vehicle emissions measurement: micro-trip analysis of non-stationary time-series
DESCRIPTION
Portable Emissions Measurement Systems (PEMS) provide direct real-time data on the emissions of an in-use vehicle under a wide range of real-world operating conditions. In recent years PEMS technology has undergone a very rapid evolution and data gathering activities are now widespread, especially in the US and Asia. But, by comparison, methods used to analyze PEMS data have received relatively little refinement over the same period. The reason for this is in part legislative, but it also reflects an issue that is much more fundamental and commonplace – if datasets are large and noisy, someone has to put a lot of work in if you want to get good information out. Current PEMS data analysis is perhaps most easily considered in terms of its two extremes, total journey and raw data analysis, as most current practices fall into one of these two categories. Here, we considered these and an alternative ‘middle ground’ approach, micro trip analysis. We also look at different micro-trips sampling strategies and some automation procedures for the routine use of such methods on a much wider range of research questions. www.its.leeds.ac.uk/people/k.ropkinsTRANSCRIPT
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MICRO-TRIP ANALYSIS OF NON-STATIONARY TIME-SERIES
Karl RopkinsInstitute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK
Contact [email protected]
2014 ITS Seminar Series
ITS, University of Leeds, June 18th 2014
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AcknowledgementsStephen Hanley
Awat Abdalla
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Micro-trip Analysis Of Non-stationary Time-series:
• Background
• Micro-trip Analyses
• Automating Micro-trip Analyses
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Micro-trip Analysis Of Non-stationary Time-series:
• Background
• Micro-trip Analyses
• Automating Micro-trip Analyses
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Non-stationary Time-series • The data sets discussed are from portable emission
measurement systems (PEMS)
• These are one example of a non-stationary time-series
• Others include:
• Portable activity measurement systems (PAMS)
• (Increasing number of large vehicle fleets)
• Aircraft Infrastructure Management System (AIMS)
• Animal tracking
• Personal GPS and mobile phone movement
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PEMS ISA StudyOne Study for one Vehicle Management system:
• Two vehicles, two fuels types
• One Intelligent Speed Adaptation (ISA) system, three operating modes (OFF, ADV, VOL)
• Three routes - but not all vehicles on all routes
• One PEMS - but additional logging
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In terms of data size:
• An individual journal generates 1,000 to 50,000 records
• A study generates 10,000s to 1,000,000s records (PEMS ISA example: 1, 080,000 records)
• PEMS data archives like those of the EPA, CARB, etc, include data from 100s of studies and real-world certification exercises
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Total Journey AnalysisComparison of measurements (summed or standardized) on a ‘per journey basis’
Approach is analogous to conventional vehicle/engine certification testing
… BUT in the real-world it is crude approach
For all routes in the PEMS ISA study, e.g.:
• We do not see anything significant in total journey data
• BUT that is not really that surprising
• There is HIGH run-to-run variation
• The impact of ISA is expected to be SMALL
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Raw Data Analysis and ModelingAnalyzing the data at the resolution it was logged at
Approach has the potential to be more informative but analysis is more labour-intensive
…and more often you are trading uncertainty
for the perception of certainty
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Raw Data Analysis and ModelingData modeling [Analyte]i = te(speedi-n , acceli-n ) + … +
te(speedi-m , acceli-m )
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Raw Data Analysis and ModelingData modeling [Analyte]i = te(speedi-n , acceli-n ) + … +
te(speedi-m , acceli-m )
Results
ISA Mode Comparison OFF vs. ADV OFF vs. VOL ADV vs. VOL
OFF ADV VOL difference % p difference % p difference % p Diesel Mondeo CO2 (g.km-1) 258.19 266.57 260.65 8.38 3.04 0.52 2.46 0.93 0.63 5.92 -1.89 0.63
All Routes CO (g.km-1) 0.12 0.12 0.12 0.01 4.00 0.61 0.001 1.41 0.49 0.004 -2.34 0.82
HC (g.km-1) 0.27 0.28 0.27 0.01 3.15 0.70 0.001 0.31 0.59 0.01 -2.61 0.82
NOx (g.km-1) 0.74 0.77 0.75 0.03 3.40 0.46 0.01 1.05 0.60 0.02 -2.11 0.64
Fuel economy
(km.litres-1) 7.75 7.68 7.73 -0.07 -0.14 0.52 -0.02 -0.63 0.54 -0.06 0.84 0.56
Petrol Mondeo CO2 (g.km-1) 370.07 388.75 373.98 18.7 4.36 0.66 3.91 0.77 0.57 14.8 -3.15 0.69
All Routes CO (g.km-1) 1.38 1.51 1.41 0.13 7.65 0.55 0.03 1.47 0.39 0.10 -5.17 0.71
HC (g.km-1) 0.68 0.72 0.69 0.04 4.42 0.65 0.01 0.72 0.43 0.03 -3.25 0.71
NOx (g.km-1) 0.25 0.25 0.25 0.01 1.98 0.57 -0.0003 -0.37 0.60 0.01 -2.16 0.64
Fuel economy
(km.litres-1) 6.41 6.27 6.38
-0.14 -2.46 0.66 -0.03 -0.74 0.57 -0.11 1.86 0.69
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Raw Data Analysis and ModelingData modeling [Analyte]i = te(speedi-n , acceli-n ) + … +
te(speedi-m , acceli-m )
Results
ISA Mode Comparison OFF vs. ADV OFF vs. VOL ADV vs. VOL
OFF ADV VOL difference % p difference % p difference % p Diesel Mondeo CO2 (g.km-1) 258.19 266.57 260.65 8.38 3.04 0.52 2.46 0.93 0.63 5.92 -1.89 0.63
All Routes CO (g.km-1) 0.12 0.12 0.12 0.01 4.00 0.61 0.001 1.41 0.49 0.004 -2.34 0.82
HC (g.km-1) 0.27 0.28 0.27 0.01 3.15 0.70 0.001 0.31 0.59 0.01 -2.61 0.82
NOx (g.km-1) 0.74 0.77 0.75 0.03 3.40 0.46 0.01 1.05 0.60 0.02 -2.11 0.64
Fuel economy
(km.litres-1) 7.75 7.68 7.73 -0.07 -0.14 0.52 -0.02 -0.63 0.54 -0.06 0.84 0.56
Petrol Mondeo CO2 (g.km-1) 370.07 388.75 373.98 18.7 4.36 0.66 3.91 0.77 0.57 14.8 -3.15 0.69
All Routes CO (g.km-1) 1.38 1.51 1.41 0.13 7.65 0.55 0.03 1.47 0.39 0.10 -5.17 0.71
HC (g.km-1) 0.68 0.72 0.69 0.04 4.42 0.65 0.01 0.72 0.43 0.03 -3.25 0.71
NOx (g.km-1) 0.25 0.25 0.25 0.01 1.98 0.57 -0.0003 -0.37 0.60 0.01 -2.16 0.64
Fuel economy
(km.litres-1) 6.41 6.27 6.38
-0.14 -2.46 0.66 -0.03 -0.74 0.57 -0.11 1.86 0.69
Small penalty for using ISA: Emissions +0.5 to +4%Fuel economy -0.7 to -2.5%
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Raw Data Analysis and ModelingData modeling [Analyte]i = te(speedi-n , acceli-n ) + … +
te(speedi-m , acceli-m )
Results
ISA Mode Comparison OFF vs. ADV OFF vs. VOL ADV vs. VOL
OFF ADV VOL difference % p difference % p difference % p Diesel Mondeo CO2 (g.km-1) 258.19 266.57 260.65 8.38 3.04 0.52 2.46 0.93 0.63 5.92 -1.89 0.63
All Routes CO (g.km-1) 0.12 0.12 0.12 0.01 4.00 0.61 0.001 1.41 0.49 0.004 -2.34 0.82
HC (g.km-1) 0.27 0.28 0.27 0.01 3.15 0.70 0.001 0.31 0.59 0.01 -2.61 0.82
NOx (g.km-1) 0.74 0.77 0.75 0.03 3.40 0.46 0.01 1.05 0.60 0.02 -2.11 0.64
Fuel economy
(km.litres-1) 7.75 7.68 7.73 -0.07 -0.14 0.52 -0.02 -0.63 0.54 -0.06 0.84 0.56
Petrol Mondeo CO2 (g.km-1) 370.07 388.75 373.98 18.7 4.36 0.66 3.91 0.77 0.57 14.8 -3.15 0.69
All Routes CO (g.km-1) 1.38 1.51 1.41 0.13 7.65 0.55 0.03 1.47 0.39 0.10 -5.17 0.71
HC (g.km-1) 0.68 0.72 0.69 0.04 4.42 0.65 0.01 0.72 0.43 0.03 -3.25 0.71
NOx (g.km-1) 0.25 0.25 0.25 0.01 1.98 0.57 -0.0003 -0.37 0.60 0.01 -2.16 0.64
Fuel economy
(km.litres-1) 6.41 6.27 6.38
-0.14 -2.46 0.66 -0.03 -0.74 0.57 -0.11 1.86 0.69
Counter-intuitively Advisory seems to have larger impact
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Raw Data Analysis and ModelingData modeling [Analyte]i = te(speedi-n , acceli-n ) + … +
te(speedi-m , acceli-m )
Results
ISA Mode Comparison OFF vs. ADV OFF vs. VOL ADV vs. VOL
OFF ADV VOL difference % p difference % p difference % p Diesel Mondeo CO2 (g.km-1) 258.19 266.57 260.65 8.38 3.04 0.52 2.46 0.93 0.63 5.92 -1.89 0.63
All Routes CO (g.km-1) 0.12 0.12 0.12 0.01 4.00 0.61 0.001 1.41 0.49 0.004 -2.34 0.82
HC (g.km-1) 0.27 0.28 0.27 0.01 3.15 0.70 0.001 0.31 0.59 0.01 -2.61 0.82
NOx (g.km-1) 0.74 0.77 0.75 0.03 3.40 0.46 0.01 1.05 0.60 0.02 -2.11 0.64
Fuel economy
(km.litres-1) 7.75 7.68 7.73 -0.07 -0.14 0.52 -0.02 -0.63 0.54 -0.06 0.84 0.56
Petrol Mondeo CO2 (g.km-1) 370.07 388.75 373.98 18.7 4.36 0.66 3.91 0.77 0.57 14.8 -3.15 0.69
All Routes CO (g.km-1) 1.38 1.51 1.41 0.13 7.65 0.55 0.03 1.47 0.39 0.10 -5.17 0.71
HC (g.km-1) 0.68 0.72 0.69 0.04 4.42 0.65 0.01 0.72 0.43 0.03 -3.25 0.71
NOx (g.km-1) 0.25 0.25 0.25 0.01 1.98 0.57 -0.0003 -0.37 0.60 0.01 -2.16 0.64
Fuel economy
(km.litres-1) 6.41 6.27 6.38
-0.14 -2.46 0.66 -0.03 -0.74 0.57 -0.11 1.86 0.69
While more consistent, these are still not statistically significant
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Vehicle Speed [ km.h−1]
Veh
icle
Acc
eler
atio
n [ m
.s−2
]
−10
−5
0
5
10
0 20 40 60 80
12346
10152131446287
12016322129639452068288611451470187523783000
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Vehicle Speed [ km.h−1]
Veh
icle
Acc
eler
atio
n [ m
.s−2
]
−10
−5
0
5
10
0 20 40 60 80
OFF
0 20 40 60 80
ADV
0 20 40 60 80
VOL
122458
11162231425878
104138181237307395506644815102612841600
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Vehicle Speed [ km.h−1]
Veh
icle
Acc
eler
atio
n [ m
.s−2
]
−10
−5
0
5
10
0 20 40 60 80
OFF
spee
d lim
it 32
ADV
spee
d lim
it 32
0 20 40 60 80
VOL
spee
d lim
it 32
OFF
spee
d lim
it 48
ADV
spee
d lim
it 48
−10
−5
0
5
10VOL
spee
d lim
it 48
−10
−5
0
5
10OFF
spee
d lim
it 64
ADV
spee
d lim
it 64
VOL
spee
d lim
it 64
OFF
spee
d lim
it 80
0 20 40 60 80
ADV
spee
d lim
it 80
−10
−5
0
5
10
VOL
spee
d lim
it 80
12245811162231425878
104138181237307395506644815102612841600
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Micro-trip Analysis Of Non-stationary Time-series:
• Background
• Micro-trip Analyses
• Automating Micro-trip Analyses
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Micro-trip AnalysisChopping total journey into a series of segments or sub-journeys and analyzing these
So, working at resolutions between the total journey and raw data levels
BUT most importantly we are retaining ‘near neighbour’ information
The approach has the potential to provide a trade-off between the two extremes of conventional analysis
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Micro-trip AnalysisMicro-trips has traditionally been more commonly used in transport modeling than transport monitoring
Relatively few examples from monitoring work
Example: DeFries and colleagues used micro-trip separation by vehicle movement start/stop time, so segments were vehicle movements steps
BUT work elsewhere, e.g. the use of rolling window averages based of CO2 emissions in EU studies, suggested (to me at least) other segmentation strategies could be worth considering
Reference: James E. Warila, Edward Glover, Timothy H. DeFries, Sandeep Kishan. Load Factors, Emission Factors, Duty Cycles, and Activity of Diesel Nonroad Vehicles. 23rd CRC Real World Emissions Workshop, San Diego, California, April 7-10, 2013.
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Other Micro-trip Separations Examples
• By Location
(and by extension by link, road feature, type, geometry or conditions, etc)
• By Vehicle Activity
•By speed, acceleration, VSP event, etc
However, the associated data handling is
potentially highly time-consuming
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This is one series of micro-trips (Marylebone Flyover, UK)
Here, we are looking at CO2 emissions (%change ISA OFF to Voluntary)
• An orange micro-trip means there is an emission penalty
• A blue micro-trip means there is an emission saving
• A red box around the micro-trip means it is statistically significant
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Most places look like these:
• Most often a small change
• Most often a penalty rather than a saving
• Most often NOT statistically significant
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But this stretch of road is different:
• Huge emission saving (30-70%)
• Statistically significant
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‘Misassignment’ of speed limit means the ISA managed vehicle is held at 30 mph on the uphill while other vehicles accelerate up hill to 40 mph…
So, the saving is a function of local geography and speed limiting…
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Micro-trip Analysis Of Non-stationary Time-series:
• Background
• Micro-trip Analyses
• Automating Micro-trip Analyses
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Sources:Rowlingson, B. and Diggle, P. (1993) Computers and Geosciences, 19, 627-655.
Bivand, R. and Gebhardt, A. (2000) Journal of Geographical Systems, 2, 307-317.
Define an irregular Polygon…
… and extract all journey data
within it
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Define micro-trip start…
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… and end regions
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So, we can sample individual journeys…
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…And then automate it so we can ‘daisy chain’ it for multiple micro-trips on multiple runs
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…BUT, once you have a step like this automated, you very quickly find extra uses for it
Three clicks: one at the center of the target roundabout, and one each at typical entry and exit points, then assume circular
areas/known radii
Here, because we want a standard area about each roundabout, we
use a simple point and click method to make
reference files
Here, we used Google Maps to
measure roundabout turning angles
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Thank youKarl Ropkins
pems.utilshttps://sites.google.com/site/karlropkins/rpackages/pems
R (Linux, Mac or Windows) http://www.r-project.org/