USERS CONFERENCEHONG KONG NOVEMBER 2000

  2. Using Emme/2 to assess the Impact of and influence the restructuring of the apartheid city City of Durban Authors: Logan Moodley City of Durban Dave McFarlane VKE Engineers

  3. Contents 1. Introduction 2. Background 3. Profile of the City 4. Existing Transport System 5. Public Transport Restructuring 6. Emme/2 Model Structure 7. Results 8. Using Emme/2 Model in a Predictive Mode 9. Concluding Remarks

  4. 1. Introduction • In 1994 first democratic government elected in South Africa • Dramatic impact on planning • New legislation Purpose of paper • To describe the methodology employed in the latest update of the Emme/2 Model • The update and methodology has been influenced by political changes • To demonstrate how the model will be used in influencing major decisions regarding restructuring and integrating the urban form of the city

  5. Locality

  6. Africa Locality

  7. Locality

  8. Locality - City of Durban

  9. Locality - City of Durban DURBAN BAY CBD

  10. 2. Background • Prior to 1994 • Six decades of separate development based on race apartheid • Different race groups lived in separately demarcated area • Distorted spatial structure • Poorest away from CBD

  11. 2. Background • Prior to 1994 • Duplication of services, public transport, schools, social facilities • Emphasis on private transport  road building • Poorest furthest away from the CBD .. But totally reliant on public transport high PT subsidy costs

  12. 40 min 25 min 10 min Durban CBD I n d I a n O c e a n INEQUITIES Effects of Apartheid Planning

  13. 2. Background Post 1994 New Government • New transport legislation • regulate • improve • promote • Steps to restructure cities • densify corridors and nodes - achieve economies of scale • infrastructure investment to support corridors • improve operational performance - tendering • Better integration • Re-calibration of Emme/2 model

  14. 3. Profile of the City • Area = 1366 Km2 • Population = 2,5 million • No. of households= 609 000 • 60% of employment close to CBD • But 30% of employees living close to CBD  long travel distances • Modal split = 57% by PT - varies from 100% to 0%

  15. 3. Profile of the City • Contributes to 9% of GDP • Port City - one million containers/annum • Other activities • tourism • commerce • subtropical fruit • sugar cane • motor manufacturing • agriculture • construction

  16. 4. Existing Transport System • Excellent road system - 3 700 km of freeway, arterial and main routes • Modes of transport • 1 500 buses, 6000 mini-bus taxis, 450 000 cars • Over the last twenty years there has been a significant shift to mini-bus taxis

  17. 4. Existing Transport System • Rail uses old heavy rolling stock • Generally PT system in a poor state • Huge inefficiencies in system mainly due to the distorted spatial structure • Currently PT subsidies - US $58 million/annum • New legislation has been enacted to restructure the PT industry

  18. Modes of Transport Rail Infrastructure Congestion - am peak Mini-bus Taxi Typical Bus

  19. 5. Public Transport Restructuring • The public transport restructuring main thrust is to establish a least cost network with optimal modes on the main corridors  reduce burden on subsidy • Leads to a more efficient and sustainable system • Supply and demand data surveyed on all public transport modes

  20. 5. Public Transport Restructuring • Basis for PT O-D matrix • High priority public transport network output Rail emphasis • O-D information plus high priority public transport network  Emme/2 model

  21. 6. Emme/2 Model Structure • NETWORK • 3 712 km of roadway • 406 km of rail • 330 zones (316 internal, 14 external) • Annotation files imported from GIS database

  22. Durban CBD I n d I a n O c e a n Emme/2 BaseNetwork

  23. 6. Emme/2 Model Structure DEMOGRAPHICS • 1996 census data • Employment and car ownership - separate sources • Prior to 1996 data collected by race and model structured by race e.g. WHBW, BHBW • Since 1996 data collected by income group - high, medium, low • Income grouping used as a proxy for car ownership and hence PT usage • This change necessitated a rethink in the structure of the model

  24. 6. Emme/2 Model Structure DEMOGRAPHICS • Detail is lost • Required simplification in trip generation and trip distribution models in order to cater for changes • Typical screenline

  25. 6. Emme/2 Model Structure TRIP GENERATION - OVERALL APPROACH • Racial classification  Income classification • Existing parameters as far as possible • Simplify model • Census data : • High income  R72 000/annum • Medium Income  R 30 000 - R72 000/annum • Low income  R0 - R30 000/annum • Why income classification ? • Trip generation  income • Car usage  income • Improved distribution of HBW trips

  26. HOME BASED WORK (HBW) TRIPS - 2 HOUR AM PEAK Productions = 0.60 * Employed residents Attractions = 0.60 * Employment 6. Emme/2 Model Structure TRIP GENERATION EQUATIONS NON-WORK (NW) TRIPS - 2 HOUR AM PEAK Productions = 0.05 * (L.Pop+M.Pop + (1.50*H.Pop)) + 0.05*(L.Emp + (2.0*M.Emp) + (4.0*H.Emp)) Attractions = (0.008 * L.Pop) + (0.024*M.Pop) + (0.039*H.Pop) (Activity zones) +( 0.591*M.Emp) + (1.182*H.Emp) Attractions = (0.008 * L.Pop) + (0.024*M.Pop) + (0.039*H.Pop) (Other zones) +( 0.117*M.Emp) + (0.234*H.Emp) TRUCK TRIPS Productions = (0.04*H.Emp) + (0.1*M.Emp) Attractions = (0.05*H.Emp) + (0.07*M.Emp) + (0.007*L. Emp)

  27. 6. Emme/2 Model Structure MODAL SPLIT • High correlation  income and car ownership • Modal split at origins based on graphs • Four modes - auto, rail, bus, mini-bus taxi • Auxillary transit mode - walk

  28. Modal Split Curve (HBW Trips)

  29. Modal Split Curve (NW Trips)

  30. Develop cost matrices Car > Travel time matrix PT > Cost of travel Both generated in previous assignment Intra-zonal costs added to each matrix The PT trip cost was refined further : Determine transposed matrix Determine minimum of original and transposed matrices This compensated for off peak direction costs TRIP DISTRIBUTION 6. Emme/2 Model Structure

  31. Simple gravity model deterrence function applied to these times/costs : F(c) = exp(-c*) Separate beta value, impedance matrices used for PT and cars Distribution undertaken for four trip types HBW - low income HBW - medium income HBW - high income NW trips TRIP DISTRIBUTION 6. Emme/2 Model Structure

  32. TRIP DISTRIBUTION 6. Emme/2 Model Structure • Distribution Method • Two dimensional matrix with two input origin matrices (car and PT) and a single destination matrix • Model distributes trips based on the deterrence matrices and relative attractiveness of car/PT for each destination • Use of INRO macro - BALMPROD.MAC • Output eight matrices (4 car, 4 PT), combined into two matrices (car, PT), for assignment

  33. CALIBRATION PROCESS 6. Emme/2 Model Structure • Iterative process TG, MS, TD, Ass • Emphasis in TD phase • Three tools used in the calibration process : 1.  value is inverse of the average (weighted ) cost value 2. Three dimensional balancing with Emme/2  origin totals • destination totals • trips crossing screenlines - 11 in total

  34. CALIBRATION PROCESS 6. Emme/2 Model Structure  this whole process was automated for the 11 screenlines for car and PT  results of the 1st 3-D balance using the first screenline was passed onto the second and so forth  origins kept same, destinations modified 3. DEMANDJ.MAC - adjustment of demand matrix based on counts (for comparison/calibration purposes only)  final matrices used in assignment not adjusted in this way

  35. Calibration Process

  36. ASSIGNMENT 6. Emme/2 Model Structure • Car assignment first with PT lines pre-loaded as Pcu value • PT assignment run second, speed of road based PT a function of car assignment speeds

  37. 7. Results • Reasonably good results • Cars 174 link counts R2 = 0.921 • Public Transport 22 screenlines R2 = 0.984 • Public Transport (buses) 22 screenlines R2 = 0.890 • Public Transport (mini-bus taxi) 22 screenlines R2 = 0.826 • Public Transport (rail) 22 screenlines R2 = 0.950

  38. Public Transport at Screenlines

  39. Link Scattergram

  40. 8. Using Emme/2 in a Predictive Mode • Simulate future scenarios • Simple trend projections to various intervention policies • Emphasis on public transport enhancement • Main areas of influence • influencing abnormal trip length frequency distribution (travel distances) • by incorporating land use strategies • bottleneck elimination

  41. Refinement and Future demographics 2.6m - 2.9m AIDS Use of PT 57% now 80% target Extension of PT network 8. Using Emme/2 in a Predictive Mode • TDM measures • rationalising PT network - using operating costs and fare income as a measure of improvement

  42. Existing Trip Length Frequency Distribution

  43. Preferred Trip Length Frequency Distribution

  44. Durban CBD I n d I a n O c e a n City of Durban Proposed Nodes and Corridors

  45. 9. Concluding Remarks • Use of Emme/2 has been the backbone in terms of determining the HPPTN • Model simplified to replicate current transport situation • In a firm position to test land use strategies • In a position to influence outcomes • Monitoring of particular parameters within Emme/2 is now easily achievable • Main tool in developing long range and short term plans for the City

  46. City of Durban THANK YOU

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