1. Fusion of Vehicle Weight and Activity Data for Improved Vehicle Emission Modeling
Kanok Boriboonsomsin, Guoyuan Wu,
Peng Hao, and Matthew Barth
University of California at Riverside
Presented at the 94th Annual Meeting of Transportation Research Board
Washington, DC
January 13, 2015

2. Introduction
Vehicle weight can significantly affect vehicle emissions, especially for heavy-duty trucks.
Heavy-duty truck weight can vary greatly.
However, the impact of vehicle weight on emissions is usually not accounted for in the past (and current??) emission modeling practices.
Up to 80,000 lbs
Around 14,000-18,000 lbs
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For example, a study based on chassis dynamometer testing found that a weight increase of X% (e.g., 100%) will result in an increase of oxides of nitrogen (NOx) emissions per unit distance of about X/2% (e.g., 50%).
So, a 16K lbs tractor without load and a fully loaded truck can have 200% difference in NOx emission factors.
In the days of MOBILE, we don’t have to worry about weight as it was not possible to model its impact on emissions. 2
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3. Opportunities for Improvements
MOVES provides a framework for the impact of vehicle weight on emissions to be modeled.
MOVES emission rates are a function of vehicle specific power (VSP), which is a function of speed, acceleration, road grade, and vehicle weight, etc.
Traffic measurement data from sensor technologies have become increasingly available, e.g.,
Weigh-in-motion
Vehicle count, classification, speed, weight, timestamp, location
Loop detector
Vehicle count, speed, timestamp, location
Timestamp and location info that comes with the data from these sensors is valuable. It allows us to correlate measurement data from different types of sensor.
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4. Objectives of Research
Explore the use of on-road truck weight measurement data in truck emission modeling.
Combine truck weight data with truck speed data to create better truck activity data input for MOVES.
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Photo credit: Arizona Department of Transportation
Weigh-in-Motion Station
Vehicle Detector Station
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5. Data Sources (1) Caltrans’ weigh-in-motion (WIM) stations
More than 100 stations with
Bending plates or piezo sensors
Double inductive loops
WIM data include
Vehicle count
Vehicle classification
Vehicle speed
Gross vehicle weight
Axle weight and spacing
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6. Data Sources (2) Caltrans’ Performance Measurement System (PeMS)
More than 16,000 vehicle detector stations (VDS)
Covering more than 22,000 directional lane miles
Each VDS reports traffic measurements every 5 minutes, e.g.,
Traffic flow (separate for cars & trucks)
Traffic speed
PeMS only reports overall traffic speed. In our previous research, we develop a method to estimate average speeds for cars and trucks separately.
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7. Data Sources (3)
MOVES driving cycles for single-unit trucks and combination trucks.
Single unit Trucks
Combination Trucks ID
Cycle Name
Avg Speed (mph)
201
MD 5mph Non-Freeway
4.6
301
HD 5mph Non-Freeway
5.8
202
MD 10mph Non-Freeway
10.7
302
HD 10mph Non-Freeway
11.2
203
MD 15mph Non-Freeway
15.6
303
HD 15mph Non-Freeway
204
MD 20mph Non-Freeway
20.8
304
HD 20mph Non-Freeway
19.4
205
MD 25mph Non-Freeway
24.5
305
HD 25mph Non-Freeway
25.6
206
MD 30mph Non-Freeway
31.5
306
HD 30mph Non-Freeway
32.5
251
MD 30mph Freeway
34.4
351
HD 30mph Freeway
252
MD 40mph Freeway
44.5
352
HD 40mph Freeway
47.1
253
MD 50mph Freeway
55.4
353
HD 50mph Freeway
54.2
254
MD 60mph Freeway
60.4
354
HD 60mph Freeway
59.4
255
MD High Speed Freeway
72.8
355
HD High Speed Freeway
71.7

8. Methodology A. WIM Station and VDS Association
C. Truck Trajectory Construction
PeMS VDS
WIM Station
Temporal footprint of the trucks at this VDS
B. Truck Record Association
Date
Time Stamp
Vehicle Class
Weight (kg)
4/15/2009
0:14:07 11
2.35E+04
00:15:02 3
2.90E+03
00:15:37 9
1.32E+04
00:16:01
1.28E+04
00:17:35
Best Estimated Arrival Time at WIM
00:18:45
1.29E+04
00:18:48
1.26E+04
00:19:33
1.36E+04
00:19:46
9.71E+03
WIM Station
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9. A. WIM Station and VDS Association

10. B. Truck Record Association
Based on estimated travel time distribution of the trucks.
There are limitations imposed by the WIM station and VDS association in the previous step, e.g.,
Small number of WIM stations  some trucks may not be recorded by any of these WIM stations.
No priori knowledge about truck travel route.
Assumptions
First-in-first-out rule applies, i.e., no over-taking.
At each time interval, weight and classification distributions of trucks at a VDS are the same as the distributions of a set of consecutive truck records at the associated WIM station.
The first assumption is to ensure that the number of trucks at a VDS and the associated WIM station matches with each other.
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11. C. Truck Trajectory Construction
Progressive trajectory propagation one second at a time.
Procedures
Select two MOVES driving cycles whose average speeds bracket the truck traffic speed at the VDS.
Create and combine speed-acceleration frequency distributions (SAFD) of the two selected cycles. Weight them in reverse proportion to how close their average speeds are to the truck traffic speed.
(Set initial speed of the first second to the truck traffic speed.)
Draw an acceleration value from the combined SAFD based on current speed and use it to calculate speed of the next second.
Repeat until the end of the truck’s temporal footprint at the VDS.

12. Speed-Acceleration Frequency Distribution
Example of HD 60mph freeway cycle (length = 1,792 seconds; average speed = 59.4 mph) %

13. Truck Operating Mode Characterization
Calculate second-by-second VSP with weight data from WIM stations.
Determine second-by-second operating mode based on EPA’s definition.
Create VSP and operating mode distributions.

14. Evaluation Case study Compare between existing and proposed methods
Freeways in Los Angeles County, CA
Entire month of April 2009
Single unit trucks and combination trucks
Compare between existing and proposed methods
Existing method
Default truck weight in MOVES
Default truck driving cycles in MOVES
Proposed method
Measured truck weight from WIM stations
Constructed truck trajectory

15. Existing Method
Weight operating mode distributions of two driving cycles in reverse proportion to how close their average speeds are to truck traffic speed (e.g., 57.1 mph).
Cycle 353
Avg. Speed 54.2 mph
Cycle 354
Avg. Speed 59.4 mph
In this example, the weight for cycle 353 is 44% while the weight for cycle 354 is 56%.
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16. Results – One Example VDS
Operating mode bin distributions – combination trucks
Existing Method
Proposed Method
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17. Results – All Los Angeles Freeways (1)
VSP distributions – combination trucks
Existing Method
Proposed Method

18. Results – All Los Angeles Freeways (2)
Operating mode distributions – combination trucks
Existing Method
Proposed Method

19. Impact on Truck Emission Inventories
Emission rates
NOx and PM2.5 emission rates from MOVES
Diesel combination trucks, 2006 model year
Results  the proposed method results in:
78% higher NOx emissions
30% higher PM2.5 emissions
Notes
These results are for this specific case study.
The emission differences would vary depending on the shape of the resulting operating mode distributions and the emission rates that are used (e.g., for a different fleet of HDTs).

20. Conclusions
Using measured truck weight in emission modeling would improve the accuracy of truck emission inventories.
Especially important for regions with freight terminals or goods movement corridors.
It is possible to combine truck weight data from WIM stations with truck speed data from loop detectors to create better truck activity data input for MOVES.
The method can be applied to other regions and technologies.
Future research may include:
Refinement of the proposed method.
Validation of the resulting operating mode distributions.

21. WIM Stations in the U.S.
Data Source:

22. Closure Thank you! Acknowledgements Contact
Federal Highway Administration (FHWA)’s Surface Transportation Environment and Planning Cooperative Research Program (STEP)
Karen Perritt, Cecilia Ho, and Michael Claggett of FHWA
California Department of Transportation
Contact
Kanok Boriboonsomsin
Guoyuan Wu