1. Travel demand forecasting tools and spatial change
Dr Edward Beukes
12 July 2017

2. BACKGROUND
Travel demand factors in Cape Town

3. Population
Distribution
Low
Medium
High
Data source:
Census 2011

4. Employment
Distribution
Low
Medium
High
Data source:
IPTN 2014

5. Household Income
Distribution
Low
Medium
High
Data source:
Census 2011

6. AMPP Trip origins Data source: CoCT IPTN 2014 Atlantis Paarl Parklands
Durbanville
CBD
Stellenbosch
Blackheath
Sea Point
Khayelitsha
Strand
Hout Bay
Muizenberg
Data source:
CoCT IPTN 2014

7. AMPP Trip destinations
Atlantis
Paarl
Parklands
CBD
Durbanville
Stellenbosch
Blackheath
Sea Point
Khayelitsha
Strand
Hout Bay
Muizenberg
Data source:
CoCT IPTN 2014

8. Private Transport Volumes FACT:
Severe congestion on inbound routes, worsening towards the CBD.
As a result of:
Shift away from rail
Inefficiencies in public transport networks
Low densities and long travel distances
No private car disincentives
FACT:
The peak period on major routes has increased from 2hours to 4hours in the last 10 years
Data source:
CoCT IPTN 2014

9. Public Transport Volumes
+- 50km
+- 36 km
FACT:
Spatial inefficiencies leads to long, costly travel distances, on public transport for the poorest residents in the city.
Tidal flow nature makes public transport expensive to operate.
The highest demand for public transport is between the south-east and CBD areas
Rail
Road-based
Data source:
CoCT IPTN 2014

10. Cape Town AMPP
Main Mode
Modal Split
Data source:
CoCT IPTN 2014

11. TRAVEL DEMAND MODELS
LAND USE IMPACTS

12. TRADITIONAL 4-STEP MODELLING
Land use activity models are a key input.
Travel demand models are used to determine the best network response to current and forecasted land use patterns.

13. TRADITIONAL 4-STEP MODELLING
Although it is widely accepted that transportation infrastructure plays a key role in location choices, this is not always accounted for in modelling practice.
Land use
response

14. LAND USE MODELLING
TRANSPORTATION IMPACTS

15. LAND USE FORECASTING - CAPE TOWN
Cape Towns Integrated Public Transport Network (IPTN) planning process incorporated the following land use assumptions:
Development densities are influenced by the densification policies.
Densities are increased around public transport facilities.
New developments limited to greenfields sites.
Data source:
CoCT IPTN 2014

16. LAND USE FORECASTING - CAPE TOWN
Key unanswered questions:
What is the development potential of brownfields sites?
What role does land use mix play on public transport operations?
What is the impact of land use forecasting assumptions on transport sustainability?
Data source:
CoCT IPTN 2014

17. COMPREHENSIVE TOD LAND USE MODEL
How to allocate future housing and employment to lower the cost of transport?
Cape Town is facing:
Increasing congestion
Increasing pollution
Increasing costs of public transport
Increasing levels of inaccessibility
Land use form at the metropolitan scale plays a huge role.
820, 000 trips, of which we have 270,000 trips to play with

18. OBJECTIVES COMPREHENSIVE TOD LAND USE MODEL Shorten trip lengths
New land use scenario generated to respond to these questions.
Purely theoretical investigation designed to extract policy insights
Increase seat renewal
Decrease travel costs
Decrease congestion
Shorten trip lengths
Increase accessibility
Locate housing near employment and vice versa
Increase transit cost efficiency
Balance bi-directional flows
Increase transit revenues
Environmental Benefits
Increase transit modal share
OBJECTIVES

19. Heuristic (learning algorithm) optimisation
Assessment and selection best from original and new variants
Stage: 1 Initialisation
Stage 2: Evaluation
Stage 3: Variation
Stage 4: Evaluation
Updating
Generic inputs: Parameters e.g. base year scenario; PTOD
Repeat until optimised
Testing new input variants
Define Goals and objectives to test performance e.g. minimise length of trip
Fun Fact:
Heuristic: meaning to "find" or "discover" refers to experience-based techniques for problem solving, learning, and discovery that give solutions that are not guaranteed to be optimal.
- Wikipedia
Randomly vary inputs of Stage 1

20. Only “unallocated” future land use growth part of optimisation
Calculate trip productions and attractions using IPTN regression coefficients
Calculate trip matrix using IPTN calibrated gravity model
Households + jobs growth (20-years)
Base year households + jobs
Base year land use
patterns kept
constant

21. Development potential of brownfields sites
Land Use
Office: m²
Parking Structure: 3 789m²
Data source:
J Petzer, CCT

22. 7% decrease in total kilometers travelled

23. 7% increase in trips aligned to the transit network

24. 23% improvement in the balance of trip productions and attractions in macrozones

25. 67% improvement in bi-directional flows

26. Given the constraints placed on the algorithm:
Non-residential
Residential
Given the constraints placed on the algorithm:
Travel to and from outlying areas should be discouraged by ensuring a good mix of residential and employment opportunities.
As far as possible, future employment opportunities should be located in the Metro – South East.
As far as possible, future residential growth should be located in and around the CBD.

27. Key policy highlights TOD should be understood at multiple scales:
Normally, the focus is on local or corridor level planning.
However, spatial problems manifest at regional or metropolitan scales as well and these, arguably, are the major drivers of costs for public transport.
Intensification of land use in the wrong location can exacerbate spatially driven transport costs
While optimizing land use diversity and intensity are important, it is even more important to encourage the right mix of land use types in the right locations.
Increasing densities in the wrong locations will result in reduced transit sustainability.
Mixed land use developments are not necessarily the best way to drive transformation
Many areas are largely single use now. We seek a mixed outcome, and so we need to promote land uses that will balance the status quo.

28. THANK YOU Dr Edward Beukes PPO: System Analysis (021) 400 1073