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Abstract

Rail infrastructure is vulnerable to extreme weather events, resulting in damage and delays to networks. The impact of heat is a major concern for the London Underground (LU) by Transport for London (TfL) both now and in future, but existing studies are limited to passenger comfort on the deep tube and do not focus on infrastructure or the vast majority of the network, which is in fact above ground. For the first time, the present empirical study examines quantitatively the statistical relationship between LU delays (by synthesizing 2011–2016 industry data) with air temperature data (from Met Office archives). A range of testing shows strong statistical relationships between most delay variables and high temperatures, though not causality. Relationships were found between high temperatures and delays associated with different asset classes on different LU lines. Track‐related delays, often the focus of high‐temperature research (i.e. track buckling), show a relationship, although this is small relative to delays caused by other assets. Using UK Climate Projections 2009 (UKCP09) and assuming a similar future performance indicates that the share of annual delays owed to temperatures > 24°C may increase in frequency and length, depending on the emissions scenario. Recommendations include extending the analysis to the LU asset scale and considering the local environment to understand failure causality in order to mitigate future heat risk. A review of how TfL and other infrastructure operators capture delays for future analysis is necessary to facilitate climate resilience benchmarking between networks. Trend lines by change in the Fc¯daily per 1°C increment in the Tc¯max by London Underground line. C&H, Circle & Hammersmith lines; W&C, Waterloo & City line.
RESEARCH ARTICLE
The impact of high temperatures and extreme heat to
delays on the London Underground rail network: An
empirical study
Sarah Greenham
1
| Emma Ferranti
2
| Andrew Quinn
1
| Katherine Drayson
3
1
School of Engineering, University of
Birmingham, Birmingham, UK
2
School of Geography, Earth and
Environmental Sciences, University of
Birmingham, Birmingham, UK
3
Transport for London (TfL), London, UK
Correspondence
Sarah Greenham, University of
Birmingham, Birmingham, B15 2TT, UK.
Email: svg868@student.bham.ac.uk
Funding information
Ferranti's time was funded by EPSRC
Fellowship, Grant/Award Number:
EP/R007365/1
Abstract
Rail infrastructure is vulnerable to extreme weather events, resulting in dam-
age and delays to networks. The impact of heat is a major concern for the
London Underground (LU) by Transport for London (TfL) both now and in
future, but existing studies are limited to passenger comfort on the deep tube
and do not focus on infrastructure or the vast majority of the network, which
is in fact above ground. For the first time, the present empirical study exam-
ines quantitatively the statistical relationship between LU delays
(by synthesizing 20112016 industry data) with air temperature data (from
Met Office archives). A range of testing shows strong statistical relationships
between most delay variables and high temperatures, though not causality.
Relationships were found between high temperatures and delays associated
with different asset classes on different LU lines. Track-related delays, often
the focus of high-temperature research (i.e. track buckling), show a relation-
ship, although this is small relative to delays caused by other assets. Using UK
Climate Projections 2009 (UKCP09) and assuming a similar future perfor-
mance indicates that the share of annual delays owed to temperatures > 24C
may increase in frequency and length, depending on the emissions scenario.
Recommendations include extending the analysis to the LU asset scale and
considering the local environment to understand failure causality in order to
mitigate future heat risk. A review of how TfL and other infrastructure opera-
tors capture delays for future analysis is necessary to facilitate climate resil-
ience benchmarking between networks.
KEYWORDS
climate change, London, railway, underground
Received: 20 June 2019 Revised: 4 December 2019 Accepted: 7 April 2020
DOI: 10.1002/met.1910
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2020 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
Meteorol Appl. 2020;27:e1910. wileyonlinelibrary.com/journal/met 1of13
https://doi.org/10.1002/met.1910
1|INTRODUCTION
Transport networks are critical for societal function,
supporting the movement of commuters, goods and ser-
vices. They contribute to the global economy, forming
5.0% and 8.9% of gross domestic product (GDP) in the
United States and Europe, respectively (European
Commission, 2016; USDT, 2016). Extreme weather events
such as floods, storms and extreme heat impact transport
services, causing delay and disruption for passengers and
freight users. Accelerated anthropogenic greenhouse gas
emissions, accountable for recent mean global tempera-
ture increases, are anticipated to affect the intensity and
frequency of extreme weather events (IPCC, 2013).
Therefore, there is an increasing need for the transport
sector to understand climate adaptation opportunities.
High temperatures affect rail network performance,
often on a regional scale, in comparison with flooding
and rainfall events, which are typically more localized
(WMO & WHO, 2015). The response of railway assets to
high temperature is complex for many reasons. First, rail-
way infrastructure incorporates a wide ranges of asset
types, including track, signalling and power supply, and
each asset type has a different operative threshold for
heat. Second, the exposure of any particular asset to heat
depends on local environmental conditions, in particular
the amount of shading (Chapman et al., 2006), which
may well be unknown at the point of data analysis.
Third, heat can impact rail assets in different ways. It can
cause direct asset failure. For example, thermal expan-
sion of track in hot weather can cause rail buckling and
train derailment (Dobney et al., 2009); and excessive heat
stress on electricity provision networks can reduce asset
life expectancy (Chapman et al., 2013).
The first few hot days of the year are also associated
with increased levels of non-specific asset failures (fail-
ure-harvesting; Ferranti et al., 2016), as are days with a
diurnal temperature range > 12C (Network Rail, 2015).
Heat also leads to accelerated ageingof an asset, reduc-
ing the assets lifespan or increasing the associated main-
tenance costs. The combination of these factors makes
attributing asset failure to high temperatures problem-
atic, not least because operator databases are not
designed to record this type of information, and can also
be subject to data-recording errors. As such, quantifying
the impact of high temperatures on railway performance
is difficult, and makes predicting and planning for high-
temperature events problematic for the operator. For
example, the environmental conditions and amount of
shading is unknown for many assets and results in its
lack of focus in the public domain (Ferranti et al., 2017).
The recurrence of extreme high temperatures in the
UK has increased in recent decades, with many record-
breaking maximum temperatures reached. According to
the UK Climate Projections 2009 (UKCP09) medium-
emissions scenario, the prospective summer heat wave
season will increase in length moving into the 21st cen-
tury, occurring annually in the UK by the 2050s, espe-
cially in the southeast of England (Sanderson and
Ford, 2017). This will be exacerbated in cities because of
the urban heat island effect (Oke, 1973; Terjung and
Louie, 1973). London is a particular risk area regarding
the performance of its transport infrastructure given that
it can experience 810C spatial variability in summer
temperatures (Holderness et al., 2013). Railway infra-
structure often has a long lifespan, and current and
future planned assets will be expected to function in this
projected future climate. Therefore, it is imperative that
railway operators plan and adapt for climate change
(Quinn et al., 2018), or else risk facing a greater cost of
climate change impacts to their assets with a reactive
approach (Chinowsky et al., 2019). Heat waves are short
in duration and can reach maximum intensity very
quickly (PHE, 2015). Hence, operators must plan in
advance to be ready for heat risks.
1.1 |The London Underground
(LU) network
LU is the oldest and one of the largest underground train
networks in the world, currently managed by Transport
for London (TfL). It comprises 402 km of track and
270 stations across 11 lines (TfL, 2017a), with 37% of its
track in deep-mined tube tunnels (Jenkins et al., 2014),
carrying 1.37 billion passengers annually (TfL, 2017b).
Network disruption, for example, because of extreme
weather, is costly to both London and the UK-wide
economy.
LU measures delays in terms of lost customer hours
(LCH): the total extra journey time, measured in hours,
experienced by Underground customers as a result of all
service disruptions with durations of two minutes or
more(TfL, 2017c). LCH is a financial metric, calculated
by considering the number of customers affected, time of
day and day of week, and adjusting the costof a cus-
tomers time (hr) accordingly. Weatheris a factor
incorporated into LCH reporting, though there is no
explicit procedure for recording the type of weather
(e.g. heat or rain) and the type of delay it has caused
(e.g. rail buckle). Those delays that can obviously be
attributed to weather, such as track flooding following
heavy rain, tend to be included, but there is no unifor-
mity in the recording process. This is particularly prob-
lematic when considering heat because it impacts assets
in many ways as outlined. As LCH is a measure unique
2of13 GREENHAM ET AL.
to TfL, it cannot be compared with other methods of
operational performance from a benchmarking perspec-
tive by other researchers and rail networks.
Although there has been significant research on the
impacts of heat on rail performance (e.g. Dobney
et al., 2009; Baker et al., 2010; Jenkins et al., 2014; Doll
et al., 2014a, 2014b; Jaroszweski et al., 2015; Ferranti
et al., 2016; Roca et al., 2016; Brazil et al., 2017; Fu and
Easton, 2018), research of the temperature impacts on
LU has generally focused on the impacts to passenger
comfort (e.g. Jenkins et al., 2014), especially across the
deep tube section of the network where the temperatures
experienced tend to be highest. Here, tunnel tempera-
tures are amplified through heat generation because of
train braking mechanisms (Ampofo et al., 2004), exacer-
bated by geology as the efficiency of London clay as a
heat sink has reduced over time (Ampofo et al., 2004;
Botelle et al., 2010). Indeed, climate projection analyses
observe LU network temperatures below ground likely to
reach 40C in parts on the hottest summer days (Arkell
and Darch, 2006). This leads to near-complete passenger
dissatisfaction with thermal environments on trains
(Jenkins et al., 2014) where train cooling alone will not
satisfy the extent of the cooling required on the LU net-
work by the 2050s to maintain operational passenger
comfort.
TfL-led risk assessments conclude that impacts to the
LU network infrastructure from extreme heat are
extremely likely to happen, with consequent moderate
impacts to the network. Particular risks projected include
the overheating of trains, platforms and stations, as well
as the failure of communication, signalling and power
assets (TfL, 2011, 2013a, 2015). However, these impacts
are poorly quantified, and there is a need for a robust
statistical analysis of the current impact of heat on
under- and over-ground assets in order to inform heat-
risk mitigation and long-term planning for climate
change.
2|METHODOLOGY
2.1 |Delay data
The database used for the analysis (Nominally Accumu-
lated Customer Hours System) captures delays for the
purpose of calculating the LCH, covering: delays in ser-
vice; trains out of service; line closures; speed restrictions;
signal failures; late start-ups; station closures; and lifts
and escalators out of service. The incidentsimpacts are
calculated through the estimated increased passenger
journey time, translated to disbenefit by multiplying by
the current value of time (TfL, 2013b). Aggregated
summary results are published periodically on the TfL
website (TfL, 2018a), approximately every four weeks.
The LU delay data provided by TfL for this analysis
covered the period between January 1, 2011, and
December 31, 2016. It contained 192,541 individual delay
records, detailing time, date, train operating line, cause of
delay and length of delay (min), including a free-text field
to capture qualitative information. Each record within
the data set described a delay, logged in real time by LU
staff at a station, depot, shed or siding.
For the purpose of aggregated analysis, columns
were added to the existing database to record additional
information for each delay. This included combined
initial delayminutes and subsequent duration
(which are two separate data columns in the original
data set); quarter of the year; and station location,
whether above or below ground (TfL, 2012). Following
discussions with TfL, the recorded location of the delay
at the station level was not included in the analysis
because of suspected inconsistencies in record-keeping.
In some cases, a delay was not recorded until the train
reached the end of its route or arrived at the depot.
Additionally, as the recording process is manual, it can-
not be guaranteed that every single delay that meets
the criteria for capture is obtained. The nature of the
LU network as a high-frequency service means there is
no schedule with which to compare delays, thus the
extent of recorded delays may not be fully represented
within the database.
The research represents the first assessment of the
impact of temperature across the whole LU network.
Although this analysis is focused on high temperatures, it
was not restricted explicitly to hot days, or indeed the
summer season, since heat-related impacts occur across a
range of temperatures and asset operational thresholds
are not always aligned to design standards (Network
Rail, 2015). As rail assets need to withstand all seasons,
the full temperature range is shown in this initial assess-
ment and the results.
In order to select only those delays where tempera-
ture could be a potential cause, the data set was reviewed
by cause category, as recorded by the LU staff member
recording the incident. As the paper is focused on asset
failure relationships exclusively, all human-induced
cause categories, such as passenger/staff illness, personal
accidents/injury and criminal activity, were removed,
leaving only those delays associated with infrastructure.
These categories were:
Asset performance (AP) power: Energy supplied inter-
nally through TfL assets.
Connect/prestige: LU passenger ticketing/fare collec-
tion system and infrastructure.
GREENHAM ET AL.3of13
Distribution network operator (DNO) power: Energy
supplied by the UK National Grids infrastructure.
Fleet: Physical train infrastructure and components;
rolling stock.
National Rail: The UKs wider rail infrastructure oper-
ated by Network Rail that subsequently impacts LU.
Station infrastructure: Physical operational compo-
nents within the boundary of a train station.
Track and civils: The track on which the trains operate
across London and reside when not in service at
depots. Civils comprises the structural support for
these tracks, such as bridges and deep-tube tunnels
(TfL, 2016).
The final data set prepared for analysis obtained from
the LU database comprised 85,509 delay incidents, equat-
ing to 44% of the full data set delays between 2011
and 2016.
2.2 |Meteorological data
Meteorological data for the same period (January
1, 2011December 31, 2016) were taken from the MIDAS
land surface data set available from the Centre for Envi-
ronmental Data Analysis (CEDA) (2017). Geographical
information system software was used to select those
weather stations from the MIDAS data set that were
located within 10 km of the LU network, as this
encompassed almost the entirety of the network. This
resulted in 10 weather stations for use.
For each weather station, several temperature variables
were available, including maximum air, ground and grass
temperatures measured twice daily at 0900 and 2100 hours.
This was the only detailed temperature data set available
for this area and time period in accordance with the delay
data. As a result, the absolute daily maximum temperatures
per day were not reflected (i.e. early/mid-afternoon). Previ-
ous studies investigating the relationship between high
temperatures and railways used air temperature data, such
as the absolute daily maximum recorded temperature
(Dobney et al., 2009, 2010; Jenkins et al., 2014) or the mean
of maximum daily temperature across a subset of weather
stations (Ferranti et al., 2016). Initial checks of the meteoro-
logical data also showed air temperature to have the most
complete series compared with those available for ground
or grass measurements for the selected weather stations.
The means of the two recorded daily temperature per day
(Tc¯
max
) were calculated against every single delay record in
order to provide a single daily temperature value per
weather station. For example, July 12, 2015, for Heathrow
Airport was observed to reach 36.7C(Kendonet al., 2016),
though the calculated Tc¯
max
was 31.9C.
The lowest Euclidean distance between every LU sta-
tion and weather station was calculated in order to syn-
thesize a single Tc¯
max
variable to every delay in the LU
database. Where a weather station failed to record data,
the next nearest weather from those selected was used, as
five weather stations ceased to record temperatures at
varying points through the duration of the study period.
This was adjusted manually referencing 5 and 10 km dis-
tance buffers around each weather station to gauge the
next closest weather station. A total of 1,072 delay inci-
dents were removed from the LU database between
December 1231, 2011, and December 2331, 2012, as no
temperature data were recorded by any weather station.
2.3 |Climate projections
The UKCP09 climate projections were used in order to
understand how the impact of temperatures may impact
LU delays in future. Projections for low (B1), medium
(A1B) and high (A1FI) emissions scenarios were obtained
from the UKCP09 (accessed in 2018). The values, produced
by the UK Meteorological Office Hadley Centre models
HadCM3, HadRM3 and HadSM3 (Murphy et al., 2007),
were divided into monthly sets of 107 values and averaged
toprovideatableofmeanchangeinCper emissions sce-
nario, per time period, per month. Baseline temperature
statistics were also compiled from the LU data set in order
to compare how the number of days annually exceeding
certain temperature thresholds could change over time, by
emissions scenario. Temperature thresholds selected were
the number of days that the Tc¯
max
>24and>27
C, as UK
rail infrastructure operators take precautionary measures
on their networks from 24C (PHE, 2015), and 27Cisthe
recognized point at which assets such as track begin to
buckle (Dobney et al., 2009).
2.4 |Derived metrics
In order to understand the impact of high temperatures,
and following discussions with analysts based at TfL, four
variables (year, LU line, cause category and location)
were cross-analysed against five delay metrics (Table 1).
Although low temperatures can have an impact on delays
and they are not the focus of the paper, low temperature
metrics were not considered.
A range of delay metrics was tested as performance is
not measured in the same way across infrastructure oper-
ators and researchers, for example: frequency or number
of delays or incidents over a given period (Ferranti
et al., 2016; Fu and Easton, 2018); delay minutes deriving
from the length of the delay (min) to the schedule
4of13 GREENHAM ET AL.
(Dobney et al., 2009; Jaroszweski et al., 2015; Ferranti
et al., 2016; Roca et al., 2016; Fu and Easton, 2018); mean
journey or subsequent delay length (Brazil et al., 2017);
overall economic/damage costs to a given network or
asset over a period of time (Baker et al., 2010; Doll
et al., 2014a, 2014b); and customer dissatisfaction
(Jenkins et al., 2014). The five metrics selected were all
obtainable directly from the LU database content, and as
such enabled direct comparison between their relation-
ships with temperature in order to identify what is
affected to a greater extent by temperature change.
2.5 |Analytical approach
Analysis was undertaken in three steps. First, regression
was used to test statistically the significance of each vari-
able and delay metric to the Tc¯
max
. Preliminary testing of
data indicated that trends were nonlinear, often parabolic
(Network Rail, 2015). Regression, therefore, comprised
three co-efficients to satisfy the quadratic equation
y=b
0
+b
1
x+b
2
x
2
, where xis the Tc¯
max
to find y, where
yis the delay metric. Variables and delay metrics were
also rationalized based on the strength of their correla-
tion co-efficients (r
2
0.5) and statistical significance
(p0.05). The hypothesis tested for all regression under-
taken is whether delay metrics increase as temperature
diverges from the mean London annual temperature of
15C (though the focus here is on the higher temperature
range). The strongest relationships were then highlighted
and investigated in more detail with further statistical
testing. Second, the delay metrics from periods of
extreme heat within the data set were reviewed. These
periods were defined as days where the Tc¯
max
was signifi-
cantly higher than the respective monthly Tc¯
max
. The
delay metrics and variables of extreme heat periods were
compared with delay metric and variable monthly means
in order to identify change in the delay distribution.
Where necessary, the individual delay incident was
reviewed for further details on the cause of the delay, and
whether the temperature was logged as part of the
incident. Finally, climate projection statistics were
reported as outlined in Section 2.3.
3|RESULTS
3.1 |Aggregate results and key findings
Each delay metric was compared with the change in the
Tc¯
max
, reporting correlation and regression co-efficients
as shown in Table 2. The mean daily metrics had a strong
parabolic relationship with change compared with tem-
perature. However, this was not the case for mean indi-
vidual delay metrics. Despite the Hc¯
daily
s high
correlation co-efficient, the same cannot be said for the
Hc¯
delay
, which has a weak parabolic relationship and no
statistical significance to change in the Tc¯
max
. The LCH is
therefore limited as a measurable metric in the context of
temperature change. All mean daily metrics report
greater values at the highest temperatures as opposed to
the lowest temperatures.
All delay metrics and variables selected for analysis
were distributed disproportionately amongst the prepared
data, suggesting that some parts of the LU network are
more susceptible to delays than others. Across the entire
prepared data analysed, there was a general improvement
in annual total number of delays and LCH over time
(from 19% to 15% and from 16% to 19% between 2011 and
2016, respectively). However, delay length increased
slightly (from 16% to 19%).
As the Fc¯
daily
indicates the strongest parabolic rela-
tionship, Figure 1 highlights two different ways this
breaks down by variables (LU line and general delay
location). The Central line has the greatest overall share
of the Fc¯
daily
(Figure 1a) and is double that of any other
LU linesFc¯
daily
when the Tc¯
max
>30
C.
LU-wide, the Fc¯
daily
is predominantly greater below
ground; however, the Fc¯
daily
above ground increases at a
greater rate from around a Tc¯
max
>20
C, reporting more
TABLE 2 Regression co-efficients of all prepared data for
analysis by change in each delay metric per 1C increment in the
Tc¯
max
r
2
b
0
b
1
b
2
Fc¯
daily
0.91** 54.59 3.02 0.12
Lc¯
daily
0.69** 1,284.10 87.47 3.16
Hc¯
daily
0.79** 40,661.00 2,298.60 91.65
Lc¯
delay
0.19* 23.29 0.42 0.01
Hc¯
delay
0.06 743.71 1.52 0.11
Note: *p0.05; **p0.01.
TABLE 1 Description of each metric tested
Metric Description
Fc¯
daily
Mean daily frequency of delays
Lc¯
daily
Mean daily length of delays (min)
Hc¯
daily
Mean daily lost customer hours (LCH)
Lc¯
delay
Mean delay length (min)
Hc¯
delay
Mean delay LCH
GREENHAM ET AL.5of13
delays above than below ground when the Tc¯
max
>24
C
(Figure 1b). These figures also show an increase in delays
at low temperatures on the Central line (Figure 1a) and
above ground (Figure 1b).
The Fc¯
daily
is also broken down by cause category
(Table 3) to identify the asset categories most susceptible
to delays. As the LU lines and network has developed
over varying timescales, asset age will also vary, so high
correlation co-efficients may indicate pre-existing vulner-
abilities. Here, the fleet-related Fc¯
daily
reports the stron-
gest parabolic relationship to the Tc¯
max
, followed by
station infrastructure, signals, and track and civils. Each
of these cause categories was further broken down by LU
line and the following highlights were drawn.
FIGURE 1 Trend lines by change
in the Fc¯
daily
per 1C increment in the
Tc¯
max
by (a) London Underground line
and (b) location. C&H, Circle &
Hammersmith lines; W&C, Waterloo &
City line
TABLE 3 Correlation and
regression co-efficients for all data by
change in the Fc¯
daily
per 1C increment
in the Tc¯
max
per London Underground
cause category
r
2
b
0
b
1
b
2
Asset performance (AP) power 0.25* 0.41 0.04 0.00
Connect/prestige 0.10 0.28 0.01 0.00
Distribution network operator (DNO) power 0.40** 0.47 0.07 0.00
Fleet 0.86** 23.69 1.33 0.05
National Rail 0.38** 0.65 0.03 0.00
Signals 0.75** 9.50 0.56 0.02
Station infrastructure 0.77** 15.58 0.75 0.03
Track and civils 0.61** 4.27 0.31 0.01
6of13 GREENHAM ET AL.
3.1.1 |The Central line is the principal
driver of the fleet-related Fc¯
daily
at every
Tc¯
max
Within the Central line, a high proportion of its Fc¯
daily
is
because of asset failures of its fleet, particularly when the
Tc¯
max
is high. Four fleet-related assets drive this trend
(automatic train control; auxiliary system; fault reporting
equipment; and heating and ventilation). Delays due to
heating and ventilation have the greatest correlation co-
efficient (r
2
= 0.77). On days when the Tc¯
max
>28
C,
there is an average of two delays because of heating and
ventilation failures, each lasting on average 13 min.
Therefore, the expected Lc¯
daily
impact to Central line
commuters on such a day is 26 min.
3.1.2 |A combination of LU lines
contributes to the overall station
infrastructure Fc¯
daily
These LU lines are the Circle & Hammersmith (C&H),
Metropolitan, Northern, and Piccadilly, all reporting
r
2
0.5. The station infrastructure Fc¯
daily
increases are
predominantly because of component failures on escala-
tors and lifts at stations on the LU lines. Escalator com-
ponent failures occur more frequently than lift
component failures, and increase at a greater rate as the
Tc¯
max
increases. The average number of delays with lift
and escalator failures when the Tc¯
max
>24
C is between
three and five. However, the average delay length of
these is < 1 min, so the delay impact to a commuter on a
hot day may be no more than 5 min.
3.1.3 |The District line drives the signal-
related Fc¯
daily
, particularly once the
Tc¯
max
>15
C
Within District line signalling assets, only one asset type
(links) is notably impacted by change in the Tc¯
max
(r
2
= 0.55). This is related to issues such as blown fuses
and cable faults to signalling equipment when in opera-
tion. The Fc¯
daily
is also greatest when the Tc¯
max
is high,
although this type of delay occurs on average less than
once per day. Other asset types are statistically signifi-
cant, though correlation co-efficients are low. However,
as there are more asset types within signals than fleet or
station infrastructure, there is a likely compounding
influence on the overall signals cause category. A links-
related delay is an average of 93 min, which, though
infrequent, has the potential to cause significant delay to
a commuter on a hot day.
3.2 |Periods of extreme heat
Three periods of extreme heat of varying lengths were iden-
tifiedinthedataset:July323, 2013; July 1, 2015; and
1215 September, 2016. Figure 2 shows that during the July
2013 heat event, track and civils delays were higher than
the July average. Several track speed restrictions (TSRs)
were enforced (94 of 153 track and civils delays recorded),
and track temperatures were recorded on the LU database
at some sites at > 42C. A total of 64 of these delays were
documented as being enforced directly because of heat-
related conditions, exceeding critical rail temperatures. The
remainder of the TSR-related delays were documented as
rail defects (apart from one, which was a tree-related track
issue), but heat-related conditions were not mentioned and
therefore cannot be assumed to be related.
During the single hot day of July 2015, the Lc¯
daily
was
double the July mean, predominantly caused by a range of
signals and fleet incidents on the District and Piccadilly
lines. Delays caused by DNO power incidents on July
1, 2015 (Figure 2), were greater than the July mean because
of two power cuts that closed one LU station for most of
the day. During September 1215, 2016, the Lc¯
daily
for this
period was double the September mean. There was a
greater number of fleet-related delays (Figure 2) because of
component failures across many different fleet assets on
the Central Line (40 of 100 fleet delays recorded).
3.3 |Climate projections
During the analysis period for the study (January
1, 2011December 31, 2016) there were nine days where
24 < Tc¯
max
<27
C and three days where the
Tc¯
max
>27
C. Table 4(a) shows that the number of days
likely to exceed these temperatures according to the
UKCP09 emissions scenarios will increase, extending the
summer season. For example, in a low-emissions sce-
nario, the Tc¯
max
>24
C only occurs between June and
mid-August; however, under a medium-emissions sce-
nario, this extends into early September by the 2080s.
This is also similar under the high-emissions scenario,
though by the 2080s the Tc¯
max
>27
C occurs regularly
between mid-July and mid-August. Consequently, with-
out adaptation or asset renewal, delay projections will
increase under all scenarios (Table 4, b, c). Current analy-
sis reveals that 3.1% of annual delays (accounting for
3.0% of the total delay length) take place when 24 <
Tc¯
max
<27
C. When the Tc¯
max
>27
C, this is an addi-
tional 1.3% for both the number and length of delays.
Assuming similar asset performance and relationships to
temperature in future, the annual proportion of delays
that take place when the Tc¯
max
>24
C in the 2080s
GREENHAM ET AL.7of13
under a high-emissions scenario could increase to 28.8%.
In addition to an increase in delays at high temperatures,
there may be a corresponding reduction in delays at low
temperatures, for there may be fewer cold days.
4|DISCUSSION
The paper highlights how different delay metrics are
affected by high temperatures on the LU network. First,
the strongest relationship lies between the Tc¯
max
and
Fc¯
daily
, as well as a relatively strong relationship between
the Tc¯
max
and Lc¯
daily
. Although the Hc¯
daily
also indicated
a strong relationship, there are external factors that are
demonstrated by no statistical significance between the
Tc¯
max
and Hc¯
delay
. Second, the distribution of delays by
tested variables is uneven. The strongest relationships lie
across assets on the Central lines fleet, signals on the
District line, and above compared with below ground as
the Tc¯
max
increases. Third, the Fc¯
daily
and Lc¯
daily
have the
potential to double compared with their monthly means
during extreme heat events under current operative
FIGURE 2 Comparison of the
relative proportion of the total number
of delays between high-temperature
periods (solid fill) and their monthly
means (diagonal fill) by cause category.
AP: asset performance; DNO:
distribution network operator
TABLE 4 (a) Number of days
annually, (b) relative proportion of the
total number of annual delays and (c)
relative proportion of the total length of
annual delays (min) estimated to exceed
the 24 and 27C thresholds in the Tc¯
max
in a typical year according to UK
Climate Projections 2009 (UKCP09)
emissions scenarios
Low (B1) Med (A1B) High (A1FI)
(a) 24 < Tc¯
max
<27
C 2030s 14 33 41
2050s 16 36 57
2080s 19 49 50
Tc¯
max
>27
C 2030s 0 0 0
2050s 0 0 6
2080s 0 0 29
(b) 24 < Tc¯
max
<27
C 2030s 5.0% 5.7% 6.7%
2050s 11.7% 12.8% 17.4%
2080s 14.6% 20.3% 17.4%
Tc¯
max
>27
C 2030s 0.0% 0.0% 0.0%
2050s 0.0% 0.0% 0.0%
2080s 0.0% 2.4% 11.4%
(c) 24 < Tc¯
max
<27
C 2030s 5.2% 5.9% 7.0%
2050s 12.2% 13.4% 18.2%
2080s 15.3% 21.2% 18.1%
Tc¯
max
>27
C 2030s 0.0% 0.0% 0.0%
2050s 0.0% 0.0% 0.0%
2080s 0.0% 2.5% 12.2%
8of13 GREENHAM ET AL.
conditions. Finally, delays caused by high temperatures
are likely to increase as a relative proportion to the total
annual delays under all climate change projection
scenarios.
4.1 |Implications for TfL
The paper has produced the first results of their kind for
the LU network. Consequently, TfL was able to its take
first steps in future heat mitigation with quantitative evi-
dence, and some of these results have been presented to
stakeholders in the UK government. The Fc¯
daily
is partic-
ularly affected when the Tc¯
max
is high. Central line trains
are around 25 years old, which is within the acceptable
lifespan (3050 years) for rolling stock (TfL, 2017d). The
identified heating and ventilation asset failures are linked
to passenger comfort and, as such, often lead to service
withdrawal. Other rail networks have experienced similar
failures, such as in Melbournes (Australia) summer in
2009 (McEvoy et al., 2012), but climatic and asset condi-
tions are not comparable with LU. Heating and ventila-
tion assets, therefore, require a greater level of
investigation in order to identify the cause of failure in
such conditions as it cannot be assumed from other case
studies.
TfL has already installed several different types of
heat-mitigation measures on the LU network. These
include new air-conditioned rolling stock across some LU
lines (District, Metropolitan, and C&H) and increased
ventilation shaft capacity on selected LU linesstation
platforms. However, it is important to note that land
availability is highly restricted within London and it
limits the extent of installing measures such as these.
Additional trackside management takes place in the form
of heat dutiesby staff who inspect track conditions and
enforce TSRs to mitigate derailment risk.
However, these measures do not yet consider all the
addressed hazards in terms of the relationships identified
in Section 3.1. For example, there are currently no mea-
sures to address the signalling failures because of high
temperatures and the long delays they cause (albeit fairly
infrequently). The signalling equipment on the District
line is reported as legacyequipment, and is degrading
due to its age (TfL, 2016), but it is not clear whether this
degradation has been accelerated by weather-related
impacts. Information and communication technology-
based assetsrisk of obsolescence and overall shorter
lifespan than assets such as rolling stock, track and brid-
ges highlights the growing importance of integrating
weather exposure into an assets life cycle costbenefit
analysis to improve its resilience. Where current signal-
ling assets are already vulnerable to failure under high
temperature conditions, it is imperative that asset
renewal processes take into account the impact of current
weather and longer time climatic change.
Furthermore, the paper highlights that heat-related
failures on non-track assets can be the greatest contrib-
utors to delay metrics: track-related delays are a small
proportion of overall delays during high temperatures
on the LU network. LU track is continually welded and
designed to be stress neutral at 27C, and no track
buckling incidents are recorded in the LU database. TfL
is thus confident in its track resilience under extreme
high temperatures (TfL, 2018b), and as track conditions
influence their buckling thresholds (Dobney
et al., 2009), this also infers that LU track is in good
condition. Nevertheless, high temperatures tend to be
combined with drier conditions. Moisture-sensitive
clays, the principal construct of London geology, can
contract, leading to track geometry distortion (Doherty
et al., 2012; Pritchard et al., 2014). This warrants further
investigation into future LU track hazards and their
risk beyond buckling.
4.2 |Implications for the rail industry
and infrastructure operators
The paper recognizes that there are clear trends and rela-
tionships between delays and high temperatures. How-
ever, it does not determine the causality of delays caused
by high temperatures because the LU data sets delay
causes can only be subjectively categorized as due to
heat. For example, testing the heat-related impact of fail-
ure harvesting as demonstrated with Network Rail data
(Ferranti et al., 2016) could not be achieved in the present
research due to LU data gaps necessary for the methodol-
ogy (quantitative specification of heat or temperature-
related key words assigned to a particular delay).
Nevertheless, the overarching relationship between
the Tc¯
max
and Fc¯
daily
for LU is comparable with that
found for Network Rail (2015). This demonstrates that
when observing similar trends, network benchmarking
could be achievable through reporting standardization.
Transport sector climate change adaptation research is
considered limited by being too generic or too specific in
subject matter for stakeholders to exploit (Eisenack
et al., 2012). What little is available at the intermediate
sectoral or regional level is not yet tailored enough to
meet the needs of stakeholders with minimal resources,
given the appetite for off-the-shelftools (Hughes
et al., 2009). Data standardization at the critical infra-
structure scale as a holistic evidence base is, therefore, an
opportunity to bridge the gap in the intermediate level of
adaptation planning.
GREENHAM ET AL.9of13
Furthermore, rail operators could benefit from pub-
lishing and reviewing their fault-reporting data with
other non-rail infrastructure operators in order to under-
stand the role of interdependencies that lead to rail
delays. Faults with energy-provision networks, for
instance, can cascade to influence the performance of rail
networks from loss of power supply to signalling assets.
Presently, the instances where externalities affect a LU
delay record may not be distinguishable, as it may not be
clear at the time of the incident to what the asset failure
was owed. Although it would be beneficial to ensure
delays are less ambiguously captured (Ferranti
et al., 2016), this is limited in the remit of the operating
staff due to interdependencies and known unknowns.
Delay recordsstandardization from the wider infrastruc-
ture network operators could help mitigate this chal-
lenge, provided that reporting is on comparable metrics.
4.3 |Implications in the wider context of
future climate change
Railway infrastructure is of growing importance as
policy-makers aim to encourage a modal shift away from
road vehicles to reduce greenhouse gas emissions. How-
ever, vehicle usage remains high, as its proportion of
users to rail/tram/metro is disproportionate at a ratio of
nearly 9:1 (European Commission, 2017). Furthermore,
global infrastructure investments remain low in compari-
son with global population growth (MGI, 2016). Over
54% of the global population lives in urban areas, and
this percentage is increasing (World Bank, 2018). Thus,
demand for transport infrastructure is also very likely to
increase. Therefore, if the present infrastructure still
remains by the end of the century, rail-related damage
costs caused by extreme weather may increase, and to a
relatively greater extent than road- and aviation-related
damage costs (Doll et al., 2014a, 2014b). In this case, reac-
tive adaptation stimulated by an extreme weather event
(Walker et al., 2015) may become more frequent. Both
the public and private sectors should consider ways in
which to integrate climate adaptation cost-effectively into
their business-as-usualactivity, such as following the
framework outlined by Quinn et al. (2018) to negate these
added associated financial costs.
There lies a particular challenge for rail and other
transport infrastructure beyond the scope of the paper.
Although the LU network is well invested and managed,
a relationship between increasing high temperatures and
increases in delay metrics is apparent. It is still uncertain
which emissions scenario trajectory is most probable,
therefore there remains a possibility that a high-
emissions scenario could be reached by the end of the
century (Sanford et al., 2014). In that case, the coupling
of urban population growth and climate change on infra-
structure could result in unprecedented pressures, and
potentially lead to network-wide operational failure at
high temperatures.
4.4 |Study limitations
The potential geographical inaccuracy of LU data records
resulted in the absence of a spatial assessment, which
would have been advantageous in this field of research.
In light of the spatial sensitivity rail infrastructure has to
local environmental context and conditions (Chapman
et al., 2006), this is a limiting factor of the present paper.
The impacts of the immediate surroundings, however,
such as tree or building shade, would be beneficial to val-
idate this inference in order to uncover delay hotspots.
This research has been undertaken using six years of
industry and meteorological data, which is a relatively
short period of time from which to draw conclusions
about the relationship between temperature and delays,
so the present research does not attribute causality. The
temperature data used here do not record the maximum
at the peak time of day, a fact that would be useful to
obtain for further detailed analysis in this research area.
Other meteorological factors such as precipitation were
omitted for the purpose of the study, and therefore the
authors cannot indicate if a delay is connected to multi-
ple weather events (e.g. a thunderstorm following a
period of extreme heat). In addition, using climate projec-
tions (which themselves are subject to a degree of uncer-
tainty) on the results from this short time period mean
that the study can only provide an indication of the likely
increases in delay metrics under extreme temperatures in
future.
The influence of interdependencies, which were
beyond the scope of the paper, further contributes to the
studys limitations.
5|CONCLUSIONS
The approach to existing research on the impacts of
weather events on rail infrastructure varies in terms of
metrics. The paper recommends that publishing delay fre-
quency and length metrics across rail infrastructure net-
works enables operational performance benchmarking
with regards to extreme weather. This knowledge sharing
on best practices to record delays could enable an
improved approach to trend analysis, and the opportunity
for benchmarking climate change preparednessin
future.
10 of 13 GREENHAM ET AL.
The London Underground (LU) network is a glob-
ally recognizable and high-profile network facing a
range of challenges, including uncertainty around cli-
mate change, capacity and future demand. To mitigate
this, recommendations include further research in
order to scope out risks to individual assets. As causal-
ity is yet to be determined, a macro-scale examination
of the London and UK-wide interdependencies and
consequential hazards that increased high temperatures
may bring could shed some light on the problem. At
the micro-scale, a spatial assessment of local geography
and environmental conditions surrounding the infra-
structure, as well as combining other meteorological
events (e.g. precipitation), may aid in the identification
of causality and support climate change adaptation
prioritization.
ACKNOWLEDGEMENTS
The authors thank the two anonymous reviewers for
helpful suggestions and feedback on the paper. The
authors are grateful to TfL for providing the data and
necessary support to produce this research. This paper is
derived from research by Greenham (2018) as part of
their Masters degree.
CONFLICT OF INTERESTS
The authors declare no conflicts of interest.
ORCID
Sarah Greenham https://orcid.org/0000-0001-7505-
5645
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How to cite this article: Greenham S, Ferranti E,
Quinn A, Drayson K. The impact of high
temperatures and extreme heat to delays on the
London Underground rail network: An empirical
study. Meteorol Appl. 2020;27:e1910. https://doi.
org/10.1002/met.1910
GREENHAM ET AL.13 of 13
... 7 The current thermal environment of the LU means that there is significant potential for recovering waste heat from tunnels, while cooling solutions are expected to gain importance as air temperatures rise in the future due to climate change, which is likely to increase train service delays. 8 This opportunity led to the development of the Bunhill WHR System, a first of its kind scheme that will recover waste energy from a ventilation shaft of the LU network, whilst also being able to supply cooling to the tunnels, as introduced in Ref. 9. This paper builds upon an analysis of the cost and carbon benefits of the WHR system, 10 and aims to estimate how the cooling effect provided by the heat recovery coils (HRC) may impact the LU environment in the future. ...
... These temperature reductions might lead to several tangible benefits for LU, such as increasing the wellbeing of passengers and staff, 25 reducing risk of train delays caused by high temperatures, 8 as well as unlocking potential for service frequency and ridership to be increased. Temperature mitigation is a key enabler to service upgrades; however, the cooling benefits from the WHR system are mostly limited to adjacent stations, meaning mitigation measures would also be required at other locations in order to achieve network-wide benefits. ...
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Recovering waste heat from urban infrastructures is becoming increasingly important as the UK strives to decarbonise heat, which remains one of the main challenges in the transition towards net zero. The Bunhill Waste Heat Recovery (WHR) System represents a first of its kind scheme that will recover waste energy from a ventilation shaft of the London Underground (LU) transport network. The system is based upon the installation of a heat recovery heat exchanger that consists of cooling coils and a reversible fan. The coils are connected to a heat pump that supplies low-carbon thermal energy to the Bunhill Heat Network in Central London. One particularly important aspect of the Bunhill WHR system is its ability to operate in a way that not only provides heating to the local heat network, but can also simultaneously supply cooled air to the LU tunnels depending on the operation of the reversible fan. The current paper estimates the potential cooling benefit that could be achieved with the WHR system based upon the development of a mathematical model. The model is able to predict the condition of the coil surface according to air inlet parameters, and this is used to calculate the latent and sensible cooling loads, which are applied to simulate how the system affects the local tunnel environment, with peak temperature reductions of up to 7.2 K being estimated for adjacent stations in 2030. The results from the investigation are presented together with recommendations for further development and future deployment of heat recovery from metro systems, as this technology could be applied across London and elsewhere to deliver significant carbon and cost savings while improving the thermal environment of railway tunnels. Practical Application This work investigates the cooling potential behind a practical project that involves recovering waste heat from the LU network. As electrification leads to an increased deployment of heat pump and district heating systems, waste heat could become a valuable resource for maximising energy efficiency, even more so when additional cooling benefits can be achieved. This paper aims to explore the impacts of cooling on railway tunnels, emphasising how secondary benefits, which are many times overlooked, could be critical to making waste heat recovery economically feasible, maximising its potential as a key technology for decarbonising heat.
... One of the most important transportation systems that, in addition to being environmentally friendly, can help reduce greenhouse gases, is the rail transportation system. Railways are a vital part of advanced societies, accounting for about 5 to 8.9 percent of GDP (Gross Domestic Product) in Europe and the United States (Greenham, et al. 2020). ...
... Greenham, et al. 2020)-(Villalba and Franco 2019)-(Ferranti, et al. 2017)-(Jaroszweski and Hooper 2014)-(Ludvigsen and Klaeboe 2014) The threshold of extreme event percentiles and potential impacts(Kaewunruen, Li and Sakdirat 2019) ...
... In 2016/2017, the Underground consumed over 1,700 GWh of electricity, with around 500 GWh of energy ending up degraded and released as waste heat [6]. The current thermal environment of the LU means that there is significant potential for recovering waste heat from tunnels, while cooling solutions are expected to gain importance as air temperatures rise in the future due to climate change, which is likely to increase train service delays [7]. This opportunity led to the development of the Bunhill WHR System, a first of its kind scheme that will recover waste energy from a ventilation shaft of the LU network, whilst also being able to supply cooling to the tunnels, as introduced in [8]. ...
... For scenarios 2, 3 and 4, which involve a combination of extract and supply modes, the average ΔTs, considering both adjacent stations, were of 1.1, 2.6 and 4.5°C, respectively, highlighting how the cooling benefit can be increased if the system operates for longer periods in supply mode. These temperature reductions might lead to several tangible benefits for LU, such as increasing the wellbeing of passengers and staff [23], reducing risk of train delays caused by high temperatures [7], as well as unlocking potential for service frequency and ridership to be increased. ...
Conference Paper
Full-text available
The Bunhill Waste Heat Recovery (WHR) System is a first of its kind scheme that will recover waste energy from a ventilation shaft of the London Underground (LU) network. The system is based upon the installation of a heat recovery heat exchanger that consists of cooling coils and a reversible fan. The coils are connected to a heat pump that supplies low carbon thermal energy to the Bunhill Heat Network in the London Borough of Islington. One particularly important aspect of the Bunhill WHR system is its ability to operate in a way that not only provides heating to the local heat network, but can also simultaneously supply cooled air to the LU tunnels depending on the operation of the reversible fan. The current paper provides an analysis of the heating and cooling duties and their associated cost and carbon savings against conventional technologies based upon a mathematical model of the WHR system. The model is able to predict the condition of the coil surface according to air inlet parameters, and this is used to calculate the latent and sensible cooling loads, which are applied to simulate how the system impacts the local tunnel environment, with peak temperature reductions of up to 7.2 °C being estimated for adjacent stations in 2030. The results from these analyses are reported, together with recommendations for further development and future deployment of heat recovery from metro systems.
... Publications by scholars in a wide range of fields indicate beneficial contact or provision of important information by TfL (e.g., Greenham et al., 2020). While no study of airborne contaminants in the New York subway indicate MTA's cooperation, TfL has solicited advice on air-quality issues in the Underground and elsewhere from university researchers (Transport for London, 2017). ...
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The public transportation workers of New York City lost their lives to COVID-19 at a shocking rate in the spring of 2020, likely abetted by their employer’s resistance to allow workers to wear masks until mere days before a region-wide lockdown was declared. We might see this death toll as a tragic outcome of uncertainty in the face of the unprecedented, yet the stance of the employer (the Metropolitan Transportation Authority, or MTA) was consistent with its longstanding reluctance to assimilate or pursue signals that suggest need for safety reforms — that is, until a worker dies. This article terms this pattern a “death-based model of organizational learning,” and situates the virus’ toll on transport workers from three angles: first, from workers’ experience of existential precarity in their workplaces, rooted in dangers workers readily problematize but which are not addressed by management; second, by showing how the MTA may modify rules following an employee fatality, at least when that death cannot be explained by individual failures alone; and third, by exploring the MTA’s longstanding hostility to health and safety research conducted in its physical and institutional bounds. These prior patterns articulated in the MTA’s response to COVID-19, such as in passivity in the face of general public health guidelines, disinterest in obvious founts of expertise to tailor its response to the pandemic, and in the eventual acceptance of a nascent public health role in light of the mounting death toll of its employees.
... Publications by scholars in a wide range of fields indicate beneficial contact or provision of important information by TfL (e.g. Greenham et al., 2020). While no study of airborne contaminants in the New York subway indicate MTA's cooperation, TfL has solicited advice on air-quality issues in the Underground and elsewhere from university researchers (Transport for London, 2017). ...
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