10.3

ANALYSIS OF WEATHER IMPACTS ON TRAFFIC FLOW IN METROPOLITAN WASHINGTON DC

Andrew D. Stern*, Vaishali Shah, and Lynette Goodwin

Mitretek Systems, Inc., Falls Church, VA

Paul Pisano

Federal Highway Administration, Washington, D.C.

1. INTRODUCTION

Anyone who uses surface transportation has been 

affected by delays caused by various forms of weather. 

Whether it is rain or snow, ice or fog, the result is

usually the same. Travel delay rises as traffic flow

decreases .

The Federal Highway Administration’s (FHWA)

Road Weather Management Program (RWMP) has

been sponsoring research into the impacts of weather 

on surface transportation. One specific research task

involved attempting to quantify the amount of travel 

delay imposed upon drivers due to the effects of

inclement weather.

This paper will describe two different methods used 

to approximate travel delay impacts of weather along 

specific roadway segments around metropolitan

Washington, D.C. Each method will use meteorological 

data sets of differing temporal and special resolutions in

conjunction with travel time data. 

The travel time data used in both models will first 

be described. Then, the two methods of analysis will be 

detailed along with their respective weather data.

Results and conclusions from both analyses are then 

presented.

2. TRAVEL TIME DATA

Travel time data were obtained by accessing and 

archiving information from the SmarTraveler web site 

(www.SmarTraveler.com ). The SmarTraveler Internet

postings are publicly available, and list by roadway

segment, travel time information as well as data on 

accidents, special programs, and road construction. 

Information is posted as early as 5:30 AM and extends

to as late as 8:30 PM, excluding weekends and some 

holidays.

The travel time data were archived at approximately 

five-minute intervals for each of the 33 Washington,

D.C. road segments for which SmarTraveler posts travel 

times . The travel time data that were used in this study 

ranged from 6:30 AM to 6:30 PM local time, Monday 

through Friday, between December 6, 1999 and May 

31, 2001. Information on accidents, special programs

and road construction were neither downloaded nor

maintained as a part of this data archive and therefore 

were not used in this study.

The geographic coverage of the Washington, D.C.

roadway network spans from the eastern suburbs in 

Maryland, across the District of Columbia to the western 

suburbs in northern Virginia. A graphical depiction of the 

road network can be seen in Figure 1.

The SmarTraveler network routinely reports travel 

time data for this region on 33 bidirectional roadway 

segments . These routes span a total of 711.8 miles . The 

average length of the segments is 10.8 miles with

maximum and minimum lengths of 25.0 and 2.6 miles, 

respectively. Of the 33 segments, 18 are freeways and 

15 are major arterials. The 18 freeway segments

constitute 472.4 of the 711.8 miles . The 15 arterial

routes constitute the remainder (239.4 miles). Appendix 

B, Table B1 provides a list and description of each road 

segment.

While data reported in the SmarTraveler network 

are not fully representative of road travel times, it is a 

credible estimation of real travel times. There are four 

factors that can impact the accuracy of the archived 

data. These are:

• The posted travel time information is a blend of 

various quantitative and qualitative data such as 

commuter call-ins using cell phones, incident

reports, and vehicle (loop) detectors. Thus,

although the data at an aggregate does represent 

the traffic characteristics of the road, it has

inherent inaccuracies compared to actual roadway 

travel times.

• In the absence of incidents, weather, road

maintenance activities, or other major events

modifying traffic demand; SmarTraveler posts

base travel times. Base travel times vary

significantly by time of day but are repeated day 

after day in the absence of traffic events.

Moreover, during extreme events, posted

increases in travel times may be less accurate as

compared to actual roadway conditions because

roadway conditions change rapidly and are not

easily captured through existing SmarTraveler

data sources. 

Corresponding author address: Andrew D. Stern,

Mitretek Systems, Inc. 3150 Fairview Park Drive

South, Falls Church, VA, 22042-4519

Email: astern@mitretek.org
• Examination of the data suggests that posted data 

during the peak traffic periods are modified much 

more frequently than during off-peak periods. For 

example, three to four distinct travel times may be 

posted during an hour of peak traffic while during 

off peak periods one travel time may be posted for 

three to four hours.

• SmarTraveler policy precludes posting of travel

times that would indicate speeds greater than the 

speed limit. Thus, travel time posting when speeds 

are greater than the speed limit are increased from 

true travel time to speed limit travel time. This has

substantive implications on the analysis of delay 

during congestion-free traffic periods.

• Although SmarTraveler updates travel times with 

regular frequency, time is required to process and 

post the data. This time produces a lag between 

actual and posted travel time.

These data limitations can significantly influence the 

analysis process and the resultant outcomes .

Figure 1 – Map of the Metropolitan Washington, D.C. Roadway Segments used in the Travel Time Analysis

3. METHOD 1: REGRESSION ANALYSIS USING

SURFACE OBSERVATIONS

The first method that was used to analyze the

effects of weather on the travel times included using 

data obtained from weather observations from three 

Washington area airports: Dulles International (IAD),

Washington National (DCA) and Baltimore/Washington 

International (BWI). A map showing the locations of

these airports can be seen in Appendix A, Figure A1.

An explanation of the data used, details of the 

analysis technique, and outcomes will be discussed in 

this section.

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Rt. 650

I-395

Rt. 50

Rt. 214

Central Ave.

Rt. 355

Rt. 97

Rt. 29

Rt. 50

Dulles Toll Road

VA 236

Suitland

Pkwy

GW 

Pkwy

Clara Barton 

Pkwy

Rt. 210

Indian Head Hwy.

Rt. 7100

Fairfax County Pkwy

Rt. 4

Pennsylvania Ave.

Rt. 1

Rt. 1

Rt. 50

I-66

I-295

I-270

I-95 

Branch Ave.

Rt. 5

I-95

GW Pkwy

Rt. 355

Rt. 185

Braddock Rd.

I-66

Dale City Exit

Gaithersburg 

Rt. 198

Powder

Mill Rd.

Berry Road Rt. 301

Centreville 

Exit (Rt. 29)

Cherry Hill

Gude Dr. Road

Wheaton 

Rt. 197

Rt.202

Rt. 301

Rt. 197

Dulles Airport Exit

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25 29

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28 27

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51

50

Rt. 650

I-395

Rt. 50

Rt. 214

Central Ave.

Rt. 355

Rt. 97

Rt. 29

Rt. 50

Dulles Toll Road

VA 236

Suitland

Pkwy

GW 

Pkwy

Clara Barton 

Pkwy

Rt. 210

Indian Head Hwy.

Rt. 7100

Fairfax County Pkwy

Rt. 4

Pennsylvania Ave.

Rt. 1

Rt. 1

Rt. 50

I-66

I-295

I-270

I-95 

Branch Ave.

Rt. 5

I-95

GW Pkwy

Rt. 355

Rt. 185

Braddock Rd.

I-66

Dale City Exit

Gaithersburg 

Rt. 198

Powder

Mill Rd.

Berry Road Rt. 301

Centreville 

Exit (Rt. 29)

Cherry Hill

Gude Dr. Road

Wheaton 

Rt. 197

Rt.202

Rt. 301

Rt. 197

Dulles Airport Exit

Dale City Exit

Gaithersburg 

Rt. 198

Powder

Mill Rd.

Berry Road Rt. 301

Centreville 

Exit (Rt. 29)

Cherry Hill

Gude Dr. Road

Wheaton 

Rt. 197

Rt.202

Rt. 301

Rt. 197

Dulles Airport Exit

48
3.1 Surface Data

The National Weather Service (NWS) and Federal 

Aviation Administration (FAA) use a semi-automated 

system to observe key atmospheric elements at most 

major airports in the United States. The Automated

Surface Observing System (ASOS), has sensors to take 

measurements of air temperature, dew point, wind

speed and direction and rainfall accumulation.

Complementary technologies, such as weather

satellites, are used to provide estimates of total sky

cover. However, human observers are maintained at

many airports to augment ASOS for such phenomena 

as the occurrence of thunderstorms, hail, or snow

accumulation or drifts.

3.1.1 ASOS Data Set

Archived hourly observations from ASOS were

obtained for the test domain from the National Climatic 

Data Center (NCD), which is the repository of weather 

data from the National Oceanic and Atmospheric

Administration (NOAA). A software program was 

developed to parse the 15,535 hourly observations into 

discrete elements. These elements included:

• Sky Cover

• Surface Visibility

• Observed Weather

• Precipitation Intensity

• Air Temperature

• Dew Point

• Wind Speed

• Hourly Rainfall Amount

A series of algorithms were created to convert the 

observational elements into categories that could be 

used for later processing. Table 1 shows the final list of 

elements that became variables in the regression

analysis routines. It was hypothesized that each of the 

variables can have a direct impact travel speed and thus 

travel time on roadway segments .

The precipitation type and intensity parameter used 

a combination of observed weather, precipitation type 

and intensity information to create the four ‘precip’ 

categories.

The ‘wind’ parameter was selected because strong 

wind gusts can affect the handling and stability of 

vehicles, especially those with high profiles.

The ‘vis’ or visibility distance parameter was used

to identify those times when surface visibilities had a 

significant impact on surface transportation.

The ‘pcon’ or pavement condition parameter was 

inferred from numerous inputs. Elements such as cloud 

cover, air temperature, humidity, precipitation intensity 

and wind speed were all used to estimate the condition 

of the pavement. Appendix B, Table B2 contains the 

decision scheme used to create these values.

Table 1 – Observational Elements Used in the Regression Analysis to Represent Surface Conditions

3.1.2 ASOS Data Quality Issues

Some data quality issues were associated with the 

use of ASOS. First, the NCDC archive was not complete 

for the analysis time period. At DCA, 20 hourly

observations were not available. At IAD, eight

observations were missing randomly through the data 

set. More importantly, all data were unavailable for an 

entire week during May of 2000. At BWI, 22 hourly

observations were missing. In most cases, the weather 

was benign and the isolated missing observations could 

be filled in by interpolation. In the case of the missing 

data at IAD, data from DCA plus radar observations 

were used to fill and estimate the missing information.

A second data quality issue was associated with 

the way that ASOS reported hourly liquid equivalent 

values during snow events. In many cases, the hourly 

amounts were underreported from the system’s tipping 

bucket rain gauge. For example, the total liquid

equivalent reported from the DCA ASOS during the 

snowstorm of January 25, 2000 was 0.18 inches. The 

human augmented liquid equivalent was 0.85 inches. At 

a 10:1 ratio, that would mean that ASOS would

underreport snow accumulations by 7 inches!

Finally, during mornings when there were heavy 

dew deposits, ASOS, on occasion, reported one or two 

hundredths of an inch of precipitation out of ‘clear skies ’. 

Name Description

precip precipitation type and intensity

0 = None

1 = Light Rain/Snow 

2 = Heavy rain

3 = Heavy Snow/Sleet

wind sustained wind speed 0 = < 30 mph 1 = > 30 mph

vis visibility distance 0 = > 0.25 miles 1 = < 0.25 miles

pcon pavement condition (inferred)

0 = Dry

1 = Wet

2 = Snow/ice

3 = Black ice

Values
These values had to be removed manually by

inspection.

3.1.3 Combining ASOS and Travel Time Data

Travel time data were collected at a resolution of 

one observation per five minutes. The ASOS

observational data were available at a frequency of

once per hour. The method to combine these two data 

sets assigned all travel time observations 30 minutes 

before or after an ASOS observation to that weather 

observation. For example on any given day, a travel 

time observation at 7:30 AM would be assigned to the 

weather observation at 7:00 AM while a travel time 

observation at 7:35 AM would be assigned to the

weather observation at 8:00 AM.

3.2 Regression Analysis

The technique used in this portion of the analysis 

was developed based on the strengths and

shortcomings of the ASOS data. Since the weather data 

could not directly correlate with specific roadway

segments (since the data were from airports),

regression models were used to predict travel times on 

the roadway segments using the weather variables

derived from the ASOS observations.

3.2.1 Analysis Steps

A two-step linear regression process was 

performed on each roadway segment by direction. First,

the travel times were regressed against the weather

variables of precipitation intensity and type, pavement 

condition, wind speed, and visibility for all ASOS sites. 

Thus, the regression models each consisted of 12

explanatory variables (3 ASOS sites x 4 variables). In 

this first step, both linear and quadratic forms of the 

precipitation and pavement weather variables were

examined. Overwhelmingly, the quadratic form

dominated in regression models and proved to be a 

better fit to the travel time data. The reduced linear

regression models were selected based on the most 

effective weather variables (those with highest tstatistics and positive coefficients). The second step 

was to reduce linear regression models for each

directed roadway segment to predict the base travel 

time and increases in travel time attributed to weather. 

The general form of the reduced regression model was:

Travel Time = Base Travel Time +

B1*(PavementAirport )2 + B2*(Wind Airport ) + .....

Average predicted delay by directed roadway

segment was calculated as the sum of the values to the 

right of the “Base Travel Time” parameter divided by the 

number of observations. The average predicted delay 

by segment and direction were aggregated to derive a 

regional estimate of the impact of weather on delay. The 

amount of delay divided by the base travel time yields 

the percent increase in travel time attributable to

weather.

This regression process was applied to two subsets 

of data: the historic peak 2-hour traffic period and the 

historic low (off-peak) 2-hour traffic period to assess the 

impacts of weather on increases in travel time (or 

delay). The off-peak period was defined for this study as 

the two-hour period with historically lowest travel times 

across the entire dataset. The peak period was defined 

for this study as the two-hour period with historically 

highest travel times across the entire dataset. Tables 

containing the average two-hour low and high traffic

times of the day and travel times can be found in

Appendix B, Tables B3 and B4, respectively.

The reduced regression models were then applied 

to a single day to illustrate the extent of weather delay 

predicted in the off-peak times during extreme weather. 

For example, the widespread snow event across the 

National Capital Area on January 25, 2000 was used. 

Regression models were fit to estimate the outcomes 

based on the most frequently occurring factors. That is, 

at extremes, model predictions may be significantly

different than the observed variable. As such, the

predicted outcomes were compared with SmarTraveler 

posted travel times. 

3.2.2 Implications of Data Quality on Analysis 

Outcomes

The greatest shortcomings in this analysis were the 

absence of information on other variables affecting

travel time beyond weather, the absence of roadway 

segment specific weather variables, and the qualitative 

nature of the travel time data. These shortcomings

result in meaningless outcomes of regression analysis 

of weather impacts during the peak period as explained 

in Section 3.2.2.1. However, the data are better suited 

to conduct an off-peak analysis since congestion,

incidents, and delay propagation effects are fewer. Yet,

there are also other factors to consider in examining the 

quality of model outcomes during the off-peak 

regression analyses. These are explored in Section

3.2.2.2.

3.2.2.1 Implication of Data Quality on Peak Traffic 

Regression Analysis

By definition, peak period hours are those with the 

highest volume of trips. Accidents are much more

frequent during the peak traffic periods as compared to 

off-peak periods and have greater impact in terms of 

increased delay during the peak traffic periods.

Subsequently, increases in travel time (termed delay) 

for a given roadway are much more likely due to

recurrent congestion, queue spillback, and incidents

during the peak period as compared to off-peak periods. 

Given the absence of information on congestion, 

queue spillback, and most importantly, incidents; 

models predicting delay during the peak period would 

be of very low explanatory power compared to similar 

models predicting delay during off-peak periods. In

addition, given that (1) weather phenomena generally 

occur for multiple hours, (2) weather variables (in this 
domain) are constant across an hour, and (3) peak

period travel times vary frequently within an hour; the 

existing weather variables prove incomplete in modeling 

or predicting peak-period delay. 

For all of these reasons, it was found that these 

data were not well suited to conduct a peak-period 

analysis of the impact of weather on delay.

Nonetheless, a preliminary regression analysis was 

conducted for the peak-period. Model R2 values were in 

the range of 0.005 to 0.05, indicating that less than 5% 

of the variability in travel time data could be explained

by the selected weather variables. This supports the

hypothesis that existing weather variables cannot

sufficiently explain variability in travel time data, and 

thus peak period analyses outcomes were meaningless.

3.2.2.2 Implication of Data Quality on Off-Peak 

Period Regression Analysis

The data were better suited to conduct an off-peak 

period analysis since congestion, incidents, and delay 

propagation effects are fewer. Moreover, travel time

changes (variability) were minimal within any given 

hour. This was the case with hourly weather variables. 

The impact of weather on delay during the off-peak 

provides significant insight as a baseline for the impact 

of weather during the peak period. Therefore, for this 

study an analysis was conducted of the impact of

weather during off-peak periods. 

It should be noted that weather effects on any given 

roadway segment were inferred based on the weather 

at the three metropolitan Washington airport sites. 

Moreover, roadway segments were from a few to as 

many as 25 miles in length and weather could vary 

within the roadway segment. In addition, some error 

was introduced in the mapping of hourly ASOS data to 

five-minute travel time data. Hence, the physical state of 

the roadway could not be fully captured.

Regression models do not capture propagation of 

delay effects from roadway to roadway. These factors 

suggest that models of delay will vary by segment. Also 

equally important is that using the multiple and

combinatorial ASOS weather sites in regression

methods can lead to data fitting, thereby overestimating 

the impact of weather on delay.

3.2.3 Analysis Outcomes

Tables B5 and B6 in Appendix B present the

reduced regression models , each model’s R2 value, and 

a measure of the goodness of fit of the regression 

model for each directional road segment. The higher the 

R

2

 value, the more the weather variables explain the 

variation in travel times. On average, the R2 value was 

0.23 across the 66 regression models. Of the 66

models, most (41) consist of three variables while some 

(16) consisted of two variables and a few (nine) 

consisted of four variables. Pavement condition (pcon) 

was the most frequent explanatory variable, appearing 

in every model. Precipitation (precip) appeared in 11 of 

the 66 models while wind speed and surface visibility 

(vis) appeared in six and five of the models,

respectively. All three-variable models, except one, are 

comprised of a variable from each of the three ASOS 

sites suggesting that conditions on the specific roadway 

are best described as a combination of conditions at the 

three ASOS sites. 

Table 2 presents the outcomes of applying the

regression models to calculate delay metrics attributable 

to weather. Across all roadways, the average roadway 

travel time was 17.0 minutes and the average percent of 

cases where weather factors cause delay is 13%. When 

weather phenomena occur, the average regional

increase in travel time is 14%, translating to a 2.2-

minute delay on a 17-minute trip. 

For the January 25, 2000 snowstorm , the predicted 

increase in travel time was 53% which translated to an 

8.3-minute delay for a 17-minute trip. The SmarTraveler 

site posted an increase in travel time for this example

regionally as 155% or a 26-minute delay on a 17-minute 

trip for the two-hour period. The reality is likely to be 

somewhere between these two values.

4. METHOD 2: ANALYSIS OF MEANS BY 

PRECIPITATION CATEGORY

A second methodology was developed with the

intent of trying to use higher resolution (both spatially 

and temporally) weather data to better approximate

roadway conditions. For this portion of the project, radar 

data from an NWS Doppler radar were used. The radar 

was located just northwest of Dulles International Airport

near Sterling, VA.

4.1 Radar Data Set

The 15,535 hours of ASOS observations were

analyzed to determine the most likely times when

precipitation could occur anywhere over the travel

network. When complete, a total of 3,352 hours of

archived radar data would be needed for the analysis. 

Archive level III, base reflectivity products (16-level/0.5 

degree elevation) were ordered from the NCDC.

4.1.1 Correlation of Radar Data to Road Segments

Using a Geographic Information System (GIS), the 

azimuth and range reference frame of the radar were

overlaid onto the roadway network (Figure 2). At this 

point, the task was to develop a translation of the GIS 

grid structure into computer code so that radar

information could be directly associated with a specific 

segment. This was accomplished in two steps:

Step 1: With the road segments overlaid on the radar 

grid, every grid that fell on a road segment was

designated as a primary grid. The azimuth and range of 

each primary grid was noted (Figure 3). A region,
exactly one radar grid in depth around the primary grids, 

was recorded as secondary grids. 

Step 2: The azimuth and range of each primary and 

secondary bin were translated into C programming

code.

The designations of grids as primary or secondary 

were used in the processing algorithm for two reasons:

1. Using a combined primary and secondary

region helped to better capture precipitation 

that was moving across a narrow road

segment.

Table 2 - Off-Peak Predicted Impacts of Weather on Delay and Travel Time % Cases With Weather Average Delay When Weather (minutes) % Increase in Travel Time When Weather Jan 25, 2000 Average Trip Delay Jan 25, 2000 % Inc. Travel Time % Cases With Weather Average Delay When Weather (minutes)

% Increase in Travel Time When WeatherJan 25, 2000Average TripDelayJan 25, 2000 % Inc. Travel Time

1 12% 3.4 12% 11.1 39% 10% 3.6 12% 12.3 41%

2 13% 2.3 11% 6.5 30% 10% 2.5 11% 8.5 39%

3 13% 2.3 26% 8.4 93% 14% 2.2 25% 8.9 98%

4 12% 2.8 16% 8.7 49% 10% 1.9 11% 6.1 35%

5 15% 0.8 5% 5.0 32% 12% 2.6 16% 10.2 64%

6 13% 1.2 8% 3.8 25% 12% 1.9 13% 6.7 44%

7 14% 3.1 11% 10.8 38% 13% 1.7 6% 7.4 25%

8 15% 0.8 6% 5.0 36% 11% 2.4 17% 8.5 60%

9 13% 1.9 10% 6.6 35% 9% 2.5 13% 8.2 43%

10 14% 1.9 9% 6.4 29% 11% 2.1 10% 8.1 37%

11 15% 1.2 9% 4.3 33% 11% 1.5 12% 5.5 42%

12 15% 1.3 5% 4.8 20% 12% 1.6 7% 5.6 23%

13 14% 1.5 25% 5.7 96% 12% 1.4 23% 5.5 92%

14 15% 2.1 15% 15.0 107% 11% 2.7 19% 9.6 69%

15 13% 1.2 9% 3.9 30% 12% 1.5 12% 5.4 41%

16 14% 3.2 9% 10.7 30% 12% 4.0 11% 19.8 57%

17 13% 1.8 14% 5.8 45% 11% 2.3 18% 8.2 64%

18 13% 1.5 15% 5.0 50% 12% 1.5 15% 5.2 52%

19 15% 1.6 20% 5.7 71% 12% 1.9 24% 6.7 83%

20 13% 0.8 8% 2.6 26% 11% 1.4 14% 5.0 50%

21 15% 2.6 18% 10.5 70% 12% 3.1 21% 12.2 82%

22 15% 3.0 17% 11.6 65% 12% 2.4 14% 8.6 48%

23 14% 1.9 10% 6.8 38% 12% 2.0 11% 7.8 43%

24 14% 2.4 17% 16.7 116% 11% 3.2 23% 11.1 79%

25 14% 2.9 24% 13.2 108% 12% 2.3 19% 8.1 67%

26 12% 4.2 25% 13.6 80% 9% 1.9 10% 1.4 7%

27 14% 1.8 17% 6.4 63% 11% 1.8 18% 6.8 67%

28 14% 3.3 22% 12.7 85% 11% 3.2 21% 11.4 77%

29 15% 1.4 7% 9.2 46% 13% 1.5 8% 7.9 40%

30 14% 2.3 11% 8.0 40% 12% 2.1 11% 7.5 38%

31 14% 2.3 15% 8.8 58% 12% 1.5 10% 5.3 35%

32 12% 2.4 13% 7.9 42% 11% 1.8 9% 6.2 33%

33 14% 3.3 11% 11.6 40% 11% 4.5 15% 16.7 58%

*delay and travel time values are those predicted by the regression models

Regional Average % Cases with Weather 13%

Regional Average Delay (minutes) when Weather 2.2

Regional Average % Increase in Travel Time when Weather 14%

Regional Average Delay Attributable to Delay Jan 25 2000 8.3

Regional Average % Increase in Travel Time when Weather Jan 25 2000 53%

Road No.

ANALYSIS OUTCOMES FOR LOW 2-HOUR REGRESSION MODELS

NORTH/EAST DIRECTION SOUTH/WEST DIRECTION
Figure 2 – GIS representation of the NWS Sterling Doppler Radar domain overlaid on the metropolitan 

Washington Road Network. The radar is located at the far left. The blue lines are azimuth lines. The boxes 

represent radar grids used in the segment analysis.

Figure 3 – The red boxes represent radar grids that lie on a road segment and are considered part of the 

“primary” segment region. The blue boxes surround the primary region and are considered part of the 

“secondary” segment region. The combination of boxes represented a segment in the radar domain.
2. The primary grids would be weighted more

heavily than the secondary grids (e.g. 0.8 vs.

0.2) when developing an average reflectivity 

value for each roadway segment

4.1.2 Z-R Processing

Doppler radars estimate rainfall rates by sensing 

the reflectivity, or amount of reflected energy returned 

from a target. There is a relationship between reflectivity 

(Z) and rainfall rate (R) through the following

relationship:

Z = aRb

Where: Z = reflectivity factor (mm6m

-3

)

R = rainfall rate (mm h-1)

a,b = coefficient, exponent of R

For this task, reflectivity data were known for each 

radar bin. Once a weighted average reflectivity value for 

each segment was calculated, the Z-R equation was 

solved for R, rainfall rate. Coding within the radar

product indicated whether the system was in

precipitation mode (six minutes/volume) or clear air

mode (10 minutes/volume). The result was an estimated 

rainfall total per volume in hundredths of an inch.

To accommodate for different seasons and drop 

size distributions, the formula coefficients were

automatically modified. A cap of 0.50 inches per radar 

volume was also used to keep estimates within reason. 

Since radar data was processed during expected

precipitation events, the chance of ground clutter

contamination was held to a minimum and the

accumulation cap was very rarely required. Table 3

shows the Z-R values and their estimated precipitation 

output.

Table 3 – Z-R relationships used for processing

Reflectivity, R

(dBZ)

Standard Rate

a=300, b=1.4

(inches/hour)

Tropical Rate

a=250, b=1.2

(inches/hour)

15 <0.01 <0.01

20 0.02 0.02

25 0.04 0.05

30 0.09 0.12

35 0.21 0.33

40 0.48 0.85

45 1.10 2.22

50 2.50 5.80

55 5.68 15.14

60 12.93 39.53

To tune the algorithm, primary and secondary bin 

regions were placed over the three area airports. Hourly

rainfall reports from the airports were used to compare 

with an hour’s worth of radar estimated accumulations. 

Appendix A, Figure A2 shows a comparison of the 

radar estimated precipitation (accumulated over an

hour) and the ASOS rain gauge value at DCA. The 

radar estimation technique was able to capture the

spikes and the trends in the gauge reading. While the 

radar estimates were generally lower than the ASOS 

observations, the onset time, ending time and

magnitude of the event were captured for the travel time 

calculations.

Figure A3 compares radar estimated and ASOS 

liquid equivalents during a light snow event at DCA. 

Again, while the radar estimated totals were lower than 

actual measurements, the timing and fluctuation in the 

precipitation was properly captured.

4.1.3 Radar Data Processing

A program was written to read the radar files, pars e 

their internal packets and unpack the run length

encoded radial reflectivity data into arrays. Appendix A, 

Figure A4 shows the graphical user interface which 

contains a reflectivity scale, informational window and a 

display that overlays the airports (white circles) on the 

radar screen. The test domain lies within the area

bounded by the two yellow radial lines.

Subroutines were written to represent each

roadway segment. Once the volume-based rainfall

estimate was generated for a segment, the data were 

written to one of 33 segment text files. Files for each of 

the area airports were also created for quality control

(Appendix B, Table B7).

It was found that the radar data set was not

complete. In order to salvage as many precipitation 

events as possible, a post-processing routine was

written to fill in data gaps by interpolation. A maximum 

of two radar volumes (12 minutes) could be missed for 

the interpolation routine to fill in the holes. An example 

of a final output file can be seen in Appendix B, Table 

B8.

4.2 Means Analysis

Most of the processed radar data were available at 

six minute intervals. The travel time data were available 

at five minute intervals. The radar data were assigned to 

the closest travel time bin prior to analysis. This process 

is s hown in Appendix A, Figure A5.

4.2.1 Analysis

Regression modeling predicted the increase in

travel time based on linear relationships between

weather variables and travel times. Radar data afforded 

the opportunity to go one step beyond because of the 

higher resolution data and simpler computations . The 

observed precipitation by roadway segment was used to 

evaluate the actual increase in travel time when

precipitation occurred. This was done by first ordering 

the observed travel times during the peak or off-peak 

periods from lowest to greatest for each segment. Then, 

a percentile of the observations was used to establish a 
baseline travel time. These baselines included eventfree travel time; a travel time representative of traffic 

without incidents or weather, but with recurrent

congestion levels. For example, in a set of 200

observations, rank ordered from lowest to highest travel 

time, the 40th in the order would constitute the 20th

percentile baseline travel time.

All of the travel time observations were then

averaged where the precipitation observations were

greater than a selected precipitation threshold (such as 

0.01 inches per volume). This value corresponds to the 

average travel time with precipitation. Comparing the 

precipitation travel time agains t the baseline event-free 

travel time yields the impact of precipitation (or travel 

time increase) due to precipitation.

A sensitivity analyses based on varying the

percentile used in the baseline travel time was 

conducted for two precipitation thresholds. The

expectation was that as the precipitation threshold

increased, the travel delay would increase. Conversely, 

as the percentile used in setting the baseline increased, 

the impact of weather would be lessened as more traffic 

events (low visibility, small incidents, etc) would creep 

into the baseline travel time.

4.2.2 Implication of Data Quality on Means Analysis

The means analysis of the impact of weather on 

travel time increases was relatively robust. However, the 

only weather element assessed was precipitation. That 

is, increases in travel time due to weather factors such 

as wind and low visibility were not assessed in this

analysis. A lesser issue was SmarTraveler’s policy of 

reporting speed limit travel times during off-peak 

periods. In such circumstances, the impact of

precipitation producing delay may not always be

captured. For example, the impact of precipitation

causing driving speeds to be reduced from 75 mph to 65

mph (where speed limits are 65 mph) would not register 

in the travel time data. Thus, this analysis may

underestimate the impact of precipitation on travel time 

increases during congestion-free traffic.

4.2.3 Means Analysis Outcomes

Appendix A, Figure A6 presents the travel time 

values associated with various percentiles for the peak 

and off-peak time periods. These values are based on 

the average travel times across the 33 roadway

segments. As demonstrated in the figure, the baseline 

travel time in the off-peak is tremendously flat at 17 

minutes while the baseline in the peak varies 

significantly corresponding to the percentile used. This 

means that the increase in travel time during the offpeak is not sensitive to the percentile used in baseline 

travel until extended beyond the 75th percentile.

In conducting the means analysis, the 25th

percentile was used as the baseline travel time. The 

minimum precipitation value used to represent wet

pavement was an estimate of 0.01 inches per radar

volume. The 25th percentile travel time during the peak 

period, averaged across the 33 roadway segments was

19.4 minutes whereas the average travel time across 

precipitation observations of 0.01 inches or greater was 

23.4 minutes. This constitutes a four-minute delay, or a 

24% increase in travel time when precipitation was 

present. The 25th percentile travel time during the offpeak period was 16.8 minutes , whereas the average 

travel time across precipitation observations of 0.01

inches or greater was 17.4 minutes, or a 3.5% increase 

in travel time when precipitation was present. If this was 

applied to the 50th percentile, then the travel time

increases were 10.7% and 3.4% for the peak and offpeak, respectively. At the 50th percentile, there would be 

a significant likelihood that some events such as small 

incidents or low visibility are incorporated into the

baseline travel time.

When a higher cutoff for precipitation (0.03 inches 

per volume) was selected, the increase in travel time did

not change substantially. During the peak, travel time 

increased to 25%, while the off-peak travel time

increased by 3.7%.

5. CONCLUSIONS

5.1 Implication of Analyses

The average impact of precipitation on peak-period 

traffic is at least an 11% increase in travel time. It is very 

likely that the impact is much closer to 25% based on 

the means analysis. Regression analyses suggest that 

during the off-peak periods , travel time increases by

approximately 13% due to an array of weather attributes 

including visibility, wind, and precipitation. The means 

analysis , measuring the impact of only precipitation,

suggests that during the off-peak periods , precipitation 

causes a 3.5% increase in travel time. This estimate is 

likely to be lower than reality, however, due to data the 

limitations discussed previously.

5.2 Next Steps

A next step in utilizing these data sets is to

generate regression models with binary variables

(variables that contain only two values). Such models 

could be used to quantify the relative impact of specific 

weather phenomena on delay. These values can also 

be useful to the traffic management community in

predicting increases in travel time when various weather 

phenomena are forecasted. However, before such

analyses are approached, an accurate travel time data 

source is needed. Another avenue of study is to

evaluate the relative impacts of weather and

incident/congestion variables. For such analyses,

however, one would require data sets on incidents and 

traffic demand that can be merged with the existing 

weather and travel time data sets.
Appendix A – Additional Figures

Figure A1 – Regional Map showing the three Airports used in the analysis and the Travel Network Domain 

(inside the red block)
Figure A2 – Comparison of Radar Estimated Rainfall to ASOS Hourly Accumulation at DCA

Figure A3 – Comparison of Radar Estimated Liquid Equivalent to ASOS Liquid Equivalent at DCA
Figure A4 – Image of the Program Created to Process all of the Radar Data into Per Volume Rainfall Totals.

Figure A5 - Process for Combining Precipitation and Travel Time Data

+ =

Time Precipitation Time Travel Time Time Travel Time Precipitation

6:32 AM 0.0000 6:30 AM 22 min. 6:30 AM 22 min. 0.0000

6:38 AM 0.0050 6:35 AM 22 min. 6:35 AM 22 min. 0.0025

6:44 AM 0.0100 6:40 AM 28 min. 6:40 AM 28 min. 0.0050

6:45 AM 28 min. 6:45 AM 28 min. 0.0100

PRECIPITATION DATA TRAVE TIME DATA

COMBINED DATA WITH

LINEAR INTERPOLATION
Figure A6 - Travel Time by Percentile Observation

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

10 20 30 40 50 60 70 80 90 100

Observation Percentile

Regional Averaged Travel Time (minutes)

Peak Period Regional Percentiles Off-Peak Regional Percentiles
Appendix B – Additional Tables

Table B1 – Description of Roadway Segments in the Washington DC Metropolitan Area Network

1 I-95/I-495 in MD btwn. Woodrow Wilson Bridge & College Park 25.0 Freeway

2 I-495 in MD between US 1 & the American Legion Bridge 17.4 Freeway

3 I-495 in VA between the American Legion Bridge & US 50 8.3 Freeway

4 I-95/I-495 in VA between US 50 & the Woodrow Wilson Bridge 13.2 Freeway

5 I-295 in MD between Laurel & East Capitol St. NE 10.9 Freeway

6 Suitland Pkwy. in MD between MD 4 & the Douglass Bridge 10.7 Freeway

7 G. W. Pkwy. in VA within I-495 (N. of DC and S. of DC) 16.5 Freeway

8 Clara Barton Pkwy. between I-495 & the Roosevelt Bridge 8.6 Freeway

9 MD 5/Branch Ave. in MD between US 301 & the DC Line 12.8 Arterial

10 MD 4 in MD between US 301 & the DC Line 12.6 Arterial

11 US 50 in MD between Bowie & Kenilworth Ave. 11.1 Freeway

12 US 1 in MD between MD 212 & the DC Line 8.0 Arterial

13 I-95 in MD between Laurel & I-495 6.1 Freeway

14 US 29 in MD between Cherry Hill Rd. & the DC Line 6.9 Arterial

15 MD 97 between Wheaton & the DC Line 4.8 Arterial

16 MD 355 between Gude Drive & the DC Line 12.0 Arterial

17 I-270 between Gaithersburg & I-495 9.1 Freeway

18 MD 214 between MD 202 & the DC Line 4.7 Arterial

19 MD 650 between I-495 & the DC Line 4.1 Arterial

20 MD 185 between I-495 & the DC Line 2.6 Arterial

21 VA 267 between Dulles Airport & I-66 14.4 Freeway

22 US 50 in VA between the VA 7100 & I-495 9.7 Arterial

23 US 50 in VA between I-495 & the Arlington Memorial Bridge 9.3 Arterial

24 I-66 between Centreville & I-495 12.5 Freeway

25 I-66 between I-495 & the Roosevelt Bridge 9.9 Freeway

26 I-95 between Dale City & I-495 14.0 Freeway

27 I-395 between I-495 & the Potomac River 9.5 Freeway

28 US 1 in VA between Kings Highway & the 14th St. Bridge 5.6 Arterial

29 VA 236 between I-495 & the King St. Metro 9.3 Arterial

30 VA 620 between the VA 7100 & I-495 8.9 Arterial

31 I-295 between I-495 & East Capitol St. 10.3 Freeway

32 MD 210 between Berry Road & the DC Line 8.4 Arterial

33 VA 7100 between Springfield Metro Station & VA 267 23.8 Freeway

Road

Number Road (SmarTraveler) Description

Length

(miles)

Road

Type
Table B2 – Decision Scheme for the Determination of Pavement Condition Using Surface Observational Data

(In each case, road conditions were assumed to be dry (category 0) unless conditions met one of the algorithm 

requirements.)

For a roadway to be classified as “wet” (category 1), a total of 16 different algorithms were used. The algorithms 

looked at different scenarios, such as:

1). If it is currently raining, then 

- There must be either measurable rainfall in the gage or visibilities must be reduced enough to 

imply accumulating rain (this avoids indicating a wet roadway with isolated trace events). 

- If the current rain only produced a trace accumulation, then we can look at the previous hour to 

see if measurable rain occurred. If it did and the temperature wasn’t too hot (for evaporation) 

and clouds were thick enough then the roads were indicated to be wet.

2). If there has only been drizzle with trace amounts of precipitation, then depending on day or night 

conditions, the roads could be wet. Examples include:

- If it has been cloudy and drizzly, and it is night time, then look back 2 to 4 hours and determine 

if there has been measurable rainfall.

- If it is during the day, then the previous 2 to 4 hour rainfall must have accumulated enough to 

keep roads wet. In this case visibility and humidity come into play.

3). If moderate to heavy rain occurred within the last 4 hours (but not within 2 hours), then

- If it is at night, there must be light winds and high humidity for wet roads, or

- If it is during the day, it must be cloudy with high humidity and temperatures below 85 degrees.

4). If it rained in the last 3 to 6 hours (above a minimum threshold) and it is at night with high humidity and 

cool temperatures, then roads are wet.

5). To cover persistence situations, if there is a long duration period of cloudy and cool weather with high 

humidity and drizzle, then roads can be wet.

The bottom line with the wet roadway scenario is that any sun during the day with moderate humidity and 

winds can lead to high evaporation rates during warm weather. Minimum accumulation thresholds were 

lowered as the temperature lowered.

A wet roadway “lag” was built in to the algorithm to allow roads to be “wet” for a time after the end of rain 

events. The duration of the lag was determined by time of day and accumulated rainfall.

For a roadway to be classified as “snowy” (category 2), 14 algorithms were used. Similar to the wet roadway 

category, time of day and surface temperature played a big part in the final determination. However, because ASOS 

has such a hard time quantifying hourly liquid equivalent precipitation, some algorithms used visibility as a predictor 

of accumulating snow. Some examples of the algorithm classes include:

1). The most straightforward situation occurs when the air temperature is below freezing and measurable 

snow has been reported during the hour (either by rain gage liquid equivalent or low visibility).

2). If it is night time and air temperatures fall between 32 and 36 degrees, then there must be a stronger 

correlation between hourly snow accumulation and lowered visibility.

3). If it is during the day, then if the air temperature is at or below freezing and it is cloudy and there is 

measurable snow, then the roads are snowy.

4). However, if it is during the day and air temperatures are between 32 and 36 degrees, then moderate or 

greater snow intensity must occur.

5). If it is not currently snowing, but it snowed within the last 2 hours, then different weights are placed on the 

time of day, amount of cloud cover, air temperature and hourly accumulated snowfall.

6). Persistence is built in to the algorithms so that multiple hours of very light snow could return a positive 

snow indication as long as cloud cover, temperature and time of day met specifications.

The snow “lag” time was longer for heavy events or nighttime snowfalls .

For the roads to be classified as “icy” (also category 2), then a series of 10 algorithms was used. These algorithms 

were separated from the snow routines because ice has different properties than snow. For example, technically you 

can have snow with air temperatures above freezing. However, freezing rain is not supposed to happen with 

temperatures above freezing (however sleet can be). For this category, freezing rain and sleet were the main 

hydrometeors. Examples of the algorithms included:

1). If it is currently icing (freezing rain or sleet) and there has been a measurable accumulation and the 

temperature is at or below freezing, the roads are icy.

2). If it is nighttime and air temperatures are between 32 and 36 degrees, then if measurable sleet occurs, 

roads are icy.

3). However, if it is daytime with above freezing temperatures, then the sleet must be occurring with at least a 

moderate intensity.

4). If it is not currently icing, but it was during the last 2 hours, then if it is nighttime with freezing 

temperatures, then roads are still icy.

5). However, if it is during the day with temperatures above freezing, then clouds must remain cloudy with 

high humidity and liquid equivalent accumulations of sleet must m eet minimum thresholds.
6). Icing algorithms look back for 4 hours to determine if temperatures, cloud cover and accumulations 

warrant an ice lag.

For roads to be classified as “black ice” (category 3), then one algorithm was used based on the following premise:

- If it is nighttime or dawn and skies have cleared during the overnight (CLR or FEW)

- Surface winds must be light (<5 mph)

- Air temperatures must start out above freezing then fall to near freezing (<=35 by dawn). This 

temperature was used since the ASOS temperature instrument is in the air and there may be a 

significant lapse rate to the ground.

- Dew points must rise to near freezing, or fog form

These conditions would indicate the potential for black ice.

Table B3 - Average Two-Hour Low (Off-Peak) Time of Day and Travel Time

Average Travel

Time in minutes

Average Travel

Time in minutes

1 12:15 PM - 2:10 PM 28.8 12:50 PM - 2:45 PM 30.4

2 9:30 AM - 11:25 AM 22.1 12:40 PM - 2:35 PM 22.4

3 11:25 AM - 1:20 PM 9.3 12:45 PM - 2:40 PM 9.3

4 11:15 AM - 1:10 PM 18.0 10:35 AM - 12:30 PM 17.8

5 7:50 AM - 9:45 AM 15.7 12:35 PM - 2:30 PM 16.3

6 10:00 AM - 11:55 AM 15.2 10:25 AM - 12:20 PM 15.5

7 11:15 AM - 1:10 PM 28.7 1:20 PM - 3:15 PM 29.3

8 7:55 AM - 9:50 AM 14.2 11:25 AM - 1:20 PM 14.6

9 10:05 AM - 12:00 PM 19.3 11:45 AM - 1:40 PM 19.2

10 10:05 AM - 12:00 PM 22.2 12:50 PM - 2:45 PM 22.2

11 9:20 AM - 11:15 AM 13.2 11:05 AM - 1:00 PM 13.2

12 9:40 AM - 11:35 AM 24.2 10:10 AM - 12:05 PM 24.2

13 12:00 PM - 1:55 PM 6.1 12:45 PM - 2:40 PM 6.2

14 1:45 PM - 3:40 PM 14.2 11:00 AM - 12:55 PM 14.3

15 10:05 AM - 12:00 PM 13.2 10:20 AM - 12:15 PM 13.3

16 9:40 AM - 11:35 AM 35.7 1:25 PM - 3:20 PM 35.6

17 9:50 AM - 11:45 AM 13.2 10:50 AM - 12:45 PM 13.3

18 10:00 AM - 11:55 AM 10.2 10:05 AM - 12:00 PM 10.2

19 10:05 AM - 12:00 PM 8.3 10:20 AM - 12:15 PM 8.3

20 9:45 AM - 11:40 AM 10.1 11:25 AM - 1:20 PM 10.1

21 1:00 PM - 2:55 PM 15.3 12:55 PM - 2:50 PM 15.3

22 12:50 PM - 2:45 PM 18.3 10:20 AM - 12:15 PM 18.2

23 11:25 AM - 1:20 PM 18.3 1:00 PM - 2:55 PM 18.2

24 1:45 PM - 3:40 PM 14.7 10:55 AM - 12:50 PM 14.5

25 1:20 PM - 3:15 PM 12.6 10:40 AM - 12:35 PM 12.4

26 12:05 PM - 2:00 PM 17.5 7:35 AM - 9:30 AM 20.0

27 11:35 AM - 1:30 PM 10.5 11:40 AM - 1:35 PM 10.4

28 12:05 PM - 2:00 PM 15.4 11:20 AM - 1:15 PM 15.3

29 1:40 PM - 3:35 PM 20.1 1:25 PM - 3:20 PM 20.1

30 10:40 AM - 12:35 PM 20.3 10:35 AM - 12:30 PM 20.2

31 12:00 PM - 1:55 PM 15.5 10:35 AM - 12:30 PM 15.4

32 10:35 AM - 12:30 PM 19.2 9:30 AM - 11:25 AM 19.1

33 10:25 AM - 12:20 PM 29.6 12:00 PM - 1:55 PM 29.5

ROAD

NO.

NORTH/EAST DIRECTION SOUTH/WEST DIRECTION

2-Hour Off-Peak 2-Hour Off-Peak
Table B4 - Average Two-Hour High (Peak) Time of Day and Travel Time

Average Travel

Time in minutes

Average Travel

Time in minutes

1 4:05 PM - 6:00 PM 35.9 7:15 AM - 9:10 AM 45.4

2 4:30 PM - 6:25 PM 35.7 7:25 AM - 9:20 AM 31.5

3 4:30 PM - 6:25 PM 12.8 4:30 PM - 6:25 PM 13.8

4 4:30 PM - 6:25 PM 25.6 7:50 AM - 9:45 AM 23.8

5 4:30 PM - 6:25 PM 23.5 7:15 AM - 9:10 AM 21.4

6 4:30 PM - 6:25 PM 20.8 7:15 AM - 9:10 AM 20.6

7 7:25 AM - 9:20 AM 40.4 7:15 AM - 9:10 AM 38.9

8 4:30 PM - 6:25 PM 17.4 7:20 AM - 9:15 AM 19.3

9 7:05 AM - 9:00 AM 25.3 4:05 PM - 6:00 PM 24.3

10 6:50 AM - 8:45 AM 27.8 4:30 PM - 6:25 PM 27.4

11 4:30 PM - 6:25 PM 18.1 7:10 AM - 9:05 AM 19.7

12 4:15 PM - 6:10 PM 29.8 7:35 AM - 9:30 AM 29.3

13 4:10 PM - 6:05 PM 8.7 7:20 AM - 9:15 AM 8.5

14 4:10 PM - 6:05 PM 18.1 7:20 AM - 9:15 AM 20.3

15 4:30 PM - 6:25 PM 15.9 7:30 AM - 9:25 AM 16.4

16 4:30 PM - 6:25 PM 42.3 7:25 AM - 9:20 AM 45.0

17 4:30 PM - 6:25 PM 18.9 7:05 AM - 9:00 AM 17.9

18 4:05 PM - 6:00 PM 12.8 7:05 AM - 9:00 AM 13.0

19 4:05 PM - 6:00 PM 12.2 7:10 AM - 9:05 AM 10.3

20 4:10 PM - 6:05 PM 13.3 7:25 AM - 9:20 AM 13.6

21 7:15 AM - 9:10 AM 21.3 4:05 PM - 6:00 PM 18.8

22 7:10 AM - 9:05 AM 24.7 4:30 PM - 6:25 PM 22.1

23 7:20 AM - 9:15 AM 24.6 4:30 PM - 6:25 PM 22.4

24 7:25 AM - 9:20 AM 24.2 4:30 PM - 6:25 PM 21.6

25 4:30 PM - 6:25 PM 18.2 3:25 PM - 5:20 PM 16.1

26 7:00 AM - 8:55 AM 25.4 4:30 PM - 6:25 PM 26.2

27 7:25 AM - 9:20 AM 19.0 4:30 PM - 6:25 PM 17.5

28 7:25 AM - 9:20 AM 21.6 4:25 PM - 6:20 PM 21.3

29 7:05 AM - 9:00 AM 23.6 4:30 PM - 6:25 PM 20.6

30 7:10 AM - 9:05 AM 27.6 4:30 PM - 6:25 PM 26.0

31 7:05 AM - 9:00 AM 20.8 7:10 AM - 9:05 AM 20.8

32 7:15 AM - 9:10 AM 24.7 4:30 PM - 6:25 PM 23.4

33 7:15 AM - 9:10 AM 39.0 7:10 AM - 9:05 AM 37.2

ROAD

NO.

NORTH/EAST DIRECTION SOUTH/WEST DIRECTION

2-Hour Peak 2-Hour Peak
Table B5. Regression Model and R2

 Values for North/East Road Directions

RoadR

2

-Value Reduced Model Equation (TT = travel time in minutes) for North/East Direction

1 0.20 TT = 28.45 +1.61x IADpcon2+1.17x DCApcon2

2 0.05 TT = 21.83 +1.41x DCApcon2+0.86 xIADprecip2

3 0.34 TT = 8.98 +1.30x DCApcon2+0.51 xIADpcon2+ 0.28x BWIpcon2

4 0.14 TT = 17.7 +1.75x DCApcon2+0.43 xIADpcon2+ 9.30x BWIvis

5 0.08 TT = 15.57 +0.36x IADprecip2+0.34 xBWIpcon2+ 0.18x DCApcon2

6 0.10 TT = 15.08 +0.70x DCApcon2+0.25 xIADpcon2

7 0.27 TT = 28.3 +1.45x DCApcon2+0.70 xBWIpcon2+ 0.56x IADpcon2

8 0.23 TT = 14.04 +0.45x IADprecip2+0.31 xBWIpcon2+ 0.12x DCApcon2

9 0.22 TT = 19.03 +0.99x DCApcon2+0.66 xIADpcon2+ 0.79x BWIvis

10 0.23 TT = 21.97 +0.82x DCApcon2+0.47 xIADpcon2+ 0.32x BWIpcon2

11 0.15 TT = 12.99 +0.82x DCApcon2+0.17 xIADpcon2+ 0.10x BWIpcon2

12 0.20 TT = 23.99 +0.85x DCApcon2+0.25 xIADpcon2+ 0.09x BWIpcon2

13 0.24 TT = 5.94 +0.82x DCApcon2+0.51 xIADpcon2+ 0.10x BWIpcon2

14 0.38 TT = 13.93 +1.84x BWIprecip2+0.42 xIADpcon2+12.11x BWIvis +

0.18 x DCApcon2

15 0.15 TT = 13.02 +0.70x DCApcon2+0.27 xIADpcon2

16 0.16 TT = 35.22 +1.99x DCApcon2+0.69 xIADpcon2

17 0.18 TT = 12.92 +1.06x DCApcon2+0.40 xIADpcon2

18 0.14 TT = 9.97 +0.91x DCApcon2+0.34 xIADpcon2

19 0.19 TT = 8.06 +0.91x DCApcon2+0.40 xIADpcon2+ 0.12x BWIpcon2

20 0.11 TT = 10.01 +0.48x DCApcon2+0.17 xIADpcon2

21 0.27 TT = 14.94 +0.96x DCApcon2+1.12 xIADpcon2+ 0.54x BWIpcon2

22 0.33 TT = 17.85 +1.07x DCApcon2+1.22 xIADpcon2+ 0.61x BWIpcon2

23 0.26 TT = 18.02 +1.11x DCApcon2+0.31 xIADpcon2+ 0.28x BWIpcon2

24 0.26 TT = 14.39 +2.30x BWIprecip2+12.64 xBWIvis + 0.32x DCApcon2

25 0.28 TT = 12.16 +1.44x BWIprecip2+0.88 xIADpcon2+ 0.86x DCApcon2+

2.69 x IADwind

26 0.13 TT = 17.01 +2.08x DCApcon2+1.32 xIADpcon2

27 0.18 TT = 10.23 +1.01x DCApcon2+0.33 xIADpcon2+ 0.26x BWIpcon2

28 0.38 TT = 14.89 +1.84x DCApcon2+1.15 xIADpcon2+ 0.18x BWIpcon2

29 0.36 TT = 19.91 +1.16x BWIprecip2+0.34 xIADpcon2+ 8.73x BWIvis +

0.15 x DCApcon2

30 0.30 TT = 19.94 +0.98x DCApcon2+0.55 xIADpcon2+ 0.48x BWIpcon2

31 0.26 TT = 15.21 +1.31x DCApcon2+0.63 xIADpcon2+ 0.26x BWIpcon2

32 0.28 TT = 18.93 +1.18x DCApcon2+0.81 xIADpcon2

33 0.22 TT = 29.17 +1.58x DCApcon2+0.80 xIADpcon2+ 0.50x BWIpcon2
Table B6. Regression Model and R2

 Values for South/West Road Directions

RoadR

2

-Value Reduced Model Equation (TT = travel time in minutes) for South/West Direction

1 0.06 TT =30.01 +1.56x IADpcon2+1.50x DCApcon2

2 0.05 TT = 22.06 +1.31x IADpcon2+0.82x DCApcon2

3 0.30 TT = 9.02 +0.94x DCApcon2+1.00 xIADpcon2+ 0.28x BWIpcon2

4 0.09 TT = 17.52 +1.10x DCApcon2+0.43 xIADpcon2

5 0.34 TT = 15.91 +1.23x DCApcon2+1.05 xIADpcon2+ 3.43x DCAwind +

0.28 x BWIpcon2

6 0.16 TT = 15.24 +0.93x DCApcon2+0.40 xIADpcon2+ 0.35x BWIpcon2

7 0.20 TT = 29.05 +0.42x DCApcon2+0.57 xIADpcon2+ 0.42x BWIpcon2+

0.39 x BWIprecip2

8 0.23 TT = 14.22 +1.33x DCApcon2+0.39 xIADpcon2+ 0.41x BWIpcon2+

3.49 x BWIwind

9 0.34 TT = 18.92 +1.33x DCApcon2+0.71 xIADpcon2

10 0.30 TT = 21.91 +0.64x DCApcon2+0.99 xIADpcon2+ 0.40x BWIpcon2

11 0.19 TT = 12.97 +0.75x DCApcon2+0.39 xIADpcon2+ 0.22x BWIpcon2

12 0.26 TT = 23.94 +0.65x DCApcon2+0.43 xIADpcon2+ 0.31x BWIpcon2

13 0.24 TT = 5.98 +0.54x DCApcon2+0.66 xIADpcon2+ 0.18x BWIpcon2+

1.02 x BWIwind

14 0.30 TT = 13.88 +1.39x DCApcon2+0.64 xIADpcon2+ 0.37x BWIpcon2

15 0.17 TT = 13.04 +0.78x DCApcon2+0.36 xIADpcon2+ 0.20x BWIpcon2

16 0.32 TT = 34.99 +2.70x BWIprecip2+1.29 xIADpcon2+ 0.51x DCApcon2

17 0.24 TT = 12.95 +1.24x DCApcon2+0.49 xIADpcon2+ 0.33x BWIpcon2

18 0.13 TT = 10.02 +0.76x DCApcon2+0.36 xIADpcon2+ 0.18x BWIpcon2

19 0.23 TT = 8.06 +0.89x DCApcon2+0.54 xIADpcon2+ 0.23x BWIpcon2+

0.95 x BWIwind

20 0.35 TT = 9.95 +0.69x DCApcon2+0.33 xIADpcon2+ 0.22x BWIpcon2

21 0.37 TT = 14.83 +1.22x DCApcon2+1.25 xIADpcon2+ 0.58x BWIpcon2

22 0.27 TT = 17.90 +1.15x DCApcon2+0.68 xIADpcon2+ 0.31x BWIpcon2

23 0.31 TT = 17.95 +0.82x DCApcon2+0.73 xIADpcon2+ 0.39x BWIpcon2

24 0.26 TT = 14.06 +0.98x IADpcon2+1.06x DCApcon2+0.74 xBWIpcon2

25 0.21 TT = 12.04 +1.17x DCApcon2+0.61 xIADpcon2+ 0.25x BWIpcon2

26 0.01 TT = 19.74 +1.43x BWIprecip2+5.40 xBWIvis

27 0.24 TT = 10.12 +1.16x DCApcon2+0.44 xIADpcon2+ 0.10x BWIpcon2

28 0.36 TT = 14.87 +1.87x DCApcon2+0.70 xIADpcon2+ 0.28x BWIpcon2

29 0.31 TT = 19.89 +0.96x BWIprecip2+0.57 xIADpcon2+ 0.18x BWIpcon2+

0.11 x DCApcon2

30 0.30 TT = 19.90 +1.08x DCApcon2+0.54 xIADpcon2+ 0.26x BWIpcon2

31 0.14 TT = 15.18 +0.84x DCApcon2+0.34 xIADpcon2+ 0.13x BWIpcon2

32 0.23 TT = 18.88 +1.15x DCApcon2+0.40 xIADpcon2

33 0.37 TT = 28.90 +2.27x DCApcon2+1.47 xIADpcon2+ 0.45x BWIpcon2
Table B7 – Example of an Intermediate Output File for Segment 1. The Average Reflectivity Values for the 

primary and secondary regions have DBZ units. Radar estimated precipitation values are located under 

“Main” and “2ndry” columns. Final reported liquid equivalent estimates are reported under the “final pcpn” 

column.

Table B8 – Example of a Final Output File for Segment 1. UTC times have been converted to Local Time. 

Actual values are tagged with “A”, Interpolated values are tagged with “I”.