Dr James Tate - Better estimation of vehicle emissions for modelling - DMUG17

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BIG telematics dataVehicle tracking

Sources:▶ Fleet surveillance e.g.

• Eddie Stobbart• Taxis*

• Insurance industry GPS and CAN/OBD

link ‘white box’ tracking

Second-by-second (1Hz) data

Young driver 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

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BIG telematics datawww.thefloow.com| insights from telematics and mass mobility analysis

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 https://www.slideshare.net/secret/km7kcqE8oHtrn9

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BENEFITSBIG telematics dataEmission 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• etc

FIGURE | Sample weekday GPS data by hour

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BACKGROUND WORKModelling vehicle movements & emissions

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BACKGROUND RESEARCHTraffic microsimulation & Instantaneous Emission Modelling

0 500 1000 1500 2000

020

4060

Spe

ed (k

m.h

1)

Shipton Rd/Water End

Salisbury Ter Leeman Rd/Station Ave

Museum Str/St Leonard's Pl

Bootham/Gillygate


0 500 1000 1500 2000

02

46

812

Fuel

(g.s

ec1

) Shipton Rd/Water End

Salisbury Ter Leeman Rd/Station Ave Museum Str/St Leonard's Pl Bootham/Gillygate Shipton Rd/Water End

0 500 1000 1500 2000

050

150

250

NO

X(m

g.se

c1)

NOxNO2


Salisbury Ter Leeman Rd/Station Ave

Museum Str/St Leonard's Pl

Bootham/GillygateShipton Rd/Water End

0 500 1000 1500 2000

01

23

Time (seconds)

PM

(mg.

sec1

) Shipton Rd/Water End

Salisbury Ter Leeman Rd/Station Ave Museum Str/St Leonard's Pl Bootham/Gillygate Shipton Rd/Water End

Time series plot of PHEM results for a sample simulated Euro 5 Bus operating the Park and Ride service 2 in the AM peak: (a) Speed, (b) Fuel consumption, (c) NOX and NO2, (d) Particle Mass (PM)

* Zallinger, M., Tate, J., and Hausberger, S. 2008. An instantaneous emission model for the passenger car fleet. Transport and 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.

0 100 200 300 400 500

020

4060

80

Spe

ed (k

m.h

1)

0 100 200 300 400 500

01

23

45

67

CO

2(g.

sec1

)

0 100 200 300 400 500

0.00

0.02

0.04

0.06

Time (seconds)

NO

X(g.

sec1

)

Modelled_CO2

Obs

erve

d_C

O2

0

2

4

6

8

0 2 4 6 8

Counts

1123468

11162231436186

121171241

UNDER-PINNING EMISSION MODELInstantaneous Emission Model PHEM*Passenger car and Heavy-duty Emission Model (Euro 0 – 6 / VI)FIGURES | Sample time series, TfL London Drive Cycle, Euro 5 small

family diesel

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CASE STUDIESBIG telematics data

1. 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)

2. Sheffield City Centre One calendar year (May 2014 – May 2015) 15,000 kms quality checked telematics data Supporting data

Met. (wind speed, direction, temp, RH, rainfall)

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METHODBIG telematics data ▶ vehicle emissions

'Raw' telematics

data

Temporal & Spatial variation in VEHICLE

EMISSIONS

DATACLEANING

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 timeslices

Day type School termtime:

- AutumnA +B - Spring A +B

- Summer A +B School half-terms(all)

Christmasholiday Easterholiday

Summerholiday Bankholidays

Special events [X, Y, Z]

DATA FORMAT

PHEM compatible

ANPR data Fleet mix and

specification

Traffic Countdata

Automatic

TIME SLICE 0000: to0600:

36 half-hour:06:0006:3007:0007:3008:0008:3009:00

etc23:30

FLEET MIX Proportions vary by

hour & week /weekend

A58(M) TURNING %

Output SATURN2015

CLASSIFIED LINK FLOWS

all segmentIDs

DIGITAL TERRAIN MAP

05. mgrid linkGRADIENTS

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BIG telematics dataHow good is the data?

www.thefloow.com proprietary data handling & cleaning processes

ITS data quality checking / cleaning / processing routines

Single journey of 56,000 kms journeys in the Leeds CAZ study

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LEEDS RESULTSPassenger car NOX Emission Factors (EFs)

FIGURE | Average (all trajectories) passenger car NOX Emission Factors (EFs)

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LEEDS RESULTSPassenger car NOX Emission Factors (EFs)

FIGURE | Passenger car NOX Emission Factors (EFs) all journeys

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LEEDS RESULTSVariation in time & space

FIGURE | Autumn term-time (first half) 08:00 – 08:30 hrs Direction South Bound

Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors

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FIGURE | Autumn term-time (first half) 08:00 – 08:30 hrs Direction North Bound


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WORK IN PROGRESSLeeds CAZ study

Key tasks: Sampling “calmer” driving trajectories estimate LGV, HGV & Bus

trajectories Weighting & scaling time & space varying EFs by classified flow levels

'Raw' telematics

data

Temporal & Spatial variation in VEHICLE

EMISSIONS

DATACLEANING

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 timeslices

Day type School termtime:

- AutumnA +B - Spring A +B

- Summer A +B School half-terms(all)

Christmasholiday Easterholiday

Summerholiday Bankholidays

Special events [X, Y, Z]

DATA FORMAT

PHEM compatible

ANPR data Fleet mix and

specification

Traffic Countdata

Automatic

TIME SLICE 0000: to0600:

36 half-hour:06:0006:3007:0007:3008:0008:3009:00

etc23:30

FLEET MIX Proportions vary by

hour & week /weekend

A58(M) TURNING %

Output SATURN2015

CLASSIFIED LINK FLOWS

all segmentIDs

DIGITAL TERRAIN MAP

05. mgrid linkGRADIENTS

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OUTLOOKBIG telematics data

SHORT-TERM: Target Case 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

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

Dr James Tate - Better estimation of vehicle emissions for modelling - DMUG17

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