# Attachment 5

IMPROVING AIR QUALITY MODELS IN NEW YORK STATE: UTILITY OF THE

1995 NATIONWIDE PERSONAL TRANSPORTATION SURVEY

### Improving Air Quality Models in New York State:

Util ity of the 1995 Nationwide Personal Transportation Survey

Nathan S. Erlbaum Planning and Strategy Group (518) 457

- 2967

NERLBAUM@GW.DOT.STATE.NY.US

New York State Department of Transportation

1220 Washington Avenue Albany, New York 12232

- 0413

May 13, 1999i Table of Contents

Introduction

................................ ................................ ................................ ................................ ...... 1

Background

................................ ................................ ................................ ................................ ....... 2

Tele

- Commuting or Work at Home

................................ ................................ ................................ .. 4

An Hourly Traffic Distribution Alternative

................................ ................................ ....................... 6

Speed From Trip Length and Travel Time Estimates

................................ ................................ ...... 10

Annual Vehicle Utilization

................................ ................................ ................................ .............. 14

Engine Mode of Oper

ation

................................ ................................ ................................ ............. 18

General Findings

................................ ................................ ................................ ............................. 24

References

................................ ................................ ................................ ................................ ....... 25

List of Tables

Table 1 Working at Home, Census v. NPTS

................................ ................................ ......................... 5

Table 2 Estimated Speeds by Stratum

................................ ................................ ................................ 11

Table 3 Estimated Speeds by Trip Purpose

................................ ................................ ......................... 13

Table 4 Number of Survey Records with Annualized Mileage

................................ .............................. 15

List of Figures

Figure 1 24

- Hour Vehicle Distribution

- Capital District Area

................................ ................ 7 Figure 2 Average Trip Length by Time of Day

- Capital District Area

................................ ..... 7 Figure 3 24 Hour Vehicle Distribution

- New York (Manhattan)

................................ ............ 8 Figure 4 Annual Vehicle Utilization

- Light Dut y Gas Vehicles

- Autos

................................ 14 Figure 5 Annual Vehicle Utilization

- Light Duty Gas Trucks

- Pickup, SUV, Van

................ 15 Figure 6 Vehicle Age Distribution

- Auto and Light Trucks

................................ .................. 16 Figure 7 Engine Mode of Operation

................................ ................................ ..................... 20 Figure 8 Duration Between Engine Starts

................................ ................................ ............. 21 Figure 9 Travel Time

................................ ................................ ................................ ............. 22 Figure 10 Existing Engine Mode of Operation

................................ ................................ ....... 231

Introduction

The New York State Department of Transportation (NYSDOT) is legislatively responsible for the management of 14% of the 112,000 miles of public roads that c arried 52% of the 118 billion vehicle miles of travel in 1996. These roads are comprised primarily of the Interstate and State Highway System which serve as the backbone for highway transportation in the state. As a state agency, the Department is concer

ned with many issues including infrastructure maintenance, safety, mobility, economic development, congestion management, and air quality. The Department

= s capital program is multi modal with investments in public transportation facilities, as well as c onstructing, operating and maintaining the highway infrastructure. Its goal is to insure transportation access and mobility to all of its citizens.

This paper will describe several analyses of the 1995 Nationwide Personal Transportation Survey

undertake n by the New York State Department of Transportation. These analyses are presented to illustrate the value of the Nationwide Personal Transportation Survey for New York State and the ability of NYSDOT to focus transportation studies on state

- based trave

l characteristics. The analyses address issues raised by the New York State Department of Environmental Conservation (ENCON), the state

= s environmental agency, and the New York State Department of Transportation. They were identified during the developme nt of the Air Quality State Implementation Plan, and related activities, such as creating vehicle miles of travel inventories, updating ENCON

=

s emission model, and the conformity analyses of the Department

= s Transportation Program.2

Background

Early travel surveys, limited primarily to automobile and truck travel, were conducted in a number of States between 1930 and 1940 and again between 1950 and 1960. As transportation planning evolved, metropolitan area surveys became more common . In 1961, a survey was conducted by the Federal Highway Administration (FHWA) to determine on a nationwide basis the characteristics of travel, and the ownership and use of automobiles. In 1969, the Nationwide Personal Transportation Survey as we know i

t was conducted. Since then this survey has been conducted almost every five years, expanding in scope and geographic coverage. In 1990 and 1995, it was possible for metropolitan areas and states to participate with FHWA to obtain additional samples for greater local coverage.

In each survey prior to 1995, New York State was represented more by its largest urban area (New York Metropolitan area), because of its sheer size within the nation than by the distribution of population in other areas

-- both ur ban and rural

--

within the State. New York State has 12 large urban areas: three of which have populations around 100,000, five between 250,000

- 500,000 in population, two between 700,000

- 800,000 in population, and one at 1.2 million in population. The

twe lfth and the largest is the ten county New York Metropolitan area with a population of 11.2 million (with six of the ten counties having populations greater than one million). Yet as urban as New York State seems, a population of 2.8 million resides in th e non urban counties making the

State the fourth most rural state in the nation in the 1990 Census.

This very diverse population distribution shows why participation in a survey such as the Nationwide Personal Transportation Survey is important for desc ribing the personal travel characteristics of the different urban and rural areas, or for characterizing the state as a whole. Given the diversity in population sizes, New York State is a microcosm of the country. The nature of the transportation problem s are diverse and transportation planning issues facing the state during the next 20 years require a broad, but detailed state

- level database. Although individual Metropolitan Planning Organizations (MPOs) have conducted area

- specific surveys over the yea rs, the most recent comprehensive statewide home interview survey for transportation was collected during the late 1960s.

The recognition of the limitations of existing data that is available to characterize travel on a

statewide and urban

- area basis and

the need to understand non urban travel led the New York State Department of Transportation (NYSDOT) to choose to become an add

- on participant with the Federal Highway Administration (FHWA) in the conduct of 1995 Nationwide Personal Transportation Survey. The 1995 Nationwide Personal Transportation Survey as conducted in New York State, surveyed 11,000 households. The sample varied in size from 425 to 650 households in the primary counties in each urban area and 1,400 households in the remaining rural co unties. The New York Metropolitan area had almost 4,000 households sampled within its ten county area. Each county represented a separate sample area varying from almost 300 to 500 households.

In addition to participating in the 1995 Nationwide Person al Transportation Survey, NYSDOT undertook two separate but related initiatives to address the transportation planning challenges facing the state. The first initiative was the acquisition of a detailed county and sub

- county forecast of demographics and b

usiness economics, and the development of a vehicle miles of travel model3

driven by these data. This model is calibrated against the Highway Performance Monitoring System travel data for each of the 12 large urban areas, and the small urban and rural aggr egated areas. The second initiative was a review of the state of the practice(s) in travel demand modeling currently in effect in each of the urban areas. Both initiatives, along with the Nationwide Personal Transportation Survey add

- on, will enable NYSD OT to better address the patterns and

characteristics of current and future travel in the state.

This paper examines five issues that arose during the reevaluation of air quality inventories and modeling for conformity analysis of the Department

= s Transp ortation Program. The first topic

examines:

$ Tele

- Commuting or Work at Home

- a comparison of results from the 1990 Census and
the 1995 Nationwide Personal Transportation Survey. It studies the emerging pattern of
increasing number of workers who A work a t home @ that suggests a possible reduction in
the number of work trips.

The remaining four topics originate from the desire of NYSDOT to reflect New York State

- based

data in ENCON

=

s use and adaptation of EPA

= s MOBILE Emission Model.

$ Hourly Vehicle Distri butions

- a comparison of hourly Nationwide Personal
Transportation Survey

- based vehicle trip distributions with hourly ground count data

$ Area

- wide Speeds

- a comparison of Nationwide Personal Transportation Survey derived
speeds with four

- step model

- base d network speeds

$ Vehicle Use

- a comparison of Nationwide Personal Transportation Survey

- based
estimates of annual vehicle usage (miles traveled) with distributions developed by the
U.S. Environmental Protection Agency for use with its MOBILE 6 Emiss ions Model.

$ Engine Mode of Operation

- a comparison of Nationwide Personal Transportation
Survey

- based estimates of area

- wide 24

- hour hot and cold starts with the four time period
estimates currently being used in the New York State Environmental Conserva tion

= s

Mobile 5b Emission Model.4

TELE

- COMMUTING OR WORK AT HOME

After the 1995 Nationwide Personal Transportation Survey (NPTS) data became available, analysis of total travel, not just journey to work travel a s in the 1990 Census, became possible. One of the first questions posed was whether tele

- commuting or working at home was affecting the journey to work. If technology such as cellular telephones, laptop computers, and Internet access typically used by th e mobile work force were having an impact, then a significant change in the number of workers working at home should appear in the data. Unfortunately, neither the Census nor the 1995 Nationwide Personal Transportation Survey specifically addressed A tele

- commuting

@ as a work activity. While this is a definite shortcoming, both surveys identified in different ways the number

of workers who worked at home.

Focusing on this issue, the 1990 Census asked about mode to work: A How did this person usually

get to work last week?

@ One possible response was A work at home. @

The 1995 Nationwide Personal Transportation Survey asked a very different question: A What is the one

- way distance from your home to your workplace? @ Possible NPTS responses included the spec ific number of

blocks or miles coded as A goes to work, @

and two alternatives when distance was not provided A no fixed work place

@ or A works at or out of home. @ The intent of the 1995 Nationwide Personal Transportation Survey category A no fixed work place

@ was meant to capture migrant workers following work, as in construction or farming. This category may also contain sales persons, such as

a manufacturer

= s representative who did not have a fixed work location, and who may not have

work from home. Unfor tunately, the 1995 Nationwide Personal Transportation Survey lacked a category to describe the classification of the work site, job, or why a person was working at home.

Table 1 illustrates that the number of workers and the percentage of those who A wor k at home @

for both the 1990 Census and the 1995 Nationwide Personal Transportation Survey for New York State. The data for New York State are summarized in this table for the sample strata with the

constituent counties noted. The number of workers who A work at home

@ in the 1995 Nationwide Personal Transportation Survey for New York State was about double that of the 1990 Census. The 1990 Census sampled 16.7% of the households in New York State. The 1995 Nationwide Personal Transportation Survey sampled 0.2% of the households in 1995, a smaller number, but reliable at

the 95% confidence level. Table 1 shows that the number of workers who A work at home

@ in New York

State has increased from 2.6% of all workers in the 1990 Census to 5.1% in the 1995 Nati onwide Personal Transportation Survey for New York State. This doubling has occurred in most areas shown in Table 1 . However, it is interesting to note that the share of the workforce working at home is highest in Ithaca, Glens Falls, Poughkeepsie, Westc

hester, Putnam and Rockland counties and the aggregate A rest of state @ area. These areas may be viewed as places where workers may commute a much longer distance to an employment location in a nearby urban area (e.g., Ithaca to Syracuse or Binghamton, Gle ns Falls to Albany, Westchester, Putnam and Rockland counties to New York City, and any of the rural counties to an urban area).

The observation that the increase in A work at home

@ is occurring across the state, and is an increasing proportion of all wo rkers in areas with A

long commutes

@ to a nearby urban area, raises a number of further policy questions. In what areas will A work at home

@ continue to increase and at what rate? In which industries, job classifications or professions are these workers en gaged? Can reasons for working at home be enumerated? Clearly the findings of Table 1 suggest that the design of the year 2000 Nationwide Personal Transportation Survey must focus more attention on these questions.5

Table 1 Working at Home, Census V. N ationwide Personal Transportation Survey

1995 Nationwide Personal Transportation Survey

Work Location 1990 Census

Table P49

- Journey to Work

(Shown by NYS 1995 NPTS Sample Stratum)

All

Workers Work@

Home % All

Workers Work@

Home %

Upstate Areas (Sample Counties)

Albany (Albany, Rensselaer, Saratoga,
Schenectady) 410,418 20,636 5.0% 382,229 8,474 2.2%

Glens Falls (Warren, Washington) 58,973 5,633 9.6% 51,864 1,933 3.7%

Utica

- Rome (Herkimer, Oneida) 150,829 8,446 5.6% 13 5,041 3,891 2.9%
Syracuse (Onondaga) 244,025 13,694 5.6% 223,650 5,295 2.4%

Ithaca (Tompkins) 49,853 3,050 6.1% 45,175 1,990 4.4%

Rochester (Monroe) 366,085 11,307 3.1% 347,088 7,403 2.1%

Buffalo (Erie, Niagara) 562,013 16,997 3.0% 531,122 9,808 1.9%

Elmira (Chemung) 40,657 1,784 4.4% 40,325 881 2.2%
Poughkeepsie (Dutchess) 136,474 8,371 6.1% 125,726 2,991 2.4%

Binghamton (Broome, Tioga) 114,967 4,920 4.3% 121,274 3,201 2.6%

Newburgh (Orange) 157,607 7,984 5.1% 141,664 3,406 2.4%

Upstate Urban Area Total 2,291,901 102,822 4.5% 2,145,158 49,273 2.3%

New York Metropolitan Area (Sample

Counties)

Bronx 447,511 16,723 3.7% 429,77 7 5,379 1.3%

Kings 966,600 30,513 3.2% 907,010 14,510 1.6%

New York 845,535 56,275 6.7% 754,148 41,102 5.5%

Queens 950,510 41,488 4.4% 918,063 13,372 1.5%

Richmond 194,047 3,751 1.9% 174,090 2,456 1.4%

New York City Total 3,404,2 03 148,750 4.4% 3,183,088 76,819 2.4%

Nassau 672,349 30,490 4.5% 650,947 16,383 2.5%

Suffolk 701,974 37,757 5.4% 652,989 12,794 2.0%

Putnam/Rockland (Combined for NPTS) 192,409 13,915 7.2% 177,973 4,481 2.5%

Westchester 439 ,844 33,930 7.7% 437,753 13,813 3.2%

New York Metropolitan Suburban County

Total 2,006,576 116,092 5.8% 1,919,662 47,471 2.5%

Rest

- of

- State 1,071,958 77,166 7.2% 972,705 39,659 4.1%

New York State 8,774,638 444,830 5.1% 8,220,613 213,222 2.6%

Source:
Unpublished 1995 NPTS Data Extracted from FHWA NPTS web site: http://www

- cta.ornl.gov/npts
1990 Census, Journey

- to

- Work, available from BTS on 1990 CTPP CD

- ROM or from Census web site :
http://www.census.gov
Prepared by:
NYSDOT, Planning and Strategy Group, March 19986

AN HOURLY TRAFFIC DISTRIBUTION ALTERNATIVE

Air Quality Analysis is a cooperative activity between the New York State Department of

Environmental Conservat ion (ENCON) which performs the emission

= s analysis, and the New York State Department of Transportation, which develops the vehicle miles of travel (VMT) inventory and related highway measures for use with the State Implementation Plan. The VMT inventory provides county level area

- wide estimates of VMT based on the Highway Performance Monitoring System (HPMS) data by rural, small urban and large urban areas, as well as by roadway functional

classification.

This section will focus on the use of the 1995 Nationwide Personal Transportation Survey for New York State (NPTS_NY) as a source for hourly vehicle distributions to provide greater detail to improve upon the traffic count distributions developed in 1992. That year, ENCON observed that

modeled emissio

ns begin to increase in the morning and then drop off by 10:00 a.m., but not rise again until early afternoon following the apparent pattern of the hourly ground count distributions.

This was in stark contrast to ozone formation that was observed to incre ase throughout the day. The Nationwide Personal Transportation Survey collected travel data across the entire day, every day for a whole year. It is possible, therefore, that the 1995 NPTS_NY could shed some light on this problem.

The 1995 Nationwide Pe rsonal Transportation Survey for New York State was summarized by urban area strata, for the proportion of hourly A personally occupied vehicle @ trips as a percent of the entire day. Since these data represent travel on all roadways within the individual a reas, a software routine was developed to construct comparable area

- wide hourly vehicle distributions from traffic counts on the State Highway System.

Figure 1 contains two curves; The first is the hourly distribution from the Nationwide Personal Transpo rtation Survey for the Capital District (the counties of Albany, Schenectady, Saratoga, and Rensselaer)

-- a typical upstate urban area. The second is the comparable area

- wide weighted average hourly traffic count distribution for the State Highway System for the same area. This figure shows that the NPTS_NY hourly distribution of vehicle trips generally follows the State Highway System traffic count pattern especially for the peak periods. However, a midday peak not present in the actual ground count dat a is observed. This peak is more typical of local non arterial traffic not typically measured in State Highway System arterial counts.

Since midday peaks occurred in the hourly distributions from the NPTS_NY for all urban areas, a computation of the a

verage hourly trip length was undertaken. If the midday peaks are representative of local traffic, it is reasoned that the average trip length would likely be shorter. Figure 2 for Capital District Area, illustrates the finding for all urban areas, the a verage trip length is shortest during the midday peak. The very high values in the early morning hours result from fewer observations and longer trip lengths.7

Figure 1

Figure 2

0 2 4 6 8 10PERCENT OF VEHICLE TRIPS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

HOUR ENDING

1995 NPTS State Highway System 

### 24 Hour Vehicle Distribution

Capital District Area*

* Albany, Schenectady, Saratoga and Rensselaer Counties

0 5 10 15 20 25 30MILES

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
HOUR ENDING 

## Average Trip Length by Time of Day

Capital District Area *

* Albany, Schenectady, Saratoga and Rensselaer Counties8

Fig ure 3 shows the area

- wide hourly distributions from both the NPTS_NY and the State Highway System for New York County.

Figure 3

As in Figure 1

, the prominent midday peak is present along with two or more significant pea ks in the evening period. This particular pattern was found in the hourly distributions for each of the five counties within New York City (NYC). The counties of Nassau and Suffolk, east of New York City
and the counties of Westchester, Rockland and Putn am, north of New York City exhibited hourly distribution patterns similar to typical urban areas in the state as depicted by Figure 1 . Depending upon the individual county within New York City, the size of the peaks varied. However, each

showed an evenin

g rush hour peak around 5 p.m., an after rush hour peak around 7 p.m., and a smaller late night peak around 10 or 11 p.m. It is probable that the characteristics of New York City, (population density, the individuality of each county as a city within a ci ty, or the nature of

local self

- contained neighborhoods) may explain the personal vehicle travel behavior noted in the evening peak, compared with that of Figure 1 for typical upstate areas. 0 2 4 6 8 10PERCENT OF VEHICLE TRIPS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

HOUR ENDING

1995 NPTS State Highway System 

## 24 Hour Vehicle Distribution

New York (Manhattan)9

These data were provided to ENCON for each of the urban areas in New York State. For the New York Metropolitan Area, the ten individual county distributions were provided. ENCON replaced the upstate, downstate, and rural hourly distributions with specific NPTS_NY hourly distributions

and tested the effects on the EPA MOBILE Emissions model output. This test showed that the modeled hourly emission results more closely correlate with the increasing hourly profile for the measured ozone data with no significant net change in the overall level of emissions.

In summa ry, the data from the Nationwide Personal Transportation Survey for New York State has provided more

reliable area

- wide hourly traffic distribution for use with ENCON

= s MOBILE Emissions model. The distribution fits the expected progression of area

- wide em issions throughout the day. The NPTS_NY has also shown that the Department

= s ground count program, when taken in the aggregate, is a good indicator of hourly arterial traffic
distributions, but not overall area

- wide traffic that includes local roads. Las tly, the NPTS_NY has identified differences in hourly distributions for the evening peak period within the five counties of New York City which requires

further examination.10

SPEED FROM TRIP LENGTH AND TRAVEL TIME ESTIMATES

One of the critical input parameters for EPA

= s MOBILE Emissions Model is the estimate of speed by roadway functional class and time of the day. All transportation projects planned for an air quality non attainment area are re quired to demonstrate conformity with the area

= s emission target. Low speeds and an increasing number of stops per mile are indicative of both high levels of congestion and emissions. As a result, NYSDOT was interested in validating and assessing the

ac curacy of the speed estimates in current usage.

In 1992, speed estimates by functional classification were developed from each of the urban area network travel demand models. They were based upon Highway Capacity Manual volume/capacity ratios (V/C), an

d empirical speed data gathered from the field. Three years later, the Nationwide Personal Transportation Survey for New York State (NPTS_NY) collected respondent trip length and travel time which could be used for computing a respondent trip speed. In a ddition, NYSDOT had the Research Triangle Institute geo

- code all trip ends during the conduct of the NPTS_NY. As a result, a separate geo

- coded data set exists for New York State.

This section compares several survey

- based speed computations and the ne twork travel demand

model

- based speeds on an urban area

- wide basis. In order to examine these different speeds, it is important first to acknowledge some problems with the data:

$ Survey

- based travel time clusters around the hour, half, or quarter

- hour per iod. Survey

based distances are reported in whole units and usually rounded to the nearest mile.

$ The geo

- coding of the origins and destinations are accurate to the street address, nearest
intersection, or zip code centroid.

$ Computation of a straig ht

- line distance between an origin and destination ignores the
actual path within the grid

- based street network that a traveler might use.

$ Speeds estimated from calibrated network travel demand model assignments are
predominantly from arterials and subjec t to the uniform applicability of capacity type
calculations and assumptions about headway and observed V/C at given speeds.
Additionally, the tolerance for link volume variations from actual traffic count volumes
may introduce variability for V/C and the refore speed calculations from network
assignments.

$ Only area

- wide speeds can be computed from the Nationwide Personal Transportation
Survey for New York State. These are obtained by dividing the reported survey trip
length by the reported travel time and for the geo

- coded records by using the computed
coordinate length between the origin and the destination (the straight

- line distance). For
comparability, the network travel demand model speeds by roadway functional class
were weighted by vehicle miles of travel to construct area

- wide speeds for each of the

urban areas or counties.11

Table 2 contains five columns that compare the computed area

- wide speed estimates for the

different urban areas or counties. The definitions for these columns are:

$ a) Sp eeds computed from all NPTS_NY trip records for personally occupied vehicle trips
using the respondent

=

s reported trip length and travel time;

$ b) Speeds computed from the respondent

= s reported trip length and travel time using
personally occupied vehicle trip records from the 82.5% of all records for which geo

coded information exists;

$ c) Speeds computed from the geo

- coded straight

- line trip length and the respondent

= s travel time using the same data records as in b);

$ d) The speed ratio derived from A (c) @ divided by A

(b) @ and,

$ e) The vehicle miles of travel weighted average area

- wide speeds used by ENCON

= s
Mobile Emissions model.

Table 2 Estimated Speeds by Stratum

(a) (b) (c) (d) (e)

All NPTS records NPTS records with O/D coordinates

Str atums(1) Survey Trip Length/ Survey Travel Time Survey Trip Length / Survey Travel Time Coordinate Trip Length / Survey Travel Time Coordinate Trip Length(c) / Survey Trip Length(b) NYS ENCON (Weighted Average Area Speeds 1992 SIP)
Upstate Areas

Al bany 28.05 29.94 23.36 78.0% 35.8

Glens Falls 29.22 30.78 25.67 83.4% 39.5

Utica

- Rome 29.29 32.14 26.11 81.2% 37.0

Syracuse 29.83 31.60 22.66 71.7% 33.7

Ithaca 28.31 31.09 25.73 82.8% 38.1

Rochester 28.97 30.64 23.03 75.2% 31.6

Buffalo 26.35 29.28 22.20 75.8% 40.5

Elmira 27.02 27.67 21.45 77.5% 35.1
Poughkeepsie 28.83 31.05 25.11 80.9% 35.8
Binghamton 29.22 30.72 26.99 87.8% 36.8 Newburgh 21.22 22.07 18.88 85.5% 36.1

Small Urban and Rural Areas

Small urban in rural counties 24.54 28.78 25.92 90.0%

Rural Counties w/o small urban 31.57 34.61 29.07 84.0%

New York City

Bronx 18.57 19.71 15.06 76.4% 21.0
Kings 17.40 18.96 12.49 65.9% 16.6

New York 19.93 19.53 14.88 76.2% 9.6

Quee ns 21.79 21.33 17.45 81.8% 17.5

Richmond 20.84 21.49 16.44 76.5% 18.7

Nassau 22.91 26.10 19.62 75.2% 17.5

Suffolk 27.98 28.63 22.75 79.5% 23.7

Putnam/Rockland 30.51 30.87 20.61 66.8% 29.5

Westchester 25.21 25.42 20.06 78. 9% 26.7

Average Ratio (c)/(b) 78.7%

SD 5.9%

(1) NYS 1995 NPTS Add

- on Stratum (Primary Urban County)12

Speeds from the NPTS_NY are computed in a fashion similar to the interval

- grouping for speed based ground counts. The trip records are s ummarized by speed intervals and the percentage of the total computed for all records within each urban area or county. The weighted harmonic average speed for each area is then computed using the midpoint of each speed interval and its percentage.

Sever al interesting observations regarding Tables 2 and 3 are noted:

$ The speeds from the selected set of survey records that have geo

- coded trip ends (b) are
not appreciably different from those of the entire survey data set (a) for New York State.

$ In less co ngested upstate urban areas , the network

- based speed estimates (e) from the
travel demand model assignments provide speeds that are on average 25% higher than
the survey

- based speeds (b).

$ In the ten individual counties within the New York Metropolitan are a, the conclusions on
speed are varied. For the five counties within New York City except the Bronx, and the

suburban counties of Rockland and Putnam the network

- based speed estimate (e) is lower
than the survey

- based speed (b). In most instances the net work speed seems to fall
between the survey speed (b) and the coordinate length estimates (c) except New York
County. In this instance, the network speed estimate (e) is half the survey estimate (b).

Differences In Trip Length Estimates

Column (d) in T able 2

, shows that the NPTS_NY straight

- line speed (c) is 78.7% of the survey length

- based speed (b) with a deviation of +/

- 5.9%. Travel time is constant in each of the speed estimates and the ratio of these two weighted average speed measures essentiall y yields the ratio of the coordinate and survey trip length. This means that the coordinate

- based trip length is 73% to 85% of the respondent

- based trip length in the survey.

Table 3 Column (d) shows that even for different destination trip purposes, t he proportional relationship of speed based upon the survey in column (b) is 76.8% of the speed based upon the coordinate or straight

- line distance (c) with a deviation of +/

- 2.2%. This indicates that trip purpose

is not a factor in this difference.

Ta ble 3 Estimated Speeds by Trip Purpose

(a) (b) (c) (d) Trip Purpose (1) Survey Trip Length / Survey Travel Time Survey Trip Length / Survey Travel Time Coordinate Trip Length / Survey Travel Time Coordinate Trip Length(c) / Survey Trip Length(b)

Work 31.80 23.95 75.3% Shop 28.04 22.64 80.8%
School Religion 26.41 20.11 76.2%

Personal Business 27.25 20.65 75.8%

Social Recreation 30.87 22.85 74.0%

Home 27.82 21.85 78.5%

AVG. ratio 76.8%

SD ratio 2.2%

(1) WHYTRP95 Re

- code Home = codes 17 Work = codes 1,2,3 Shop = codes 4 School Religion = codes 5,6 Personal Business = codes 7

- 10 Social Recreation = codes 11

- 1613

Straight

- line distance between two points v iews the urban street grid system from the standpoint of the Pythagorean Theorem. Evaluating a right triangle with values of two sides between 0.25 and 7.0

in one

- quarter increments shows that the ratio of the hypotenuse to the sum of the other two sides
is 77.7%, with a variation of +/

- 7.4%. Therefore, the difference between straight

- line or coordinate distance and survey trip length may be attributed to highway system geometry and not respondent estimation error, because people do not generally travel in a straight line.

In a somewhat related analysis that examined a GIS based network routing solution for geo

- coded trip data in the Syracuse urban area, a much more interesting finding was discovered regarding the accuracy of respondents

= reported trip length estimates. The average NPTS_NY personally occupied vehicle trip length in the Syracuse area is 9.08 miles. For personally occupied vehicle trip records with intersection geo

- coded origins and destinations, 63% of the time the respondent

= s

estimate of trip length was longer than the network

- based trip routing solution by no more then 5%. Moreover, the respondent

= s trip length estimate was 5

- 10% longer than the routing solution 36% of the time. Only 1% of the respondents exceeded the network routin g solution by more than 10%. A 5% error in the respondent

= s trip length, converts to a difference of less than a half mile. Considering that the respondent is probably rounding distance to at least the nearest mile, this difference suggests that the respo ndent

= s estimate may be very reliable.

The examination of the Nationwide Personal Transportation Survey for New York State and its geo

coded data leads to the conclusion that the network travel demand model estimated speeds are a reasonable approach. How ever in the less congested upstate urban areas, the network speeds tend to provide higher

calculated area

- wide speeds than the survey would suggest. In the more congested counties within the New York Metropolitan area, they are much closer to the survey

- b ased area

- wide speeds

. The only exception is in New York County (Manhattan) which requires further examination. Straight

- line trip length appears to be less accurate than the respondent

= s trip length estimate. However, the GIS network

- based trip routin

g solution for intersection geo

- coded data in the Syracuse urban area suggests that the respondent

= s estimate is an accurate estimate of the actual trip length.14

ANNUAL VEHICLE UTILIZATION

Vehicle emis

sions are a factor of vehicle age, type, and annual usage during the year. Emissions vary by vehicle model year, as well as a model year

= s proportion of the total vehicle population. Vehicle age distributions by vehicle type are readily obtainable from the New York State Department of Motor Vehicles registration data files, but actual vehicle usage is not. The New York State Department of Transportation was concerned that national level data recommended for use

with EPA

=

s MOBILE 6 Emissions Model would not be appropriate for New York State. One of the component data sets in the Nationwide Personal Transportation Survey is the vehicle file. The vehicle data from this file were examined to compare the average annualized vehicle odometer readings with the

U.S. distributions and fitted data being supplied with EPA

= s MOBILE 6

Emissions Model.

Figures 4

, shows this comparison for light duty gas vehicles (LDGV), A autos. @ Figure 5

, shows this comparison for light duty gas trucks (LDGT), A pickups, sports ut

ility vehicles, and vans. @

Both figures show the raw data and the fitted exponential curves for both the U. S. and New York State.

Figure 4

0

1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000Annualized Mileage

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Vehicle Age NYS_LDGV(raw)

NYS_LDGV(fitted 13)

US_LDGV(raw)

US_LDGV(fitted) 

## Annual Vehicle Utilization

Light Duty Gas Vehicles - Autos15

Figure 5

Figures 4 and 5

contain the fitted exp

onential curves calibrated against the New York State and U.S. average annualized vehicle odometer readings. The figures show that fitted EPA data is significantly different from the fitted New York State data. Of 75,217 vehicle records in the U.S.

data set, average annualized mileages were computed for 42.7% of the vehicles. Of the 17,606 vehicle records in the New York State data set (which are part of the national set), average annualized mileages were computed for 46.2% of the vehicles.

Examinat ion of the New York State data set shows extremely wide variations in annualized mileage for vehicles older than 14 or 12 years of age (not shown) respectively, for both autos or light trucks.

Table 4 shows the average number of data points represented b y each average annualized mileage point for all vehicle ages and those in Figures 4 and 5 . Variability in a data set due to outliers or, in this case, too few observations, significantly influences the ability to fit a reliable curve to the raw

data. Alt hough these observations are readily identifiable in the New York State data set, the number of observations used in the EPA analysis for MOBILE 6 is not known. A larger data set drives average annualized mileage variation toward the mean in each age coho rt and significantly reduces the variability in the data. If this were a problem in the EPA data set, its resolution is unknown, because the data do not exhibit extreme values for average annualized mileage. As a 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000Annualized Mileage

0 1 2 3 4 5 6 7 8 9 10 11 Vehicle Age NYS_LDGT(raw)

NYS_LDGT(fitted 11)

US_LDGT(raw)

US_LDGT(fitted) 

## Annual Vehicle Utilization

Light Duty Gas Truck - Pickup,SUV,Van16

result, the equations fitted for the New York State data were done for vehicle ages 0

- 13 years for
autos, and 0

- 11 years for light duty trucks as in Table 4

.

Table 4 Number of Survey Records with Annualized Mileage

Age Cohort LDGV

Avg. # of

records Age Cohort LDGT Avg. # of

records

0

- 13 391 0

- 11 164

14

- 62+ 15 12

- 77+ 12

Examination of the resultant fitted curves in Figures 4 and 5 , showed that the New York State average annualized mileage does not decline as rapidly as the national data set, especially for autos, and that newer light trucks in general have a lower rate of use than in the nation. Figure 4 shows that auto use in New York State remains higher over a longer period than for the U. S. The initial annual utilization rates for autos and light trucks ar e noticeably lower in the first three years than it is for the U. S. Lastly, while light duty trucks have a lower initial average annualized mileage it rises above the national average as the vehicle moves into

the 7

- 10 year age group.

Figure 6 compares

the age distributions for light duty autos and trucks in the 1996 national fleet, with the 1997 New York State Department of Motor Vehicles A Vehicles in Operation

@ data, and the 1995 vehicle age distributions from the NPTS_NY. The proportion of 5

- 10 year

- old vehicles in the New York State fleet exceed that for the nation based on the area under the curve for these age cohorts. In addition, for all vehicle age cohorts 2

- 10 years, the New York State vehicle fleet has higher distribution proportions than th e national of which it is part. This indicates that a vehicle

= s retention in the fleet lasts longer in New York State. It is possible that this is the result of the high level of public transit use, primarily in New York City and the lower per capita veh icle ownership that

results. The rate of vehicle retention coupled with the difference in annual usage rates for autos and light trucks in Figures 4 and 5

will affect emissions.17

Figure 6

0%

1% 2% 3% 4% 5% 6% 7% 8% 9% 10%Percent

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Age US Mobile 6

NY 1997 DMV

NY NPTS 

### Vehicle Age Distribution

Auto and Light Trucks18

ENGINE MODE OF OPERATION

Esti mates of hot and cold start percentag es are the most problematic of the input parameters to the

Mobile Emissions Model . In particular, the range of cold start modes of operation can have as much impact on emissions as speed and ambient temperature input ranges; yet it is more difficult to assess. The current Mobile 5b Emissions Model in use by the New York State Department of Environmental Conservation (ENCON) relies upon percent cold proportions established in 1992. These data were based upon estimates derived from several studies described in the literature dating from the late 1970s to the late1980s. The percentage of hot and cold starts were synthesized for four time periods. The time periods or bands reflected peak and off peak periods in a typica l 24

- hour traffic volume distribution. The data were prepared for three highway categories (Interstate and Expressway, Arterial, and All Other Roadways). The data were also weighted by the vehicle miles of travel in each highway category. At the time th is approach relied upon engineering judgement and the best available data.

The 1995 Nationwide Personal Transportation Survey in New York State (NPTS_NY) provided the opportunity to reexamine this issue both geographically and temporally within a 24

- hour period.
Because the 1995 NPTS_NY was a residential home interview

- based survey, it was only possible to

compute area

- wide estimates. This approach required the computation of the number of hot and cold starts for the trips taken by each vehicle in the household. By sequencing the trips for the individual vehicles, the duration between the end of one trip and the beginning of the next can be calculated. If the duration between trips was greater than 60 minutes, a cold start was determined.

A cold star

t was assumed for the first trip of the day for all vehicles.

Engine mode of operation was classified into four categories. These categories are based on the length of time (duration) between the ending of one trip and the beginning of the next and in the trip length measured in minutes. The 1995 NPTS_NY trip travel time was compared to nine minutes to reflect the 505 second Federal Test Procedure (FTP) engine start and driving cycle.

The duration is more

then 60 minutes:

$ Cold Start

- Cold Mode ( CS_C M )

- vehicle started cold , driven less than nine minutes.

$ Cold Start

- Hot Mode ( CS_HM )

- vehicle started cold , driven more than nine minutes.

The duration is less

then 60 minutes:

$ Hot Start

- Cold Mode ( HS_CM )

- vehicle started hot , driven less than nine min utes. $ Hot Start

- Hot Mode ( HS_HM )

- vehicle started hot , driven more than nine minutes.

Figure 7 , shows the 24

- hour distribution of the four engine modes of operation for statewide vehicle trips as a percent of all vehicle trips, and the percentage of hour ly vehicle trips. Categories CS_HM and HS_HM when taken together represent the hot stabilized emission mode of vehicle
operation.

Figure 8 , uses the duration between engine starts to classify hot or cold starts. The data are presented for statewide vehi cle trips as a percent of all vehicle trips, and for the percentage of hourly vehicle trips.19

Figure 9 , shows the number and percent of statewide vehicle trips less than and more than nine minutes in duration for vehicle trips as a percent of all vehicle trips, and the percentage of hourly vehicle trips.

The 24

- hour distribution of statewide vehicle trips in Figures 7, 8, and 9 , reflects the temporal traffic distribution

described in Figure 1

. The morning, midday, and evening peaks are evident in these figures. Figure 2

and 9 describing average trip lengths and trip time also show consistency. However, differences in the percent of vehicle trips in hours one and 24 are displayed in Figures 7, 8 and to a lesser extent Figure 9

. This may result from too

few trips being reported in this period, or problems with coding trips that did not start or end

on the A travel day. @

Figure 10

shows the hot/cold start distribution from the New York State Department of

Environmental Conservation

= s Mobile 5b emissions model for A All Other Roads

@ as the hourly percentage of vehicle trips (in this roadway category statewide). The short individual trip lengths in the Nationwide Personal Transportation Survey best reflect the A All Other Roads

@ category (Minor Arterial, Maj or and Minor Collectors and Locals) rather than the longer distance trips that would more likely use the A

Interstate or Expressways @ and/or A Principal Arterial @

roadways. Figure 10

, suggests that the peak period cold start cold mode, the midday cold start hot mode, and the midday stabilized mode (CS_HM and HS_HM) estimates currently being used, shared some similarity with those periods in the engine mode of operation data obtained from the NPTS_NY 24

- hour statewide distribution in Figures 7, 8, and 9 . Var iation throughout the day as shown in Figure 10 , is obviously not handled well by the current hot and cold start estimates. The availability of a 24

- hour distribution from the NPTS_NY for these data, as well as developing these data for different metropol itan areas within New York State, will clearly have an impact on the accumulation of emissions throughout the day

. The continuous 24

- hour statewide distribution obtained from the NPTS_NY, also suggests that

the hot and cold start estimates for the other tw o roadway categories may need to be reexamined in light of these findings

.20

Figure 7

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%Percent of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

CS_CM HS_CM CS_HM HS_HM Engine Mode of Operation

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Hourly Percentage of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

CS_CM HS_CM CS_HM HS_HM Engine Mode of Operation21

Figure 8

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%Percent of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

Cold Start Hot Start 

### Duration Between Engine Starts

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Hourly Percentage of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour Ending

Cold Start Hot Start 

### Duration Between Engine Starts22

Figure 9

0.0%

10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%Hourly Percentage of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

<9 Minutes >9 Minutes Travel Time 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0%Percent of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

<9 Minutes >9 Minutes Travel Time23

Figure 10

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Hourly Percentage of Vehicle Trips

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour Ending

CS_CM HS_CM Stabilized 

### Existing Engine Mode of Operation

NYSDEC - Mobile 5b (All Other Roads)24

General Findings

The 1995 Nationwide Personal Transp ortation Survey for New York State provides a valuable metropolitan area, and in the case of the New York Metropolitan area, a county data set to address a variety of transportation air quality related questions. Perhaps the most important finding is that a state specific survey has a major role in addressing state transportation issues, especially those related to using state values in EPA

= s MOBILE Emission Model.

$ The survey shows that the number of workers working at home in 1995 is 5.1% of all

workers

- - double that reported in the 1990 Census.

$ The survey also suggests that while the number of workers working at home is increasing,
the real increase may be occurring with those workers who have long distance commutes
to other urban areas for employment.

$ The findings have demonstrated that this survey can serve as a source for area

- wide hourly

vehicle distributions.

$ These area

- wide hourly vehicle distributions capture local traffic that is not part of arterial

distributions available from traffic counts on the State Highway System.

$ The survey based, hourly vehicle distributions have improved the correlation between
hourly emission model results and measured ozone data.

$ Area

- wide speed estimates developed from the survey are a useful measure for testin g the
reasonableness of the network travel demand model speed estimates.

$ Area

- wide speed estimates from the survey also indicate that the network

- based speed
methodology may overestimate speeds in less congested areas, and provide comparable
speeds in mor e congested areas. However, speeds in New York County (Manhattan) may
require further examination.

$ A GIS network routing solution for geo

- coded NPTS_NY vehicle trip data in the Syracuse
urban area suggests that the respondents

= trip length estimate may i ndeed be an accurate
estimate of how far they travel. However, this requires further confirmation by examining

other urban areas in the data set.

$ Analysis of the vehicle file in the NPTS_NY reveals that national average annualized
mileage estimates for a

uto and light truck usage and the age proportion of these fleets
differ noticeably from those for New York State.

$ Analysis of the engine mode of operation from the NPTS_NY discloses a 24

- hour
distribution that is significantly different from that currentl y being used. Implementation
of this distribution will impact the accumulation of emissions throughout the day.25

REFERENCES

1995 NPTS User

= s Guide for The Public Use Data Files, Nationwide Personal Transportation Survey, October 1997, USDOT, FHWA FHWA

- PL

- 98

- 002.

Code Book for New York Add

- on, 1995 Nationwide Personal Transportation Survey, prepared for New York State Department of Transportation by Research Triangle Institute August 1997

1995 Nationwide Personal Transportation Survey for New York Sta te digital data set, prepared for New York State Department of Transportation by Research Triangle Institute, August 1997.

1995 Nationwide Personal Transportation Survey for New York State geo

- coded data set, prepared for New York State Department of Tran sportation by Research Triangle Institute, August 1997.

Update of Fleet Characterization Data for Use in MOBILE 6, Final Report, May 11, 1998, U.S. Environmental Protection Agency, Motor Vehicle Emissions Laboratory, Ann Arbor, Michigan,

Ref. SJ007260.

New York State Registrations in Force 1997, New York State Department of Motor Vehicles.

New York State Department of Transportation 24 hour Short Count Data File for TF#3 records

1986

- 1998.

1990 Census Transportation Planning Package CD

- ROM for New Yor k State, United States Department of Transportation, Bureau of Transportation Statistics.

Internal Communications between the New York State Department of Environmental Conservation and the New York State Department of Transportation.

New York State 1990

Base Year Carbon Monoxide and Ozone Precursor On

- Road Mobile Source Inventory, Revised Report, New York State Department of Environmental Conservation, April

1993.

Mobile 5b Users Manual, September 1996, United States Environmental Protection Agency.