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Table 3.1. Locations of Candidate Studies
Candidate Data Set Dimensions MI DC DE MD NJ NY PA VA WV
Project 2. ACAS FOT
Project 5. RDCWS FOT
Project 6. 100-Car Study
Project 7. DDWS FOT
Project 8. NTDS
Project 11. NTNDS
were collected in southeastern Michigan, including Detroit and data to describe the surrounding traffic), the availability of
surrounding suburbs and rural areas, resulting in 2,500 h of weather, traffic condition, crash, and work zone data in related
video data. For Project 6, data were collected in the Washing- states is investigated to provide a consistent data set across the
ton, D.C., northern Virginia, and Maryland areas, resulting studies and to avoid potential subjective bias brought by data
in 43,000 h of video data. For Project 7, drivers were recruited reductions.
in Roanoke, South Boston, Stuarts Draft, and Cloverdale, Weather data can be reliably obtained by acquiring data
Virginia, as well as in Charlotte, North Carolina. Data were from a nearby weather station. Figure 3.2 shows the locations
collected for long-haul (cross-country) trips, as well as for of weather stations in the related states. As can be seen from the
overnight express (out-and-back) operations throughout map, weather stations are densely located and thus there is a
the Mid-Atlantic area, resulting in 46,000 h of video data. high possibility of linking a vehicle location to a nearby weather
For Project 8, drivers were recruited at trucking centers in station through GPS data. Out of 592 weather stations, 253 are
Charlotte, Kernersville, and Henderson, North Carolina, and either Automated Surface Observing System (ASOS) stations
in Roanoke, Gordonsville, and Mount Crawford, Virginia. or Automated Weather Observing System (AWOS) stations.
Data were collected for trucking runs throughout the Mid- Weather stations using ASOS are located at airports. ASOS
Atlantic region, resulting in 14,500 h of video data. For Proj- is supported by the Federal Aviation Administration (FAA),
ect 11, data were collected in the New River and Roanoke the National Weather Service (NWS), and the Department of
Valleys in Virginia, resulting in 16,644 h of driving data, Defense (DOD). The system provides weather observations that
including 10,754 h of teen driving data. include temperature, dew point, wind, altimeter setting, visibil-
ity, sky condition, and precipitation. Five hundred sixty-nine
FAA-sponsored and 313 NWS-sponsored ASOS stations are
Quality of External Data installed at airports throughout the United States. The weather
Using in-vehicle video data to help assess the role of driver reports by ASOS that could be used in this study are of METAR
behavior in nonrecurring congestion requires analyzing not type (Aviation Routine Weather Reports) and contain precipi-
only the vehicle and driver data but also the complementary tation type, precipitation intensities (in./h), and visibility read-
data. For instance, it has been documented that weather affects ings (m). ASOS visibility measurements are performed at 30-s
driving behavior and performance and leads to longer follow- increments using a forward scatter sensor to compute 1-min
ing distances, thereby decreasing throughput at intersections average extinction coefficients (sum of the absorption and scat-
and resulting in longer travel time. Another factor that has tering coefficients). For this purpose, a photocell that identifies
been shown to affect crash risk is the occurrence of a prior the time of day (day or night) is used to select the appropri-
incident. Driver behavior in the vicinity of traffic control devices ate equation for use in the procedure. ASOS computes a 1-min
also contributes to nonrecurring congestion. Finally, previous average visibility level that is used to compute a 10-min moving
studies in the Los Angeles conurbation have shown that more average (MA) visibility level. This value is then rounded down
vehicle-hours of delay result from extraordinary and acciden- to the nearest reportable visibility level. The system uses
tally occurring traffic disturbances (nonrecurring) than from precipitation identification sensors to determine the type
regularly occurring network overloading during typical daily of precipitation (rain, snow, or freezing rain). Precipitation
peak hours (recurring). intensity is recorded as liquid-equivalent precipitation accumu-
Although some of the complementary data can be obtained lation measurements using a Heated Tipping Bucket (HTB)
from data reduction (e.g., the traffic condition is a variable gauge. The HTB has a resolution of 0.01 in. and an accuracy of
recorded by data reductionists while they were viewing video ±0.02 in., or 4% of the hourly total, whichever is greater.
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Figure 3.2. Weather station locations.
AWOS is one of the oldest automated weather stations Count Program. In Pennsylvania, there are 116 continuous
and predates ASOS. It is a modular system utilizing a central stations out of 30,661 count stations. Figure 3.5 shows a sub-
processor that receives input from multiple sensors. Oper- set of the traffic count stations in Pittsburgh. Figure 3.6 shows
ated and controlled by the FAA, state and local governments, traffic count stations in Delaware.
and some private agencies, the AWOS reports weather infor- A sample of the longitudinal and latitudinal information
mation at 20-min intervals but does not report special obser- of one traffic count station on link ID 507002 is shown in
vations for rapidly changing weather conditions. Depending Table 3.2. Table 3.3 demonstrates a sample of the raw traf-
on the different varieties, AWOS observes different indices. fic counts collected by that station at 15-min intervals by
The most common type, AWOS-III, observes temperature vehicle class in the state of Virginia. Traffic conditions (e.g.,
and the dew point in degrees Celsius, wind speed and direc- traffic density and level of service) can be inferred from the
tion in knots, visibility, cloud coverage and ceiling up to counts. The longitudinal and latitudinal fields can then be
12,000 ft, and altimeter setting. Additional sensors, such as used to link in-vehicle GPS data and traffic count data.
for freezing rain and thunderstorms, have recently become Some states have location information for the stations listed
available. as mileposts and street names that can be digitized when
Traffic count data are available in all the states that were necessary.
studied. The state of Virginia has extensive locations of traf- Crash data are readily available for every state, although
fic count stations. There are more than 72,000 stations, 470 some have stringent data privilege requirements. Some states,
of which collect continuous data. A subset from the Virginia such as New Jersey and Michigan, have online crash databases
Count Station list--the traffic count locations in the city of from which the information can be downloaded. The District
Richmond--is shown in Figure 3.3. The traffic count stations of Columbia DOT coded their crashes with work zone infor-
in West Virginia are shown in Figure 3.4. There are approx- mation if there was a work zone in the surrounding area
imately 60 permanent count stations in West Virginia, and when the crash happened. Table 3.4 provides a crash sample
one-third of the state is counted each year in a Coverage from Washington, D.C. Because of space limitations, only
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Figure 3.3. Traffic count stations in Richmond, Va.
Figure 3.4. Traffic count stations in West Virginia.
