AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-3559-2018High spatio-temporal resolution pollutant measurements of on-board vehicle emissions using ultra-fast response gas analyzersOn-board Fast Gas MeasurementsIrwinMartinhttps://orcid.org/0000-0001-6205-4600BradleyHarryDuckhouseMatthewHammondMatthewPeckhamMark S.msp@cambustion.comCambustion Ltd., Cambridge, CB1 8DH, UKnow at: Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen PSI, SwitzerlandMark S. Peckham (msp@cambustion.com)20June20181163559356718August201720December201710May201827May2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/3559/2018/amt-11-3559-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/3559/2018/amt-11-3559-2018.pdf
Existing ultra-fast response engine exhaust emissions analyzers have been
adapted for on-board vehicle use combined with GPS data. We present, for the
first time, how high spatio-temporal resolution data products allow transient
features associated with internal combustion engines to be examined in detail
during on-road driving. Such data are both useful to examine the circumstances
leading to high emissions, and reveals the accurate position of urban air
quality “hot spots” as deposited by the candidate vehicle, useful for
source attribution and dispersion modelling. The fast response time of the
analyzers, which results in 100 Hz data, makes accurate
time-alignment with the vehicle's engine control unit (ECU) signals possible.
This enables correlation with transient air fuel ratio, engine speed, load,
and other engine parameters, which helps to explain the causes of the
emissions “spikes” that portable emissions measurement systems (PEMS) and
conventional slow response analyzers would miss or smooth out due to mixing
within their sampling systems. The data presented is from NO and
NOx analyzers, but other fast analyzers (e.g. total hydrocarbons
(THC), CO and CO2) can be used similarly. The high levels of
NOx pollution associated with accelerating on entry ramps to
motorways, driving over speed bumps, accelerating away from traffic lights,
are explored in detail. The time-aligned ultra-fast analyzers offer unique
insight allowing more accurate quantification and better interpretation of
engine and driver activity and the associated emissions impact on local air
quality.
Introduction
Urban air quality is of current concern in many of the world's cities
, with a particular focus on the health effects of
particulate and NOx emissions , and
governments are facing punitive fines for breaching agreed air quality limits
. Internal combustion engines in vehicles contribute
significantly to this air quality problem and many cities
have multiple monitoring stations for mapping their air quality on a low
temporal resolution basis.
The measured pollutants at such monitoring stations are affected by the
dispersion of the pollutant from its source (e.g. vehicle tailpipes), with
both climatic and traffic conditions causing variations in the measured air
quality . Some urban authorities issue “live” contour
maps superimposed on city street maps to inform the population of the current
air quality and enable the general population
to plan their travel routes accordingly. Techniques have also been developed
to mount mobile gas analyzers on vehicles to map the location of the worst
offending streets .
Ultra-fast response gas analyzers were first developed in 1987 with a time
response (i.e. the time taken for the output signal to fall from 90 to
10 % of its full scale following a 100 to 0 % step input signal to
the device, hereafter referred to as T1090), of a few milliseconds
and have been used since then for cyclically resolved
analysis of cold start combustion and other very transient engine phenomena
(e.g. gear changes, restarting of combustion, and emissions optimization).
Such fast response allows measurement of the transient emissions within a
single engine exhaust stroke. The deployment of ultra-fast analyzers combined
with accurate global positioning service
GPS; and engine control unit (ECU) data
enables the location of tailpipe emissions spikes to be positioned with
enhanced spatial accuracy, and the combination of the engine data helps to
explain the mechanisms causing the emission of such pollutants.
Urban driving is set by the legislation to constitute a significant portion
(no less than 29 % by distance) of a real driving emissions (RDE) test
(EU Commission, Article 2016/427, ) in recognition of
society's reliance on urban transport. Urban driving generally includes
numerous transient features. For example, acceleration away from traffic
signals, stop/start congested traffic, traffic calming measures such as speed
bumps, and awaiting clearance from oncoming traffic to proceed down narrow
streets. The accelerations and decelerations intrinsic to negotiating such
impedances often have associated spikes of emissions (e.g. a single gear
change comprises a series of engine speed and load transients). This
contrasts strongly with a typical cruise cycle on uncongested highways, where
few transients occur (except perhaps for overtaking) and the tailpipe
emissions can be relatively low over a much larger distance
.
It is worth noting that engine emissions are typically very high when the
engine is first started until the catalyst-based aftertreatment system
becomes active. In gasoline vehicles, this is largely temperature dependent
taking approximately 30 s in modern vehicles but many hundreds of
seconds for the selective catalytic reduction (SCR) NOx abatement
systems in modern diesel engines. During this warm-up time, engine pollutants
pass to the environment largely unabated. Thereafter, aftertreatment systems
are generally excellent at cleansing engine exhaust pollutants during steady
state engine conditions.
The vehicle's ECU data (specifically the exhaust or inlet mass flow,
discussed in more detail below) have been used to convert the raw analyzer ppm
concentration measurements to a g s-1 and
g km-1 value, which is a more relevant data product for atmospheric
modelling of the pollutant dispersion.
One of the applications of this technique is to help resolve emissions
calibration issues on-board a vehicle. Portable emissions measurement systems
(PEMS) have been developed by a number of manufacturers to comply with the
latest EU emissions regulations (EU Commission, Article 2016/427,
) but their response times are of the order of 1 s,
with a further “delay time” owing to the transit time of sample gas from
its source to the analyzer. Their response times are further compromised by
the additional pipe volumes required to support the exhaust mass flow
measurement system such that the resulting response time makes the emissions
data difficult to align accurately with ECU and spatially accurate GPS data.
Therefore, it is much more challenging to resolve accurately emissions spikes
due to the smoothing effect of PEMS.
One of the main challenges of RDE test work is the unavoidable variability in
testing conditions. The emissions on a given route can be affected by any of
the following list of factors and more; fuel blend, ambient temperature,
pressure and humidity, driving harshness, time and dates of travel
(prevailing traffic conditions), type of vehicle, or timing of gear changes
. To solve RDE transient emissions problems, vehicle
manufacturers are identifying the type of transient features which are
causing emissions issues, replicating the transient within the controlled
conditions of a laboratory and then solving the issue, often with the use of
fast response emissions analyzers. Such analysis is beyond the scope of this
paper. The technique which will be discussed in this paper is the
instrumentation of ultra-fast gas analyzers for tailpipe sampling and the
identification of urban road features conducive to producing high emissions
for multiple vehicles. In addition, this data could be used by city councils
or planning authorities to improve current road layouts or influence future
developments to improve urban air quality.
Schematic showing the layout of the various components required for
the on-board fast gas analyzer measurements. Dashed lines represent
electrical connections, and fluid connections are represented by solid
lines.
Instrumentation
The methodology outlined in this paper is generally applicable to any vehicle
with reasonable access to the exhaust system. This paper contains minimal
discussion about the engine and aftertreatment causes of these emissions to
keep the focus on the technique and the instrumentation. The case studies
discussed herein are reliant on data obtained using a single two-channel
analyzer operated in a configuration described below and demonstrates some,
but not all, potential features of interest when using such a technique.
Figure is a schematic showing the layout of the various
components. A GPS module is fixed to the roof of the car, and connected to
the logging computer using USB. GPS data points are subject to the standard
errors inherent to GPS . These errors include but are not
limited to atmospheric water vapour causing propagation delays in the radio
signal and multipath errors where the signal reflects off nearby buildings,
confusing the receiver. Dilution of precision due to the unfavourable
positioning of satellites is also a possible source of error, meaning it can
be very difficult to quantify the inaccuracy of any measurements taken. At
any time the accuracy of the GPS measurement could be between 2.5 and
10 m circular error probable (CEP). However, GPS is a precise
measurement technique with finest achievable resolution of 11cm at
52∘ N. GPS is logged at 10 Hz, though this was set to
1 Hz for the London drive cycle due to this being an initial test of
the technique. The 100 Hz emissions data was provided by a
two-channel fast-response chemiluminescence analyzer
CLA;, situated in the rear of the vehicle cabin,
powered via an inverter from a large capacity 12 V battery so as to
avoid unnecessarily loading the engine. The analyzer data was binned
according to GPS midpoint such that on average the mean of 10 analyzer
concentration values are mapped onto a single GPS point, resulting in a high
precision spatio-temporal emissions measurement (rather than just one
concentration measurement per spatial point). The analyzer was reconfigured
from its standard laboratory layout to minimize its size and power
consumption, making it suitable for on-board use. Engine data was logged from
the vehicle's on-board diagnostics (OBD) port, which enables access to the
controller area network (CAN) data from the ECU. These data are available at a
maximum rate of 10 Hz.
The 1100 mAh, 12 V battery, and 1000 W inverter
supplied power to the gas analyzer for > 120 min. Not shown in the
schematic are two video cameras with audio recording: one aimed at the gear
selector and another aimed forwards through the front windscreen. The video
cameras record to internal SD cards which are later time-aligned to the fast
on-board measurement data.
The analyzers were tested for vibration signal insensitivity by logging the
output to calibration gas while subjecting the rear of the vehicle to shock
vibrations whilst stationary. All data are logged to one computer running a
custom data acquisition program written in LabVIEW 2015. The data acquisition
system merges the analogue, CAN, and GPS data on a common time-base for ease
of processing (logged at 10 Hz). The program also outputs a
spatially resolved dataset where each data point corresponds to a physical
location with all faster data averaged between each time step.
Regarding sampling location, the gasoline emissions data recorded for this
study was taken from an emissions development vehicle which was being used to
establish direct links between engine operating parameters and tailpipe
emissions; a sampling pipe fitted through the vehicle floor to two sampling
points, one before the three-way catalyst and one post catalyst but
pre-muffler. For engine calibration applications, having pre and
post-catalyst sampling is essential to understanding aftertreatment operation
and excellent time alignment. For air quality applications, analyzing
post-catalyst data is more beneficial. The diesel data was taken from a
vehicle for which floor drilling was not favoured and therefore the tailpipe
data was taken post-muffler. The effect of the diesel vehicle's muffler on
the measured ppm signal yielded a delay of approximately 0.4 s equivalent to
an artificially early appearance of emissions of about 3 m distance in the
pollutant deposition positioning at 30 km h-1 vehicle speed. This
could be corrected for by post-processing if desired. For air quality
interpretation alone, tailpipe measurements are adequate, but pre-catalyst
measurements are required to correlate engine and driver inputs more
accurately to these emissions.
Overview maps showing NOx emissions from the diesel and
gasoline vehicles driven around London (a) and
Cambridge (b). Tailpipe emissions higher than 1 gkm-1
per GPS point are circles coloured in black. Points of interest are numbered:
(1) traffic light, (2) motorway ramp, (3) speed bumps (Map tiles by Stamen Design, under CC BY 3.0. Data by OpenStreetMap, under ODbL.).
Diesel-fuelled vehicle
Diesel emissions were measured from a Euro 5 compliant, 2.0 L, diesel
passenger car with 96 000 km of operation, fuelled with standard UK pump
diesel fuel . The vehicle was in good
repair and in current use with no form of NOx aftertreatment (e.g. a
catalytic convertor). For fleet context, diesel vehicles classified Euro 5
and earlier account for approximately 87 % of the UK's licensed diesel
cars in 2016 . One channel of the analyzer was configured for
measurement of [NO] and the other for measurement of
[NO] + [NO2] = [NOx] via an NO2 converter
(which decomposes NO2 to NO). The sampling pipes from these two
channels were connected to a single stainless-steel sample pipe entering the
vehicle's tailpipe, penetrating 200 mm inside the rear muffler. The
resulting T1090 response time of this sampling arrangement was
approximately 20 ms, but engine exhaust transients are heavily damped
by mixing in the muffler volume. However, depending on exhaust flow rate,
transients can be seen with an observed T1090 rise time of
approximately 100 ms, suggesting that an installation of this manner
is suitable for this application. This sampling technique measures the
tailpipe concentrations at street level (e.g. before turbulent dilution in
the vehicle's wake). The emissions data was logged at 100 Hz.
The London route was chosen because it has been used by Transport for London
for emissions studies in the past (unpublished), and the drive duration was
2.5 h for the 53 km of this route (shown in
Fig. a). An urban route around Cambridge, UK passing near
continuous air quality monitoring stations was also designed and used for
comparison of diesel and gasoline vehicle emissions (shown in
Fig. b).
Gasoline-fuelled vehicle
Gasoline emissions were measured with a Euro 4 compliant, 1.6 L,
turbocharged GDI passenger car with 80 000 km of operation, fuelled with
standard UK pump unleaded gasoline fuel
. The vehicle was in good repair and in
current use. For fleet context, gasoline vehicles classified Euro 4 and
earlier account for approximately 66 % of the UK's licensed gasoline cars
in 2016 .
The sampling points on the gasoline vehicle were different from the diesel
vehicle as it was anticipated that the NO2 emissions would be
relatively low (NO2 being mainly a byproduct of diesel-powered
internal combustion engines) . The first channel of
the fast CLA was fitted upstream of the three-way catalyst in the exhaust
(hereafter referred to as “engine-out”) and the second channel was fitted
downstream of the three-way catalyst (upstream of the muffler, hereafter
referred to as “tailpipe”).
Time series of NOx, NO, emissions in g s-1 of the
diesel vehicle with vehicle speed of a traffic light pull-away in central
London, UK. The red box represents the spatial component of the graph data,
shown in the context of the city area.
The gasoline vehicle was driven around the Cambridge route and by comparing
the engine-out to the tailpipe data, the conversion efficiency of the
three-way catalyst could be calculated.
Results
Continuously logged data (two-channel gas measurement, OBD data, and GPS
location) when combined with exhaust mass flow (see below) result in two
main data products: (1) gaseous mass emissions (e.g. NOx
in gs-1), with vehicle speed and other diagnostic data, and
(2) spatially binned exhaust mass emissions in gkm-1. Data
product (1) is essentially gas analyzer “raw data” converted into mass, and
the time resolution is very high, limited only by the analogue data
collection of the gas analyzers (and for vehicle speed, limited by the
CAN-bus). Data product (2) is limited by the GPS acquisition speed (e.g.
10 Hz), and the emissions data are binned according to GPS
midpoints. In binning the data, the mean values between each GPS bin midpoint
are calculated, resulting in an average mass emission across the GPS point.
Mass air flow (MAF) data have been used when available from the ECU (i.e. the
diesel Euro 5 vehicle), and where MAF data are not available from the ECU it
has been calculated using intake air temperature, engine speed, manifold
pressure, the dimensions of the engine, air / fuel ratio, and an
estimation of volumetric efficiency based on empirical calculations (i.e. the
gasoline Euro 4 vehicle).
Since September 2017, EU vehicle emissions legislation requires new vehicles
to comply with RDE requirements (EU Commission, Article 2016/427,
) in an attempt to make vehicles less polluting over an
extended (and largely unpredictable) set of operating conditions compared
with the standard drive cycles which have been used to date e.g.
NEDC;. One of the main challenges in capturing the more
dynamic conditions of RDE is the harsh transient driving behaviour for which
fast response emissions analyzers are well suited. For the purposes of
illustrating the applicability of this technique, the results have been
broken up into several representative aspects of real world driving,
highlighting areas of interest shown in Fig. that require
fast measurement to capture transient phenomena: (1) traffic lights,
(2) motorway ramps, and (3) speed bumps.
Emissions measurements associated with coming off, traversing, and
rejoining a motorway shown in three sections; (1) exiting the motorway,
(2) crossing under the motorway, and (3) rejoining the motorway. Panel (a) shows the fast NO emissions (g s-1) with vehicle speed on
the right axis, and panel (b) shows the route of the vehicle, coloured by
NO emissions in gkm-1. Note the different units on the scales for
each plot, and that values over 0.05 gkm-1 are all coloured
black in order give sufficient dynamic range on the colour scale.
Traffic lights
Figure shows the diesel NOx emissions increasing
from a 2.0 ± 0.8 mg s-1 baseline when stationary, associated
with sustained lean operation of the engine during the idle period, to around
70 ± 6 mg s-1 during the accelerative phases (increased engine
load) following each gear change after pulling away from the traffic lights.
The NOx emissions increase slightly after vehicle speed increases
(i.e. accelerates) at around 3266 s due to gaseous mixing and gas
transit time in the exhaust of the vehicle. This would be avoided by sampling
upstream of the muffler, where sampling port installation, not desirable on
all vehicles, is required. Figure b is a satellite view of
central London, with NOx emissions in gkm-1 shown as a
function of colour of the mean value binned per GPS mid-point, for the route
driven (driving direction indicated). The section of the drive shown in
panel (a) is contained within the red box.
Motorway ramps
Figure shows the data collected on the exit and entry slip
road to a 70 mph (∼ 112 km h-1) dual-carriageway. In
panel (a), a time series plot shows NO emissions from the Euro 4 gasoline
engine alongside vehicle speed. Phase 1 of the manoeuvre shows the
deceleration off the dual-carriageway on approach to the first roundabout. As
expected when slowing down, load on the engine is very low, and emissions are
therefore minimal. Phase 2 shows the navigation of the first roundabout
followed by an acceleration and gear-change between the two roundabouts.
Immediately after the gear change, a very short duration spike of NO can be
seen at 923 s. The magnitude of this spike is in excess of
120 ± 36 mg s-1 – a considerable emissions peak (for reference,
the current emissions standards – Euro 6 at time of publication – are
60 mg km-1NOx for gasoline and 80 mg km-1 diesel).
Using much slower conventional PEMS equipment
(T1090∼ 1 s), this highly time-resolved event would
be significantly delayed, and smoothed out over a longer period. The true
magnitude of this event would also be missed due to its short duration and
its spatial location would be difficult to place. The inclusion of
simultaneous GPS data identifies such emissions hot spots spatially. The
scale of the spatial markers on Fig. correlate colour to the
mean NO emissions at that time and location (i.e. values over
50 mg kg-1 are coloured the same as at 50 mg kg-1). A black
data point can be seen on the exit of the first roundabout to show the
emissions at 923 s. A further spike in emissions was observed at the
second roundabout due to a second deceleration, followed by an acceleration.
Phase 3 shows NO emissions spikes correlating with gear changes and high load
acceleration as the vehicle joins the main dual-carriageway. The increased NO
emissions with each high load acceleration following each gear change are
easily visualised on the GPS map plot in Fig. b.
Panel (a)NOx and NO emissions in g s-1 with
vehicle speed, showing the transient behaviour associated with driving over
three speed bumps in immediate succession. Panel (b) shows the geolocation
of each speed bump (shown in a red box) with a coloured GPS trace showing
emissions in g km-1.
Panel (a)NOx and NO emissions in g s-1 with
vehicle speed, showing the transient behaviour associated with driving over a
single speed bump in detail. The geolocation plot in panel (b) shows the
emissions in g km-1 associated with the vehicle's negotiation of the
speed bump.
Speed bumps
The data from the diesel vehicle most clearly shows the location of speed
bumps, due to the significantly higher NOx emissions associated
with all accelerative phases (i.e. higher signal compared with gasoline
measurements for the same test).
Figure shows NOx and NO emissions and vehicle
speed against time, with a spatial plot of NOx emissions in
mg kg-1 in panel (b). The NOx and NO emissions vary with
vehicle speed over three subsequent speed bumps, labelled numerically on both
the time-series and spatial plots. The nature of speed bumps is to force the
driver to slow significantly before accelerating back up to cruising speed,
and are often located immediately outside schools or in residential areas as
a safety measure. The largest emissions are again associated with the
acceleration which occurs immediately following each speed bump, as the
driver tends to accelerate towards the speed limit. The acceleration is
briefly interrupted by a gear change which is easily identified by the
significant reduction in NOx emissions followed by an immediate
sharp increase. The fast response time of this setup illustrates the high
temporal resolution of each of these fast transient features.
Figure shows the characteristics associated with a
single speed bump. Where decelerations are fairly sharp and fuel shut-off
occurs, a sharp drop in NOx is observed as there is no combustion
producing NOx emissions. Further, the drop in emissions at
2218 s is due to a gear change. The inclusion of traffic calming
measures such as speed bumps outside schools may reduce average vehicle road
speeds, but appears to increase local pollution significantly.
Error Propagation
An error propagation has been conducted for the data taken, using the general
propagation of errors formula , based on known uncertainties
for the gas analyzer, and for the diesel, data-sheet values for the on-board
mass flow meter and assumed values for λ (air–fuel ratio) . This yielded values of
±5.8 % at the maximum point and approximately ±100 % for the
baseline. For the gasoline, a comparison was made to a Bosch flow metre of
known uncertainty and errors calculated from this, with data-sheet values for
λ in this case. This yielded values of ±25.6 % at the maximum
point and approximately ±140 % for the baseline. Uncertainties have
been provided for all stated values.
Discussion and conclusions
Ultra-fast response engine exhaust emissions analyzers have been adapted for
on-board vehicle use, and when combined with OBD and GPS data allow, for the
first time, numerous transient features associated with the on-road driving
of internal combustion engines to be examined in detail. On-board, the
analyzer's sampling rate of 100 Hz captures emissions transients that
would otherwise be lost or smeared when using conventional PEMS equipment or
other slower analyzers. The ultra-fast analyzers therefore present a time
resolution improvement of two orders of magnitude over PEMS. In addition,
PEMS GPS data are recorded at 1 Hz, whereas the location data in this
study is logged at 10 Hz. This realises a 10× spatial
resolution benefit in the logged location data and a 100× analyzer
response time benefit, which matches the resolution of the GPS. The
contribution of these aspects will give a vast spatio-temporal improvement.
For an application such as the identification of pollution “hot-spots” for
the improvement of urban air quality, it is important to understand any road
features which promote transient accelerator pedal input on a local scale,
and therefore using a measurement technique able to sample many points
throughout a short duration transient is fundamental to understanding its
causes.
The analyzers were adapted for on-board use by reducing their size and power
requirements far below the original laboratory specification thereby allowing
for at least a 2 h operating interval. Further, OBD and GPS data were
logged simultaneously with exhaust emissions such that the aforementioned
transient features can be analyzed with a high temporal resolution and with
precise location. Using exhaust mass flow (or a derived value), NOx
concentrations were converted to gs-1, and then binned into the
simultaneously logged GPS points as gkm-1, resulting in a
spatio-temporal data product that can be overlaid onto satellite mapping
services (e.g. Google Maps, OpenStreetMap) for the easy identification of
emissions hot-spots. A selection of hot-spots was explored in the data
analysis, giving insight into the transient features associated with the high
emissions at these locations. Traffic lights, motorway ramps, and speed bumps
were three examples of daily driving conditions that benefit from fast-gas
measurement in terms of identifying under what engine conditions spikes in
emissions occur, and the resultant emissions hot spots in terms of geographic
location. Namely, the accelerations (including their gear changes) caused by
traffic conditions and road layout are the cause of these high emissions.
NOx was chosen for this study as being one of the main urban air
quality pollutants of concern, but fast response THC, CO and CO2
analyzers can also be used in a similar manner.
Further Work
Further improvements have already been made to the spatial accuracy of the
positioning data. Real-time kinematic (RTK) GPS has been successfully
implemented allowing an accuracy of < 1 cm to be achieved in
favourable conditions, with an increased 15 Hz logging frequency.
This has further increased the benefit of the spatial emissions positioning
of this technique.
This paper discusses the specific technique required to take high
spatio-temporal emissions data but consideration of the implications of such
data is important. GPS plots of emissions hot spots could provide important
information for local air quality councils and transport planning
authorities. However due to the relative set-up times of such testing it is
unlikely to be a useful tool for in-service compliance checks. If used
correctly, emissions maps could provide recommendations on changes to improve
local air quality. The most obvious change, from a purely scientific point of
view, would be the removal of all speed bumps as these cause transients that
contribute significantly to high NOx levels. Their placement close
to schools or pedestrian areas reinforces this argument due to the direct
impact on local air quality. However, such a change would have other political
and safety implications far beyond the scope of this paper. Further,
improvements through synchronization of traffic lights in order to streamline
traffic flows may be possible. A greater uptake in driverless or at least
intelligent vehicles that could feedback to a centralised traffic control
centre would aid this. Drivers could also shoulder some responsibility for
emissions improvement, thus education in better driving techniques or even the
inclusion of a driving quality metric into licensing tests could be
beneficial. Although this study includes several tests on two differing
vehicles, the data set is insufficient to allow the above hypothesis to be
tested. Further work in repetition of specific road elements in different
vehicles, and employing different driving styles, is needed.
Data available upon request from the corresponding author.
The authors declare that they have no conflict of
interest.
Acknowledgements
We would like to thank Transport for London (TfL) for supplying a Central and
West London candidate route. Edited by:
Folkert Boersma Reviewed by: three anonymous referees
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