An unapologetically technical conference, DMUG remains the key annual event for experts in this field. Unmissable speakers will be examining topical issues in emissions, exposure and dispersion modelling.
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Dr James Tate - Better estimation of vehicle emissions for modelling - DMUG17
1.
2. BIG telematics data
Vehicle tracking
2
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
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
• etc
FIGURE | Sample weekday GPS data by
hour
6. BACKGROUND RESEARCH
Traffic microsimulation & Instantaneous Emission Modelling
6
0 500 1000 1500 2000
0204060
Speed(km.h
1
)
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
0246812
Fuel(g.sec
1
)
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
050150250
NOX(mg.sec
1
)
NOx
NO2
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
0123
Time (seconds)
PM(mg.sec
1
)
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)
7. * 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
0 100 200 300 400 500
020406080
Speed(km.h
1
)
0 100 200 300 400 500
01234567
CO2(g.sec
1
)
0 100 200 300 400 500
0.000.020.040.06
Time (seconds)
NOX(g.sec
1
)
Modelled_CO2
Observed_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
UNDER-PINNING EMISSION MODEL
Instantaneous 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
8. CASE STUDIES
BIG telematics data
8
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)
10. BIG telematics data
How good is the data?
10
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
11. LEEDS RESULTS
Passenger car NOX Emission Factors (EFs)
11
FIGURE | Average (all trajectories) passenger car NOX Emission Factors
(EFs)
12. LEEDS RESULTS
Passenger car NOX Emission Factors (EFs)
12
FIGURE | Passenger car NOX Emission Factors (EFs) all journeys
13. LEEDS RESULTS
Variation in time & space
13
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
14. LEEDS RESULTS
Variation in time & space
14
FIGURE | Autumn term-time (first half) 08:00 – 08:30 hrs Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
15. LEEDS RESULTS
Variation in time & space
15
FIGURE | Autumn term-time (first half) 12:00 – 12:30 hrs Direction South Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
16. LEEDS RESULTS
Variation in time & space
16
FIGURE | Autumn term-time (first half) 12:00 – 12:30 hrs Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
17. LEEDS RESULTS
Variation in time & space
17
FIGURE | Autumn term-time (first half) 17:00 – 17:30 hrs Direction South Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
18. LEEDS RESULTS
Variation in time & space
18
FIGURE | Autumn term-time (first half) 17:00 – 17:30 hrs Direction North Bound
Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
19. WORK IN PROGRESS
Leeds CAZ study
19
Key tasks:
Sampling “calmer” driving trajectories estimate LGV, HGV & Bus
trajectories
Weighting & scaling time & space varying EFs by classified flow levels
20. OUTLOOK
BIG telematics data
20
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