This document discusses using big telematics data from vehicle tracking to assess vehicular emissions. It provides details on: - Sources of telematics data like fleet surveillance and insurance data. - Benefits like accounting for local driving conditions like traffic flow and weather in emission assessments. - Methodology used to clean the data, model instantaneous emissions, and scale emission factors based on fleet mix and traffic flows. - Case studies conducted in Sheffield and Leeds examining variability in driver behavior and emissions by time of day, weather, and other factors. - Ongoing work to refine estimates and model scenarios like potential clean air zones.

2. BIG telematics data
Vehicle tracking
2
Sources:
• Fleet surveillance e.g.
• TfL iBus data
• Eddie Stobbart
• Taxis*
• Insurance industry
• GPS and CAN/OBD link
‘white box’ tracking
• Second-by-second (1Hz)
• Youngdriver bias
• Data anonymised
* Nyhan, M., Sobolevsky, S., Kang, C., Robinson, P., Corti, A., Szell, M., Streets, D., Lu, L., Britter, R., Barrett, S., Ratti, C. 2016. Predicting vehicular
emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model. Atmospheric Environment 140
(2016) 352-363. http://dx.doi.org/10.1016/j.atmosenv.2016.06.018

3. BIG telematics data
www.thefloow.com| insights from telematics and mass mobility analysis
3 Chapman, S. 2016. Vehicular Air Pollution: Insights from telematics and mass mobility and analysis. The Floow
Ltd. Routes to Clean Air Conference, Bristol, October 2016

4. BENEFITS
BIG telematics data
4
Emission assessments
account for local, real-
driving conditions:
• Network-wide: No
boundaries
• Vehicle acceleration,
deceleration, cruising &
idling
• Variability in traffic flow
• Month of year
• Day of week
• Hour of day
• Holidays
• Special events
• Weather
FIGURE | Sample weekday GPS data by
hour

5. BACKGROUND WORK
Modelling vehicle movements & emissions
5

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Passengercar and Heavy-duty Emission Model (Euro 0 –6 / VI)FIGURES | Sample time series, TfL
London Drive Cycle, Euro 5 diesel MPV
Modelled_NOx
O
b
served_NOx
0.00
0.01
0.02
0.03
0.00 0.01 0.02 0.03
Counts
1
1
2
3
5
7
11
16
23
34
51
75
111
165
244
361
535
Modelled_CO2
O
b
served_CO2
0
2
4
6
8
0 2 4 6 8
Counts
1
1
2
3
4
6
8
11
16
22
31
43
61
86
121
171
241
Zallinger, M., Tate, J., Hausberger, S. 2008. An instantaneous emission model for the passenger
car fleet. Transport & Air Pollution conference, Graz 2008
Moody, A., Tate, J. 2017. In Service CO2 and NOX Emissions of Euro 6/VI Cars, Light- and
Heavy- duty goods Vehicles in Real London driving: Taking the Road into the Laboratory.
Journal of Earth Sciences and Geotechnical Engineering 7(1):51-62 01 Jan 2017.

7. CASE STUDIES
BIG telematics data
7
• Leeds Clean Air Zone study
• One calendar year (May 2015 – May 2016)
• 56,000 kms quality checked telematics data
• Supporting data
• Automatic Traffic Count (ATC) data (Leeds CC on A58M)
• Log special events, incidents etc.
• Turning proportions from 2015 traffic model (SATURN)
• Detailed fleet analysis from ANPR study (April 2016)
• Met. (wind speed, direction, temp, RH, rainfall)
• Sheffield City Centre
• One calendar year (May 2014 – May 2015)
• 15,000 kms quality checked telematics data
• Supporting data

8. SHEFFIELD RESULTS
Variability in driver behaviour by HOUR of day
8
FIGURE | Variation in positive VSP with HOUR of the day
NOTE: Vehicle Specific Power (VSP) is the sum of the engine loads (aerodynamic
drag, acceleration, rolling resistance, hill climbing) divided by the mass of the vehicle

9. SHEFFIELD RESULTS
Variability in driver behaviour on HOLIDAYS
9
FIGURE | Variation in positive VSP with type of DAY / HOLIDAY

10. SHEFFIELD RESULTS
Influence WEATHER conditions
10
FIGURE | Variation in
positive VSP with
RAINFALL
NOTE: Local, hourly weather data obtained from UK Met Office datasets
FIGURE | Variation in
positive VSP with
TEMPERATURE

11. LEEDS CLEAN AIR ZONE STUDY 2017
METHOD
11
'Raw'
telematics
data
Temporal &
Spatial
variationin
VEHICLE
EMISSIONS
DATA
CLEANING
Kalman filter > SPEED
& ACCELERATION
+ GRADIENT
INSTANTANEOUS
EMISSION MODEL
[PHEM]
LINK EMISSION
FACTORS (EFs)
grams.km-1 all
vehiclesub-types
WEIGHTING
& SCALING EFs
by local Fleet Mix & Flow
in time slices
Day type
School term time:
- AutumnA + B
- Spring A + B
- Summer A + B
School half-terms (all)
Christmas holiday
Easter holiday
Summer holiday
Bank holidays
Special events [X, Y, Z]
DATA
FORMAT
PHEM compatible
ANPR data
Fleet mix and
specification
Traffic
Count data
Automatic
TIME SLICE
00:00to 06:00
36 half-hourperiods:
06:00
06:30
07:00
07:30
08:00
08:30
09:00
etc
23:30
FLEET MIX
Proportionsvary by
hour & week /
weekend
A58(M)
TURNING %
Output SATURN
2015
CLASSIFIED
LINK FLOWS
all segment IDs
DIGITAL
TERRAIN MAP
0.5m grid
link GRADIENTS

12. METHOD
BIG telematics data ▶ vehicle emissions process (START)
12
'Raw'
telematics
data
DATA
CLEANING
Kalman filter > SPEED
& ACCELERATION
+ GRADIENT
INSTANTANEOUS
EMISSION MODEL
[PHEM]
Day type
School term time:
- AutumnA + B
- Spring A + B
- Summer A + B
School half-terms (all)
Christmas holiday
Easter holiday
Summer holiday
Bank holidays
Special events [X, Y, Z]
DATA
FORMAT
PHEM compatible
ANPR data
Fleet mix and
specificationTIME SLICE
00:00to 06:00
36 half-hourperiods:
06:00
06:30
07:00
07:30
08:00
08:30
09:00
etc
23:30
DIGITAL
TERRAIN MAP
0.5m grid
link GRADIENTS

13. METHOD
BIG telematics data ▶ vehicle emissions process (END)
13
Temporal &
Spatial
variationin
VEHICLE
EMISSIONS
INSTANTANEOUS
EMISSION MODEL
[PHEM]
LINK EMISSION
FACTORS (EFs)
grams.km-1 all
vehiclesub-types
WEIGHTING
& SCALING EFs
by local Fleet Mix & Flow
in time slices
ANPR data
Fleet mix and
specification
Traffic
Count data
Automatic
FLEET MIX
Proportionsvary by
hour & week /
weekend
A58(M)
TURNING %
Output SATURN
2015
CLASSIFIED
LINK FLOWS
all segment IDs

14. SUPPORTING DATA
Traffic flow on A58(M): LEEDS CC Automatic Traffic Count (ATC) site
14

15. BIG telematics data
How good is the data?
15
• Pair contrasting North-South journeys (3 of 56,000 kms data)

16. BIG telematics data
How good is the data?
16
• Pair contrasting North-South journeys (3 of 56,000 kms data)

17. LEEDS RESULTS
Passenger car NOX Emission Factors (EFs)
17
FIGURE | Average (all trajectories) passenger car NOX and NO2 Emission
Factors (EFs)

18. LEEDS RESULTS
Passenger car NOX Emission Factors (EFs)
18
FIGURE | Passenger car NOX Emission Factors (EFs) all journeys

19. LEEDS RESULTS
Variation in time & space
19
FIGURE | Autumn term-time (first half) 08:00 Q >GB> Direction South Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

20. LEEDS RESULTS
Variation in time & space
20
FIGURE | Autumn term-time (first half) 08:00 Q >GB> Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

21. LEEDS RESULTS
Variation in time & space
21
FIGURE | Autumn term-time (first half) 12:00 Q 5B> Direction South Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

22. LEEDS RESULTS
Variation in time & space
22
FIGURE | Autumn term-time (first half) 12:00 Q 5B> Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

23. LEEDS RESULTS
Variation in time & space
23
FIGURE | Autumn term-time (first half) 17:00 Q 5IB> Direction South Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

24. LEEDS RESULTS
Variation in time & space
24
FIGURE | Autumn term-time (first half) 17:00 Q 5IB> Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

25. WORK IN PROGRESS
Leeds CAZ study
25
• Key tasks:
• Sampling “calmer” driving trajectories  estimate LGV, HGV & Bus
trajectories
• Weighting & scaling time & space varying EFs by classified flow levels
• Clean Air Zone scenarios
'Raw'
telematics
data
Temporal &
Spatial
variationin
VEHICLE
EMISSIONS
DATA
CLEANING
Kalman filter > SPEED
& ACCELERATION
+ GRADIENT
INSTANTANEOUS
EMISSION MODEL
[PHEM]
LINK EMISSION
FACTORS (EFs)
grams.km-1 all
vehiclesub-types
WEIGHTING
& SCALING EFs
by local Fleet Mix & Flow
in time slices
Day type
School term time:
- AutumnA + B
- Spring A + B
- Summer A + B
School half-terms (all)
Christmas holiday
Easter holiday
Summer holiday
Bank holidays
Special events [X, Y, Z]
DATA
FORMAT
PHEM compatible
ANPR data
Fleet mix and
specification
Traffic
Count data
Automatic
TIME SLICE
00:00to 06:00
36 half-hourperiods:
06:00
06:30
07:00
07:30
08:00
08:30
09:00
etc
23:30
FLEET MIX
Proportionsvary by
hour & week /
weekend
A58(M)
TURNING %
Output SATURN
2015
CLASSIFIED
LINK FLOWS
all segment IDs
DIGITAL
TERRAIN MAP
0.5m grid
link GRADIENTS

26. OUTLOOK
BIG telematics data
26
SHORT-TERM: TargetCase Study applications
• Traffic management interventions
• Variable Speed Limits (VSL) & ‘Smart’ motorways
• Demand management to alleviate congestion
• Smoothing traffic flow including ecoDriving
• Complex, unstable, congested networks
• Challenging to observe & model traffic flow e.g. Leeds Inner Ring Road
LONG-TERM:
• Network wide, system approach
• Real-time fusion of telematics, fast IEM & in-situ flow monitoring
• All vehicle types: Buses (e.g. iBus London) and HGVs