Institute for Transport StudiesFACULTY OF EARTH AND ENVIRONMENT

Inaugural Lecture: Professor Simon Shepherd

How can modelling help resolve transport challenges?

Outline

• Signals and bus priority

• HOV lanes

• Road Pricing

• Strategic models – system dynamics

• Greenhouse Gas reduction

• Electric Vehicle take up

• Challenges

Social dilemmas

Dawes (1980)

“Social dilemmas are characterised by two properties:

(a) The social payoff to each individual for defecting behaviour is higher than the payoff for cooperative behaviour

(b) All individuals in society receive a lower payoff if all defect than if all cooperate”

Transport is a form of social dilemma

Early days

Quinn, Montgomery, May 1988

Empirical study of traffic control in Bangkok looking at queue management versus manual (police) control.

• Over-saturated conditions called for new strategies

• Key was to avoid blocking back during green phase

• Automatic signals were seen to be 6% better in terms of delay than police control.

• Happy police could go home half an hour early!

Data collection

All done without “big data”

Iterative process between

data and model

My PhD thesis

Based on Ramp metering approachby Papageorgiou in Paris.

Developed in micro-simulation and testedIn field in Leeds and Turin with two realSystems – SCOOT and SPOT

On Site in Turin

Adapted to grid networks

Gridlock prevention strategy

35% reduction in delay

On the Box

Simon Box -can humans do better than signal controllers?

BBC the One Show 2013

Simple experiments seem to suggest that Humans can do better in simple cases

Fig. 2 The test site of Downtown San Francisco: (a) real network; (b) simulation model; (c) partitioning of the network into 3 reservoirs.

Also saw between 10-40% reduction in travel times – but note problems in 1970s with this in Nottingham zone and collar experiment

Konstantinos Aboudolas , Nikolas Geroliminis. Perimeter and boundary flow control in multi-reservoir heterogeneous networks Transportation Research Part B: Methodological, Volume 55, 2013, 265 - 281

San Francisco 2013

Reflection on thesis

Three future situations:

(1) Network efficiency through traffic responsive signals with auto-gating for over-saturated periods

(2) Improve network efficiency and manage demand with road pricing

(3) Improve network efficiency for public transport with priority at signals whilst creating delays for private car to restrict demand for car use

“It is the author’s belief that concentrating on the short term benefits of strategies restricts the initial scope of strategies to be investigated… ignores long term impacts of chosen strategies”

Shepherd (1994)

Look elsewhere for inspiration “PIG DATA”

PRIMAVERA – first for Leeds

Field trials of bus priority and queue management strategies in Leeds +Turin.

Both systems improved bus times by 10% in Leeds

SPOT also reduced car travel times by 11-30% in Turin

Model under-estimated savings compared to field trials.

SPOT now in over 30 cities in Europe

SCOOT has other methods of gating and bus priority

High Occupancy Lanes

Another first for Leeds

Leeds modelling

Used SATURN network model to explore various scenarios

Diverted about 16% of traffic to other corridors with little effect on total network

We found that 3+ would be better than 2+

Savings in real life were 2-3 minutes along the corridor – confirming model results

We also tested a motorway scheme as per Madrid which gave negative benefits overall

Site visits are important

Salzburg Austria

Lots of sources/sinks in the data from existing model

Site visits essential when modelling

Remember to think about failure mode

Road Pricing

The judgmental approach to cordon design

Most road pricing schemes use cordons

• Designed using professional judgment

But performance depends critically on cordon location

• e.g. London: threefold difference in economic benefits depending on number, location of cordons, screenlines

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Edinburgh judgmental design

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Langrangian curve

Second best – optimisation approach

GA

Competitive environment

Mutation

Look for short cut

Aim to develop a method between judgement and GA based approach but which uses theory

Top 15 Marginal Cost tolls gave high proportion of first best benefits

Could this information be used in designing a closed cordon?

Does it transfer to larger networks?

Display SLA using bandwidths

Short cut performance

Doubles benefit compared to judgemental cordon

Achieved 93% of GA optimal result

Transfers to other networks

Strategic models and system dynamics

CLD example

Simple example

Eggs

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Simple example 2

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Stocks and flows

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Chicken and eggs model

Note :

Populationbirths deaths

birth rate death rate

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Population : Current

Simple population model

PopulationYoung

births aging young

average time in young

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aging middle aging old

average time in middle average time in old

initial popinfant

initial popmiddle

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fox food availability

fox foodrequirements

average fox life

fox consumptionof rabbits

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fox mortalitylookup

fox births fox deaths

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rabbit births

rabbit crowding

carrying capacity

average rabbit liferabbit birth rate

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lookup

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MARS is a Land Use Transport Interaction model

Transport sub-model

Land use residential location sub-model

Land use workplace location sub-model

Rent, Land price, Available land

Accessibility

Spatial distribution residents

Spatial distribution workplaces

Basis of MARS

Means of transport(Use) Car

FUR

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Slow

Core city

Built up structure Transport structure

Car

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* Residing, leisure, etc.

Uses*

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Advanced systems - CITYMobil

Gateshead Tyne and Wear

Cybercar PT feeder

In 2035, introduction of cybercar results in local impacts:

• Car– 8% peak decrease, 30% off peak decrease

• Bus– 36% peak decrease, 50% off peak decrease

• Rail– 193% peak increase, 170% off peak increase

• Slow- 29% peak decrease, 45% off peak decrease

EU Level model

Energy pricesBiofuel supplyEnergy investment

GDPTransport demandTransport energy demand

Energy prices

Integrated assessment of policy scenarios: GHG-TransPoRD modelling approach

Policy scenario

Technology by mode

Investment in R&D and new production

National policies

Urban policies

ASTRAIntegrated economy-transport-environment model

TREMOVEEnvironmental impact model and vehicle fleet model

POLESWorld energy model

MARSUrban land use and transport model

Energy pricesVehicle fleet

composition

Example W&C visionary

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Do-nothing

W&C Vis ionary No Culture Change

W&C Vis ionary with Culture Change

Behavioural impact

Urban policy is limited without some form of behavioural change!

Other Technology scenarios

Note REF case index 104

Max efficiency and market led

EV – Electrification

Hydrogen Fuel Cells take off

Ambitious technology with strong transport policy

Optimistic technology scenarios only get to a 55% reduction target for 2050

Needs the behavioural change with visionary policy to achieve a 75% reduction

Example – uptake of Electric Vehicles

Struben and Sterman (2008)

Sensitivity to word of mouth

Word of mouth between CV drivers is crucial for success – as was marketing

Some of the conclusions

BAU assumptions are crucial!

Subsidies have no real impact in BAU but are crucial in a failing market – but expensive!

If EVs take off then we see significant loss of fuel duty = £10bn p.a. 2050 in most optimistic case.

Revenue preserver per vehicle could range between £300-£650 p.a. by 2050.

A further 9% reduction in emissions from CV gives similar results in terms of CO2 at much lower cost to government.

Supply Chain DisruptionWilson (2007)

Highway maintenanceFallah-Fini et al, (2010)

Load anddeterioration factors

Highwaydeterioration rate

Desiredmaintenance budget

Budget allocated tomaintenance operations

Highwayimprovement rate

Area of the highwayunder distress

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Delay inmaintenance

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budget shortfall

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Desired highwaycondition

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Maintenance Fix

AcceleratedDeterioration

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Approach suggests less costly preventative maintenance rather than more expensive (deferred) corrective maintenance should bring benefits to the system as a whole

Airline business cycles (Liehr et al (2001)

A negative feedback loop with two delays can result in cycles without any growth.

Long delivery times and long aircraft life mixed with need to maximise loading causes cycles even without changes in demand.

Click icon to add chart

Bivona et al 2010 – Bus fleet management example

Comparison of 2 scenarios

1. Reduce all budgetsDon’t replace retiring maintainersReduce training activitiesIncrease planned stoppages formaintenanceOnly replace buses which reach endof life

2. Dispose of old buses nowBut invest in some new buses.Devote 15% time to train rookiesReduce planned stoppages for maintenanceUse out-sourcing

MacMillan et al (2014)

B1 – thought to be dominant loop – more cyclists more injuries – fewer cyclists

Results for various scenarios

Regional cycle networks/ self explaining roads – not enough to overcomethe safety in numbers or changing norm threshold. Arterial segregated bike lanes more effective – note total serious injuries increase (top right) butper cyclist reduced (bottom left).

Future challenges

How should models be used?

Modelling

tools

Top down

process

Long term

strategies

Bottom up

process

Short term

strategies

Signals – short term

HOV lane – more substantial change

But in these cases models were linked with implementation

Road pricing – is this short term?

Strategic/longer term

GHG reduction, future systems, land use etc

Needs collaboration between modeller and decision maker!

Consider feedback between systems and users at different levels

How should models be used (2)?

Social/transportdilemma

s

LeadersDecision makers

Long term

impacts

Sum ofIndividual behaviou

r

Short term

symptoms

A match with social dilemmas

Need to change behaviour of individuals and decision makers

Avoid short termism and fixes that then fail

Change resistance as the Crux -Harich (2010)

Social forces which favour change are inter-linked with those which favour resistance to change

The higher the leverage point the higher the system will resist changing it. (Donella Meadows 1999)

Changing agent goals scores most on leverage point to solve the problem

Taxes and regulations score less well on leverage point analysis

Suggests we need to work on stakeholders and users together

Technology or behaviour change?

Is there a resistance to change here?

And finally

“System dynamics helps us expand the boundaries of our mental models so that we become aware of and take responsibility for the feedbacks created by our decisions”, Sterman (2002).

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Thanks for listening

Any questions?