Transport Modelling for Rapid Development

Transport Modelling for Transport
and Land Use Sustainability:
lessons and challenges from East
Asia and the Middle East
Richard Di Bona
Independent Consultant and
Director (part-time), LLA Consultancy Ltd, Hong Kong
4th Annual San Francisco Bay Area ITE
Modeling Workshop, San Francisco,
USA, 1 April 2011

Contents
1. Why listen to me?
2. Modellers’ / forecasters’ failings
3. Challenges caused by rapid development
4. Frequent oversights in planning and their
implications
5. Relationship between urban form and
appropriate transit types
6. Uncertainty and the need for multi-
perspective strategic forecasting
7. Transport policy
Transport Modelling for Transport and Land Use Sustainability:
lessons and challenges from East Asia and the Middle East

Why Listen To Me?
• Rapid development can lead to boom-bust
cycles/ other displacements (“black swans”)
• Lessons from “before”, “during” and “after”
events w.r.t. how to model transport systems
• Given long-run nature of strategic forecasts,
similar challenges likely in “stable & mature”
cities over a forecast horizon (e.g. sub-prime)
• Two-way learning between “developed” and
“developing” environments!


“Success is dangerous. One begins to
copy oneself, and to copy oneself is
more dangerous than to copy others.
It leads to sterility”
Pablo Picasso


But have Modellers been Successful?
Looking just at toll roads, i.e. models with a
focus on one route only:
• Average initial year traffic ≈ 70% of forecast
– (Bain, R. & Wilkins, M. “Credit Implications of Traffic Risk in Start-Up Toll Facilities”,
Standard & Poor’s, September 2002)

• Forecast error often consistent over years 1-5
– (Bain, R. & Polakovic, L, “Traffic Forecasting Risk Study Update 2005: Through
Ramp-Up and Beyond”, Standard & Poor’s, August 2005)

• And remember, at this seminar we’re looking
at assessing “game changing” initiatives,
which may be system-wide!

Are We Simply Biassed?
Differences in outturn patronage (1st year):
• ≈ 82% of lender-commissioned forecasts
• ≈ 66% if commissioned by others
– (Bain, R. & Wilkins, M. “Credit Implications of Traffic Risk in Start-Up Toll Facilities”, Standard &
Poor’s, September 2002)

• Suggests forecast bias/ influence by clients
(agreeing those GDP/ land use assumptions?)
– (Brinkman, P.A. The Ethical Challenges and Professional Responses of Travel Demand
Forecasters, PhD Dissertation, University of California at Berkeley, 2003)

• Yet practitioners have only very weak
acceptance of bias in their work
– (Di Bona, R.F. What are the Key Risks Associated with Private Investment in Start-Up Toll Road
Projects in Developing East Asian Economies?, MBA Dissertation, Henley Management
College, 2006)


Groupthink and Agency Theory?
To make (new) things happen requires optimism
• But do we lose too much of our skepticism?
Are benefits/ returns overstated to get approval?
• It’s our chance to bring our new ideas to fruition
• Or client’s staff’s chance for a success bonus
Are we afraid of emphasising critical risks to
superiors or breaking with “group consensus”?
Note: Every 5 years, 80% of businesses fail, so
transport modellers are not alone!


“We must be skeptical even of our
skepticism”
Bertrand Russell


What is Rapid Development?
• Rapid population growth (people or %)
• Rapid economic growth
• Substantial redevelopment: new population
&/or business centres, new satellite towns
• What about major new transport
infrastructure – another “game changer”?
– Hong Kong Airport Core Programme (airport, rail,
expressway and land use developments)
– Maybe in the USA: High Speed Rail (stations and
surrounds) or even PRT roll-out?


Keeping Track of Rapid Development
• Which data are still up-to-date?
• Can institutions and transit operators cope?
• Can they react to events?
– Changing traveller behaviour and preferences
• Can they meet obligations, such as:
– Bus network restructuring
– Interchange facilities planning & development
• Is transit viable during periods of change? (i.e.
when patterns are unsettled)
• Do agencies have authority, capability and time
to manage/ control/ plan/ review/ enforce?
• Do we really consider the above in our models?

Implicit Acceptance of Eternal Boom
• “We will not return to the old boom and
bust”, Gordon Brown, March 2007
– and over 100 times subsequently!
• “But this time/ country is different”
• Skyscraper Theory
– (Lawrence, A. “The Curse Bites: Skyscraper Index Strikes”,
Dresdner Kleinwort Benson Research, March 1999)


Can We Reliably Forecast Outcomes?
Development ≠ Occupancy:
• Lag-times for uptake
• Competition between new sites

Oversupply?
↓
Partial completion

But where? Uncertainty!

Forecasting or Merely Extrapolation?
• Models based upon past data
• Legacy transport systems for past needs
• At best, model revision/ re-basing is “catch up”
• Trip generation, distribution, mode choice
characteristics prone to change
• Affordability can change (decreasing during
“boom” and then increasing after “slump”)
– Income levels & real disposable income
• Danger of extrapolating a boom to develop
infrastructure which opens during a slump

Before and After Studies are Critical
• Assess forecasting performance of models
• Identify strengths and weaknesses
• Ask where improvement is feasible
– But remember we can never be perfect!
– Planning data or behavioural parameters?
• Singapore North East (metro) Line – had a major
before & after study as it tied-in with major land
development:
– Planning data / uptake of units was an issue
– Lag-times/ ramp-up under-estimated
– Most mode-purpose distribution functions stood up well
– But those related to social activities and land uses initially
underdeveloped in the area performed poorly – people
had to travel further to fulfill such activities

Adaptive Expectations and the Cycle
After boom:
During late boom:
An economy or • Fewer projects
• Over-forecast
• Less readiness
a new industry • Many failures
to cope with
(e.g. High • Lots of projects
recovery
started, many
Speed Rail) fewer projects
completed

Pre-boom: During early boom:
• Some growth • Under-forecast
• Profits/ success • Profits/ success
• Leads to more projects • Yet more projects


Catch 22: Example of Early Railways
In UK, many railway companies set up & went bankrupt
• Yet those towns without railways faded
• So they were good to have but not to invest in
In the US, experience was similar but arguably more
extreme as railways opened up the mid-west and west
• Exhibited cyclical booms and crises: cycles are
“particularly violent in the case of… emerging
industries and emerging companies, which grow and
evolve rapidly” (Faber, M. Tomorrow’s Gold, CLSA Books, 2002)
US High Speed Rail, & urban PRT to face similar issues?


Forewarned is Forearmed
• If we’re aware of problems we can start to
address them
• But there are also other frequent oversights
which we can try to avoid
• Minimise risk where we can


Frequent Planning Oversights
• Can happen in almost any project
• Design guidance notes/ best practice
checklists can help
• But rush-to-open can exacerbate problems
(e.g. not all components ready at same time)
• Are the following examples really considered
in our transport models?


Mid-Valley / Abdullah Hukum
• Mid-Valley Megamall, in the suburbs of Kuala
Lumpur:
– Opened November 1999
– shopping mall, offices, hotel (now 2 hotels)
– 1.7m sq.ft. leasable space: once Asia’s biggest mall
• Abdullah Hukum Station on Putra LRT (now
Kelana Jaya LRT):
– Line opened September 1998
– Achieved a small fraction of predicted patronage


Proximity but Severance
Abdullah
Hukum Mid-Valley
Station Megamall
250m
(800 feet)

Kuala
Lumpur
River

But did Mid-Valley load onto LRT in the model(s)?

Abdullah Hukum Station

Elevated Station, so should be easy to build a pedestrian
footbridge!

Following Criticism, KTM to the Rescue…

August 2004: mall served by Komuter rail, following criticism

Lack of Integration
• Narrow definition of project interests:
“compete” rather than “collaborate”
– Despite transit requiring accessible land use
– Despite developments having increased worth/
value from accessibility
• Not an isolated case, e.g. Bangkok Skytrain
– Initially no walkways into adjacent buildings
– And even: no escalators to stations / platforms
– Yet not taken account of in pre-opening modelling


“Compete” between Transit Lines!?
Sadly, this can be even worse…
• Bangkok Skytrain “versus” Blue Line Subway
– Actual resistance to common ticketing
– Significant vertical distances
• Manila LRT: Metrorail (N-S) & Megatren (E-W)
– Connect via a shopping mall!
• Kuala Lumpur Sentral Stations & others
• System exit; walk (cross-road); new ticket
– Yet shown as simple interchange!
Do models simplify the hurdles travellers face in the
immediate vicinity of stations?


Bangkok, Thailand
Skytrain:
• above ground
• no aircon

Blue Line:
• Deep under ground
• Cavernous halls:
– Assumed to house
shops, but don’t East Asia and the Middle East
lessons and challenges from

Kuala Lumpur Sentral Stations

400 metres, not
counting in-
station distance,
one highway
crossing


It Might Look Good on a Map, but…
In Amman, buses were “highly accessible”, as
most homes/ offices were near to routes, but:
• Buses tended to leave terminals once full
• So not possible to board mid-route
• Bus route structure was a legacy of how
Amman used to be
• And as a hilly city, walking usually
inconvenient
– Philadelphia founded by Romans on 7 hills
• Buses seen as “mode of last resort”

Amman, Jordan


Building a Metro is not Enough
In Tianjin, a number of new metro lines planned,
but bus companies (all under the public Tianjin Bus
Group) resistant to change, e.g. route restructuring
• Buses slow and inconvenient – another legacy
network
• Like Amman, piecemeal changes to bus routes:
– Tacking on new development areas
– Not providing wholly new, direct/ convenient routes
• Cycling seen as much quicker and more reliable
• Like Amman, buses seen as “social need” rather
than a means to persuade drivers out of cars

Tianjin, China


Public Transport – A Mode of Last Resort?
Traditionally, PT for those without access to a car
• Social need and low cost
• Poor comfort, poor service
• This is not attractive to car users
• Low fares may be counter-productive:
– If I can afford to drive when the bus fare is 50 cents
– I can still afford to drive when it’s 25 cents
• And there may be more social stigma attached to
low cost buses, especially when poor quality
Stigma effects not usually modelled/ modellable!


Access/ Egress – Oft Forgotten!
Are these incorporated into your
models?


Better Pedestrian Networks are Possible
Hong Kong’s
Elevated
Walkway
Network

And Hong Kong’s
Mid-Levels
Escalator

Inter-Modal Integration
Fractal approach:
• Begin with strategic routes/ demand analysis
• Then feeder modes/ routes
• Potential congestion issues (transit & traffic)
• Then work downwards to “short distance”
Interchange is undesirable relative to point-to-
point journeys, so minimise inconvenience
Remember viability of interim networks, during
phased development

Superblocks
In Bangkok & Dubai, deriving often from land ownership
or development patterns:
• No through roads or functional road hierarchy
Typically large roads in-between:
• Trunk and local roads at the same time
• Significant frontage interference
• Substantial community severance


Additional Vehicle Mileage
Even a short journey becomes long:
• Walkers may need to find a taxi to cross road
• Drivers may have to drive a long way round


Assessing Extent of Excess Mileage
Combine transport modelling and GIS:
• Trip matrices for interzonal demand
• Compare crow-fly distances (GIS) with skimmed
distance and time
• If good pedestrian links developed, for short
crow-fly trips transfer to walk may be possible
• Also consider walk to/from car parking (if not in-
building)
• Due to lack of pedestrian data capture,
spreadsheet-based estimates may need to be
imposed onto models

Urban Form & Development Density
• Determines realistic potential patronage and
requirements for feeder services
• High density, ribbon development is ideal
• Concentrates demand onto corridors, either:
– Naturally (Hong Kong); or,
– Strong advance planning (Singapore, Curitiba)
• Then work out a realistic transit system


High Density Ribbon Development

• Hong Kong is naturally
suited to high capacity, high
frequency rapid transit
• But this is augmented by
strong pedestrian planning
also!

Cost versus Capacity

Source: Montassar DRAIEF-SYSTRA; World Bank


Uncertainty & Forecast Perspectives
Transport demand forecasts in general are
beset with uncertainty, including:
• Land use/ planning data
• Economic growth
• Population growth
• Vehicle ownership
• Transport policy variables, including tolls and
fares
• Timing of competing & feeder routes

A spread of probability:
• Central Case (for transport planning)
• Low Case (for financing) – remember ramp up!
• High Case (for station design/ system sizing)
Though ideally
use a range of
qualititatively
different
scenarios also



Central Case

Low Case

Maximum Likely
Demand Model

2015 2020 2025 2030


Value-Capture Opportunities
• Property development (Hong Kong model)
• Shops in stations (but don’t congest stations)
• Advertising, but perhaps not too much:
– Livery adverts can interfere with branding
– Obtrusive advertising can be unpopular
• Joint ticketing
– Hong Kong’s “Octopus” smart card is also used for
grocery shopping


Transport Policy
• In the long term anything is possible, but what is
feasible?
• As, in the short term constraints are the norm:
– Political unacceptability of “stick” measures on car
usage
– Election cycles: can de-rail initiatives
– Resistance to reorganisation
– Takes time to reform institutions
– Land resumption/ compensation/ resettlement
• Be skeptical developing base case assumptions

Transport Policy and Modelling
• Heavy infrastructure can be popular with
politicians
– Photo-op: opening a bridge or train station
• Supporting policy measures may be electorally
unpopular, especially in advance
– E.g. road pricing
• Essential to have policy variables in models
– Parking charges, tolls, car ownership & fuel costs, etc
• Demonstrate increased economic and financial
ROI from infrastructure if combined with
transport policies

Thank You

Richard Di Bona
Director, LLA Consultancy Ltd (Hong Kong)
and Independent Consultant
rfdibona@yahoo.com; richard@lla.com.hk

Transport Modelling for Rapid Development

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