Improving Air Quality Models in New York State: 

Utility 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, 1999
i

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 Operation.............................................................................................................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 Duty 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 .......................................................................23
1

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 carried 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 concerned 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 constructing, 

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 

undertaken 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 travel 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 development 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 it 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 urban 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 

twelfth 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 the 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 describing 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 problems 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 years, 

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 counties. 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 Personal 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 business economics, and the development of a vehicle miles of travel model 
3

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 aggregated 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 NYSDOT 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 Transportation 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 Awork at 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 Distributions - 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-based 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 Emissions 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 as 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 the 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 Atele-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: AHow did this person usually 

get to work last week?@ One possible response was Awork at home.@ The 1995 Nationwide 

Personal Transportation Survey asked a very different question: AWhat is the one-way distance 

from your home to your workplace?@ Possible NPTS responses included the specific number of 

blocks or miles coded as Agoes to work,@ and two alternatives when distance was not provided Ano 

fixed work place@ or Aworks at or out of home.@ The intent of the 1995 Nationwide Personal 

Transportation Survey category Ano 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. Unfortunately, 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 Awork 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 Awork 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 Awork at home@ in New York 

State has increased from 2.6% of all workers in the 1990 Census to 5.1% in the 1995 Nationwide 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, Westchester, Putnam and Rockland counties and the aggregate Arest 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, Glens 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 Awork at home@ is occurring across the state, and is an

increasing proportion of all workers in areas with Along commutes@ to a nearby urban area, raises a 

number of further policy questions. In what areas will Awork at home@ continue to increase and at 

what rate? In which industries, job classifications or professions are these workers engaged? 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. Nationwide 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% 135,041 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% 429,777 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 3,404,203 148,750 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 1998
6

AN HOURLY TRAFFIC DISTRIBUTION ALTERNATIVE

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

Environmental Conservation (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 emissions 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 increase 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 Personal Transportation Survey for New York State was summarized by 

urban area strata, for the proportion of hourly Apersonally occupied vehicle@ trips as a percent of the 

entire day. Since these data represent travel on all roadways within the individual areas, 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 

Transportation 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 data 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 average 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 

10 

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

1995 NPTS State Highway System

24 Hour Vehicle Distribution

Capital District Area*

* Albany, Schenectady, Saratoga and Rensselaer Counties

0 

5 

10 

15 

20 

25 

30 

MILES

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 Counties
8

Figure 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 peaks 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 Putnam, 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 evening 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 city, 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 

10 

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

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 summary, 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 emissions 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. Lastly, 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 required 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 

accuracy 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), and 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 addition, 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 network 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 period. Surveybased 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 straight-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 subject 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 therefore 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) Speeds 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 geocoded 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

Stratums(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

Albany 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 

Queens 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 summarized 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.

Several 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 congested 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 area, 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 network 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 Table 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 essentially 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, the 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.

Table 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- 16
13

Straight-line distance between two points views 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 routing 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 respondent=s estimate may be very reliable.

The examination of the Nationwide Personal Transportation Survey for New York State and its geocoded data leads to the conclusion that the network travel demand model estimated speeds are a 

reasonable approach. However 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-based 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 routing 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 emissions 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), Aautos.@ Figure 5, shows

 this comparison for light duty gas trucks (LDGT), Apickups, sports utility 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 

16000 

Annualized 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 - Autos
15

Figure 5

Figures 4 and 5 contain the fitted exponential 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. 

Examination 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 by 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. Although 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 cohort 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 

20000 

Annualized 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,Van
16

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 are 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 AVehicles 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 the 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 vehicle 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 Trucks
18

ENGINE MODE OF OPERATION

Estimates of hot and cold start percentages 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 typical 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 this 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 start 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_CM) - 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 minutes.

$ 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 hourly 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 vehicle 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 Atravel day.@

Figure 10 shows the hot/cold start distribution from the New York State Department of

Environmental Conservation=s Mobile 5b emissions model for AAll 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 AAll Other Roads@ category (Minor 

Arterial, Major and Minor Collectors and Locals) rather than the longer distance trips that would 

more likely use the AInterstate or Expressways@ and/or APrincipal 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. Variation 

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 metropolitan 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 two 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 Operation
21

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 Starts
22

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 Time
23

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 Transportation 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 testing 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 more 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 indeed 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 auto 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 currently 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 State 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 Transportation 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 York 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.