emme2 conference

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2nd ASIAN EMME/2 USERS CONFERENCE HONG KONG NOVEMBER 2000

Contents: 

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

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 1. Introduction

Locality: 

Locality

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Locality

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Locality

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Locality - City of Durban

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Locality - City of Durban DURBAN BAY CBD

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 2. Background

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 2. Background

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Effects of Apartheid Planning

2. Background: 

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

3. Profile of the City: 

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

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 3. Profile of the City

4. Existing Transport System: 

4. Existing Transport System 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 Excellent road system - 3 700 km of freeway, arterial and main routes Modes of transport

4. Existing Transport System: 

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

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

5. Public Transport Restructuring: 

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

5. Public Transport Restructuring: 

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

6. Emme/2 Model Structure: 

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

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Emme/2 BaseNetwork

6. Emme/2 Model Structure: 

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

6. Emme/2 Model Structure: 

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

6. Emme/2 Model Structure: 

6. Emme/2 Model Structure 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 TRIP GENERATION - OVERALL APPROACH

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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) 6. Emme/2 Model Structure

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

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Modal Split Curve (HBW Trips)

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Modal Split Curve (NW Trips)

TRIP DISTRIBUTION: 

TRIP DISTRIBUTION 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 6. Emme/2 Model Structure

TRIP DISTRIBUTION: 

TRIP DISTRIBUTION 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 6. Emme/2 Model Structure

TRIP DISTRIBUTION: 

TRIP DISTRIBUTION 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 6. Emme/2 Model Structure

CALIBRATION PROCESS: 

CALIBRATION PROCESS 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 6. Emme/2 Model Structure

CALIBRATION PROCESS: 

CALIBRATION PROCESS  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 6. Emme/2 Model Structure

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Calibration Process

ASSIGNMENT: 

ASSIGNMENT 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 6. Emme/2 Model Structure

7. Results: 

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

Public Transport at Screenlines: 

Public Transport at Screenlines

Link Scattergram: 

Link Scattergram

8. Using Emme/2 in a Predictive Mode: 

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

8. Using Emme/2 in a Predictive Mode: 

8. Using Emme/2 in a Predictive Mode TDM measures rationalising PT network - using operating costs and fare income as a measure of improvement

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Existing Trip Length Frequency Distribution

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Preferred Trip Length Frequency Distribution

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Proposed Nodes and Corridors City of Durban

9. Concluding Remarks: 

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