transport modelling for transport and land use sustainability lessons and challenges from east...

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? Transport Modelling for Transport and Land Use Sustainability:


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!

Transport Modelling for Transport and Land Use Sustainability:


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


During early boom:

• Under-forecast

• Profits/ success

• Yet more projects

Pre-boom:

• Some growth

• Profits/ success

• Leads to more projects

During late boom:

• Over-forecast

• Many failures

• Lots of projects

started, many

fewer projects

completed

After boom:

• Fewer projects

• Less readiness

to cope with

recovery

An economy or a new industry (e.g. High Speed Rail)

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


Mid-Valley

Megamall

Abdullah

Hukum

Station

Kuala

Lumpur

River

250m

(800 feet)

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

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


Uncertainty & Forecast Perspectives 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

Uncertainty & Forecast Perspectives

2015 2020 2025 2030

Central Case

Low Case

Maximum LikelyDemand Model


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

[email protected]; [email protected]


mailto:[email protected]

mailto:[email protected]

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