Passenger and Commercial Transportation Methodology
Background
This document provides data sources and methodology for the greenhouse gas emissions inventory for passenger and commercial vehicles.
StreetLight data
StreetLight Data is a transportation analytics platform that uses aggregated location-based services (LBS) data from cell phones and navigation/GPS data to deliver insights on travel patterns. For this project, we used StreetLight to find the volume of traffic going from each city or township unit (CTU) to each CTU for personal and commercial traffic during 2019.
Vehicle Miles Traveled
To find the estimate number of vehicle miles traveled (VMT), we used the estimated number of vehicles and the average trip length in miles for all origin - destination pairs. VMT is calculated as follows:
\[VMT = trips\times length\]
where
\[trips = \text{number of trips}\\length = \text{average trip length in miles}\]
Passenger vehicle miles traveled
StreetLight provides multiple metrics to describe travel patterns. For passenger vehicle traffic, we used StreetLight Volume, which represents an estimated number of vehicle trips. StreetLight uses machine learning models to estimate the expected seasonal changes at a given location and then applies their Average Annual Daily Traffic (AADT)1 values to calibrate seasonal changes to an estimated volume (StreetLight Data 2019b). Expected seasonal changes are generated from permanent traffic recorders, which count vehicles constantly. Under 500 counters are used to calculate seasonal changes, but a validation study in Hennepin County yielded promising results for origin-destination analyses (StreetLight Data 2019b). Council researchers reviewed StreetLight’s methodology for all metrics used and consulted with StreetLight support staff to ensure the best possible accuracy.
The origin-destination analysis specified personal traffic only for all months in 2018, and was filtered to include all days of the week during all hours. The resulting analysis had a sample size of approximately 9 million devices and 51 million trips. StreetLight Volume and the average trip length for each origin - destination pair were multiplied to get VMT.
Commercial vehicle miles traveled
StreetLight does not provide StreetLight Volume for commercial vehicle analyses at this time. To measure volume for commercial traffic, we used the StreetLight Index, a relative measure of traffic volume. The Index is “a normalized value that takes into account variation in sample size across space and time” (StreetLight Data 2019d). Though the StreetLight Index does not indicate the number of trips, it can be calibrated to estimate the number of trips using calibration zones with AADT data. StreetLight compares the AADT calibration values for a given zone with StreetLight’s sample size for the same zone, and creates a calibration factor to apply to the entire analysis (StreetLight Data 2019c). We generated a calibration zone set for commercial traffic by selecting road segments with both AADT and vehicle classification data.
The Minnesota Department of Transportation (MNDOT) operates vehicle classification stations across the state, which provide both the volume of traffic on a given road segment and the breakdown of volume by vehicle type. We obtained this breakdown using data from MNDOT’s Yearly Volume Trends With Truck Distribution report series and classified medium and heavy-duty vehicle types.2 Then, we selected only the stations within the metro with observations in years 2016, 2017, 2018, and/or 2019. Finally, we joined this data with MNDOT’s Average Annual Daily Traffic (AADT) road segments by station ID. The road segments sampled include multiple road functional classes and segments in all seven metro counties.
We then ran the same origin-destination analysis as for passenger vehicles, but specifying commercial traffic only and segmenting results by medium and heavy-duty traffic. We then calculated VMT with the same method as for passenger vehicles, by multiplying the number of trips by average trip length in miles.
Greenhouse Gas Emissions
Greenhouse gases are any gases that trap heat in the atmosphere (US EPA 2019). To attribute greenhouse gas (GHG) emissions to individual municipalities, we followed protocol set by ICLEI (Local Governments for Sustainability USA) (Local Governments for Sustainability USA 2013). We used method TR.1.A, which requires origin-destination data. The equation is as follows
\[Emissions=\frac{1}{2}T_{o} + \frac{1}{2}T_{d}\]
where
\[T_o=\textrm{grams of emissions for each GHG from all trips originating in the community}\\T_d=\textrm{grams of emissions for each GHG from all trips terminating in the community}\]
In this protocol, the total emissions for a trip are split between the origin and destination CTU. For trips that both begin and end in the same CTU, all emissions are attributed to the given CTU. The advantage of this method is emissions from a trip that passes through a CTU without stopping are not attributed to the intermediary CTU. For example, emissions from a trip starting in Minneapolis, passing through Brooklyn Center, and ending in Brooklyn Park would be split between Minneapolis and Brooklyn Park and not attributed to Brooklyn Center. This method prevents emissions being attributed to communities that high-traffic roads, such as highways and interstates, pass through.
Direct emissions
There are a three greenhouse gases produced from burning gasoline and diesel: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The United States Environmental Protection Agency (EPA) provides the estimated volume of gas emitted in grams from gallons of fuel burned (US EPA 2015).
Global Warming Potential (GWP)
Some Greenhouse gases have greater effect on the climate than others. In addition to estimating direct emissions, we also used the Global Warming Potential (GWP) metric to find the CO2 equivalent for CH4 and N20 emissions. GWP is “the time-integrated [radiative forcing] due to a pulse emission of a given component relative to a pulse emission of equal mass of CO2” (Myhre et al. 2013). In other words, the GWP is an index of the effect of a given gas relative to that of CO2. For example, one gram of N2O is equivalent to 298 grams of CO2 over 100 years.
The GWP is usually integrated for 20, 100, and 500 years. We opted to use GWP100, as it is the metric primarily used in the US (US EPA 2016).
Regional Emissions Rates
Emissions rates for our region were calculated using the EPA’s Motor Vehicle Emissions Simulator (MOVES), in combination with the Council’s regional travel demand model, Minnesota Department of Vehicle Services county vehicle registration data, and the Minnesota Pollution Control Agency vehicle age distribution. Each of these inputs helps the model estimate the characteristics of vehicles on the road in our region. The model takes into account differences in fuel economy (miles per gallon) depending on a vehicle’s age and size, as well as its fuel intake (diesel or gasoline). The results are specific to the conditions of our region, and so are more accurate than national averages.
Passenger vehicle emissions rates
Passenger vehicles include passenger cars, motorcycles, and passenger trucks (pick-up trucks). Data from 2019 is used in the most recent version of this dataset.
,Commercial vehicle emissions rates
Medium duty
Medium duty vehicles include intercity buses, school buses, transit buses, motor homes, light commercial trucks, and refuse trucks. Data from 2018 is used in the most recent version of this dataset.
Heavy duty
Heavy duty vehicles include single and combination long and short haul trucks. The listed regional average rate is weighted according to the proportion of gasoline and diesel heavy duty vehicles, which is used in the final calculations. Data from 2018 is used in the most recent version of this dataset.
Final data
Calculation algorithms for data manipulation and VMT and GHG emissions calculations were written in the statistical language R by Council data scientists and researchers. Two functions were developed: calculate_vmt(), which takes in StreetLight O-D analysis results and returns vehicle miles traveled for each CTU, and calculate_emissions(), which applies grams per mile emissions rates to vehicle miles traveled.
calculate_emissions() output is as follows
| Variable | Description |
|---|---|
| zone | The CTU name |
| vmt_same | Vehicle miles traveled for trips beginning and ending in the given CTU |
| vmt_origin | Vehicle miles traveled for trips starting in the given CTU |
| vmt_destination | Vehcile miles traveled for trips ending in the given CTU |
| vmt_zone_total | Total vehicle miles traveled for the given CTU |
| year | The year emissons are being estimated for |
| type | “personal” or “commercial” |
| vehicle_weight | “Passenger”, “Medium Duty”, or “Heavy Duty” |
| total_co2 | the total grams of CO2 attributed to the given CTU |
| total_ch4 | the total grams of CH4 attributed to the given CTU |
| total_n2o | the total grams of N2O attributed to the given CTU |
| total_co2_w_equiv | the total grams of CO2 and CO2 equivalent attributed to the given CTU |
Authors
Liz Roten, Associate Data Scientist Liz.Roten@metc.state.mn.us
Mauricio Léon, Senior Researcher Mauricio.Leon@metc.state.mn.us
Acknowledgements
Special thanks to these folks for ongoing consultation and streamlined data access.
Mark Filipi - Manager, Technical Planning Support, Metropolitan Council
Nicole Sullivan - Data Scientist, Research, Metropolitan Council
Ian Vaagenes - Research Analysis Specialist, Minnesota Department of Transportation
Genna Gores, Jon Wergin, and the rest of the folks at StreetLight Data Support
This document was last updated 2020-07-16 by Liz Roten.
© Metropolitan Council
References
Assessment, and Standards Division. 2016. “Population Activity of on-Road Vehicles in MOVES2014.” Technical Report EPA-420-R-16-003a. Office of Transportation and Air Quality: US EPA.
Local Governments for Sustainability USA. 2013. “Appendix D: Transportation and Other Mobile Emission Activities and Sources in U.S. Community Protocol for Accounting and Reporting of Greenhouse Gas Emissions.” Version 1.1.
Myhre, G. D., D. Shindell, F.-M. Br’eon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, et al. 2013. “Anthropogenic and Natural Radiative Forcing in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.” Edited by T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, V. Bex, Y. Xia, and P. M. Midgley. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA: The Intergovernmental Panel on Climate Change.
StreetLight Data. 2019a. “StreetLight AADT 2018 V3 Methodology and Validation White Paper.” White Paper Version 3.0.
———. 2019b. “StreetLight Volume Methodology & Validation White Paper.”
———. 2019c. “How Does Single Factor Calibration Work?” StreetLight Data Support. https://support.streetlightdata.com/hc/en-us/articles/360024521892-How-does-Single-Factor-Calibration-Work-.
———. 2019d. “StreetLight Index.” StreetLight Data Support.
US EPA. 2015. “Greenhouse Gases Equivalencies Calculator - Calculations and References.” Government. United States Environmental Protection Agency. https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references.
———. 2016. “Understanding Global Warming Potentials.” Overviews and Factsheets. US EPA. https://www.epa.gov/ghgemissions/understanding-global-warming-potentials.
———. 2019. “Overview of Greenhouse Gases.” Overviews and Factsheets. US EPA. https://www.epa.gov/ghgemissions/overview-greenhouse-gases.
StreetLight’s AADT is generated using proprietary machine learning techniques. StreetLight uses location-based services trip data, navigation GPS trips for personal and commercial vehicles, population estimates from the US Census, OpenStreetMaps feature geographies, weather patterns, and nearly 10,000 permanent loop counters (reliable, local vehicle counters) in 26 states across the United States (StreetLight Data 2019a).↩︎
MNDOT classifies vehicles by their size and number of axles. However, StreetLight classifies commercial vehicles by weight, where vehicles between 14,000 lbs and 26,000 lbs are considered medium-duty, and vehicles greater than 26,000 lbs are heavy-duty. In addition, the EPA’s Motor Vehicle Emissions Simulator (MOVES) has their own, slightly different vehicle classification system (Assessment and Standards Division 2016, Section 2.2). After reviewing MNDOT’s visual definitions of commercial vehicles, we defined MNDOT vehicle types 4 and 5 as medium-duty and types 6-13 as heavy-duty. We believe this configuration aligns most closely with both StreetLight’s and MOVES’ vehicle classifications schemes.↩︎