7  Data sources

7.1 StreetLight Data

StreetLight Data is a transportation analytics platform that uses aggregated location-based services (LBS) data from cell phones, GPS data, and connected vehicle data to deliver insights on travel patterns. For this project, we used StreetLight to find the volume of traffic (number of vehicles) and average trip length for passenger and commercial vehicles.

The Metropolitan Council used StreetLight for our 2018 inventory.

For ease of access, we used {streetlightR} to interact directly with the StreetLight API. {streetlightR} is an open-source R package maintained by Council staff. We also used StreetLight InSight®, an online interface for running StreetLight analyses. Our subscription to StreetLight is part of MnDOT’s transportation analysis with regional data for informed strategies program. StreetLight falls second, only behind government data sources, in the data quality table (Table B.2).

Using the API, we uploaded the CPRG county geography and then performed an origin-destination analysis, in which all counties were both an origin and destination. This resulted in detailed vehicle counts for traffic between and within each of the counties. The data were then pulled and cleaned for estimating passenger and commercial VMT. The data were filtered to ensure all day types and day parts were included in the VMT calculations. Analyses were configured to include all months in 2021 and the most recent metric version (R131-M129) available was used.

All StreetLight API operations are recorded in R scripts: stl_upload_zone_sets.R, stl_run_analyses.R, and stl_fetch_analyses.R.

7.1.1 Passenger

For passenger data, we used StreetLight Volume - an estimate of the number of vehicles. StreetLight uses a sample of devices with location-based services (LBS) and vehicles with location technology (connected vehicle data) to model number of vehicles (StreetLight Data 2024).

The models that make up StreetLight Volume predict vehicle volumes by combining location-based services (LBS) and connected vehicle trips with contextual features that represent the street network, demographics, climate, and other geographic characteristics (StreetLight Data 2023a). The models are validated against data permanent traffic counters across the country, including in the study area.

StreetLight provides a rough sample size for the entire analysis, as shown in Table 7.1.

Table 7.1: StreetLight passenger travel analysis sample size

Data periods	Mode of travel	Approximate device count	Approximate trip count
Jan 01, 2021 - Dec 31, 2021	All Vehicles LBS Plus - StL All Vehicles Volume	1,038,000	125,411,000

7.1.1.1 Trip length validation

StreetLight returns not only vehicle volume, but also trip attributes, like trip length. We then use this to estimate vehicle miles traveled, by multiplying volume by average trip length for each origin-destination pair.

Figure 7.1: StreetLight origin-destination passenger trip length matrix

StreetLight also provides an estimation of the overall trip distance distribution. Use the widget below to find the distributions of various origin-destination pairs.

Figure 7.2: StreetLight passenger trip length distribution widget

Select an origin and destination county to visualize

Origin county

Destination county

To test logical validity of average trip lengths, we will compare the minimum distance between each origin and destination with the average trip length. These should correlate.

In cases where the origin and destination counties are not adjacent, the average trip length is consistently higher than the minimum distance between the counties.

Figure 7.3: Avg. trip distance and minimum distance between counties

We can also compare these distances with the observed average trip distance from the Met Council Travel Behavior Inventory (TBI). Read more about the TBI in Section 7.4.

Figure 7.4 shows a strong positive correlation when comparing origin-destination pairs. Note that TBI origin-destination pairs with fewer than 30 observed trips were removed due to low sample size.

Figure 7.4: Avg. trip distance, Travel Behavior Inventory and StreetLight

We would also expect that large counties will have longer trip lengths and smaller counties will have shorter trip lengths.

Comparing trip distance and county area, we see a weakly positive correlation (the larger the county, the longer the average trip).

Figure 7.5: Avg. distance for trips within county and county area

7.1.2 Commercial

StreetLight does not provide StreetLight Volume for 2021 commercial vehicle analyses. To measure volume for commercial traffic, we used the StreetLight Index, a relative measure of traffic volume, calibrated using AADT values to result in traffic volume (StreetLight Data 2023b).

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 2023d). We generated a calibration zone set for commercial traffic by selecting road segments with both AADT and vehicle classification data in both MN and WI counties within the CPRG study area. Read more about state DOT vehicle weight distribution data in Section 7.2.2.

Table 7.2: StreetLight commercial travel analysis sample size

Data periods	Mode of travel	Vehicle weight	Approximate device count	Approximate trip count
Jan 01, 2021 - Dec 31, 2021	Truck - StL Calibrated Truck Index	Medium	N/A	1,514,000
Jan 01, 2021 - Dec 31, 2021	Truck - StL Calibrated Truck Index	Heavy	N/A	605,000

7.1.2.1 Trip length

StreetLight calculates trip length in the same manner as passenger trips.

Figure 7.6: StreetLight origin-destination medium-duty trip length matrix

Figure 7.7: StreetLight origin-destination heavy-duty trip length matrix

Figure 7.8: StreetLight commercial trip length distribution widget

Select an origin and destination county to visualize

Origin county

Destination county

7.1.2.2 Calibration

StreetLight classifies commercial vehicles by Federal Highway Administration (FHWA) gross vehicle weight range (GWVR) classes: where vehicles between 14,000 lbs and 26,000 lbs (Class 4 to Class 6) are considered medium-duty, and vehicles greater than 26,000 lbs (Class 7+) are heavy-duty (StreetLight Data 2023c).

EPA’s Motor Vehicle Emissions Simulator (MOVES) has their own, slightly different vehicle classification system (USEPA 2016).

After reviewing MnDOT’s visual definitions of commercial vehicles, we defined MnDOT vehicle types 4-7 as medium-duty and types 8-13 as heavy-duty. We believe this configuration aligns most closely with both StreetLight’s and MOVES’s vehicle classifications schemes.

However, vehicles falling in FHWA class 7 (> 26,000 lbs, < 33,000 lbs) are classified as medium duty by state DOTs, and heavy duty by StreetLight. This discrepancy is relatively small, and is unlikely to heavily influence emissions reported.

Table 7.3: Vehicle weight classifications by data source

Gross vehicle weight rating (lbs)	FHWA	DOT	StreetLight
<6000	Class 1: <6,000 lbs	Light-duty	Light-duty
10,000	Class 2: 6,001-10,000lbs	Light-duty	Light-duty
14,000	Class 3: 10,001-14,000 lbs	Light-duty	Light-duty
16,000	Class 4: 14,001-16,000 lbs	Medium-duty	Medium-duty
19,500	Class 5: 16,001-19,500 lbs	Medium-duty	Medium-duty
26,000	Class 6: 19,501-26,000 lbs	Medium-duty	Medium-duty
33,000	Class 7: 26,001-33,000 lbs	Medium-duty	Heavy-duty
>33,000	Class 8+: >33,001 lbs	Heavy-duty	Heavy-duty

To create the calibration dataset, we found the ratio of passenger/medium/heavy-duty vehicles at traffic count stations within our study area using state DOT data. You can read more about vehicle classification data in Section 7.2.2.

7.1.3 Total vehicle volume validation

To validate our county origin-destination VMT data, we can compare the county totals to the DOT reported values from MnDOT (MnDOT 2021a) and WisDOT (WisDOT 2021). Note that these data include all vehicle types, both passenger and commercial.

Figure 7.9: County vehicle miles traveled and StreetLight Volume

7.1.4 Limitations

• The data used for passenger traffic contains “all vehicles”, not just passenger vehicles, meaning that commercial vehicles may be double counted. As a default, StreetLight suggests that users use a ratio of 96/2/2 (96% passenger, 2% medium, 2% heavy). We could apply a scaling factor of 0.96 to the passenger data to account for this.
• Commercial vehicle classifications schemes differ across data sources, though the scale of this effect is unknown.

7.2 State DOT data

As required by federal law, Minnesota and Wisconsin state departments of transportation (MnDOT and WisDOT) report various traffic measures for planning, forecasting, and various analysis endeavors.

7.2.1 Vehicle miles traveled

Vehicle miles traveled (VMT) is a standardized measure created by multiplying average annual daily traffic (AADT) by centerline miles. AADT is an estimate of the total vehicles on a road segment on any given day of the year in all directions of travel. VMT and AADT are common traffic measures and standardized across the United States.

MnDOT and WisDOT derive VMT using traffic counts from continuous and short term traffic monitoring sites. These raw counts are adjusted by multiplying seasonal, day-of-week, and axle adjustment factors WisDOT (2023). Data is not collected for every site every year, but the data are sufficient for year-over-year comparisons.

These data were compiled from MnDOT and WisDOT county level reports. MnDOT provides Excel workbooks with VMT by county and route system on their website. These were downloaded, filtered to include the relevant counties, and aggregated to the county level by summing VMT by county/route system. Processing code can be found in mndot_vmt_county.R.

WisDOT publishes PDF tables with county-level VMT. These were downloaded and data was extracted using {tabulizer}, an R package interfacing with the Tabula PDF extractor library. Processing code can be found in wisdot_vmt_county.R.

Figure 7.10: County vehicle miles traveled

7.2.2 Vehicle distribution by weight

To calibrate the generalized StreetLight Index to get commercial vehicle counts, we created a set of spatial lines (roads) to calibrate StreetLight’s metrics. For each calibration road, we found the proportion of passenger, medium-, and heavy-duty vehicles in the most recently available year, up to 2021.

State DOTs operate vehicle classification stations, which provide both the volume of traffic on a given road segment and, for some locations, the breakdown of volume by vehicle type. We obtained this breakdown using data from MnDOT (MnDOT 2021b) and WisDOT (WisDOT 2020) reporting websites.

MnDOT provides AADT road segments, which align with station identification numbers. Wisconsin does not readily supply AADT road segment data, so as suggested by the Wisconsin cartographers office (State Cartographer’s Office 2021) we pulled OpenStreetMaps road data (OSM version 0.6).

Then, we selected only the stations within the study area with observations in the last five years (2017-2021). Finally, we joined this data with AADT WisDOT (2021) road segments by station ID. The road segments sampled include multiple road functional classes and segments in all counties. All traffic sensor stations pulled were permanent, continuous monitoring sites. Data were cross-validated by verifying AADT and weight distribution fields on MnDOT and WisDOT traffic mapping applications.

Data were processed in R scripts: wisdot_stations.R, mndot_extract_yearly_volume_trends.R, mndot_stations.R, calibration_lines_points.R.

Figure 7.11: StreetLight calibration locations and values

Only 27 calibration roads were used for this inventory due to data availability constraints.

Figure 7.12: Vehicle weight distribution at calibration points

7.2.3 Limitations

• AADT/VMT data rely on modeling, and not every site will have new observed data every year.
• AADT/VMT are generally estimated for high-use arterial roads and highways, leaving local roads out.
• We may want to consider using non-permanent counters and/or counters from just outside the study region to increase the total number of calibration roads.

7.3 EPA MOVES

Emissions rates for our region were calculated using the EPA’s Motor Vehicle Emissions Simulator (MOVES) (USEPA 2016). MOVES calculates emissions factors using Council’s regional travel demand model, Minnesota Department of Vehicle Services’ county vehicle registration data, and the Minnesota Pollution Control Agency’s 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.

MOVES is a high-quality, government modeling system with strong data inputs and outputs. We requested this data from our MOVES coordinator who ran the model and shared the resulting table with us. The model run covers the entirety of the 7-county metro for years 2018 and 2019 ¹, using MOVES version 2014B.

The resulting table provides grams of CO₂, CH₄, and N₂O per vehicle mile traveled. We imported, processed, and saved the data in an R script, epa_moves.R. CO₂ equivalence (CO₂e) values are derived using global warming potential (GWP) values. See Section A.2 for more details.

Table 7.4: Emissions in grams per mile traveled by vehicle weight in the Twin Cities region. EPA MOVES.

Vehicle weight	MOVES year	Grams CH₄ per mile	Grams N₂O per mile	Grams CO₂ per mile	Grams CO₂e per mile
Passenger	2019	0.01	0.01	353.43	356.44
Medium	2018	0.01	0.01	473.24	476.25
Heavy	2018	0.02	0.04	1,212.36	1,223.91

7.3.1 Comparsion with EPA GHG Emissions Hub (2021)

For comparison, we pulled the emissions per mile rates from the Local Greenhouse Gas Inventory Tool (LGGIT), which align with the 2021 EPA GHG Emission Hub (USEPA 2021).

Table 7.5: Grams of emissions per mile by vehicle type and fuel type. EPA GHG Emission Hub (2021)

Vehicle type	Vehicle model year	Fuel type	Grams CH₄ per mile	Grams N₂O per mile	Grams CO₂ per mile	Grams CO₂e per mile
Passenger Car	2013	Gasoline	0.01	0.00	364.32	365.77
Passenger Car	2014	Diesel	0.03	0.02	315.12	321.21
Light Truck	2007	Diesel	0.00	0.00	461.99	462.40
Heavy-Duty Vehicle	2007	Diesel	0.01	0.04	787.81	799.84

To directly compare overall passenger emissions rates, we applied a weighted average to the EPA GHG Hub emissions rates for passenger vehicles according to the regional fleet gasoline-diesel distribution (98% gasoline, 2% diesel). Learn more about the regional fleet in Section 7.4.1.

Figure 7.13: MOVES and GHG Emissions Hub per mile emission rates by vehicle weight

7.3.2 Limitations

• This edition of MOVES is outdated relative to our estimation year (2021).
• We are not breaking out vehicles by fuel type; instead, we are aggregating based on the regional fleet. This may result in more inaccuracies.
• MOVES only accounts for vehicles that are registered in the 7-county metro area, so does not account for vehicles on regional roads, but registered elsewhere. However, the traffic generated from those vehicles is detected in the regional travel demand model.
• MOVES values were last updated in 2019. We anticipate using a more recent version of MOVES for the CCAP.

7.4 Travel Behavior Inventory

The Metropolitan Council Travel Behavior Inventory (TBI) is a bi-annual household survey of around 7,500 families in the 7-county Twin Cities metro and three neighboring Wisconsin counties. Information on people, households, trips, and vehicles are collected (Metropolitan Council 2021). This survey was active in the field from May 22, 2021 to February 5, 2023.

Data were imported directly from a Council-maintained public GitHub repository. The calculations below were verified from other Council analysis projects using the same dataset. Exactly 7,745 households with homes in any of the 11 CPRG counties were included in this subset of the TBI dataset.

TBI survey statistics were processed in tbi_survey_stats.R.

Table 7.6: 2021 TBI household survey geographic distribution

Household county	Estimated number of households	Estimated number of households standard error	Estimated percentage of households in county	Estimated percentage of all households in county standard error	Sample size
Hennepin	506,749	14,883	35.10%	0.99%	3,573
Ramsey	209,722	9,467	14.52%	0.65%	1,804
Dakota	161,097	11,679	11.16%	0.76%	652
Outside CPRG area	149,827	12,223	10.38%	0.79%	475
Anoka	128,264	11,620	8.88%	0.76%	450
Washington	94,091	8,075	6.52%	0.55%	377
Scott	51,448	7,005	3.56%	0.48%	158
Carver	34,501	5,751	2.39%	0.39%	139
St Croix	34,456	5,753	2.39%	0.39%	108
Sherburne	34,579	5,602	2.39%	0.38%	102
Chisago	20,014	4,523	1.39%	0.31%	63
Pierce	19,143	4,564	1.33%	0.31%	49

7.4.1 Regional fleet characteristics

We used 2021 TBI data to determine the regional average vehicle age and distribution of diesel and gasoline passenger vehicles of households in the CPRG study area.

Vehicles were classified into two broad fuel categories - diesel and gas + all other fuels (including gasoline, electric, flex-fuel, hybrid, and plug-in hybrid) - to best match the average miles per gallon table specifications in the EPA Local Greenhouse Gas Inventory Tool (LGGIT). The resulting value is on par with recent statistics from the Bureau of Transportation Statistics (BTS), which calculates the average passenger vehicle age in 2021 to be 12.1 years (BTS 2023).

TBI data were cleaned to only include vehicles with complete data and model year 1980 or later. Vehicles with a fuel type “Other (e.g., natural gas, bio-diesel)” were removed due to low sample size.

Regional fleet statistics were processed in tbi_vehicle_stats.R.

Table 7.7: Median vehicle age and proportion of all regional vehicles by grouped fuel type

Fuel type	Median vehicle year	Median vehicle year standard error	Estimated number of vehicles	Estimated number of vehicles standard error	Estimated percentage of all vehicles	Estimated percentage of all vehicles standard error	Sample size
Gas + all other fuels	2013	0	2,340,466	62,664	98.80%	0.21%	10,657
Diesel	2014	2	28,400	4,927	1.20%	0.21%	128

Figure 7.14: Regional vehicle fleet model year by fuel type

7.4.2 Average trip distance between counties

The average trip distance for the entire region is 6.28 miles (standard error 0.14), based on a sample of 117,870 trips.

Trips with distances over 720 miles (the equivalent of 12 hours of driving at 60 miles per hour) were removed. Only Minnesota CPRG counties were available for analysis.

We used the TBI to validate StreetLight’s average trip length. See Section 7.1.1.1 for more detail.

TBI trip distances were processed in tbi_trip_length.R.

Table 7.8: Regional mean trip distance. 2021 TBI.

Mean trip distance (miles)	Mean trip distance standard error	Estimated number of trips	Estimated number of trips standard error	Distance variance	Sample size
6.28	0.14	8,567,961	88,994	157	117,870

Origin-destination pairs with fewer than 30 observed trips were removed.

Figure 7.15: TBI origin-destination trip length matrix

Table 7.9: Mean trip distance by origin-destination county

Origin-Destination pair	Sample size	Mean trip distance (miles)	Mean trip distance standard error	Estimated number of trips	Estimated number of trips standard error	Distance variance
Hennepin-Hennepin	46,479	4.26	0.07	2,985,758	48,078	27.14
Ramsey-Ramsey	20,577	3.34	0.13	1,114,590	28,257	42.16
Dakota-Dakota	9,442	3.22	0.11	793,420	30,618	12.18
Anoka-Anoka	4,476	3.52	0.20	613,754	30,689	18.40
Washington-Washington	5,711	3.44	0.12	525,961	21,624	10.55
Scott-Scott	1,899	4.67	0.46	248,429	18,964	26.04
Ramsey-Hennepin	4,035	11.20	1.31	183,738	10,688	901.47
Hennepin-Ramsey	3,997	11.30	0.63	181,794	10,332	158.39
Hennepin-Anoka	1,279	12.11	0.80	149,876	15,673	83.96
Anoka-Hennepin	1,261	11.97	0.65	149,460	14,876	51.39
Carver-Carver	2,099	8.56	3.71	145,007	14,525	1,520.02
Ramsey-Washington	1,463	11.98	1.34	103,637	8,819	327.17
Hennepin-Dakota	1,354	14.66	0.69	97,582	9,736	45.67
Washington-Ramsey	1,426	9.26	0.54	94,323	8,217	40.85
Sherburne-Sherburne	702	23.63	9.00	93,336	10,987	4,486.87
Dakota-Hennepin	1,336	14.31	0.64	87,681	8,402	43.37
Anoka-Ramsey	720	9.89	0.73	81,528	12,577	37.48
Ramsey-Dakota	1,120	9.99	0.64	76,215	9,282	38.45
Ramsey-Anoka	704	10.58	0.70	74,680	12,067	43.31
Carver-Hennepin	611	12.26	1.00	72,673	9,638	127.24
Hennepin-Carver	593	12.04	1.16	72,544	9,776	83.13
Dakota-Ramsey	1,124	9.48	0.64	71,539	8,617	34.82
Chisago-Chisago	510	7.82	1.77	49,698	7,362	116.60
Scott-Hennepin	413	12.95	1.54	49,451	8,581	70.36
Hennepin-Scott	394	10.76	1.16	47,265	7,937	54.46
Dakota-Scott	341	8.95	1.01	43,366	8,317	43.06
Scott-Dakota	326	7.88	0.78	39,340	7,775	22.29
Washington-Dakota	350	15.81	1.41	31,371	6,521	57.95
Dakota-Washington	360	15.18	1.74	29,921	6,026	61.85
Washington-Hennepin	333	21.17	1.24	25,661	4,285	75.59
Hennepin-Washington	302	20.46	1.12	25,492	4,699	45.93
Anoka-Sherburne	104	14.51	1.75	22,934	6,720	82.99
Scott-Carver	116	8.17	1.20	15,669	3,917	36.37
Chisago-Washington	144	12.88	2.13	14,555	3,566	91.39
Carver-Scott	110	6.79	1.06	13,873	3,750	19.51
Washington-Chisago	147	11.11	1.33	13,417	3,462	42.53
Washington-Anoka	107	21.55	2.19	12,164	2,730	166.10
Hennepin-Sherburne	134	27.64	1.68	12,018	2,491	91.69
Anoka-Chisago	66	27.29	4.86	11,404	3,744	204.36
Anoka-Washington	108	22.20	2.31	11,324	2,820	100.45
Sherburne-Anoka	90	14.21	2.64	8,554	2,896	81.15
Sherburne-Hennepin	128	28.05	1.87	8,341	1,848	92.68
Ramsey-Chisago	61	40.56	2.48	8,293	3,062	71.40
Scott-Ramsey	59	30.16	3.35	6,423	2,764	59.68
Chisago-Anoka	60	17.61	3.94	5,806	2,519	104.53
Dakota-Carver	54	29.35	0.64	5,160	2,602	15.12
Carver-Dakota	48	34.51	0.83	4,367	2,183	21.57
Anoka-Dakota	61	27.49	2.28	3,814	1,669	45.18
Dakota-Anoka	51	25.23	0.89	3,557	1,237	22.57
Washington-Carver	17	55.43	4.28	3,136	1,906	107.93
Chisago-Ramsey	57	33.40	0.83	2,921	1,632	16.25
Chisago-Hennepin	48	49.92	3.57	2,886	1,696	58.15
Sherburne-Ramsey	33	40.05	3.31	2,886	905	110.23
Ramsey-Scott	58	29.36	2.66	1,911	1,073	24.57
Hennepin-Chisago	56	53.25	2.73	1,868	1,422	42.25
Ramsey-Sherburne	27	46.18	2.35	1,714	676	58.87
Ramsey-Carver	22	35.72	0.97	1,606	1,165	25.08
Anoka-Scott	25	33.48	4.44	1,527	627	108.80
Carver-Washington	16	39.31	0.40	1,431	562	2.70
Carver-Ramsey	17	31.04	0.38	1,412	1,152	6.93
Scott-Anoka	18	37.54	1.26	1,217	514	24.36
Dakota-Chisago	16	45.18	1.21	261	113	14.53
Chisago-Dakota	15	46.62	2.71	156	84	34.06

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———. 2021b. “Yearly Volume Trends With Truck Distribution.” MnDOT Website. https://www.dot.state.mn.us/traffic/data/data-products.html.

———. 2023. “TFA Data Collection Methods.” Government. 2023. https://www.dot.state.mn.us/traffic/data/coll-methods.html#TVPO.

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———. 2023b. “StreetLight Index.” Industry. https://support.streetlightdata.com/hc/en-us/articles/360018552772-StreetLight-Index.

———. 2023c. “Truck Travel Mode.” Industry. https://support.streetlightdata.com/hc/en-us/articles/8358369620507-Truck-travel-mode.

———. 2023d. “What Is Single Factor Calibration?” Industry. https://support.streetlightdata.com/hc/en-us/articles/360019181272-What-is-single-factor-calibration-.

———. 2024. “All Vehicles Travel Modes.” Industry. https://support.streetlightdata.com/hc/en-us/articles/360034538232-All-Vehicles-travel-modes.

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———. 2021. “GHG Emission Factors Hub.” https://www.epa.gov/system/files/documents/2023-04/emission-factors_sept2021.pdf.

WisDOT. 2020. “Wisconsin Vehicle Classification Data.” wisconsindot.gov. https://wisconsindot.gov/Pages/projects/data-plan/traf-fore/default.aspx.

———. 2021. “Wisconsin Vehicle Miles of Travel (VMT).” https://wisconsindot.gov/. https://wisconsindot.gov/Pages/projects/data-plan/veh-miles/default.aspx.

———. 2023. “Transportation Planning Manual - Chapter 9 Traffic Forecasting, Travel Demand Models and Planning Data.” Government. Bureau of Planning and Economic Development, Traffic Forecasting Section. https://wisconsindot.gov/Documents/projects/data-plan/plan-res/tpm/9.pdf.

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1. Passenger vehicles were updated to 2019. Commercial vehicles use 2018 values.