improving the forecasting of passenger rail demand...•rising fares vs falling fares (reference...

Strictly confidential

Improving the forecasting of passenger rail demand

Oxera Community

Andrew Meaney, Partner

Matthew Shepherd, Senior Consultant

19 November 2015

Overview

Introduction

Department for Transport

National Grid

Insights from behavioural economics

Beyond PDFH?

19 November 2015Strictly confidential 2

19 November 2015Strictly confidential

The Passenger Demand Forecasting Handbook (PDFH)

Overview

• summarises many years of research into the drivers of rail demand and how

these drivers affect the demand for passenger rail travel in Great Britain

• elasticity values are combined with data on flows and with forecasts of the

growth in the drivers of demand over the forecasting period

• regularly updated to include new research commissioned by the

Passenger Demand Forecasting Council (PDFC)

• PDFC brings together the train operating companies, Network Rail, the

Department for Transport, Transport Scotland, the Office of Rail and Road,

Transport for London and the Passenger Transport Executive Group

• used by the DfT, NR, TOCs, franchise bidders and ROSCOs

• the framework for most of the passenger demand forecasts in the GB rail

industry

3

19 November 2015Strictly confidential

PDFH

Forecasting performance

4

0.80

0.85

0.90

0.95

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12

Passen

ger

KM

s—

all f

low

s (

ind

ex

20

05

/06

=1

.00

)

Actuals WebTAG

Department for Transport

National Grid



Moving Britain Ahead

Improving the forecasting of passenger volumes in the rail sectorInsights from other transport modes

November 156

Moving Britain Ahead November 157

Forecasting is inherently uncertain


Forecasting GDP growth


Demand forecasting in different transport modes

Roads Investment Strategy

Rail

Airports Commission


A decade of virtually flat growth in road traffic

-4

-2

0

2

4

6

8

10

12

14

16

18

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Year

High growth in car ownership

Suez crisis

OPEC oil embargo Strong economic growth

Early 1990s recession

Fuel protests

Fuel price spike and recent economic

downturn

Chart TRA0101e: Year-on-year growth in motor vehicle traffic in Great Britain, from 1949

Source: National Road Traffic Survey, Department for Transport


Understanding the trends in road travel and implications for future

road demand

If people continue to move into cities

Improving land-use in cities

Higher costs of car travel in cities

Technology changing how and why we travel

Trends across different groups

Source: Understanding the drivers of road travel: current trends in and factors

behind roads use, January 2015

0

10

20

30

40

50

60

Billi

on

pa

ss

en

ger

kil

om

etr

es

Source: ORR

Beeching

report

1980s

recession

1970s

recession

1990s

recession

Privatisation

2000s recession

Hatfield

crash

Rail travel since 1950


Long term benefits forecasting

Long term (high uncertainty)Complex

Model

Demand

Year


Representing uncertainty

Monte Carlo simulation

Risk-based analysis

‘What if’ scenarios

Scenario planning

Fan charts

Varying appraisal periods


Representing uncertaintyMonte Carlo simulation

Source: The Economic Case for HS2, October 2013


Representing uncertaintyProbabilistic forecasting


Representing uncertaintyScenario planning


Questions

What other possible ways are there for representing and

communicating uncertainty in long term rail demand

forecasting?

To what extent are the concepts of peak car and market

saturation relevant for rail in the long term?

Is there inherently something special about rail that makes it

different to other modes?


Sources

https://www.gov.uk/government/collections/transport-appraisal-and-strategic-modelling-tasm-research-reports#valuing-journey-improvements

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/73143/aviation-demand-forecasting.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/389960/understanding-and-valuing-the-impacts-of-transport-investment-progress-report-2014.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/411471/road-traffic-forecasts-2015.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/395722/understanding-the-drivers-road_travel.pdf

https://www.gov.uk/government/collections/transport-appraisal-and-strategic-modelling-tasm-research-reports#valuing-journey-improvements

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/73143/aviation-demand-forecasting.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/389960/understanding-and-valuing-the-impacts-of-transport-investment-progress-report-2014.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/411471/road-traffic-forecasts-2015.pdf

https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/395722/understanding-the-drivers-road_travel.pdf


Improving the forecasting of passenger volumes in the rail sectorInsights from other transport modes

Future Energy Scenarios 2015

Marcus Stewart

Energy Supply and Demand Manager

The value of scenarios

Annual FES development cycle

The 2015 scenarios

2x2 approach

Rules assumptions and levers

The 2015 scenarios

PESTE assumptions

26

Gone Green demand assumptions

27

Economic Growth Rate Wholesale Electricity Prices Retail Electricity Prices Wholesale Gas Prices Retail Gas Prices Coal Wholesale Prices Power & Gas Demand Assumptions

High High High (cost of L Carbon Gen) Medium (low demand + Carbon £) High (Policy intervention – tax) Low (low demand + Carbon £)

Residential Air Conditioning Uptake Low (Elec £) Time of Use Tariff Uptake High (Elec £, H envir) Smart Metering Deployment High (H innovation, H envir) LED Lighting High (H innovation, Elec £, H Envir) Smart Appliance Deployment High (H innovation, H income) Appliance Efficiency High (H innovation, H envir) Micro generation – PV, micro Wind, hydro

High (H innovation, Elec £)

Micro CHP – on site demand Low (Gas £, H envir) CHP – on site demand H biomass, L Gas (Elec £, H envir) Electric Vehicle Deployment High (desirable product, H envir) Gas Vehicle Deployment High (HGVs – economic, no

altrenatives) Public Transport Decarbonisation High (air quality, H envir) Low Carbon Heating Appliances High (Elec £ Vs Gas £, H envir) Loft Insulation Deployment High (L cost, policy driven) Cavity Wall Insulation Deployment High (L cost, policy driven) Solid Wall Insulation Deployment High (H cost, policy driven) New Building Regulations High (policy driven) District Heating Deployment High (Policy driven) Home Energy Management Systems (HEMS)

High (L cost, H innovation, desirable product)

Bottom up development of scenarios

Changing electricity

consumption patterns

Annual electricity consumption

at the meter TWh/yr

Gauging historic performance

• The following chart shows historic demand from 1990 (Note the pre 2004 data is E&W uplifted to GB demand). It also show National Grid forecasts from 2005, 2008, 2011 and 2013. These are significant forecasts as they attempted to forecast the effects of the 2088/09 recession and the then expected “bounce back”:

30

Early 1990s

recession and

bounce back

Flattening

demand due to

energy efficiency

and price factors

Between 1993 and 2002

demand grew every year

on average 1.5%

2008/09

recession

Anticipated

bounce

backs

31

Thank you – please get in touch

[email protected]

http://fes.nationalgrid.com/

Future Energy Scenarios 2015

mailto:[email protected]

http://fes.nationalgrid.com/

Behavioural economics: implicationsfor rail demand

Oxera Community

Leon Fields, Senior Consultant

Pantelis Solomon, Consultant

19 November 2015


Overview

• context

• introduction to behavioural economics

• implications for rail demand forecasting

• applications to demand forecasting

19 November 2015 33Strictly confidential

Psychology and economics

19 November 2015 34

Preferences

Decision-making

Choice

2. Beliefs,

learning and

memory

Internal information

3. Thinking

and

reasoning

4. Behaviour1. Perception

External information

Source: Niels, G., Van Dijk, R. and Fields, L. (2013), ‘Behavioural economics and its impact on competition policy: A

practical assessment’, Competition Law Journal, 3; and Oxera (2013), ‘Behavioural economics and its impact on

competition policy: a practical assessment with illustrative examples from financial services’, prepared for the

Netherlands Authority for Consumers and Markets, May.


Assumptions of traditional economics

19 November 2015 35

Context-independent

Self-interested

Perfect recall of past

Objective probability

Time-consistent Objective about

future risks

Formal reasoning

Fully rational

Use all available

information

Follow optimal path

Resist short-term

urges

Preferences

Decision-making

Choice

2. Beliefs,

learning and

memory


3. Thinking

and

reasoning



Do not procrastinate



19 November 2015 36

Context-dependent

(framing matters)

May be ‘other-regarding’

Imperfect (and

biased) recall

Subjective

probability

Potentially

time-inconsistent

Overconfidence,

over-optimism,

over-extrapolation

Both formal and

informal reasoning

Both automatic and

reflective

Use heuristics

(rules of thumb)

Part-optimising; part-

satisfying; part-inert

Conflict between

short-term urges and

long-term plans

Preferences

Decision-making

Choice

2. Beliefs,

learning and

memory


3. Thinking

and

reasoning



Potential for mistakes


The three key features of behavioural economics

1. Preferences depend on context (reference-dependence)

• relative payoffs matter

• people dislike losses more than they like gains (loss aversion)

• social comparisons (e.g. status, fairness)

2. Decision-making involves taking shortcuts

• ‘instinct’; ‘gut feel’

• heuristics uses selected information or part-calculations

• interaction with beliefs

• ‘availability’—recalling experience or recent events

• ‘representative’—‘how much A looks like B’

• following the herd

3. Intention versus action


Two-systems approach to decision-making

19 November 2015 38

System I (automatic) System II (reflective)

Uncontrolled Controlled

Effortless Effortful

Associative Deductive

Fast Slow

Unconscious (lack of self-awareness) Conscious (self-aware)

Skilled (pre-learned) Rule-following

Source: Based on Thaler, R. and Sunstein, C. (2008), Nudge: Improving decisions about health, wealth, and happiness,

Yale University Press. See also the dual-process model described in Figure 1 of Kahneman, D. (2002), ‘Maps of

bounded rationality: a perspective on intuitive judgment and choice’, Nobel Prize Lecture, Stockholm, 8 December.

Automatic Heuristics Full deliberationversus


What biases can result?

19 November 2015 39

1. Framing affects preferences

• loss aversion sensitivity to information frame

• can lead to status quo bias, default bias and inertia

2. Instinct and heuristics can be wrong

• representativeness bias errors

• availability bias; optimism bias; confirmation bias

• herd behaviour may not be rational

3. Too much information is as bad as too little

4. ‘Now versus later’ decisions can be difficult

• I may not know what is in my long-term best interests

• I may not act in my best interests (present bias)


Caution: biases are hypotheses

The importance of empirical techniques

• consumer biases

• hypotheses; and a matter of degree!

• testing a hypothesis with actual data; but where do we get the data

from? Four main approaches:

• surveys (stated preference)

• lab experiments

• field experiments

• natural experiments (revealed preference)

• randomised controlled trials (RCTs)

19 November 2015 40

All the approaches have their respective pros and cons

care is required


Demand forecasting elements

Demand estimation, interventions, demand simulation

19 November 2015 41

Estimation Interventions Simulation

Estimating elasticities

• exogenous variables

(e.g. GDP, car ownership)

• endogenous variables

(e.g. fares, cross-price,

service frequency, reliability)

• construction of value of

time, generalised cost

• by segment (e.g. peak v off-

peak; business v leisure)

Methodologies

• stated preference (e.g. logit)

• revealed preference

Decision variables

• fares

• frequency

• quality

• reliability

• punctuality

Forecasting demand

• baseline demand (driven by

changes in exogenous

factors only)

• baseline demand +

interventions (e.g. changes

in price, frequency)

• by segment

• sensitivity analysis

(e.g. monte carlo)

Methodologies

• static models (long-term)

• dynamic models (annual)


Behavioural economics

Implications for rail demand (I)

• disaggregation of elasticities• passenger responses depend on context

• consumer response to price changes is context-dependent• rising fares vs falling fares (reference point)

• nominal vs real changes in prices (‘money illusion’)

• value-of-time is context-dependent• advance-purchase (system II) versus turn-up (system I) may imply different

elasticities

• once a journey decision has been made, people may be prepared to tolerate

unforeseen delays (shrouding, loss aversion)

• reliability and planned vs unplanned delays (ambiguity aversion)

• sensitivity to journey time will depend on quality of service (e.g. wi-fi)

• beliefs (even if incorrect) matter• e.g. beliefs regarding car vs rail punctuality



Implications for rail demand (II)

• heuristics are often used to make journey choices

• passengers do not weigh up all route and ticketing options; they often

choose the most familiar option

• recent important events (e.g. large delays, accidents) can have a

disproportional impact on beliefs about rail punctuality (availability

heuristic)

• information provision and salience can have significant effects on

demand

• behavioural biases can cause demand to be inelastic

• people adopt habits that can be hard to change—e.g. a particular

route (status quo bias)

• consumers may be reluctant to switch away from car because they

have already made the expense (sunk cost fallacy)



Implications for elasticity estimation?

• stated preference methods may only uncover beliefs

• preferences and decisions are context-dependent (e.g. do not capture

the ‘in the moment’ effect)

• difference between stated intention and actual behaviour

• no real time value or financial consequences

• potential framing effects of questions

• revealed-preference methods usually only uncover behaviour

• noise in what drove the decision (e.g. new service or external factors)

• decisions versus beliefs versus preferences

• a potential solution?

• laboratory experiments?

• randomised controlled trials?



Implications for interventions?

• information provision

• framing, salience, real-time

• instilling loyalty and reducing perceived search

• smart ticketing

• quality enhancements

• free Wi-Fi and sensitivity to journey time

• customer segmentation

• advance-purchase versus turn-up customers

• warning: sophisticated versus naïve consumers and outcomes

• testing interventions (once again…)

• laboratory experiments?

• randomised controlled trials?


How do customers respond to fare changes?

Real vs nominal changes

19 November 2015 46

• standard PDFH model assumes that passengers

respond to real change in rail fares and GDP

• behavioural economics suggests that consumers

tend to think in nominal terms—‘money illusion’

• framing (a wage rise of 1% in a zero inflation

environment vs a 2% raise when inflation is 1%)

• nominal representations more appealing and

salient

• appraisal of different options depends on

reference point

• use GB passenger rail demand data to estimate

consumer demand using nominal fares and

nominal GDP

• forecast performs significantly better with

nominal as opposed to real demand drivers

(see figure)

12

17

22

27

32

37

199

8Q

1

200

0Q

1

200

2Q

1

200

4Q

1

200

6Q

1

200

8Q

1

201

0Q

1

201

2Q

1

201

4Q

1

Pa

sse

nge

r jo

urn

eys (

m)

Nominal ECM forecast Real ECM forecast

Outturn

Nominal vs real forecasts

Source: Oxera (2015), ‘Do passengers respond to fare changes? Does inflation matter?’.


How fast do customers respond to fare changes?

Short term vs long term

19 November 2015 47

• traditional economics assumes that consumers

have all the information they need when making

decisions

• however, changes in demand drivers may take

time to take effect

• information lags and processing costs

• habits, status quo bias

• avoidance of cognitive effort and loss

aversion

• use GB passenger rail demand data to estimate

how long it takes for consumers to respond to

price changes

• a change in one of the demand drivers will

cause demand to adjust back to its

equilibrium value by 62% in the next period

-7

-6

-5

-4

-3

-2

-1

0

t+1 t+2 t+3 t+4 t+5

Pe

rce

nta

ge

ch

an

ge

in

de

ma

nd

Response from 10% fares

increase at period t

Source: Oxera (2013), ‘Responding slowly to change? Passenger rail demand in Great Britain’.


Implications for demand forecasting

Bias Implications for demand forecasting

• present bias

• status quo bias

Stated preference surveys may overestimate

elasticity by placing respondents in an ‘advance-

purchase’ mode

• reference dependence

• loss aversion

• framing

Demand forecasting improves if it includes variables

that are relevant to consumer decision-making

(e.g. nominal vs real prices)

• sunk cost fallacy Large sunk cost expense may cause reluctance to

switch across travel modes (e.g. car vs train travel)

• heuristics Demand is based on perceptions (e.g. train

punctuality); response to service improvements may

take time; framing of information matters


Contact:

Leon Fields

+44 (0) 1865 253014

[email protected]

www.oxera.comFollow us on Twitter @OxeraConsultingOxera Consulting LLP is a limited liability partnership registered in England

No. OC392464, registered office: Park Central, 40/41 Park End Street,

Oxford, OX1 1JD, UK. The Brussels office, trading as Oxera Brussels, is

registered in Belgium, SETR Oxera Consulting Limited 0883 432 547,

registered office: Stephanie Square Centre, Avenue Louise 65, Box 11,

1050 Brussels, Belgium. Oxera Consulting GmbH is registered in

Germany, no. HRB 148781 B (Local Court of Charlottenburg), registered

office: Torstraße 138, Berlin 10119, Germany.

Although every effort has been made to ensure the accuracy of the

material and the integrity of the analysis presented herein, the Company

accepts no liability for any actions taken on the basis of its contents. No

Oxera entity is either authorised or regulated by the Financial Conduct

Authority or the Prudential Regulation Authority. Anyone considering a

specific investment should consult their own broker or other investment

adviser. We accept no liability for any specific investment decision, which

must be at the investor’s own risk.

© Oxera, 2015. All rights reserved. Except for the quotation of short

passages for the purposes of criticism or review, no part may be used or

reproduced without permission.


Behavioural biases (I)

Bias Description Example

Present bias People often have a preference for

immediate gratification, and overvalue

the present over the future. As the

consumer can regret such choices later,

their preferences are ‘time-inconsistent’.

Consumers may over-emphasise short-term or upfront discounts over

future savings. This means that, when assessing, they may be more likely

to choose tariffs or products that offer cash-back rewards or short-term

special offers (e.g. first three months free).

Reference-

dependence and

loss aversion

Consumers may not assess outcomes in

their own right, but rather as gains and

losses relative to a reference point.

Psychologically, losses are felt roughly

twice as much as gains of the same

magnitude. As a result, many consumers

under-weigh gains and over-weigh

losses.

Consumers may overestimate the potential costs of switching and

underestimate the potential benefits, making them less likely to be engaged

and active.

Consumers may estimate benefits with reference to their current bill (e.g.

being willing to switch to save £10 in relation to a £25 phone bill, but not in

relation to a £100 energy bill, even though the saving is the same).

Status quo bias People prefer their current option. A tendency to select default options even when the effort of making a

different decision is low. This means that consumers with automatically

renewable contracts are more likely to let their contracts roll over, even

where they can exit these contracts easily.

Regret and other

emotions

Many people act to avoid ambiguity or

stress. Their choice can also be distorted

by temporary strong emotions (e.g. fear).

Buying expensive insurance for peace of mind, even though you are very

unlikely to need it.

Choice of hospital being distorted by strong emotions (e.g. fear, shock).

19 November 2015 50

Source: UK Regulators Network (2014), ‘Consumer Engagement and Switching’.


Behavioural biases (II)

Bias Description Example

Over-extrapolation People may make predictions on the

basis of only a few observations, when

these observations are not

representative.

Consumers may use just a few years of past returns as a basis for judging

future returns and making investment decisions, without considering the

extent to which past returns reflect chance and particular circumstances.

Projection bias People may expect their current tastes

and preferences to continue in the future,

and underestimate the possibility of

change.

Consumers may tie themselves into long mobile phone contracts,

expecting their preferences and financial circumstances to remain the

same in the future. Consumers may not anticipate that they will have

difficulty controlling their future credit card spending, or overestimate the

amount they expect to save.

Framing, salience

and limited

attention

Even when the economic benefits of

particular choices are identical,

consumers may make different choices

depending on how the decision problem

is framed. Attention is also drawn to

particularly salient aspects of a situation

that can have a marked influence on

choice.

Consumers might overestimate the value of a packaged bank account or

bundle of communications services because they are presented in an

attractive way that highlights benefits and under-emphasises charges.

Consumers may not pay attention to disclosure letters, as important

information is buried in heavy paragraphs of text or hidden in small print.

Decision-making

rules of thumb

Consumers can simplify complex

decision problems by adopting specific

rules of thumb (heuristics).

When choosing from a wide range of options, consumers may choose the

most familiar, well-known brands (for instance) and avoid the ambiguous or

uncertain, or pick the first option on a list.

When choosing from a list of hospitals or providers displayed on a

comparison website, consumers choose the first option or one that is

familiar to them, such as their local hospital or a well-known brand.

19 November 2015 51

Source: UK Regulators Network (2014), ‘Consumer Engagement and Switching’.



Additional factors

Oxera Community

Andrew Meaney, Partner

Matthew Shepherd, Senior Consultant

19 November 2015

Additional factors—technology


• wi-fi, real-time information, Uber, driverless or electric cars…

• what impact does changing technology have on passenger demand?

• changes the way people value travel time (differently between different modes

and no mode)

• changes (monetary) costs

• changing costs are accounted for in PDFH (at least in theory)

• for example, through car journey time or cost metrics

• changing values of time likely to have two effects:

• different elasticities

• different levels of generalised cost

• elasticity increases as alternative options (including do not travel) become more

attractive (video-conferencing technology, etc.)

• increased productivity means value of travel time declines, implying demand

increases even if travel time remains constant

Additional factors—employment/population


• growth in public sector and corporate/financial services stimulates rail travel,

as these sectors tend to favour central locations that are more accessible by

rail1

• accounting for local changes in employment mix and level may improve

demand forecasts

• local (city centre) data is available from the ONS to provide a benchmark

• density of activity in urban centres, combined with differentials in rent and

house prices, may contribute to commuting patterns

1 MVA (2009), ‘Regional flows: regional rail demand elasticities’, September.

Additional factors—factors that affect car usage


• car cost and car journey time captured in the PDFH framework

• punctuality of trains is captured, but punctuality/reliability of cars is not

• data potentially available from a number of sources

• Google?

• Traffic Master?

• enables us to build a picture of congestion on the road network at a very

granular level

• what elasticity to use?

• little research on the effects of road congestion on rail demand

• forecasts of congestion

• the DfT’s NTM or Highways England

Additional factors—behavioural changes


• 1995: 20-year-old men drove

on average 8,300 miles per

year

• 2005: 20-year-old men drove

on average 6,400 miles per

year

Suggests a change in the

relationship of young men with

their car?

Implies an increase in the car

cost elasticity over time as cars

become more optional?

Source: RAC Foundation (2012), ‘On the Move – making sense of car

and train travel trends in Britain’.

19 November

2015


Improving forecast accuracy

Where next?

• status quo

• well-understood framework, but does not forecast well

• many bidders in franchises amend the elasticities routinely

• some omitted factors

• future research?

• big issues

• who drives the agenda?

• what are the alternatives?

• randomised trials (smartcards)

• experiments (smartcards)

• machine learning using less aggregated data

• household-based approach

57

Contact:Andrew Meaney+44 (0) 1865 [email protected]

www.oxera.comFollow us on Twitter @OxeraConsultingOxera Consulting LLP is a limited liability partnership registered in England

No. OC392464, registered office: Park Central, 40/41 Park End Street,

Oxford, OX1 1JD, UK. The Brussels office, trading as Oxera Brussels, is

registered in Belgium, SETR Oxera Consulting Limited 0883 432 547,

registered office: Stephanie Square Centre, Avenue Louise 65, Box 11,

1050 Brussels, Belgium. Oxera Consulting GmbH is registered in

Germany, no. HRB 148781 B (Local Court of Charlottenburg), registered

office: Torstraße 138, Berlin 10119, Germany.

Although every effort has been made to ensure the accuracy of the

material and the integrity of the analysis presented herein, the Company

accepts no liability for any actions taken on the basis of its contents. No

Oxera entity is either authorised or regulated by the Financial Conduct

Authority or the Prudential Regulation Authority. Anyone considering a

specific investment should consult their own broker or other investment

adviser. We accept no liability for any specific investment decision, which

must be at the investor’s own risk.

© Oxera, 2015. All rights reserved. Except for the quotation of short

passages for the purposes of criticism or review, no part may be used or

reproduced without permission.Strictly confidential

improving the forecasting of passenger rail demand...•rising fares vs falling fares (reference...

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