Measuring and Valuing
Convenience and Service Quality
A review of global practices and challenges
from the public transport sector

16

Discussion Paper 2013 • 16

Richard Anderson, Benjamin
Condry, Nicholas Findlay,
Ruben Brage-Ardao, Haojie Li
Imperial College,
London, United Kingdom

Measuring and Valuing Convenience
A Review of Global Practices and Challenges from the
Public Transport Sector

Discussion Paper No. 2013-16

Prepared for the Roundtable on Valuing Convenience in Public Transport(12-13 September 2013, Paris)

Richard ANDERSON, Benjamin CONDRY, Nicholas FINDLAY,
Ruben BRAGE-ARDAO, Haojie LI
Imperial College London, United Kingdom

RevisedDecember 2013

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

ABSTRACTThe term “convenience” is often used in relation to transport; the assumption being thata “convenient” service will be more attractive. Hence those responsible for the
specification and supply of public transport have an interest in optimising the level ofconvenience provided to passengers. However, what makes a service convenient isgenerally neither well defined, nor understood. Basic attributes such as network size,
service frequency, journey time and pricing alone cannot explain passenger demand for
public transport modes. Other factors of convenience play a key role in influencing
demand and mode choice but they are often more complex and harder to define,measure and value. In this paper we first consider the meaning of the term conveniencein transport, and urban public transport in particular, including the attributes which it
encompasses.
We argue that the good measurement of public transport convenience and service quality
is a pre-requisite to its valuation and ensuring more optimal policy decisions andmanagement actions to maximise convenience and hence demand. We focus on theurban public transport operator and its measurement of convenience by reviewing thepractical experience gained from over 20 years of international benchmarking with morethan 50 metro, bus and suburban rail operators in large cities around the world.
Specifically, we review the current standards and practices from the urban railway
industry in measuring convenience and provide examples of how such performance inmetro operations varies globally. It is demonstrated that current practice in many cities
remains too operationally based, despite there being an opportunity for much morecustomer focused measures using the greatly increased data availability from newtechnologies.
The experience of the UK railway industry in valuing convenience related attributes isdiscussed. Here, a common framework for demand forecasting has been developed
combining service quality and convenience measures with other service attributes toeffectively measure the “attractiveness” of the service to customers. The paper concludes
by considering the implications and opportunities for public transport operators,
authorities and regulators worldwide in better measuring, valuing and managing public
transport convenience in order to better meet mobility needs.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITYTABLE OF CONTENTS

1. INTRODUCTION ........................................................................................ 7
1.1
1.2

Defining Convenience in Public Transport ............................................... 8
Public Transport Convenience and Service Quality ................................... 9

2. MEASURING CONVENIENCE ..................................................................... 11
2.1
2.2
2.3
2.4

European Standards EN13816 and EN15140 ......................................... 11
Recent Experiences and Practices from the Urban Rail Industry .............. 14
Case Studies of Worldwide Practice in Measuring Convenienceand Service Quality ........................................................................... 18
Benchmarking Public Transport Convenience and Service Quality ............ 22

3. VALUING CONVENIENCE.......................................................................... 31
3.1
3.2
3.3
3.4
3.5
3.6

Measuring Convenience by Looking at the Impacts on Demand .................... 31
The British Rail Industry Passenger Demand Forecasting Framework .......... 31
PDFH Methodology .................................................................................................... 32
Examples from the PDFH ......................................................................................... 33
Understanding the Relative Impact of Convenience Attributes ................. 36
Applicability of the PDFH Methodology to Other Public Transport Systems 37

4. CONCLUSIONS ......................................................................................... 38
ACKNOWLEDGEMENTS.................................................................................. 41
REFERENCES ............................................................................................... 41
APPENDIX A - ............................................................................................. 44

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

1. INTRODUCTION

In this discussion paper we review the practical experience of measuring and valuing
convenience in two transport sectors: the operators of public transport systems in urban
areas, and the railway industry in Great Britain.
In this section we firstly define convenience and service quality in the context of theroundtable. Section 2 of the paper then explores the practices that metro, bus and rail
operators from around the world have adopted to measure the service they provide froma customer focused perspective. We draw on the practical experience of over 50 metro,bus and suburban rail operators in large cities. These operators participate in aninternational benchmarking programme which has been led by Imperial College for nearly
20 years and which measures and evaluates the comparative performance of public
transport. International standards that define the attributes of convenience and service
quality are reviewed, and we present the most common and innovative measures usedby metro operators in particular. It should be emphasised that our experience is mainly
based on observations from public transport operators, rather than their authorities.
The challenges and outcomes of benchmarking convenience between metro operators arealso discussed in Section 2, with some recent empirical research that has sought toquantify the responsiveness of demand to service quality and in turn, some the factors
which also affect convenience in metros. We explain how metros and their authorities
might better measure some convenience attributes, giving examples from London, Hong
Kong and Paris. In recent years, public transport operators have been gifted withsignificantly better data, for example from ticketing and signalling systems that offer
opportunities for better service quality measurement.
In Section 3 we examine the specific experience of the rail industry in Great Britain in
attempting to value convenience related attributes and consider whether any of theapproaches could be better adopted by urban transport operators. This framework isbased on a variation of standard transport planning principles and economic theory, but
has been specifically adapted and calibrated for the industry through an on-going
research programme conducted over more than three decades. In addition to measuring
and valuing the core variables such as journey time, frequency, interchange and fares,“softer” factors such as the provision of information, rolling stock quality and passenger
information are also captured within a common metric. All major industry parties,
including operators, government, transport authorities and the regulator sign up to thiscommon framework and set of values, meaning the business cases can be developed
using mutually agreed parameters.
Finally, in Section 4, we draw conclusions based on lessons from the experience of thetwo sectors studied on why convenience matters for public transport and how it might bebetter measured and valued.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

1.1

Defining Convenience in Public Transport

The term “convenience” is often used in relation to transport: It is generally assumed
that a convenient service is more desirable and may therefore lead to increased demand,
both by attracting additional customers away from alternatives and generating new trips.
Hence convenience matters for public transport; to be attractive it must meet the needsof users, catering for ever rising expectations in an environment of increasing
competition. This makes convenience vital in helping to ensure long term viability of
public transport, through increases in demand, revenue, public support and acceptability.
Therefore ‘convenient public transport’ is important to define and understand, particularly
when devising strategies and policies to encourage mode shift. However, what makes aservice convenient is not always well understood, nor is there a universal definition ofwhich attributes come under the definition of convenience. Crockett and Hounsell (2005)
noted that convenience is an ambiguous concept in public transport, often showing ahigh degree of overlap with other service attributes.
The Oxford English Dictionary defines convenience and convenient as:“convenience [noun].. the state of being able to proceed with something without
difficulty…the quality of being useful, easy, or suitable for someone…a thing that
contributes to an easy and effortless way of life..”“convenient [adjective]…fitting in well with a person’s needs, activities, and plans..
involving little trouble or effort…helpfully placed or occurring..”
Thereby to be useful and suitable, the public transport service needs to be available totake passengers where they want to go at the time they wish to travel. This is facilitatedby access and egress via helpfully placed and available (occurring) boarding and alighting
points, and a network, timetable and operating hours fitting with activities which give riseto travel demand. A suitable service must also be reliable, punctual, and provide anappropriate level of comfort.
For many, the car, offering door-to-door mobility, symbolises the very essence of
convenient travel, allowing the traveller to proceed without difficulty. Huey and Everett(1996) found convenience to be the greatest benefit of car use. Yet most public transport
trips involve multiple journey stages or intermodal changes (Wardman et al, 1997). To
be easy to use, a public transport service needs to offer effective integration as well asfeatures including suitable information and appropriate ticketing.
Fundamentally, in transport terms proceeding without difficulty, or with little effort canbe assumed to be synonymous with attributes of generalised cost and time,encompassing all dimensions of access (Brons et al. 2009), egress (Wardman et al,2007), travel time (Wardman, 2011), wait time, congestion, as well as service-specificfactors including measurable and more subjective (Eboli and Mazzulla, 2011) service
quality attributes (Whelan and Johnson, 2004; Litman, 2008).

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

Convenience can be related to all stages of the trip, from initial planning to arrival at thedestination. Berry et al. (2002) conceptualise service convenience as consumers’ timeand effort perceptions relating to the purchase or use of a service and defined five typesof convenience – decision, access, transaction, benefit and post-benefit. They explain
how benefits of convenience and burdens of inconvenience relate to saving or wastingtime and/or effort and argue that “A firm’s […] operations can dramatically influence
consumers’ perceptions of service convenience.” Poor public transport service quality likecrowding (Waldman and Whelan, 2011) might therefore be assumed to impact negatively
on the perception of convenience.
Crockett et al. found that “it is possible to consider convenience in rail travel as anembodiment of four themes: access/egress, station facilities/environment, frequency ofservice/scheduling and interchange between train services”. They also note that there isa considerable overlap between a broader definition of convenience and other travel
factors including reliability, which they sought to differentiate in the context of rail travel.
Others have considered a narrower view of convenience as independent of time,
reliability and comfort (Noland and Kunreuther, 1995), yet analogous with the door todoor convenience of car travel. Earlier research considered convenience solely as afunction of the number of stages within a trip (Watson, 1972). Litman (2008) refers to‘Comfort and Convenience’ as more qualitative factors, possibly implying that these are
separate from journey time components.
For the purpose of this paper we assume that convenience relates to the whole journey,
including access and egress, and also other subjective factors such as perceived value(Lai and Chen, 2011, and de Ona et al, 2013). Therefore, all dimensions of public
transport users’ generalised travel time equation are considered, including but not
restricted to, time, interchange and “softer” quality attributes. Monetary cost (fare) is
excluded.
1.2

Public Transport Convenience and Service Quality

Although convenience is not necessarily synonymous with service quality, for simplicity,
we use the term “convenience” in this paper to encapsulate both the wider scope of
convenience as well as attributes of service quality. This is consistent with the scope ofservice quality defined in the two European Standards created to help define (EN13816,
2002) and measure (EN15140) service quality, which cover all attributes of the public
transport service, as detailed in Table 1.1 and discussed further in section 2.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

Table 1.1 Eight attributes of service quality as defined by EN 13816 (adapted)
Availability

Extent of the service offered in terms of geography, time (operating
hours) frequency and transport mode

Accessibility

Access and egress to/from the public transport system includinginterface with other transport modes

Information

Systematic provision of knowledge about the system to assist theplanning and execution journeys

Time

Aspects of time relevant to the planning and execution of passenger
and train journeys, including journey time, punctuality and reliability

Customer Care

Service elements introduced to match the requirements of any
individual customer, including staff reaction to customer complaints andkindness of staff

ComfortIncluding crowding, cleanliness and service elements introduced for the
purpose of making public transport journeys as comfortable as isreasonably possible.

Security

Offering safety and security to customers for the whole journey

Environmental Impact

Effect on the environment resulting from the provision of a public
transport service (pollution and noise)(Source: Adapted from: European Committee for Standardisation, 2002)

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

2. MEASURING CONVENIENCE

Service quality and convenience is of increasing importance to all businesses, including
public transport organisations. It influences customer satisfaction, passenger demand,investment decisions and revenue. As described above, convenience is difficult to define
and encapsulates a broad range of attributes.
However, to deliver an appropriate level of convenience, and hence to make the service
attractive to passengers, operators and authorities must ensure that the quality delivered
meets the needs and expectations of both existing and potential users. To achieve this, aclear understanding of travel behaviour and consumer needs and expectations isrequired, together with an accurate quantification of the strengths and weaknesses of theservice. Therefore, it is essential to measure the quality of the service provided so that
improvements aimed at enhancing user satisfaction and increasing market share can bemost effectively targeted. However, developing accurate measures is a complex task,
since it involves understanding perceptions and attitudes.
Availability of the service and the provision of adequate capacity are at the forefront of
convenience, particularly in large, dense urban areas. High level measures can include
frequency, operating hours, network structure, reliability (ensuring that passengers
arrive at their destination on time) and comfort (including crowding). Ensuring that public
transport is accessible to all, especially for people with special needs is vital toencouraging public transport use. Accessibility can be measured in terms of ease ofgetting to and from stops, ease of boarding and alighting and of obtaining a ticket.
In this section of the paper we firstly review the European Standards for public passenger
transport service quality, EN13816 and EN15140, in the context of the framework they
provide to measure convenience in public transport. We then summarise the different
convenience and service quality measures adopted by metro, bus and rail operators fromaround the world, and assess these against the criteria in the European Standards.
2.1

European Standards EN13816 and EN15140

The European Standard EN13816 provides a useful theoretical and practical frameworkfor organisations to define and set convenience targets. It offers guidance on
methodology for setting targets and measuring quality, and provides a comprehensive
list of areas that together make up the service quality delivered to customers. The list ofareas can help organisations ensure that they are considering the whole customer
experience. For example, whilst aspects of journey time may be the most obvious
aspects of convenience, customers are also affected by issues such as ease of obtaining
information, and operating hours. The eight aspects of customer service quality as
defined by EN13816 were presented previously in Table 1.1.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

The EN13816 Quality Loop
The diagram below illustrates the Quality Loop set out in EN13816, which defines a clear
process to ensure that the service provided can meet the needs of existing and potential
users most effectively, and therefore be as convenient as possible.
Figure 1.1 EN13816 Service Quality Loop

(Source: European Committee for Standardisation, 2002)

The Quality Loop links the perspective of the users (“Service Beneficiaries” 1) with that of
the operators and authorities (“Service Partners”) by setting out the steps by which thelatter can most effectively meet the needs of the former, thus maximising theconvenience of the service. The aim of the public transport provider should be to‘minimise the gap’ between the service quality sought, targeted, delivered and perceived.
The first stage, “service quality sought” represents the ideal service to meet the needs ofusers – this could be considered as the “convenience maximising” service. It is vital thattransport providers understand the needs of users and, ideally, attempt to deliver a
service which best matches these needs – therefore the “service quality targeted” should
be as close as possible to the “service quality sought”.
Of course the service delivered will not normally fully match the level targeted – e.g.
there will be delays. To understand how well the service actually delivered matches thetargeted level, a comprehensive process for the measurement of performance isrequired. These must be objective measures; they are not equivalent to satisfaction.
Service quality perceived by customers will tend to reflect the service actually delivered,hence satisfaction measurement relates the level of service perceived with the level of
1.

(It should be noted that “Service Beneficiaries” in the quality loop includes the community as well as
customers – the ideal service needs to balance the needs of both, including in relation to aspects
such as environmental impacts and cost).

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

quality sought, the final link in the Quality Loop – this measures how well the service
meets customers’ expectations. Since satisfaction relates quality perceived with quality
sought, there is no direct link between satisfaction and the planned service (targeted
quality). Therefore it is possible to have low satisfaction scores even where the level ofservice provided exactly matches that targeted – i.e. everything works according to plan.
Hence the quality loop illustrates the distinction between customer satisfaction which is asubjective measure of success, and performance measurement, which is an objective
measure of success. Both types of measurement are required to understand how well theorganisation is serving the customer.
EN13816 and EN15140 Guidance on Measuring Convenience
According to EN13816, any good service quality definition should be relevant, specific,
and customer focused. Decisions about what to monitor should be based on customer
priorities: each measurement should have a specific purpose. Measurements must be
relevant to the goal of improving service quality as measuring for measurement’s sake isa waste of resources. Definitions of success are needed; these should be grounded inintelligence about what level of service will please customers.
The related European Standard, EN15140 (Public passenger transport - Basicrequirements and recommendations for systems that measure delivered service quality)
provides recommendations on service quality measurement in the framework of
EN13816. The focus is on advising operators and authorities how to set measurement
processes, formulate specific indicators and set clearly defined targets.
Public transport providers must understand what is important to the customer. For
example, EN15140 recommends that customer expectation surveys are conducted inorder to determine and weight attributes to reflect what is most relevant to customers. It
advises that the design of measurement systems should balance the customers’ view
with the use of the system as an internal management tool to achieve quality targets.
Crucially, a key instruction of EN15140 is that “the level of achievement shall be
expressed, where appropriate, as a ratio of passengers affected”. The standard
recommends that operators split peak and off-peak measurements, giving greater weightof importance to services in the peak period (where more passengers are affected).
EN15140 advises that when quality indicators are used in a contractual relationshipbetween a transport authority and an operator, the measurement processes should beunderstood and agreed on by the contractual partners with clear allocations ofresponsibility in the contract.

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2.2

Recent Experiences and Practices from the Urban Rail Industry

The purpose of this section of the paper is to summarise the convenience measures
adopted by metro, bus and rail operators from around the world. Benchmarking researchby Imperial College London for the CoMET and Nova benchmarking groups shows that
metro operators have adopted a broad range of attributes to measure and understandthe service they provide to customers. This allows operators and authorities to evaluate
the service from a more customer focused perspective, although as we shall see, many
are more customer-oriented than others. Since the data comes under a strict
confidentially agreement for the groups, graphs and figures shown are anonymised asfollows:•

Am – North and South America•

Eu – Europe•

As - Asia

We necessarily focus on the experience of the urban metro groups who have agreed toshare knowledge with OECD. At the time of this paper, the CoMET and Nova groups
comprised 31 metros from North and South America, Europe and Asia (See Appendix A).
The groups were initiated in 1994 and are focused on using benchmarking to identify andshare best practices in operations and management. In 2012, CoMET and Nova metroscombined carried over 22 billion passengers, therefore their contribution to theeconomies of major world cities is considerable and optimising the generalised cost of
travel is essential. Interest in understanding the measurement of convenience isunderstandably high and the delivery of higher levels of quality is increasingly being seen
as a key to an operator’s effort to position themselves more effectively in the market.
As part of the CoMET and Nova process and using publicly available data, twenty-one
metros responded to a survey in which they were asked to report the specific Service
Quality and Customer Satisfaction measures used in their metro. The most common
performance measures reported are shown in Table 2.1 below.
Table 2.1 Top 10 Service Quality Indicators Measured by CoMET and Nova Metros

Top 10 Most Frequently Measured Service Quality Indicators

Proportion of (%) Metros

Escalator And Lift Availability

76%

Train Delay: Measured At 2 And 5 Minute Delay Thresholds

48%

Ticketing Service Availabiltiy/Failure Rate/Time Taken

48%

Level Of Crowding

43%

Cleanliness Of Stations/Trains

43%

AFC Gate Availability/Failure Rate

43%

Availability/Quality Of Staff

43%

Passenger Journeys On Time: Measured At 2 And 5 Minutes Delay Thresholds

38%

Information At Stations

38%

Waiting Time

33%

(Source: Community of Metros /Nova Group of Metros /Imperial College London)

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Escalator and lift availability, train punctuality, ticketing service availability and crowding
are the most frequently measured indicators. The most common indicators are broadly
speaking very operationally focused, with only 38% of metros reporting that theymeasure the reliability of the service as perceived by passengers.
Indicators relating key measures of convenience, such as in-vehicle travel time, accessand egress and interchange are less common, we expect because they are less easy tovary in an operational context (the planning authority rather than the operator may
measure such attributes instead). Waiting time and the level of crowding are importantsources of customer inconvenience, yet are measured by only a minority of operators.
Provision of capacity is a key element of convenience although many very busy metros inlarge cities did not report that they measure any crowding or capacity indicators.
Examples of specific indicators used in CoMET and Nova metros are shown in Table 2.2
below. The most common indicators are shown in addition to some more innovative or
good practice measures in each Service Quality area as defined by the EN13816.
Common measures used by metros include many indicators that relate to the
management of the system to achieve targeted service quality, such as trains on time,
frequency, proportion of scheduled headways achieved, and the proportion of scheduled
kilometres achieved. More customer focused indicators, but less common, include theproportion of passenger journeys arriving on time, ‘Lost Customer Hours’ and ‘Excess
Wait time’, which we discuss in the sections below.

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Table 2.2 Top 10 Service Quality Indicators Measured by CoMET and Nova Metros

Availability

Accessibility

Information

Most Common Indicators Used byMetrosInnovative / Good Practice Measures byMetros (Eu=European Metro, Am=American,
As=Asian)% of rolling stock available for service
in the peak period

Number of unplanned full station
measured each service day (Eu)

% of actual service delivered thatmeets scheduled service

Occasions when passengers exceed the maximum
capacity of a station (Am)Car kilometres between train failurecausing delays ≥ 5mins

Peak headway targets by line (minimum interval
between two trains) (As)% of escalators and elevators available
for service% of customers affected by the unavailability ofescalators (Eu)

% of Ticket Vending Machines available
across the network

Target: 96% passengers should not get stuck in lift
for +15mins (Eu)

Availability of dynamic passenger
information in stations and trains (forservice disruptions)% of passengers that have access to real time
travel information during service interruptions (Eu)

Mystery Shopper Survey to evaluate
quality of passenger information

closures-% of staff interactions that offer correct ticketing
and route information (Eu)
Excess Journey Time (EJT) (Eu)Time

% of trains operated on time (2,3 and5 minutes delay threshold)% of passenger journeys on time (2,3
and 5 minutes delay threshold)

Lost Customer Hours (LCH) (Eu)
Excess Wait Time (EWT) (Eu)
Passenger affected ratio (% passengers delayed by
5 minutes or more) (As)% of passengers that waited less than the reference
headway (non-peak hours) (Eu)Ratio of complaints / passenger
Customer Care

Passenger enquiry response time - X%of customer complaints addressed
within X number of days
Overall Customer Satisfaction Score
Crowding density: average number ofpassengers standing per m2 trains inmost heavily loaded section in peak
periodComfortTemperature on trains and in stationmust not exceed pre-set standards
Perceived cleanliness rating in stations
and trains (survey)
Incidence of fatalities to staff andpassengers

Security

Environmental
ImpactRate of passenger
passenger)

accidents

Incidencestations

in

of

crime

No indicators reported

trains(perandGeneral Perceived Quality Index: overall index is
calculated weighting the rating of each aspectaccording to its importance (Eu)Monitoring and evaluation: % of satisfaction (rating
3 and above) in Supervisors’ monitoring/evaluation
at Customer Service Officers’ call handling (As)Maximum crowding on the train in peak hour, line
by line, peak direction: must not exceed 100% ofplanning standard (As)% of Peak Services at above 135% seat capacity(As)
Agreed standard between operator and regulator
that there should be no more than 4 passengers per
m2 in the train (Eu)
Criminal cases that result in system interruption,
influencing passengers’ safety and property securityin every 1 million passenger kilometres (As)Perceived security rating (regarding assault androbbery) (Eu)Area of graffiti removed (as m2) (As)No indicators reported(Source: Community of Metros /Nova Group of Metros /Imperial College London)

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Table 2.3 below presents the proportion of Service Quality indicators measured by CoMET
and Nova metros, split for European, Asian and American (North and South) metros. The
eight categories represent the areas outlined in EN13816 (as described above).
Table 2.3 Service Quality Measurement Areas Measured by CoMET and Nova Metros.
% of Asian Metros
Which Measure This
Category% of European Metros
Which Measure This
Category% of American(North/South) Metros
Which Measure This
Category

Availability

75%

100%

75%

Customer Care

63%

100%

75%

Accessibility

50%

67%

75%

Time

63%

89%

100%

Comfort

75%

100%

75%

Safety and Security

63%

89%

75%

Information

63%

89%

75%

Environmental impact

0%

0%

0%

(Source: Community of Metros /Nova Group of Metros /Imperial College London)

Availability (e.g. minimum frequency of service achieved), customer care (e.g. standard
timescales for staff response time to passenger queries or complaints), Accessibility (e.g.
availability of lifts/ escalators) and time (e.g. Excess Journey Time / Excess Waiting
Time) are the most commonly measured service quality areas within CoMET and Nova,
with 81% - 86% of all metros having some service quality measurement in thesecategories. Environmental impact was the only area not measured by any metro.
It is clear that there is more service quality measurement in European metros where
there is often a greater contractual or regulatory relationship between the operator andthe authority. For example, some of the more novel measurement practices in London
Underground stem from regulatory standards set up as part of the Public Private
Partnership (PPP) contracts. In general, Asian metros have far less comprehensive
measures, although there is a strong focus on operationally focused, time-based
indicators (such as punctuality, reliability and the percentage of trains operated).
In many cities, there are limited alternatives to the fast service provided by the metro soa decline in service quality may cause passenger dissatisfaction and/or political
discomfort, rather than any significant drop in demand (at least in the short term).
Therefore, an independent regulator is sometimes needed to monitor service quality,
which is then linked to financial rewards and/or penalties. Although incentive regulation
is a relatively recent mechanism in metro operations, it is becoming more prevalent in
the bus sector and is very common in regulated utility sectors.

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Setting clear targets is an effective management tool for reaching targeted quality levels.
EN13816 recommends that operators should adopt specific targets for each indicator,
with clearly defined measurement processes. This is important to highlight, because not
all CoMET, Nova or IBBG members reported both these elements in their service quality
measures. Indeed, some urban transport operators only use customer satisfaction
surveys, so have no defined objective quality indicators.
Setting a specific target to reach may provide a better incentive for improvement, which
is often stipulated in operating performance contracts. Paris RATP has a strict set of key
performance indicators and service quality targets written into their operating contract
with the Transport Authority (STIF). These targets, which are set annually over a threeyear period, lead to a financial bonus or penalty depending on whether their overall
performance is above or below the stated threshold (RATP Activity Report, 2012).
Leading CoMET and Nova metros such as Metro de Santiago and Hong Kong MTR have
implemented a continuous improvement processes and culture in their organisations.
Each month performance is compared against previous levels.
2.3
Case Studies of Worldwide Practice in Measuring Convenience andService Quality
We next look at some of the more innovative measures of convenience and service
quality in more detail, with examples from the United Kingdom, other European
countries, North America and Asia.
The UK Experience: Transport for London / London Underground / London
Buses
The impact of service quality on the potential to generate passenger growth (revenue)
remains a strong focus in London. Contracting out of bus services by Transport for
London (TfL) and the now-defunct London Underground (LU) Public Private Partnerships(PPPs) have both been catalysts for new and more inventive measures of convenience.
The strong focus on measuring and valuing performance in the UK is based arounddetailed appraisal requirements for government funding. Detailed measurements include
the Journey Time Metric (JTM) and Lost Customer Hours (LCH) used by LU and Excess
Wait Time (EWT) used by London Buses.
LU measures ‘excess journey time’ through their Journey Time Metric (JTM) (London
Transport, 1999). The JTM was developed to measure customers’ overall journey time on
the network. Each journey is broken down into constituent parts; access from entrance
to platform, ticket queuing & purchase, platform wait time, on train time, platform toplatform interchange and egress from platform to exit. Times for these elements can be
disaggregated by line and time band. Using information from the Passenger Origin &Destination Survey, LU is able to derive a passenger demand matrix; thereby estimating
how many passengers travel on a particular line section or along a certain stationpassageway. Hence they can calculate the average time for any given journey stage.
Each JTM also has a Value of Time weight associated with it dependent on how thecustomer perceives the activity. Changes in the scheduled values for components of the
JTM can reflect capital improvements, for example by re-designing stations to shorten
walk links or the use of faster trains. Non-capital initiatives, for example better
management of station dwell times or the provision of station assistants to reduce ticket

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queues, can have an immediate impact on excess time and even scheduled time in thelonger term.
Lost Customer Hours (LCH) measures the total additional time (summed for all
customers) resulting from all service disruptions of two minutes or more, due any cause.
For example, a two minute delay at a busy central station in the morning peak costs
significantly more LCH than a two minute delay on a Sunday evening in the suburbs, as
more passengers are affected. The measure takes into account the duration, location andtime of the disruption to estimate the total “cost” in terms of customer time, directly
measuring the impact on passengers. It reflects whether or not metro assets areavailable and was the primary measure used for assessing the PPP Infrastructure
Company’s performance in improving the day-to-day availability.
Excess Wait Time (EWT) is a measure of regularity used by London Buses. EWT is definedas the difference between the actual wait time (AWT) and scheduled wait time (SWT).
The lower the EWT, the more likely it is that passengers will not wait more than thescheduled time and will perceive the service as regular, hence it is a measure ofperceived regularity. It is objective, relatively easy to communicate to passengers,
represents all customers and penalises very long headways (bad for customer
convenience). However, this is only valid for regular scheduled headways.
Of four regularity measures tested in the International Bus Benchmarking Group (IBBG)
by Imperial College London, EWT was considered the most statistically robust and theonly method that fully incorporates the customer perspective (Trompet et al, 2011).
Other service regularity indicators tested included wait assessment, service regularity
and standard deviation of the difference between the actual and scheduled headways, ameasure related to headway adherence, but with the output expressed in minutes.
The Contemporary European Experience
In general, European Metros have a more comprehensive approach to measuring
convenience and service quality, with many following the EN13816 standard closely.
Many European metros in CoMET and Nova have an inclusive approach to setting Service
Quality benchmarks, to ensure the service standards are upheld. Service Quality
standards are often dictated by transport authorities and governments through an
operating contract. Several metros used a range of measures based around EN13816,
aiming to provide internal incentives and to pledge a high quality service to customers.
For example, all lines in Metro de Madrid are individually certified by AENOR (Spanish
Standards / Certification Authority), in accordance with EN13816.
There is a strong Service Quality commitment in the new (2012-2015) operating contract
between Paris RATP and the transport authority (STIF). Financial incentives are enforced
based on a range of indicators which broadly conform to EN13816. Specific targets areset for each indicator, including a minimum standard. If RATP exceeds this minimum, aprogressive bonus is applied. However, if the specified target is not met on any measure,a penalty is applied. The focus on service quality within the contract ensures thatstandards are upheld and Metro, RER and bus services meet the needs of their (growing)
customer base. RATP sometimes uses higher targets than those set by the authority forinternal management (RATP Activity Report, 2012).

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The new contract between STIF and RATP includes more performance indicators than theprevious one; 141 performance indicators compared to 79. More weight is given topunctuality and regularity (43% of service quality indicators based on punctuality,
compared to 29% previously) and customer satisfaction now also has a higher weight(RATP Activity Report, 2012). Crucially, the contract includes indicators that measure theimpact of the service on the passenger. For example, a target or minimum threshold is
set for passenger waiting time (% of passengers who waited less than the reference
headway) in the off peak period. Similarly, a ticketing (Automatic Ticket Machine)
availability threshold has been set to measure convenience of ticket purchase.
The Contemporary Asian Experience: Hong Kong MTR
Some Asian metros are improving their service quality measures in response to changing
regulatory environments and a need to be continuously customer facing. Best practice
metros measure both operational and customer focussed indicators. Hong Kong MTR
measure a ‘Passengers Affected Ratio’, representing the number of passengers on trains
delayed by five minutes or more. Passenger numbers are based on fifteen-minute
average loading figures per line from the Automatic Fare Collection system (AFC). Traindelay is measured directly from the signalling system and collated by the control centre.
While operational and technical indicators are useful, they can misrepresent the customer
perspective, hence there is often a mismatch between public perception and punctuality
indicators. Service reliability measures such as mean distance between failures (MDBF)
typically focus on the frequency with which incidents or delays occur. However, to truly
understand reliability, measures that capture the total impact of incidents to trains andultimately to customers, such as train hours delay and passenger hours delay, areneeded (Barron et al, 2013). This is important because the impact of incidents increases
exponentially with the duration. Furthermore, the context of incidents is critical; incidents
that occur during peak times or at busy locations have much greater impacts than thoseat the outer end of a long metro line late on a Sunday night.
Metros such as Hong Kong MTR exhibit a balanced and detailed approach to measuring
time and reliability based indicators, considering both customer and operationally focusedmeasures. As well as the ‘passenger affected ratio,’ there is strong focus on measuringpunctuality (the proportion of trains that run on time) and reliability (the mean distance
between incidents causing delay to service) at two and five minute thresholds, which are
more useful for measuring the technical performance of assets.
Frequency, Capacity and Crowding
Passengers in Excess of Capacity (PiXC) is an aggregate metric used to measure
crowding levels for Train Operating Companies (TOCs) in Great Britain. PiXC is derived
from the number of passengers travelling in excess of capacity on all services passing a
critical load point (above a critical threshold) divided by the total number of people
travelling, expressed as a percentage. The critical load points aim to represent the mostcrowed section of the journey, normally the approach to – or departure from, major
termini, and cover only the three hour morning and evening peaks (Department forTransport (DfT), United Kingdom, 2012).

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In a metro context in large and dense cities, capacity provision and crowding can be key
measures of how convenient the public transport service is for potential customers as
they can significantly affect the total generalised costs of trips. Surprisingly, specificcrowding and capacity measures do not feature prominently in most CoMET and Nova
metros’ indicators. Only seven out of twenty-one CoMET and Nova metros measurecapacity or crowding. A further two metros include crowding in their Customer
Satisfaction scores. An example is a Chinese metro which measures the degree ofcrowding on trains in the peak period by relating real time passenger demand by linewith the maximum capacity of the line in the peak hour in the peak direction. If thedegree of crowding exceeds the threshold, they use this indicator to identify overcrowded
trains and hence determine where capacity increases are needed. Sydney Trains
measures the proportion of peak services above 135% of seated capacity. Some
European metros have a standard to limit crowding to four passengers per m2 during thepeak period.
Previous work by Imperial College (Graham et al, 2009) used a dynamic panel model toestimate the effect of fares, income and quality (capacity and frequency) on demand fora sample of 22 metros, using time series data from a 13 year period. The key results are
summarised in Table 2.4, below:
Table 2.4 Metro Elasticities of Demand.

Demand withrespect to:

Elasticity (shortrun)

Elasticity (longrun)

Income0.026+0.183Fares-0.047-0.331

Quality of service(using car km /route km)

0.072

0.507

(Source: Graham et al, 2009)

The estimated long run income elasticity was small but positive (0.18), indicating that
metros are perceived as normal goods (demand increases as incomes rise). Long-run
quality of service elasticities (here, +0.51 using capacity: car km per route km) werepositive and substantially higher than the absolute value of fare elasticities (-0.33).
However, increasing speed appeared to have little effect on demand. The implication wasthat increasing capacity, rather than fare reductions or reducing in-vehicle time, may bemost effective in increasing patronage. Of course, an average elasticity of demand toservice quality of 0.51 across all metros, for all time periods might suggest a muchhigher elasticity for busy metros during peak periods and may imply that passenger
demand is highly sensitive to crowding factors in terms of their generalised cost.
Sensitivity tests on Chinese metro costs and revenues, using these elasticities (Anderson
et al, 2012), demonstrated that operating cost recovery from fares would be increased
significantly by maximising use of fixed capacity by increases in train frequency andcapacity.

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It is notable that only 50% of CoMET and Nova metros, when surveyed in 2009, knew
their own elasticities of demand to price (values received are shown in Figure 2.1). It is
probable that such information is better known by transport authorities, but this may
indicate that key policy and service decisions, and the case for investment, could bemade better by operators if a greater understanding of passenger demand and revenue
was known.
Figure 2.1 Known Metro Fare Elasticities (2009): Responsiveness of demand to price, as
given by CoMET and Nova metros.

Source: (Anderson et al, 2010 / CoMET and Nova Benchmarking Groups)

2.4

Benchmarking Public Transport Convenience and Service Quality

The CoMET, Nova, ISBeRG and IBBG groups include metros, suburban railways and bus
systems of many different characteristics, but many share fundamentally the samechallenges and issues which provides for a wealth of experience and knowledge that
operators can share with each other.
The benchmarking process, facilitated by Imperial College London, is centred on a Key
Performance Indicator (KPI) system, which enables universally consistent andunderstandable comparisons between different organisations. However, it involves not
only the comparison of performance, but also the identification of best practices, helping
support decision making and improve internal management. This leads to a better
understanding of the differences between operators, to improve internal motivation, settargets for better performance and identify high priority problems, strengths or
weaknesses. All areas of the business are covered, including finance, operations andsafety as well as aspects related to convenience.

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Benchmarking convenience involves many challenges. Firstly, the subjective dimension ofmany convenience attributes implies that it is not always clearly defined or the definitionsof individual organisations may present significant differences. People from different
cities or countries may have different habits, customs and expectations regarding
convenience. Having different understandings of the concepts also compromises the
direct comparability of data between organisations. Moreover, convenience is a dynamic
dimension as the expectations of the customers are not fixed and may become moredemanding depending on the progress in other sectors (for example, with theintroduction of new technology, air conditioning and increasing comfort standards).
Despite these challenges, the CoMET and Nova KPIs provide a number of measures that
can help us define objective levels of convenience. We present a selection of examples
below. These have been anonymised to maintain confidentiality, using the codes definedin Section 2.2).
The first example, car km between incidents causing a delay of 5 minutes or more, isshown in Figure 2.2. A key determinant of metro quality and customer satisfaction is theextent to which trains and therefore passengers are delayed. This KPI measuresreliability in terms of incident frequency, regardless of delay duration (provided the delay
was 5 minutes or more). Total reliability should also consider the length of delays andhow many passengers are affected. This is an important KPI in CoMET and Nova due tothe huge disparities in performance observed, and the significant year-on-year
improvements in several metros.
Figure 2.2 Car kilometres between incidents causing delay of equal to or more than 5
minutes. Also known as Mean Distance Between Failures (MDBF).

3.5

Car Kilometres Between Incidents Causing Delay (> 5 Minutes)(MDBF) (2012)

Car km (millions)

3.0
2.5
2.0
1.5
1.0
0.5
0.0

(Source: Community of Metros /Nova Group of Metros /Imperial College London)

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However, car km between incidents not a true measure of convenience to passengers as
it is operationally focused and does not necessarily reflect the service experienced bycustomers. It is therefore important to measure the impact of train delays on
passengers. The table below demonstrates the hierarchy of time based delay indicators
considered by the CoMET and Nova KPI system, in increasing order of customer focus:
Table 2.5 Measuring Train and Passenger Focussed Delay Incidents
Trains On Time / Total Number Trains (Terminal Station Only)

On Time Arrival at En-Route

Trains On Time / Total Number Trains (at any point En-Route)

Average Delay per Train

Number of Minutes of Train Delay Versus Number of Trains
Affected by Delay
Train Hours Operated / Hours of Train Delay

Passengers Affected
Passenger OTP
Passenger Hours Delay

Average Number of Passengers per Train (Loading) and Timeand Location of Train Delay
Passenger Hours’ Delay / Passenger Journey

Increasing Customer Focus

On Time Arrival at Final Destination

Passenger Journeys On Time / Passenger Journey(Source: Community of Metros /Nova Group of Metros /Imperial College London/ (Barron et al., 2013).

To measure delays from a more customer focused perspective appropriate data needs tobe collected in a sufficient level of detail. Figure 2.3 presents an overview of incident anddelay data that 22 metros provided for a recent survey (Barron et al, 2013). Of themetros that responded, all were able to provide the most basic data such as the numberof delay incidents, but very few could provide detailed data on the impact of delays on
trains and passengers.

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Figure 2.3 Overview of availability of incident data: CoMET and Nova Survey, 2012 (Barron
et al, 2013)

Availability of Incident Data
Number of Number
incidentsof incidents
Mean distancedistance between
between failures
failures
Mean
Incident cause Incident Cause
Incident date

Incident datestart time
Incident start Incident
time
Durationdegraded
service
Durationof ofdegraded
service

Data Item

until problem
is identified- until -problem
is identified--until
serviceis isrestored
until normal
normal service
restored
Durationof stopped
service
Durationof stopped
service- until -problem
is identified
until problem
is identified
until normal
normal service
restored--until
serviceis isrestored

Total impact
onimpact
trains on trains
Total- numberof trains- numberofaffectedtrains affected- total train delay- total train delay

Total
impacton passengers
Total
impacton passengers- total- passengers
affectedaffected
total passengers- total passengerdelay- total passenger
delay

0

2

4

6

8
10 12 14
Number of Metros

16

18

20

22

(Source: Community of Metros /Nova Group of Metros /Imperial College London/ (Barron et al., 2013).

Despite difficulties of data collection limiting its availability for many metros, the current
benchmarking methods enable us to estimate the impact of incidents on delay topassengers. Figure 2.4 shows the proportion of passenger journeys on time for CoMET
and Nova metros in 2012. It is notable that this is estimated by metros for thebenchmarking groups, yet not often used as an internal performance measure. In thiscase, those operators with incidents concentrated in the peak hours (affecting morecustomers), will have a significantly lower proportion of passenger journeys on time. The
data shows that the average daily commuter in a high reliability metros such as Hong
Kong is delayed by 5 minutes or more only every two years, yet once every 2 weeks in a
typical European metro. However, current measurement methodologies exclude delays
caused by congestion in stations; further research and more precise measurement fromticketing systems might reveal a higher level of passenger delay than currently recorded.

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Figure 2.4 Metro Passenger Delay: passenger journeys on time per passenger journey.

Passenger Journeys On Time per Passenger Journey (2012)
100%

95%
90%
85%
80%

75%
70%
65%
60%

(Source: Community of Metros /Nova Group of Metros /Imperial College London)

The importance of measuring delays from a passenger perspective is often poorly
understood by operators, which can lead to incorrect management decisions and focus.
As shown by Figure 2.5, if ‘Metro A’ managed it service based on the number ofincidents, as opposed to their impact on customers, they would see the biggest challenge
as rolling stock, yet delays caused by passengers have a greater impact on customer
delay. Barron et al (2012) state that “this supports the hypothesis that number ofincidents is not an accurate proxy for effect on passengers. Therefore, use of aperformance indicator that specifically addresses passengers is indeed a necessary prerequisite for passenger-focused management of incidents”.

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Figure 2.5 Number of metro delay incidents and passengers affected by delays – sample
from an anonymous metro.

Metro A: Number of Incidents and Passengers Affected
Passengers(2009+2010)
Incidents('000)
500

150

400
100
300

200

50

100
0

Rolling Stock

SignallingNumber of Incidents

Other
Caused by
Caused by
Equipment
Staff
Passengers
Total Passengers Affected ('000)(Source: Community of Metros /Nova Group of Metros /Imperial College London)

Another aspect of importance for customer convenience is the generalised cost
components of interchange penalties and waiting times, as discussed in Section 1. Using
the benchmarking data we can compute the average number of interchanges for eachjourney on the metro. As networks expand, as they are doing so rapidly in China andIndia, increasing reach offers greater access to the metro, but the complexity of the
network can result in more interchanges on each trip. A poorly designed network can add
additional generalised cost to passengers, as well as increasing unit costs to theoperator. More widely, the transport authority must consider improving the total journey,
including all access and egress modes. Figure 2.6 shows the number of interchanges(between lines) per passenger journey and how it relates to network length; we observea positive correlation.

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Figure 2.6 Number of interchanges per metro passenger journey and network size

Number of Changes per Passenger Journey Versus Network Size (2012)

Passenger Boardings per Passenger Journey

1.8
1.7
1.6
1.5

1.4
1.3
1.2
1.1
1.0
0

50

100

150

200

250

300

350

400

450

500

Network Length (km)(Source: Community of Metros /Nova Group of Metros /Imperial College London)

Finally, Figure 2.7 demonstrates a further element from the benchmarking which
represents a common problem for emerging metros in Asia: delivering sufficient peak
period frequency to maximise capacity and minimise crowding (where many new metros
are not exceeding 24 trains per hour). European and South American metros havegenerally better optimised design and operating practices to take full advantage ofmodern signalling technology and maximise the frequency of trains during the peak hour.
This can be seen clearly in Figure 2.7 where twelve of the fifteen metros with the highest
service frequencies are in Europe and South America; for example, 33 trains per hour are
operated on London’s upgraded Victoria line. Maximising frequencies is importantbecause metros exhibit ‘strong returns to density’ (Graham et al, 2003): maximising
capacity can increase efficiency and reduce subsidy requirements for metros in large
cities with very high levels of passenger demand.

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Figure 2.7 Maximum Peak Hour Train Frequency (CoMET and Nova metros, 2012).

(Source: Community of Metros /Nova Group of Metros /Imperial College London)

However, when attempting to maximise train frequencies it is important to understandthe relationship between capacity utilisation and delays. Research (Melo et al, 2011)confirms that increasing available line capacity (e.g. with new signalling), so as toprovide some slack, limits the impact of delays and increases reliability. Conversely,
running more trains without any increase in line capacity leads to more delays. Table 2.6,
below, shows the results of recent statistical analysis of delays by metro line, taking into
account a number of operating and demand characteristics. The results also demonstratethat technology has a significant influence on the number of delays. Crucially, however,from a generalised cost point of view, maximising utilisation of line capacity (frequency)
increases delays yet investing to add the same change in available capacity reducesdelays by a greater degree.

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Table 2.6 The sensitivity of metro delay incidents to technology and demand factors

Parameter% Change in Delay Incidents (Meandistances between failures causing adelay > 5 minutes)+1 Year of Rolling
Stock Age

0.7% - 2% depending on model+1 train per hour inthe peak period+3.5%+1 train per hour
practical capacity-5.0%

Moving from manual
to automatic train
operation (ATO)-26%+10% passengers+3.0%(Source: Melo et al, 2011 / Subsequent analysis by Imperial College London using CoMET and Nova data)

These benchmarking examples have shown that trading off the value and demand impact
of delays against crowding and increased waiting times is a process that only a few
metros undertake. Metros naturally focus on the objective measures of service quality
against which they are regulated or managed by their authorities, commonly measures oftrain delay. Although we have not surveyed metros to identify their decision making
process, other research shown above (e.g. in Table 2.3) demonstrates that metros,
particularly in Asia, are not measuring the impact of delays and crowding on passengers
well.
If good measurement is the pre-requisite to good valuation of such conveniencemeasures, we argue that insufficient attention is given to minimising generalised cost /journey time. Good practice is nonetheless observed in cities such as Paris and London:
Transport for London uses its Business Case Development Manual (described in Section3) to give guidance on values to attribute to changes in passenger generalised costs forinvestments improvements. In summary, we argue that greater management attention
by operators to the measurement and valuation of attributes of most important forpassenger convenience is required in many cities and that such analysis should not beleft only to the authority to undertake. In Section 3 we look at how convenience
attributes can be valued, taking the specific example of the rail industry in Great Britain.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY3.

VALUING CONVENIENCE

In Section 2 we looked at how convenience can be measured for public transport. In thissection, we show how similar attributes of the service can be valued through thequantification of the effects on passenger demand. Taking the example of the extensive
demand forecasting framework used by the British rail industry, we demonstrate howthese impacts can be quantified and describe some of the evidence.
3.1

Measuring Convenience by Looking at the Impacts on Demand

One means of measuring how well public transport meets the needs of existing andpotential customers is to look the level of demand for the service. All else being equal,
we would expect more people to use a convenient service than an inconvenient one.
This relationship between convenience and demand means that transport providers havea direct commercial interest in the level of service they offer to customers. The railway
sector in Great Britain has invested considerable effort to understand this relationship.
They need to know how best to target investment to maximise the impact on demand,and hence revenue from passengers. This was as important for the nationalised British
Rail in the 1970s and 80s as it is for the private sector operators today; funding
pressures from government can provide as strong an incentive as the need to maximisecommercial profits.
Specifically, this evidence helps us understand convenience from two perspectives.
Firstly, it demonstrates that improving the level of convenience attracts more people tothe service, proving that investment to make the transport system more convenient canhelp increase the overall use of public transport, helping to meet wider social,environmental and economic objectives. Secondly, it provides quantified evidence ofwhat customers find most important when making travel decisions and the relative
importance they place on different attributes of the service.
Although the demand forecasting experience from the British railway sector can only
provide high level guidance on the convenience of public transport more broadly, we
consider that the approach, and much of the evidence is likely to be transferable ingeneral terms, and the wealth of quantified evidence available makes it especiallyvaluable.
3.2

The British Rail Industry Passenger Demand Forecasting Framework

A substantial volume of quantitative and qualitative research has been undertaken over
several decades to understand the impact of a broad range of service attributes on
demand. Attributes include aspects of the service which may be classed as convenience,
including frequency, reliability, quality and crowding. The evidence is collated in the
Passenger Demand Forecasting Handbook (PDFH). The document is updated on a regularbasis with new and revised evidence; the latest edition contains 500 pages.

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Transport for London (TfL) use a similar body of evidence to understand demand impactsof service changes and quality enhancements on the London Underground, buses and theother modes for which they are responsible. This is contained in TfL’s Business Case
Development Manual (BCDM).
All major industry bodies participate in the PDFH including train operators (TOCs), theinfrastructure provider (Network Rail), government (Department for Transport andTransport Scotland) and the regulator (ORR). The broad participation helps ensure thatthe evidence is accurate and unbiased. Participants jointly fund a research programme todevelop the evidence and keep it up to date. PDFH also takes input from other academic
work and research undertaken independently by the participating organisations.
The research underpinning the PDFH includes both stated preference and revealed
preference work with passengers, as well as substantial econometric and similar analysis.
The substantial volume of research on which the PDFH is based means that mostattributes have been the subject of a number of different studies, increasing robustness.
The high level of use of the evidence across the industry also means that significant
practical validation of the findings is carried out.
The PDFH is a confidential document; hence the evidence is described here in generalterms only. It is not possible to quote specific values, however, as noted above thesewould not generally be directly transferable. Third parties can however apply for licenced
access.
3.3

PDFH Methodology

The primary methodology used in the PDFH is based on elasticity to time and cost (fare).
Most evidence is expressed in units of time. Each variable, including those related toconvenience, is converted into an equivalent amount of travel time (expressed as“Generalised Journey Time”), weighted to reflect relative importance. Elasticities to time
are then applied to estimate changes in demand, based on the principle that reductions
in journey time lead to increases in demand, as follows:
Ij = (GJTnew/GJTbase)e
Where:
I is the index for change in demand,
GJTnew is the weighted Generalised Journey Time after a change to the service,
GJTbase is the weighted Generalised Journey Time before the change to theservice,and e is the elasticity to time

Generalised Journey Time (GJT) has specific definition in PDFH, different to that often
used in conventional transport planning theory. The concept is analogous to Utility theory
in economics (but of reverse sign). Basically GJT is a measure of the “attractiveness” ofthe service to customers. The lower the GJT the more attractive the service is tocustomers. E.g. a shorter journey time, higher frequency, more comfortable, or cheaper
service will have a lower GJT. The use of common units (time) means that it is also
possible to compare the relative importance of different variables in terms of their impact
on demand.

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PDFH evidence (eleasticities and weightings for specific service attributes) is
disaggregated according to market segment: journey purpose (business, leisure andcommuting), journey length and geography (e.g. typically passengers around major
cities respond differently to those in rural areas).
There are separate elasticities to price (fare), used to estimate the impact of changes inticket price. This is assessed separately from the time based attributes – i.e. values oftime are not used to combine cost and time-based elements. Here we focus on the time
based elements only.
3.4

Examples from the PDFH

The key areas in the PDFH relating to convenience are:


(Station to Station) Journey timeFrequencyInterchange

Punctuality and Reliability

CrowdingRolling stock and station quality

The evidence on each of these areas is considered below.
Journey time, frequency and interchange
In-vehicle time (effectively travel time), waiting time (a function of service frequency)
and interchange are combined in the PDFH framework as a single measure of Generalised
Journey Time (GJT) which can be calculated for each origin destination flow. Individual
GJT elements can also be weighed to account for further quality attributes, including
crowding levels and rolling stock quality, as described below.
The evidence shows that passengers are very sensitive to GJT, as might be expected.
Elasticities of demand to GJT have typically been found to be in the range of around -0.7
to -1.1, depending on market segment. An elasticity of -1.0 means that the increase indemand for the service is directly proportional to the reduction in GJT – i.e. a 10%
reduction in GJT would lead to a 10% increase in demand for the service.
For short distance services, such as in urban areas, the evidence shows that the impact
of changes frequency can be very significant. Often it may be easier, and cheaper, toimprove the attractiveness of the service by means of frequency enhancement thanthough reduction in actual travel time, consistent with our findings from research on
metros describes in Section 2.3.4. This is in addition to any further impacts resulting
from the increased capacity usually associated with higher frequencies.

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The example shown in Table 3.1, based on PDFH evidence2, illustrates the relativechanges in journey time and frequency required to achieve the same impact on demand,
assuming an elasticity to GJT of -1.0. This involves a base scenario with of a service
operating every 10 minutes, and a travel time of 15 minutes. The evidence suggests thatthe impact on demand due to doubling the frequency to every 5 minutes will be the sameas reducing the travel time from 15 to 10 minutes (excluding capacity impacts). This
demonstrates that frequency has a very large impact on demand, and therefore
convenience. This is particularly important when considering off-peak service levels.
During off peak periods many trips may be more discretionary (compared to trips to/fromwork).
Table 3.1 Relative changes in journey time and frequency required to achieve thesame impact on demand

Base
Scenario
Doubling
Frequency
Journey5
minutes(50%
faster)Travel Time

Frequency% Change
in GJT

DemandEvery 10 min

Generalised
Journey Time
(GJT)
25 min15 minN/A(Existing)

15 minEvery 5 min

20 min-20%+20%

10 minEvery 10 min

20 min-20%+20%(Source: Imperial College London, based on UK rail industry experience/PDFH)

The PDFH evidence also shows that the need to interchange can have a major negative
impact on demand, suggesting that passengers find this especially inconvenient. Eachchange of trains is equivalent to an absolute minimum of 10 minutes additional journey
time, over and above the actual connection time between trains. For most journeys theimpact is even greater. For regular travellers using high frequency urban public transport
we would expect the negative impact to be less, although as we saw previously (Figure2.6) journeys on some urban networks can involve a high number of interchanges.
As an example, consider a journey from A to C which involves a 10 minute journey from
A to B, with a 5 minute wait at B for the next service, and a journey time of 15 minutes
from B to C. The total elapsed time from A to C would be 10 + 5 + 15 = 30 minutes.
However, with an interchange penalty of 10 minutes, applied in addition to this, the total
GJT would be 40 minutes – i.e. the service from A to C including the interchange could be
expected to be as attractive to passengers as a direct service from A to C taking 40
minutes (and therefore attract a similar level or demand).
Crowding
Impacts of changes in crowding are estimated using a weighting factor applied to the invehicle component of GJT. Larger weighting factors represent higher levels of crowding.
In a crowded vehicle with 4 to 6 passengers standing per m2, the impact for thosepassengers standing is equivalent to a 2 to 3 times increase in in-vehicle time. This2.

Due to the confidentiality of PDFH, only approximate values have been used for this example. These
are intended to represent a “typical” service; PDFH includes specific values for individual market
segments.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

means that the negative impact on demand from crowding can be equivalent to a 2 to 3
times increase in travel time for affected passengers; crowded services are, as we would
expect, significantly less attractive.
The negative impacts of crowding for rail services start even when there is still plenty
space on board trains. Evidence shows that there are marginal detrimental impacts on
demand once around 75% of seats are taken. All else being equal, passengers are more
likely to travel if they have a choice of seat.
For urban transport such as metros, we might expect the negative impacts of crowding to
be lower, since journeys are normally shorter than on mainline railways, but the effect isstill likely to be significant. In Section 2.3.4 we saw that the demand impact of capacity
provision on metros can be substantial. One of the implications of this evidence is that aswell as making the service more comfortable for existing users, reducing crowding levels(e.g. by running longer trains) will make the service more attractive, leading to anincrease in demand. Therefore there is a degree of feedback, where a proportion of any
new capacity provided is filled with new passengers attracted to the (now) less crowded
service. Providing additional capacity by means of shorter headways, rather than higher
capacity vehicles, will increase this effect further as new demand is attracted byincreased frequencies as well as the less crowded conditions on board.
As explained in section 2, above, analysis of CoMET and Nova data (Graham et al, 2009)
revealed an average elasticity of demand with respect to capacity of +0.51, with far
higher elasticities expected for busier metros during peak periods. The economics andefficiency of metros improves significantly the greater the extent to which fixed costs are
met with increasing levels of capacity and revenue (Graham et al, 2003).
Punctuality and Reliability
Punctuality and reliability are addressed in the PDFH by adding a weighted value of“average lateness” to the in-vehicle time component of GJT. Average lateness is themean magnitude of delay to a service. As an example, a delay of 10 minutes every 5days would equate to an average lateness of 2 minutes (10/5 = 2). The evidence showsthat each additional minute of average lateness is equivalent to several times that of anadditional minute of scheduled journey time, which is reflected in the weighting factors.
Therefore the negative impact on demand of 2 minutes average lateness would beequivalent to adding much more than 2 minutes to the scheduled journey time every day
if the service were never delayed.
Rolling Stock Quality and Station Facilities
The PDFH also contains evidence on the “softer” quality attributes of both trains andstations, including cleanliness, comfort and information. The impacts of these attributes
have been found to be very small relative those for journey time, frequency, interchange
and crowding. Typically they are equivalent to a reduction in travel time of a few
percentage points, with a similar level of impact on demand. However, some of these
attributes may be relatively easy to improve, and may often be less costly than – forexample – increasing average speeds to obtain faster journey times. High quality
passenger information in particular has been shown to have a relatively significant
impact on attractiveness of the service.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

3.5

Understanding the Relative Impact of Convenience Attributes

Since the majority of convenience related attributes are converted into common units oftime in the PDFH, it is possible to compare their impacts on a common basis, effectively
providing an indication of their relative importance to customers.
Figure 3.1 provides an illustration of this, based on an example service operating every
10 minutes with and end-to-end travel time of 15 minutes, as in Table 3.1. The y-axis
represents the expected percentage change in demand as a result of each of theindividual service enhancements. Although based on sample PDFH evidence, thisexample is included for illustrative purposes only. Actual impacts will depend on theexisting level of service, the changes being made and specific local circumstances.
However, it is clear from the graph that a unified approach to valuing service attributes
using common units can provide a powerful management tool for assessing the relative
benefits of different enhancement options.
In the specific example given, a relatively high level of crowding (around 4 passengers
per m2) was assumed, leading to significant benefits from crowding reduction by meansof extra capacity. Reliability improvements (assumed here as a 2 minute reduction in
average delay) and price (20% reduction assumed), are shown to have a smaller impact
on demand than the core attributes of capacity, frequency and journey time. Similarly,
the figure shows that for a typical service, relative impacts of attributes such as
information and other soft factors including comfort and cleanliness are relatively small.
However, these can often be cost effective to address and may be easier to improve inthe short term; they are still important for convenience.
Figure 3.1 Example illustration of the relative impact of selected convenience attributes
on demand(Source: Imperial College London, based on UK rail industry experience/PDFH)

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY

3.6
Applicability of the PDFH Methodology to Other Public Transport
Systems
The PDFH methodology and evidence has been developed specifically for the rail industry
in Great Britain, with values calibrated to this sector based on substantial evidence. Railusers in Great Britain may not be typical of public transport users more broadly and there
are also specific factors relating to the service and areas within which it operates. For thisreason, we would not expect all PDFH evidence to be directly applicable elsewhere.
However, we have demonstrated that in areas such as journey time, frequency andcrowding, the evidence appears similar to our findings from research on metros (e.g.
Graham et al, 2009).
However, the basic principle of valuing service attributes in common units of time, withspecific weightings applied, should be broadly applicable. In fact this method is
consistent with conventional transport planning theory and demand modelling. It should
be feasible to determine equivalent valuation of individual service attributes for otherpublic transport systems, although the research required should not be under estimated.
A potential issue in attempting to value convenience based on observed impacts on
demand is that some users may be captive to public transport, with no choice about
whether to use it, even if it is not convenient. This may be a greater issue where there
are limited alternatives available (demand for rail in Great Britain is relatively elastic). In
these circumstances, demand related impacts may be small and therefore difficult tomeasure. However, even where existing users are fully captive to public transport theevidence from those places where they do have a choice helps us understand what is
important. In general, we could expect all public transport users to have a similar view ofconvenience, even if local conditions and the range of alternatives available mean thatthey have limited opportunity to change their travel behaviour in the short term.
Also, although public transport users may be captive to the mode in the short term, andmay have little choice other than to use the service provided – even if not convenient –there is often a large turnover in passengers over time. Recent research in the UK(Mason, Segal and Condry, 2011) found a “churn rate” of close to 25% over two years in
the commuter marker. I.e. over a two year period, one-quarter of rail commuters
stopped using the service and were replaced by a similar number of new users. In thelonger term, people make choices on work and home location, as well as car ownership,
which impact on their use of public transport. Often these locational decisions are heavily
influenced by the availability, and especially the convenience of transport. Since theavailability and convenience of public transport can influence lifestyle decisions, it is clearthat a more convenient service will tend to attract more passengers in the longer term.
Increasing wealth in many countries, and greater competition in the transport market,
means the ability of users to change travel patterns and modes is likely to increase in
future. Customers may have little choice today, but if the service is not convenient they
may cease to use it as soon as more attractive alternatives become available to them.
Another potential issue is that some convenience attributes may not have a significant
impact on demand, but may still be important for other reasons. Certainly there may besome attributes that will make the service more attractive for passengers, but not
influence their behaviour. However, if an attribute of the service has no effect on thedecisions of even the most discretionary travellers, with little observable impact on
demand, then it may be reasonable to conclude that this has no relevance to passengers.
Understanding the relative impact of different measure will help those responsible for
specification and provision to focus efforts on the aspects with greatest impact.

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4. CONCLUSIONS

It is generally assumed that a convenient service is more desirable and will therefore
lead to increased demand. This is supported by empirical research including evidence
from the railway industry in Great Britain. Rising customer expectations and increasing
competition make optimising convenience important to help ensure long term viability of
public transport, through increases in demand, revenue, public support and acceptability.
It is important to understand the relative impact of changes to the different attributes of
convenience (what is more or less important) so that both transport operators andauthorities can focus on defined areas within constrained resources.
However, what makes a service convenient is not always well understood, nor is there auniversal definition of which attributes come under the definition of convenience. We
define convenience as encompassing all attributes which influence the attractiveness ofthe service to customers; thus covering all elements of the conventional generalised costequation, including access, egress, frequency and crowding, as well as “softer” factors
such as comfort and information.
We argue that it is a pre-requisite that convenience must be measured before it can bevalued and managed optimally. Using the case of the metro industry, we have shownthat to date, public transport operators are is still, relatively too operationally focused interms of the attributes of service which they are measuring and acting on. There areseveral reasons for this; firstly, historically metrics such as on-time performance atterminals have been easy to measure by operators and regulate by authorities: better
technology was required to better measure the customer experience. Secondly,
incentives within the industry have not been perfectly aligned towards the customer.
These constraints, however, are changing rapidly and operators in Europe in particular,
and no doubt elsewhere in the world, are exhibiting innovative approaches to themeasurement and valuation of convenience. The key catalysts have been: improved andbetter specified regulation and contracting regimes (such as in Paris and, earlier, for bus
services in London), technology (particularly ticketing, signalling and remote monitoring
systems) and the development of European standards such as EN13816. It is arguably
important that financial incentives are present and strong enough to encourage operators
and authorities to become more customer-focused, whether through either body taking
revenue risk and/or or bonus/malus regimes for the operator.
It is important that strategic transport planning decisions concerning passengers’
generalised cost are not simply left for the transport authority to decide; for example
metros carrying revenue risk would benefit significantly from a better understanding of
the impacts of frequency and capacity on demand. It is necessary for the design ofperformance measurement systems to consider the objectives, aims and desired
outcomes and then develop a measurement system around that: to measure attributes
that are important to (potential) customers. The operator must still have operational
measures of performance (to see if the operator is delivering what it plans to);38

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITY‘operational excellence’ is still very much required. There can be unintended
consequences of operators ‘gaming’ a contractual performance measurement system inorder to maximise performance only in regulated or contractual areas, to the exclusionand detriment of unmeasured service quality attributes; it is our professional judgement
such a situation is arising in some cities, particularly for newer metros.
We have shown that there are regional differences in the scope of conveniencemeasurement between metro operators and that a more comprehensive and customer
oriented approach tends to be present in Europe. However, such metros have had time
to develop their management systems.
Crowding can be chronic in the metros of many large cities and research for the railway
industry in Britain shows that this is a large component of the generalised cost of peaktravel. The experience of metro operators suggests that this attribute is rarely wellmeasured and specified by operators and authorities in terms of its demand andgeneralised cost impact. Measurement of train delay at terminals, without measuring its
impact on passengers is common but not good practice, yet today’s technology isavailable to measure and mange such an important element of service quality. For
metros, data from ticketing systems should now permit a greater understanding ofjourney times from gate to gate (origin station to destination) yet such information is not
currently well reported or used by metro operators (we do not know the extent of suchanalysis at an authority level, however). We conclude from our evidence from metros’
measurement of convenience and service quality that operators worldwide could do much
more to measure (and later value and act on) the variability and reliability of journey
times, using new ticketing and gate data. This appears to be a significant opportunity for
future analysis and research. The urban bus industry, however, is using GPS data andtechnology to better manage and measure wait times.
Newer Asian metros are expanding rapidly, therefore their focus is necessarily to stabilize
operational performance; in future years we may see a more comprehensive attention
and measurement of more customer-facing attributes. Sharing experience from moreestablished metros in Europe (London, Paris) and elsewhere in Asia (e.g. Hong Kong) will
be beneficial to both new metros and their authorities in ensuring that the economic
potential of mass transit in large cities is optimised.
Even where the measurement of convenience is better, what is less common still is thevaluation of the related service quality attributes. The experience of the UK railway
industry’s Passenger Demand Forecasting Handbook (PDFH) shows what can be achieved
with sufficient data and analysis. Transport for London’s relatively comprehensive‘Business Case Development Manual’ is good practice worth emulating elsewhere,
although extensive research on the demand response to the generalised cost componentsof trips is required.
The valuation of the individual components of convenience enables transport providers tofocus improvements on the aspects which have the greatest impacts. Even well-definedservice specifications and quality measurement processes can lead to the wrong areas
being targeted from improvements if the real long term effects are not sufficiently
understood.

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Nonetheless, many metros are instinctively and increasingly customer facing in their
management actions notwithstanding shortcomings in the measurement and valuationsof passengers’ convenience. For example there is a significant trend towards optimisingavailable information where operators understand that that the service starts when thepassenger plans to use the metro service. Finding ways to estimate the value of such
attributes is not easy, yet their effect on demand has been shown to be significant. It
could be argued that customer expectations of convenience and service quality arealways changing and therefore operators and authorities need to be receptive tochanging circumstances and available technologies in their measurement and valuation.

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MEASURING AND VALUING CONVENIENCE AND SERVICE QUALITYACKNOWLEDGEMENTS

The authors wish to thank the CoMET and Nova metro benchmarking groups forparticipating in the data collection process and for agreeing to share knowledge for this
discussion paper.

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APPENDIX A - LIST OF COMET AND NOVA METROS THAT PARTICIPATE IN THE
BENCHMARKING FACILITATED BY IMPERIAL COLLEGE LONDON

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