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To: OLIS Students and City of Eugene From: CLMPO: Josh Roll Date: 3.6.13 Subject: Regional and State Fleet Composition Comparison The following information is presented regarding the observation that fuel consumption at Eugene fuel stations has decreased by more (in percentage terms) between 2003 and 2011, relative to Eugene’s closest neighbor Springfield as well as the state and other jurisdictions within the state. Three attributes (Vehicle Ownership, Age, Fuel Efficiency) of the fleets were examined in order to gain a better sense of how the vehicles in each jurisdiction may be affecting fuel consumption. The outputs are broken down into All Vehicles and Vehicles Newer than 1978 since the Environmental Protection Agency (EPA) data on fuel efficiency only had data on vehicles from 1979 onward. The proportion of vehicles older than 1978 was typically low, ranging from 2%-9% depending on the jurisdiction and snap shot year (See Table A.)

Table A.

Data Definitions

• DMV data – Data received from the City of Eugene describing the fleet make, model and year counts by jurisdiction by snapshot year. Make and model are in coded terms which truncate the names, e.g. “Toyota” and “Tacoma” would be “TOY” and “TAC”.

• EPA data – Data pulled from EPA website and existing data sources describing vehicle fuel efficiency by operating mode by make, model, engine size (cylinders), and year. The make and model are in literal terms meaning they are full descriptive of the make and model.

a. City – City Driving Mode b. Highway – Highway Operating Mode c. Combo – Combination of both City and Highway Operating Mode

• Population Research Center Data (PSRC) population data – This data comes from Portland State University PSRC and are estimates of population by jurisdiction for each year of interest. http://www.pdx.edu/prc/annual-population-estimates

Proportion of Vehicles Older than 1978 2003 2004 2005 2006 2007 2008 2009 2010 2011 Eugene 8% 7% 6% 5% 5% 4% 4% 4% 3% Springfield 9% 8% 7% 6% 6% 5% 5% 4% 4% Tigard 4% 4% 3% 3% 3% 2% 2% 2% 2% WashingtonCounty 5% 4% 4% 3% 3% 3% 3% 2% 2%

1. Vehicle Ownership Using DMV data and PSRC population data rates of vehicle ownership were calculated for each jurisdiction for the beginning and end years of available DMV data. Available DMV data on the number of vehicles is summarized in Table 1.1. It appears that the number of vehicles increased by less in percentage terms than any of the other jurisdictions.

Table 1.1

Vehicles Newer than 1978 Number of Vehicles by Jurisdiction

2003 2004 2005 2006 2007 2008 2009 2010 2011

% Change from 2003-2011

Eugene 142,810 144,996 147,887 150,464 152,522 152,389 151,475 150,464 150,176 4.90% Springfield 53,382 54,383 55,774 57,032 58,051 58,241 58,055 57,394 57,227 6.72% Tigard 45,474 46,581 47,240 49,056 50,054 49,643 49,342 49,085 48,957 7.11% WashingtonCounty 362,099 369,374 382,697 398,495 412,628 411,272 408,853 411,860 417,804 13.33%

Table 1.2 describes population by jurisdiction for 2003 and 2011. Interim years can be found at the PSRC website.

Table 1.2

Population by Jurisdiction

2003 2004 2005 2006 2007 2008 2009 2010 2011


Eugene 143,910 157,010 8.34% Springfield 54,720 59,695 8.33% Tigard 45,130 48,415 6.79% WashingtonCounty 472,600 536,370 11.89%

Using the number of vehicles and the population we can then compute a vehicle ownership rate to see if the number of vehicles per person is changing and if that change is different across jurisdictions. Table 1.3 describes the rates for 2003 and 2011 and the change over that time. Eugene decreased its vehicle ownership rate by 3.75% with Springfield also decreasing its vehicle ownership rate by only by 1.76%. Both Tigard and Washington County both increased their vehicle ownership rates by 0.35% and 1.64% respectively.

Table 1.3

Vehicle Ownership Rate by Jurisdiction

2003 2004 2005 2006 2007 2008 2009 2010 2011


Eugene 0.992 0.956 -3.75% Springfield 0.976 0.959 -1.76% Tigard 1.008 1.011 0.35% WashingtonCounty 0.766 0.779 1.64%

2. Vehicle Age Using the DMV data a summary of vehicle age by snapshot year and by jurisdiction was created. Table 2.1 and Figure 2.1 describes this summary. Contrary to what would be expected if the fleet in Eugene was in facto newer and more efficient, the average age for vehicles 1978 and newer, Eugene and especially Springfield fleets are older by 2-3 years compared to the two other jurisdictions. The calculated average is consistent with the data from another DMV data set LCOG had on hand that details make, model and year for the entire state. Table 2.2 below details the average vehicle age by county. One thing to take note of is the relative stability in the age of vehicles up until the recession which seems to have caused people to hang onto their cars for longer resulting in greater average age for all jurisdictions. (Table 2.1)

Table 2.1

Vehicles Newer than 1978 Average Age of vehicles Only Vehicles newer Year 2003 2004 2005 2006 2007 2008 2009 2010 2011 Eugene 8.8 8.9 9 9.1 9.3 9.5 9.9 10.3 10.6 Springfield 9.5 9.6 9.7 9.8 10 10.3 10.8 11.2 11.6 Tigard 6.8 7 7.1 7.2 7.3 7.6 8.1 8.5 8.9 WashingtonCounty 7.1 7.2 7.3 7.4 7.5 7.8 8.2 8.6 8.8

Figure 2.1

Vehicle Age

0

2

4

6

8

10

12

14

2003 2004 2005 2006 2007 2008 2009 2010 2011

Year

Yea

rs O

ld

Eugene

Springfield

Tigard

WashingtonCounty

Table 2.2

2011 Statewide DMV Data Average Vehicle Age County

11.8 Baker 10.8 Benton 9.9 Clackamas 11 Clatsop

11.6 Columbia 12 Coos

12.1 Crook 11.4 Curry 10.8 Deschutes 12 Douglas

11.3 Gilliam 12.3 Grant 12.3 Harney 11.4 Hood River 11.2 Jackson 12 Jefferson 12 Josephine

11.9 Klamath 12.4 Lake 11.4 Lane 11.1 Lincoln 11.8 Linn 11.7 Malheur 11.1 Marion 11.3 Morrow 9.8 Multnomah 8.4 Out of Country 6.3 Out of State

10.8 Polk 11.6 Sherman

11.7 State Owned

Vehicle 11.4 Tillamook 11.3 Umatilla 11.4 Union 11.6 Wallowa 11.8 Wasco

9 Washington 12.2 Wheeler 11.4 Yamhill

3. Fleet Efficiency Using a process coded in the R open source software platform, CLMPO ‘decoded’ the DMV data so that a city, highway, and combo operating mode fuel ratings could be appended to the data from official EPA data on mile per gallon by make, model, and year. The vehicle make and model information within the DMV data was presented in a coded form as explained above in the data description section whereas the EPA data had full descriptions of the vehicle make and model. This reality make a simple join of the data impossible and required the implementation of some fuzzy logic within the processing. Fuzzy logic works by identifying a pattern to search for within a sample data set. So if we have a coded make and model for a Toyota Tacoma is TOY and TAC respectively then we search using those coded values and should identify the pattern “TOY” for Toyota (TOY = TOYata) and TAC for Tacoma (TAC = TAComa). The search is hierarchical and first limits the search by year before trying to find a Make match but once the process limits the records by year and Make then a model is searched for within a usually small number of available possibilities. The process is described in Figure 3.1

Figure 3.1

Determine Vehicle

Characteristics

Legend

Data

Attributes

Function

DMV Data

EPA Data

EPA Data By YearVehicle Record

Search Year

Search MakeCoded Make Coded Model

Year

Search Model

EPA Data By Year and Make

EPA Data By Year and Make and Model

Decoded Vehicle Record Model(s)

The process was not able to assign an MPG to any vehicles older than 1979 because there was not data on MPG available for those vehicle years. After removing vehicles older than 1979 from the DMV data, there were still a proportion of the records in which an MPG could not be assigned. Table 3.1 details the percentage of vehicles by year and by jurisdiction where no MPG was assigned and which will be removed from further analysis. These vehicles are vehicles that were not in the EPA data likely because they were heavier duty trucks that made deliveries but would not typically be included in the EPA data. Motorcycles were also not assigned an MPG though they typically represent a small proportion of vehicles and vehicle miles traveled.

Table 3.1

Vehicles without an assigned MPG as Percentage of all vehicles newer than 1979

2003 2004 2005 2006 2007 2008 2009 2010 2011 Eugene 34% 8% 9% 9% 9% 10% 10% 10% 10% Springfield 32% 8% 9% 9% 10% 10% 10% 10% 10% Tigard 34% 7% 7% 8% 8% 9% 9% 9% 9% WashingtonCounty 34% 7% 8% 9% 9% 9% 10% 10% 10%

It is unclear what was going on with the data in the 2003 sample but the data appears to be worse than the other years resulting in a much higher proportion of vehicles not getting an MPG assigned. The rest of the years for all jurisdictions are reasonable, I think, with up to 10% of the data needing to be removed, leaving the analysis with a 90% sample. A major limitation to the assignment process is that the DMV data did not have engine size or the number of cyclinders. These two attributes can affect a vehicle’s MPG considerably and reduce the clarity by which the MPG assignment was done. Without engine size and cylinder information, all MPGs for a particular make and model were averaged and then assigned to that vehicle. This is a major limitation to the final MPG assignment because it could be that of all the Toyota Tacomas in any of the jurisdictions, most of them are 4 cylinder with an MPG of 19 in the city where as the 6 cylinder are MPG rating is 16 resulting in an average of 17.5. These differences are not great but some vehicles engine sizes can have a much bigger effect, sometimes being different by as much as 10 MPG or more. Also, in some cases the fuzzy logic was not able to distinguish between very similarly named model types. For instance the coded model type for a Volkswagon Passat is “PAS” which the logic was unable to distinguish between a “Passat” and a “Passat Wagon”. Not sure how much of an issue this is but there is more variation between models with different engine sizes than there is in different versions of the same model like in the example of the Passat.

The results of averaging the MPG by operating mode by year and jurisdiction seem to indicate very little difference in average fleet efficiency between the jurisdictions for all of the driving models. Further, the average fleet efficiency did not change considerably between 2003 and 2011, only by between 2% and 5% depending on the mode and jurisdiction. Tables 3.2 – 3.4 and Figures 3.2 – 3.4 detail these outcomes for city, highway and combo driving modes.

Table 3.2

Mpg Change over time for City

2003 2004 2005 2006 2007 2008 2009 2010 2011 Change from 2003 - 2011

Eugene 17.61 18.19 18.12 18.09 18.1 18.17 18.22 18.28 18.39 104% Springfield 17.47 17.99 17.86 17.75 17.69 17.68 17.69 17.68 17.75 102% Tigard 17.53 17.92 17.86 17.81 17.82 17.91 18 18.07 18.21 104% WashingtonCounty 17.55 18 17.94 17.89 17.94 18.04 18.12 18.21 18.35 105%

Figure 3.2

MPG for City

17

17.2

17.4

17.6

17.8

18

18.2

18.4

18.6

2003 2004 2005 2006 2007 2008 2009 2010 2011

Years

Ave

rage

Fle

et M

PG

Eugene

Springfield

Tigard

WashingtonCounty

Table 3.3

Mpg Change over time for Hwy

2003 2004 2005 2006 2007 2008 2009 2010 2011 Change from 2003 - 2011

Eugene 23.64 24.35 24.26 24.19 24.17 24.24 24.28 24.33 24.46 103% Springfield 23.49 24.17 24 23.84 23.75 23.73 23.73 23.72 23.78 101% Tigard 23.54 24.14 24.1 24.02 24.02 24.11 24.21 24.27 24.44 104% WashingtonCounty 23.57 24.21 24.18 24.1 24.13 24.24 24.33 24.44 24.61 104%

Figure 3.3

MPG for Hwy

22.8

23

23.2

23.4

23.6

23.8

24

24.2

24.4

24.6

24.8

2003 2004 2005 2006 2007 2008 2009 2010 2011

Year

Ave

rage

Fle

et M

PG

Eugene

Springfield

Tigard

WashingtonCounty

Table 3.4

MPG Change over time for Combo

2003 2004 2005 2006 2007 2008 2009 2010 2011 Change from 2003 - 2011

Eugene 19.81 20.45 20.38 20.33 20.33 20.4 20.45 20.51 20.61 104% Springfield 19.68 20.25 20.12 19.99 19.92 19.91 19.92 19.91 19.96 101% Tigard 19.73 20.21 20.16 20.1 20.11 20.19 20.29 20.35 20.5 104% WashingtonCounty 19.75 20.29 20.24 20.18 20.22 20.32 20.41 20.5 20.65 105%

Figure 3.4

MPG for Combo

19

19.2

19.4

19.6

19.8

20

20.2

20.4

20.6

20.8

2003 2004 2005 2006 2007 2008 2009 2010 2011

Year

Ave

rafe

Fle

et M

PG

Eugene

Springfield

Tigard

WashingtonCounty

In regards to the question spawning this analysis, is Eugene’s fleet more efficient?; the answer appears to be in the case for city driving mode yes but only marginally. For the other driving modes not so much and certainly not considerably.

This outcome should not be surprising since much in the way of engine efficiency for vehicles has been sacrificed for power over the last few decades. In an article put out by Bureau of Transportation Statistics it, the author demonstrates how fuel efficiencies have not gone as far as many would like since the 1970s. The graph in figure 3.5 doesn’t include years beyond 2006 but a vast change would be unexpected.

Figure 3.5

Source: U.S. Bureau of Transportation Statistics

4. Discussion The above results do not answer the ultimate question regarding the reason for less fuel consumption at Eugene stations compared to observed fuel consumption data from Springfield and the state stations. Of the fleet characteristics measured the analysis, the results of looking at age and mileage would indicate the opposite of what is observed in the fuel consumption data. The results from looking at vehicle ownership for the analysis, however, would support what is being observed in the fuel consumption data. There are certain limitations in the way the data was processed in order to arrive at average fleet MPGs which lend less credibility to that part of the analysis. However, any even a highly reliable overall fleet MPG would still need to be considered knowing that the number of vehicles anyone particular vehicle traveled would have implications on the fuel consumption. For instance, the average fleet MPG might be 20 MPG but vehicles with 15 MPG drive 95% of the vehicle miles traveled rendering the average less meaningful. The answer to the question regarding Eugene’s substantial drop in fuel consumption is likely multifold. Less vehicle ownership, maybe a more efficient fleet (certainly more efficient relative to 2003, though maybe not relative to other jurisdictions), the kinds of travel (inner city versus long distance), mode shift(more travel done by active modes and transit), less trips because fewer people are employed(Lane County unemployment rate 8.2% whereas Washington County is

6.9%), and other factors that make the question very complicated. Another issue in all of this could be that fuel consumption for Eugene and Springfield should not be separated since they are in a single metropolitan area where residents from one area purchase fuel from the other. It’s impossible to tell if that works out to a balance or if Eugene residents buy more from Springfield or vice versa. More work is definitely needed and the question is very interesting.

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