Traffic Analysis Toolbox Volume XI: 

Weather and Traffic Analysis, Modeling and 

Simulation 

PUBLICATION NO. FHWA-JPO-11-019 December 2010

U.S. Department of Transportation 

Federal Highway Administration 

Office of Operations 

1200 New Jersey Avenue, SE 

Washington, DC 20590
Notice 

This document is disseminated under the sponsorship of the U.S. Department of Transportation in the 

interest of information exchange. The U.S. Government assumes no liability for the use of the information 

contained in this document. 

The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names 

appear in this report only because they are considered essential to the object of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve Government, 

industry, and the public in a manner that promotes public understanding. Standards and policies are used 

to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA 

periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality 

improvement. 
i 

Foreword 

Inclement weather has significant impacts on transportation system. For example, studies have 

shown reductions in roadway capacity, increased drivers’ response and reaction times, reductions 

in travel demands, etc. during inclement weather. Studies have also shown that incorporating 

inclement weather into transportation operations and managements could improve transportation 

system performances. 

This document is developed to guide traffic engineers and transportation operations managers in 

analyzing and modeling weather impacts on transportation system. It is expected that by utilizing 

the weather module, better estimates and predictions of real world traffic parameters during 

inclement weather conditions can be made, leading to appropriate measures for improving 

highway safety and mobility in inclement weather. 

This document serves as Volume XI in the FHWA Traffic Analysis Toolbox. Preceding volumes 

in the toolbox include: Volume I: Traffic Analysis Tools Primer, Volume II: Decision Support 

Methodology for Selecting Traffic Analysis Tools, Volume III: Guidelines for Applying Traffic 

Microsimulation Modeling Software, Volume IV: Guidelines for Applying CORSIM 

Microsimulation Modeling Software, Volume V: Traffic Analysis Tools Case Studies - Benefits 

and Best Practices, Volume VI: Definition, Interpretation, and Calculation of Traffic Analysis 

Tools Measures of Effectiveness, Volume VII: Predicting Performance with Traffic Analysis 

Tools: Case Studies, Volume VIII: Work Zone Modeling and Simulation—A Guide for DecisionMakers, Volume IX: Work Zone Modeling and Simulation – A Guide for Analysis, and Volume X: 

Localized Bottleneck Congestion Analysis Focusing on What Analysis Tools Are Available, 

Necessary and Productive for Localized Congestion Remediation. 

 
ii 

Technical Report Documentation Page 

1. Report No. 2.Government Accession No 3. Recipient’s Catalog No 

FHWA-JPO-11-019 

4. Title and Subtitle 5. Report Date 

Traffic Analysis Toolbox Volume XI: 

Weather and Traffic Analysis, Modeling and Simulation 

December 2010 

6. Performing Organization Code 

7. Author(s) 8. Performing Organization Report No. 

Byungkyu “Brian” Park, Ph.D. 

Theresa K. Jones 

Stephen O. Griffin 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Science Applications International Corporation (SAIC) 

8301 Greensboro Drive, Mailstop E-12-3 

McLean, VA 22102 

University of Virginia 

Department of Civil & Environmental Engineering 

P.O. Box 400742 

Charlottesville, VA 22904-4742 

11. Contract or Grant No. 

10.Sponsoring Agency Name and Address 13. Type of Report and Period Covered 

Federal Highway Administration 

Office of Operations 

Room E86-205 

1200 New Jersey Avenue, SE 

Washington, DC 20590 

14. Sponsoring Agency Code 

15. Supplementary Notes 

FHWA Technical Monitors: Roemer Alfelor, C.Y. David Yang, Edward Fok and Paul Pisano. 

16. Abstract 

This document presents a weather module for the traffic analysis tools program. It provides traffic 

engineers, transportation modelers and decisions makers with a guide that can incorporate weather 

impacts into transportation system analysis and modeling. The module describes how users can 

implement weather analysis using mesoscopic or microscopic traffic simulation modeling tools. It also 

includes weather and traffic data sources and discusses various weather responsive traffic operations 

and management strategies. 

17. Key Words 18. Distribution Statement 

weather, modeling, simulation, traffic analysis No restrictions. 

19. Security Classify. (of this report) 

Unclassified 

20. Security Classify. (of this page) 

Unclassified 

21. No. of Pages 

 87 

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 
iii 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2

 square feet 0.093 square meters m2

yd2

 square yards 0.836 square meters m2

ac acres 0.405 hectares ha 

mi2

 square miles 2.59 square kilometers km2

 

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3

yd3 cubic yards 0.765 cubic meters m3

MASS 

oz ounces 28.35 grams g 

lb pounds 0.454 kilograms kg 

T short tons (2000 lb) 0.907 megagrams (or “metric ton”) Mg (or “t”) 

TEMPERATURE (exact degrees) 

F Fahrenheit ሺܨ െ 32ሻ ൊ 1.8 Celcius C 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2

 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2

m2

 square meters 10.764 square feet ft2

m2

 square meters 1.195 square yards yd2

ha hectares 2.47 acres ac 

km2

 square kilometers 0.386 square miles mi2

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3

MASS 

g grams 0.035 ounces oz 

kg kilograms 2.202 pounds lb 

Mg (or “t”) megagrams (or “metric ton”) 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) 

C Celcius 1.8ܥ ൅ 32 Fahrenheit F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2

 candela/m2 0.2919 foot-Lamberts fl 

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2
iv 

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v 

Table of Contents 

Introduction and Purpose ............................................................................................................ 1

1.0 Why Weather? ................................................................................................................... 3

2.0 Weather Impacts on the Transportation System ............................................................ 5

2.1. Macroscopic Level Impacts ............................................................................................. 5

2.2. Microscopic Level Impacts .............................................................................................. 6

3.0 Traffic Analysis Tools Incorporating Weather ............................................................... 9

3.1. Overview .......................................................................................................................... 9

3.1.1. Macroscopic Analysis ............................................................................................... 9

3.1.2. Mesoscopic Analysis ................................................................................................ 9

3.1.3. Microscopic Analysis.............................................................................................. 10

3.2. Mesoscopic Analysis ...................................................................................................... 11

3.2.1. Mesoscopic Traffic Simulation Model Setup ......................................................... 11

3.2.2. Data Preparation ...................................................................................................... 13

3.2.3. Traffic Model Calibration for Normal Conditions .................................................. 14

3.2.4. Calibration for Weather Impacts ............................................................................. 17

3.2.5. Performance Measures ............................................................................................ 20

3.2.6. Weather Model Implementation and Analysis ....................................................... 20

3.3. Microscopic Analysis ..................................................................................................... 26

3.3.1. Microscopic Traffic Simulation Model Set Up ...................................................... 26

3.3.2. Data Preparation ...................................................................................................... 28

3.3.3. Traffic Model Calibration for Normal Conditions .................................................. 28

3.3.4. Calibration for Weather Impacts ............................................................................. 34

3.3.5. Performance Measures ............................................................................................ 36

3.3.6. Model Implementation and Analysis ...................................................................... 36

4.0 Data Sources for Weather and Traffic Analysis ........................................................... 39

4.1. Weather Data Sources .................................................................................................... 39

4.2. Traffic Data Sources ....................................................................................................... 41

4.3. Intelligent Transportation Systems Data Sources .......................................................... 45

5.0 Weather Responsive Traffic Operations and Management Strategies ....................... 48

5.1. Advisory Strategies ........................................................................................................ 49

5.2. Control Strategies ........................................................................................................... 49
vi 

5.3. Treatment Strategies ....................................................................................................... 50

6.0 Case Study ........................................................................................................................ 52

6.1. An Implementation of DYNASMART in Hampton Roads, VA. – Mesoscopic 

Simulation ................................................................................................................................. 52

6.1.1. Case Study Summary .............................................................................................. 52

6.1.2. Description of the Study ......................................................................................... 52

6.1.3. Demand Adjustment ............................................................................................... 57

6.1.4. Analysis and Results ............................................................................................... 58

6.1.5. Conclusions ............................................................................................................. 60

6.1.6. Guidance ................................................................................................................. 61

6.2. Traffic Signal Operation under Inclement Weather – Signal Optimization and 

Microscopic Traffic Simulation ................................................................................................ 61

6.2.1. Case Study Summary .............................................................................................. 61

6.2.2. Description of the Study ......................................................................................... 62

6.2.3. Analysis and Results ............................................................................................... 64

6.2.4. Conclusions and Recommendations ....................................................................... 70

6.2.5. Guidance ................................................................................................................. 70

References .................................................................................................................................... 72

 
vii 

Table of Figures 

Figure 3-1 Sample DynusT Network in Portland, OR (DynusT, 2010) ....................................... 12

Figure 3-2 Flow Chart of Model Set Up ....................................................................................... 12

Figure 3-3 Calibration of Both Supply and Demand Data Process Flow Chart (Kunde, 2000) ... 15

Figure 3-4 Supply Parameter Calibration Procedure .................................................................... 21

Figure 3-5 Travel Time Comparisons under Three Weather Conditions ..................................... 24

Figure 3-6 Weather Events during Peak Hour .............................................................................. 25

Figure 3-7 Link Speed Comparisons ............................................................................................ 26

Figure 3-8 Calibration and Validation Process Flow Chart .......................................................... 29

Figure 3-9 Initial Evaluation Process ............................................................................................ 30

Figure 3-10 Example of X-Y plot with Acceptable Results ......................................................... 32

Figure 3-11 Concept of Acceptable Region .................................................................................. 34

Figure 3-12 Relationship between Speed and Volume for Base and Weather Cases ................... 37

Figure 4-1 Snapshot of Clarus Portal ........................................................................................... 39

Figure 4-2 National Climatic Data Center Website ...................................................................... 40

Figure 4-3 UM Weather Homepage ............................................................................................. 41

Figure 4-4 Archived Data Management System (ADMS) Homepage ......................................... 42

Figure 4-5 Performance Measurement System (PeMS) Homepage ............................................. 43

Figure 4-6 PORTAL Homepage ................................................................................................... 44

Figure 4-7 Daily Statistics for I-5 North ....................................................................................... 44

Figure 4-8 Minnesota Department of Transportation Traffic Data Webpage .............................. 45

Figure 4-9 INRIX Homepage ....................................................................................................... 46

Figure 5-1 WRTM Concept of Operations Flow Chart ................................................................ 48

Figure 6-1 Hampton Roads DYNASMART-P Network .............................................................. 53

Figure 6-2 Description of Weather Data in DYNASMART-P ..................................................... 54

Figure 6-3 Snapshots of WAF.dat files......................................................................................... 56

Figure 6-4 DYNSMART-P Program ............................................................................................ 57

Figure 6-5 Hampton Roads DYNASMART-P Program Snapshot............................................... 58

Figure 6-6 Simulated and Observed Speeds ................................................................................. 60

Figure 6-7 Dorset Street ................................................................................................................ 63

Figure 6-8 Vermont Route 15 ....................................................................................................... 64

Figure 6-9 Impact of Inclement Weather Duration on Operational Benefits (Dorset Street) ....... 69

Figure 6-10 Impact of Inclement Weather Duration on Operational Benefits (Route 15) ........... 69

 
viii 

Table of Tables 

Table 2-1 Weather Impacts on Roadways and Traffic Operations ................................................. 5

Table 2-2 Macroscopic Level Impacts on Traffic Operations ........................................................ 6

Table 2-3 Microscopic Level Impacts on Traffic Operations ......................................................... 6

Table 3-1 Calibrated Supply Parameters ...................................................................................... 22

Table 3-2 Weather Adjustment Factor Coefficients ..................................................................... 23

Table 3-3 Demand Adjustment due to Inclement Weather Conditions ........................................ 25

Table 3-4 Minimum Number of Simulation Repetitions .............................................................. 31

Table 6-1 Weather Sensitive Demand Parameters ........................................................................ 54

Table 6-2 Weather Impacts on Traffic Demand ........................................................................... 58

Table 6-3 RMSE between Observed and Simulated Speeds ........................................................ 59

Table 6-4 Parameters Adjusted During Calibration of Simulation Models .................................. 65

Table 6-5 Reduction Factors for Saturation Flow Rate and Free Flow Speed ............................. 66

Table 6-6 Performance Measures used for Benefit Assessment ................................................... 67

Table 6-7 Benefits of Special Timing Plans – CORSIM Results for Dorset Street ..................... 68

Table 6-8 Benefits of Special Timing Plans – CORSIM Results for Vermont Route 15 ............. 68
1 

Introduction and Purpose

We live in a part of the world where inclement weather conditions occur, affecting traffic 

operations. Traffic behaves differently when inclement weather is thrown into the mix, and to 

say that traffic will always be affected in one particular way is not true because there are various 

types of inclement weather. This weather module was developed as part of FHWA’s Traffic 

Analysis Tools (TAT) program to guide traffic engineers and transportation operations managers 

in analyzing and modeling weather impacts on highway traffic movement. Users of this TAT 

weather module will learn how to calibrate traffic stream parameters (e.g., free flow speed, speed 

at capacity, and saturation flow at capacity) based on weather-related parameters (e.g., 

precipitation type, precipitation intensity level, and visibility). By utilizing the weather module, 

better estimates and predictions of real world traffic parameters during inclement weather 

conditions can be made, leading to appropriate measures for improving highway safety and 

mobility in inclement weather. 

The purpose of the weather TAT module is to provide Traffic Management Center (TMC) 

operators and traffic engineers with practical guidance for implementing proper traffic 

operational strategies specific to inclement weather conditions. 

Traffic Analysis Toolbox Volume XI includes six chapters. The following provides a brief 

description of each chapter. 

Chapter 1, entitled “Why Weather?”, presents a brief overview of the topics covered in this TAT 

weather module. Topics included in Chapter 1 include: weather impacts on the transportation 

system, the available traffic analysis and modeling tools that incorporate those impacts, and the 

benefits of incorporating weather analysis into traffic operations and management strategies. 

Chapter 2 elaborates on the discussion of weather impacts on the transportation system. 

Examples of these weather impacts are given on two traffic analysis levels: the macroscopic 

level and the microscopic level. The macroscopic level impacts discussed in this chapter include 

the weather impacts on traffic stream parameters, such as capacity, volume and speed at 

capacity, for an aggregated group of vehicles. The section on microscopic-level impacts 

addresses weather impacts on traffic parameters that correlate with the individual vehicular 

movements (e.g., lost startup time and saturation headway). 

Chapter 3 starts with a brief overview of the three main types of traffic analysis tools (i.e., 

macroscopic, mesoscopic, and microscopic) that describes the capabilities and limitations for 

each type of tool. It then provides procedures for conducting weather-related traffic analyses 

using mesoscopic and microscopic traffic simulation tools (note that macroscopic tools are not 

considered as they have not been employed in weather impact analysis). 

Chapter 4 presents examples of available weather, traffic, and Intelligent Transportation Systems 

(ITS) data sources that can be used to conduct a weather-related traffic analysis. Discussions of 

Clarus, the Highway Performance Monitoring System, and the connected vehicles initiative are 

included in this chapter. 

Chapter 5 discusses on Weather Responsive Traffic Management (WRTM) Concept of 

Operations and existing traffic management strategies that would benefit from weather impact 

analysis. 
2 

Finally, Chapter 6 provides two case studies that were conducted to analyze weather impacts on 

traffic operations using mesoscopic and microscopic traffic modeling tools. In the first case 

study, DYNASMART-P, a mesoscopic traffic modeling tool, was used to develop models that 

estimated and predicted inclement weather impacts on freeway segments in the Hampton Roads 

region of Virginia. The second case study used two microscopic traffic modeling tools, CORSIM 

and SimTraffic, to evaluate weather-specific signal timing plans for four New England corridors. 
3 

1.0 Why Weather?

Weather conditions—consisting of rain, fog, snow, wind, and extreme temperatures—affect 

traffic operations and safety for a significant portion of the year in the United States. About 24 

percent of all crashes in the United States are weather-related, resulting in more than 673,000 

injuries and about 7,400 fatalities each year (Pisano et al., 2009). Inclement weather impacts 

roadway surface conditions and driver behavior, which result in increased delays and crash risks. 

Information about these impacts is widely available from past literature and recent studies, but 

this knowledge has not been fully incorporated into current traffic analysis tools. Because 

weather has significant effects on the transportation system, it should be taken into consideration 

when analyzing traffic operations. This section briefly discusses the impacts inclement weather 

conditions have on the transportation system, the available traffic analysis and modeling tools 

that incorporate those impacts, and the benefits of incorporating weather into traffic operations 

and management strategies. 

Traffic conditions are usually at their best in ideal weather. Such conditions are defined by the 

following characteristics: dry roadway, good visibility (greater than 0.25 miles), no precipitation, 

and winds less than 9.94 mph (Zhang et al., 2004). Of course the ideal weather condition is not 

guaranteed every time a trip is made on the road. A chain effect occurs when weather is 

involved: a weather event changes roadway conditions (e.g., reduced visibility and pavement 

friction), which leads to a reduction in traffic supply parameters (e.g., lower free-flow speeds and 

capacities), which in turn affect traffic flow (e.g., higher delays and lower average speeds). 

A significant indicator of the effects of inclement weather on the transportation system is traffic 

demand. This is the volume of traffic that is seen daily, or the number of drivers that use the 

traffic facility. In most cases, not including commuter traffic, inclement weather indirectly 

correlates with traffic demand. As the severity of adverse weather increases, traffic demand tends 

to decrease. In a study conducted on I-35 in northern rural Iowa, researchers extracted traffic 

counts for several snowy days that received more than 1 inch of snow in 24 hours. The study 

presented the following results: I-35 experienced a 20 percent reduction in traffic volumes on 

snowy days with low wind speed and good visibility, and an 80 percent reduction in traffic 

volumes on snowy days with high wind speed (as high as 40 mph) and poor visibility (less than 

one-quarter of a mile) (Maze et al., 2005). 

The occurrence of inclement weather conditions may be more significant for some cities in the 

United States. According to Kyte et al. (2001), the National Weather Service provides records of 

rain days for 284 sites in the United States for at least 10 years; these records show that nearly 

one-third of the sites have rainy days that occur about 34 percent of the year and 15 percent of 

the sites have rainy days occurring 41 percent of the year. Potential traffic impacts such as sideswipe crashes, t-bone crashes, and rear impact crashes may be attributed to impaired visibility 

and reduced road surface friction due to the frequency and intensity of rain. If rainfall occurs for 

a significant portion of the year for some of these sites then it will be beneficial for traffic 

engineers to incorporate the impact of inclement weather into their capacity and level of service 

analyses. 

Many studies have shown the impacts of weather on traffic, depending on the severity of rain, 

snow, or other conditions. In addition, researchers have already incorporated such weather 

impacts in analyses using traffic simulation modeling tools, such as DYNASMART, DynaMIT, 
4 

AIMSUN, CORSIM, INTEGRATION, PARAMICS, VISSIM, etc. Adjustable weather factors 

allow these model tools to simulate realistic traffic situations in inclement weather. Furthermore, 

weather databases provide adequate weather data to deploy the weather module for traffic 

operations and management. 

Weather data sources, such as Clarus, can be used by traffic engineers as a means of 

incorporating inclement weather impacts into existing traffic analysis tools. Clarus is designed to 

display current and forecasted weather-related data for a particular region. Its main goal is to 

decrease the effects of inclement weather on all road and transit users and operators. With 

Clarus, current users can obtain weather-related data for 38 States in the United States and 3 

provinces in Canada that uses a common format. 

The connected vehicles initiative could be an essential tool in making inclement weather 

information networks and the transportation system a more cohesive unit. Connected vehicles, a 

US Department of Transportation initiative, is designed to create a network that connects 

vehicles to infrastructure and wireless devices. It is proposed that connecting these three 

elements will enhance safety and mobility and reduce the negative effects of transportation on 

the environment. 

Steps have been taken by transportation agencies to incorporate weather into traffic operation 

and management strategies. Current weather-responsive traffic operation and management 

strategies range widely from motorist advisory (alert and warning systems) to traffic signal 

control strategies, all of which are used to facilitate travel in inclement weather. Motorist 

advisory, alert, and warning systems make drivers more aware of current and impending weather 

and pavement conditions through passive warning systems, active warning systems, en-route 

weather alerts, pre-trip road condition information, and pavement condition information (Batelle 

et al., 2010). A survey showed that about 92 percent of respondents would slow down if the high 

wind warning was activated (Kumar and Strong, 2006). In traffic signal control strategies, traffic 

signals are modified to optimize traffic operations during inclement weather. This may be 

performed by changing the interaction between detection systems and traffic signal control 

systems, implementing weather specific traffic signal timing plans, or programming weatherresponsive ramp metering timing parameters. Traffic operation and management strategies that 

incorporate the results from weather responsive traffic analyses have the potential to provide 

operational benefits. These results can inform, support, and improve advisory strategies, control 

strategies, and treatment strategies. 
5 

2.0 Weather Impacts on the Transportation System

It is generally understood that weather events affect roadway and traffic operations. FHWA 

summarized such impacts under various weather events as shown in Table 2-1. 

Table 2-1 Weather Impacts on Roadways and Traffic Operations 

Weather Events Primary Roadway Impacts Primary Traffic Operation Impacts 

Rain, Snow, Ice, Sleet, 

Hail & Flooding 

Reduced pavement friction 

Lane obstruction & submersion 

Reduced visibility 

Infrastructure damage 

Reduced roadway capacity and speed 

Increased delay and speed variance 

Increased crash risk 

Road restrictions & closures 

Strong Winds Reduced visibility due to blowing 

snow/dust 

Lane obstruction due to windblown debris & drifting snow 

Reduced vehicle performance 

Increased delay 

Reduced traffic speeds 

Increased crash risk 

Bridge restrictions & closures 

Fog, Smog & Smoke Reduced visibility Reduced speeds & increased delay 

Increased speed variability 

Increased crash risk 

Lightning & Extreme 

Temperatures 

Infrastructure damage Traffic control device failure 

Loss of power/communications services 

Rain substantially impacts drivers, vehicles, and roadways. Accumulated water on the roadway 

can greatly decrease the friction between the treads of a tire and the roadway surface. In addition, 

visibility is reduced to a poor quality due to raindrops falling onto the windshields of vehicles 

and by water that is dispersed upward from the roadway surface by the tires of nearby vehicles. 

In these conditions, it is not uncommon to find reductions in capacity and speed at capacity. For 

example, a study by Rakha et al. (2008) showed rainfall reduced capacity by 10-11 percent. 

Like rain, snowy conditions affect the operations of the transportation system. Snow reduces the 

roadway surface friction, making traveling difficult. Due to reduced roadway friction, drivers 

tend to decrease their traveling speeds which cause headway to increase and saturation flow to 

decrease. Perrin et al. (2001) and Agbolosu-Amison et al. (2004) conducted studies to analyze 

the impacts of snowy conditions on traffic flow, which included lower speeds and saturation 

flow rates. These studies show that the maximum reduction of saturation flow rate was 21 

percent for snowy conditions. 

2.1. Macroscopic Level Impacts

Inclement weather conditions affect the transportation system on a macroscopic level. Table 2-2 

presents the impacts of rain and snow on macroscopic parameters typically analyzed in traffic 

operations. 
6 

Table 2-2 Macroscopic Level Impacts on Traffic Operations 

Typical Impacts 

Weather Event 

Rain Snow 

Capacity -4 ~ −30% −3 ~ −27% 

Volume −20% −6 ~ −26% 

Speed at Capacity −8 ~ −14% −5 ~ −19% 

Saturation Flow −2 ~ −6% −4 ~ −21% 

(Sources: Agbolosu-Amison et al., 2004; Dalta and Sharma, 2008; Prevedourous and 

Chang, 2004; Rakha et al., 2008; Samba and Park, 2009) 

These impacts vary by time-of-day and day-of-week. Generally, traffic flow during daytime peak 

hours is much heavier than daytime off-peak hours. Dalta and Sharma (2008) indicated that 

commuter roads obtained lower reductions in traffic volume during daytime peak hours (-6 to 10 

percent) than during daytime off-peak hours (-10 to -15 percent). This may be attributable to the 

larger number of work-related trips (necessary trips) made during these peak hours. 

Inclement weather also impacts speed at capacity. A recent study conducted by Rakha et al.

(2008) shows reductions in speed at capacity for varying intensities of rain and snow. Maximum 

reductions ranged from 8 to 14 percent and 5 to 19 percent for rainy and snowy conditions, 

respectively. Reductions are due to weather-related roadway impacts such as reduced visibility 

and lower roadway surface friction. 

2.2. Microscopic Level Impacts

Inclement weather increases lost startup time, increases saturation headway, and reduces freeflow speed, ultimately affecting the flow of traffic. The decline in traffic flow volumes during 

inclement weather conditions is noticeable on time-sensitive facilities such as signalized 

intersections, where fewer vehicles travel through the green phase of an intersection approach 

because the signal timing is not adjusted to accommodate inclement weather conditions. Table 2-

3 presents the microscopic traffic impacts typically observed in inclement weather conditions. 

Table 2-3 Microscopic Level Impacts on Traffic Operations 

Typical Impacts 

Weather Event 

Rain Snow 

Lost startup time +7.6 ~ 31.5% +18.5 ~ 65.2% 

Saturation Headway +2.5 ~ 13.2% +4.4 ~ 30.9% 

Free-Flow Speed −2 ~ −9% −3 ~ 36% 

(Sources: Agbolosu-Amison et al., 2004; Perrin et al., 2001) 

Roadway surface quality degrades significantly under severe snowy conditions. When snow 

accumulates on roadways, vehicles have less tire traction, ultimately causing initial movements 

to stall. These conditions lead to slower startup times for vehicles. For example, due to inclement 

weather, lost startup times increased by an average of 23 percent (Perrin et al., 2001) and 

saturation headways also increased by a maximum of 30.9 percent (Agbolosu-Amison et al., 

2004). 

Drivers’ reducing their speeds during heavy rain or snow is a clear indication that inclement 

weather affects driver behavior. The more severe the inclement weather condition, the more 

likely drivers are to adjust their behavior behind the wheel to accommodate their comfort levels. 

For example, drivers respond to poor environmental conditions (e.g., low visibility and high 
7 

winds) by reducing their free flow speeds. One study found that free-flow speeds for all vehicles 

decreased by 7.5 percent and 18.1 percent during foggy and snowy conditions, respectively 

(Liang et al., 1998). 

 
8 

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9 

3.0 Traffic Analysis Tools Incorporating Weather

3.1. Overview

One universally correct method to conduct traffic analyses cannot exist due to the fact that all 

traffic analyses do not share the same objectives. Objectives are an important deciding factor in 

choosing a type of traffic analysis. For instance, if a study’s objective is to simulate traffic flow 

for multiple regions of a State, analysts need to choose a traffic analysis tool that can handle the 

amount of data that is involved in the study. The three main types of analysis are: macroscopic 

analysis, mesoscopic analysis, and microscopic analysis. This section defines each analysis type 

and discusses their modeling capabilities and limitations. 

3.1.1. Macroscopic Analysis

If there is one thing to take away from learning about transportation operations, it is that flow, 

speed, and density are all related to each other. The conditions of two will affect the third traffic 

stream parameter. For instance, if drivers on a highway are able to travel at their free-flow speeds 

and maximum density has not been reached, then the flow of traffic will run smoothly. When 

users incorporate macroscopic simulation models into their traffic analyses, they are analyzing 

the relationship among the three traffic stream parameters. 

When we consider the word “macroscopic,” we think “large scale.” Accordingly, a key feature 

of most macroscopic models is their ability to model large study areas. Using the flows, speeds, 

and density measures of a large network, macroscopic models can provide simple representations 

of the traffic behavior in that network. Because these models do not require detailed data such as 

driver characteristics, model set up can be done quickly and the simulation can output results in a 

timely manner (Traffic Analysis Toolbox Volume I – Alexiadis et al., 2004). 

Although the ability of macroscopic models to output a simple representation of traffic flow in a 

timely manner is considered a benefit, it is considered a limitation as well. Macroscopic models 

can simulate traffic stream parameters (i.e., flow, speed, and density) on a large scope, but they 

cannot model detailed behavior in individual vehicle movements (e.g., saturation headway and 

lost startup time). 

The FHWA Traffic Analysis Toolbox lists the commonly used macroscopic simulation models 

and can be found at the following link: 

 FHWA Traffic Analysis Tools – Macroscopic Simulation Models 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol1/sectapp_a.htm#a5 

Note that no literature was found in the weather-related analysis using macroscopic modeling. 

This is because weather impacts cannot be effectively analyzed using macroscopic tools. As a 

result, this chapter does not discuss incorporating weather in macroscopic analysis. 

3.1.2. Mesoscopic Analysis

Macroscopic models can only provide so much detail in simulating real world traffic conditions. 

In some cases, research requires more in-depth simulation results. This is where mesoscopic 

models come into play. These models have the ability to model large study areas but they 

provide users with more detailed information than macroscopic models. 
10 

More detailed traffic scenarios can be modeled using mesoscopic simulation models. For 

instance, users have the capability to model diversion routes from major roadways (e.g., 

freeways and highways) to other road types (e.g., signalized arterial). This could not be 

accomplished using macroscopic models. 

With these capabilities come weaknesses. One key limitation in using mesoscopic models is the 

inability to model detailed operational strategies such as a coordinated traffic network. This 

operational strategy involves programming the traffic signals at several intersections so that the 

flow of traffic is optimized (i.e., drivers do not receive a red signal at each intersection they 

approach). Such an operation would be better suited in a microscopic model because it would 

require more detailed data. Mesoscopic models provide users with higher accuracy in simulating 

real world traffic behavior than macroscopic models, but microscopic models simulate real world 

traffic behavior with higher accuracy than mesoscopic models. 

Commonly used mesoscopic simulation models include those from the DYNASMART and 

DYNAMIT family. Recent studies of inclement weather impacts on the transportation system 

have incorporated mesoscopic analysis with these models. The FHWA Traffic Analysis Toolbox 

provides additional information on mesoscopic simulation models at the following link: 

 FHWA Traffic Analysis Tools – Mesoscopic Simulation Models 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol1/sectapp_a.htm#a6 

3.1.3. Microscopic Analysis

When research requires heavily detailed analysis of real world traffic behavior, users would use 

microscopic simulation models. These models are intended to simulate the movement of 

individual vehicles, which can be done by using car-following models, longitudinal motion 

models (e.g., acceleration and deceleration models), gap-acceptance models, and lane-changing 

models. 

Microscopic models allow users to simulate the stochastic nature of traffic. The drivers that you 

share the road with are not going to drive in the same manner as you. Their thinking patterns and 

comfort levels will vary for each traffic scenario presented to them. Incorporating driver 

behavior data is essential to simulate traffic conditions with the highest accuracy. 

The ability of microscopic models to simulate traffic behavior with high accuracy is a benefit but 

also a weakness. In order to gain such a high level of accuracy, microscopic simulation models 

require substantial amounts of roadway geometry, traffic control, traffic pattern, and driver 

behavior data. Providing this amount of data will limit users to modeling smaller networks than 

those that can be modeled in macroscopic and mesoscopic analyses. The required input data will 

also cause each simulation run to take a very long time to output results. 

The FHWA Traffic Analysis Toolbox lists commonly used microscopic simulation models. 

These include: CORSIM, VISSIM, AIMSUN, and PARAMICS. For additional information on 

microscopic simulation tools please refer to the following sites: 

 FHWA Traffic Analysis Tools – Microscopic Simulation Models 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol1/sectapp_a.htm#a7 

 Traffic Analysis Toolbox Volume IV: Guidelines for Applying CORSIM 

Microsimulation Modeling Software 

http://ops.fhwa.dot.gov/trafficanalysistools/tat_vol4/vol4_guidelines.pdf 
11 

3.2. Mesoscopic Analysis

As noted, this chapter does not cover macroscopic analysis as weather impacts cannot be 

effectively analyzed using macroscopic tools. 

Mesoscopic analysis is an emerging method for simulating and studying traffic. As explained in 

the 3.1 overview section, macroscopic simulation is a highly aggregated method for analyzing 

traffic that assumes all vehicles on the roadway have the same characteristics. However, this 

method is not appropriate to predict and understand changes happening at the vehicular level. 

Microscopic analysis looks at every individual vehicle and its unique characteristics. This 

method makes identifying changes and conflicts among vehicles easier; however, it requires a 

great deal of computing power and is most effective in smaller geographic networks. 

One advantage of mesoscopic analysis is the ability to analyze larger geographic areas than 

microscopic analysis while still providing some of the detailed data that macroscopic analysis 

cannot provide. Mesoscopic analysis also allows for the analysis of road segments, multiple 

routes within a network, basic signalized intersections, freeways and ramps. 

The major disadvantage to mesoscopic analysis is the heavy data requirement. Mesoscopic 

analysis requires almost as much data as microscopic simulation and for large geographic 

regions the data requirements are comparable to those of transportation planning studies. 

Another disadvantage to mesoscopic analysis is that some complex traffic features currently 

cannot be simulated well, such as sophisticated traffic signals. 

3.2.1. Mesoscopic Traffic Simulation Model Setup

There are a number of software packages available for mesoscopic traffic modeling. In the 

United States, the more commonly used software are the Traffic Estimation and Prediction 

Systems (TrEPS) tools, formerly known as Dynamic Traffic Assignment (DTA) tools. These 

tools include DynaMIT-P, DynaMIT-X, DYNASMART-P, DYNASMART-X, and DynusT. 

Some other mesoscopic tools are listed below. This TAT module focuses primarily on TrEPS 

tools. 

Mesoscopic Traffic Simulation Software Options 

 CONTRAM (Continuous Traffic Assignment Model) 

http://www.contram.com 

 DYNAMIT-P, DYNAMIT-X, DYNASMART-P, 

DYNASMART-X, DynusT: 

http://www.dynamictrafficassignment.org 

 VISTA (Visual Interactive System for Transport Algorithms) 

http://www.vistatransport.com/ (revision date June 18, 2009) 

(Source: Traffic Analysis Toolbox Volume II – Jeannotte et al., 2004) 

A typical network in any of these programs might look something like Figure 3-1. It clearly 

shows the extent of the geographic area that this model can analyze: an area much larger than a 

typical microscopic simulation would be able to handle. 
12 

Figure 3-1 Sample DynusT Network in Portland, OR (DynusT, 2010) 

The basic principles of traffic simulation vary little from one type of traffic simulation to the 

next. The overall procedure of developing and applying traffic simulation modeling to a traffic 

analysis consists of seven steps, four of which are part of the model set up. A flow chart of the 

model set up for traffic analysis is shown in Figure 3-2. 

Figure 3-2 Flow Chart of Model Set Up 

(Source: Modified from Traffic Analysis Toolbox Volume III - Dowling et al., 2004) 

3.2.1.1. Project Scope

An important first step for any project is assessing its scope. When it comes to traffic modeling, 

thoroughly scoping out the project can be very useful in deciding what traffic analysis tool is best 

for the goals of the project. Projects that require modeling of large geographic areas, faster 

1 Project Scope

- Define project purpose

- Identify influence areas

- Select model

- Estimate staff time

2 Data Collection

- Traffic volumes

- Base maps / inventory

- Field observations

3 Base Model Development

- Input data

- Develop quality assurance

4 Error Checking

- Review inputs

- Review animation
13 

computing times, or networks with several routes for drivers to take should consider using 

mesoscopic modeling; the drawback being a loss in fidelity in the output results. The 

microscopic simulation section further elaborates on this topic. 

3.2.1.2. Data Collection

Setting up the model cannot be done until all of the necessary data are complete. All of the 

model setup information falls into one of three groups: Network, Control, or Movement. 

Network: 

The network data contain all of the links and nodes that geographically build the network. Links 

represent roadways and nodes and points on the map where multiple roads connect. A node 

could be an intersection, a freeway ramp, or simply a point where the road curves to make for a 

more accurate representation of the roadway network. 

Control: 

The control data are needed for intersections where signals or signs are used to govern vehicle 

movements. The data would include information on the location and timing of traffic signals or 

the locations of Stop or Yield signs. It also includes data for ramp metering or variable message 

sign (VMS) information being provided to drivers. 

Movement: 

The movement data are also necessary for intersection control and define how a vehicle moves 

when at an intersection. These data work hand-in-hand with the Control data to accurately move 

vehicles throughout the network. 

3.2.1.3. Base Model Development

To complete this step all of the data that were collected need to be organized and formatted 

correctly into the proper program-specific input data so that the modeling tool will be able to 

read and use the data without problems. Mesoscopic traffic models can be very data intensive 

and require a large number of input data in order to build an entire network. 

3.2.1.4. Error Checking

The primary purpose for Error Checking is to ensure that the model being developed will 

accurately simulate what is occurring or will occur in real networks. Data calibration and 

validation will be discussed in detail in the coming sections. 

3.2.2. Data Preparation

To prepare a mesoscopic simulation model, data are typically needed for estimating supply and 

demand parameters. Data for supply parameters are speed, volume, and density from each 

segment type of the transportation network that is being studied. Data for demand parameters 

(i.e., origin destination demand matrix) are the historical origin-destination (OD) matrix and 

observed counts. 

Conducting data preparation allows for quality assurance in the input data of the study. It is made 

up of review, error checking, and the reduction of the data collected in the field (Dowling et al., 

2004). Data verification and validation should be performed during the data preparation step. 

The following are data verification and validation checks: 
14 

Data Verification and Data Validation 

 Geometric and control data should be reviewed for apparent 

violations of design and/or traffic engineering practices. Sudden 

breaks in geometric continuity (such as a short block of a two-lane 

street sandwiched in between long stretches of a four-lane street) 

may also be worth checking with people who are knowledgeable 

about local conditions. Breaks in continuity and violations of 

design standards may be indicative of data collection errors. 

 Internal consistency of counts should be reviewed. Upstream 

counts should be compared to downstream counts. Unexplained 

large jumps or drops in the counts should be reconciled. 

 Floating car run results should be reviewed for realistic segment 

speeds. 

 Counts of capacity and saturation flow should be compared to the 

HCM estimates for these values. Large differences between field 

measurements and the HCM warrant double checking the field 

measurements and the HCM computations. 

(Source: Traffic Analysis Toolbox Volume III - Dowling et al., 2004) 

3.2.3. Traffic Model Calibration for Normal Conditions

Calibrating the mesoscopic traffic model is based on the same principle as calibration of any 

model. Real data such as vehicle counts, speed studies, and travel time data; the Highway 

Capacity Manual standards; and historic origin and destination demand data are all useful for 

calibrating and validating the model. Figure 3-3 briefly outlines the process of calibrating supply 

and demand data for a mesoscopic simulation. Figure 3-3(a) shows calibration as a three-step 

process: disaggregated level, sub-network level, and system level calibration. The disaggregated 

level calibration deals with calibration over entire segments of the speed-density relationships 

and the capacity. As shown in Figure 3-3(b), the sub-network calibration discusses more specific 

procedures. The sub-network calibration is the process of estimating and calibrating demand on 

the segment level. Finally, system-level calibration is calibrating supply and demand parameters 

on the network scale to ensure that the supply and demand match up with one another. 
15 

(a) Framework for supply and demand calibration 

(b) Subnetwork calibration 

Figure 3-3 Calibration of Both Supply and Demand Data Process Flow Chart (Kunde, 2000) 

Some specific methods for calibrating both the supply and demand parameters are discussed 

further below. 
16 

3.2.3.1. Supply Parameters

Supply parameters are the characteristics of a roadway that describe its ability to move vehicles: 

capacity, speed, flow. Supply parameters are determined by some type of traffic flow model. 

Traffic flow models dictate how vehicles move in general. Individual vehicle movements are not 

simulated in mesoscopic models. Mesoscopic traffic flow models use a macroscopic traffic flow 

model. 

The following model is the traffic flow model used by DynaMIT software to determine the 

supply parameters (Park et al., 2004). This model uses the speed and flow relationship. 

ݍൌݍ௕௢௦, ݑ ൌ ݑ ௙if ௤೚್ೞ

௨೚್ೞ

൑ ݇଴ (3-1)

௦௢௕ݑൌݍ

ە

ۖ

۔

ۖ

ۓ

݇௝௔௠ · ቎1 െ ቆݑ௕௢௦

௙ݑ

ቇ

ଵ

ఈ

቏

ଵ

ఉ

൅ ݇଴

ۙ

ۖ

ۘ

ۖ

ۗ

if ௤೚್ೞ

௨೚್ೞ

൐ ݇଴ (3-2)

Where: 

݋ݍܾݏ ൌ field flow (vph) 

݋ݑܾݏ ൌ field speed (vph) 

ݍ ൌ estimated flow (vph) 

ݑ ൌ estimated speed (mph) 

ݑ݂ ൌ free flow speed (mph) 

݇0 ൌ free flow density (veh/mile/lane) 

݆݇ܽ݉ ൌ jam density (veh/mile/lane) 

ߙ and β=parameters 

This is a two-regime function, which means that free-flow conditions and non-free flow 

conditions are treated separately. In the free flow regime u is the free flow speed while curve 

fitting is done to estimate speed in the second regime. The parameters α, β are determined by 

plotting and examining the data collected. DYNASMART and DynusT use similar traffic flow 

models. 

As noted, to calibrate the supply parameters for normal conditions, speed, flow, and density data 

from a real network during normal conditions need to be collected. The data should then be 

input into one of the model equations shown above. The model equation could be transformed 

into a linear model so that linear regression analysis can be performed and values for traffic flow 

model parameters can be determined (i.e., α and β in speed-flow relationship equation). This 

process can be repeated if necessary to improve the accuracy of the supply parameters. This 

process is well illustrated in the supply parameter calibration procedure. 
17 

Supply Parameter Calibration Procedure 

Step 1 Process observation data 

Step 1.1 Categorize the traffic data (speed and occupancy), for each location. 

Step 1.2 Convert occupancy into density 

Step 1.3 For each location, perform Steps 2 through 5. 

Step 2 Fit the data into a dual-regime model. For initial kbp of 10 vpmpl, do the following: 

Step 2.1 Divide the data set into to subsets based on the initial kbp, that is, the first and 

second regime observations. 

Step 2.2 For the first regime, the free-flow speed, uf , is estimated as the mean of the 

speeds. Root mean squared error for speeds is also calculated. 

Step 2.3 For the second regime, set v0 and kjam based on the observations, that is, the 

minimum speed observed and maximum density observed. 

Step 2.4 Transform the second regime data, speed and density, as follows: 

ܻ ൌ lnሺݒ ௜െ ݒ଴ሻ,ܺ ൌ ln ቆ1 െ ݇଴

݇௝௔௠ቇ

Let ܾ ൌ ln൫ݒ ௙െ ݒ଴൯

Step 2.5 Perform linear regression of the function ܻ ൌ ߙܺ ൅ ܾ to estimate α and b.

Step 2.6 Recover vf from the estimated b, that is ݒ ௙ൌ ݁௕ ൅ ݒ଴

Step 2.7 Calculate R-squared value for the second regime. 

Step 2.8 Calculate difference in estimated speeds at the joint of two regimes by 

comparing uf in the first regime and the modeled speed value at kbp in the second 

regime. 

Step 3 Increase kbp by 1 vpmpl and repeat Step 2.1 to 2.8 until kbp becomes 30 vpmpl. 

Step 4 Find the optimal value of kbp based on Measures of Effectiveness (MOEs) of the fitted 

models for each regime and joint fit observations for the entire models. 

Step 5 Choose the function that best fits the data set for each weather condition. 

(Source: Incorporating Weather Impacts in Traffic Estimation and Prediction Systems, Mahmassani et al., 2009) 

3.2.3.2. Demand Parameters

A 2008 study by Park et al., titled Online Implementation of DynaMIT: A Prototype Traffic 

Estimation and Prediction Program, clearly describes the procedures for calibrating demand 

parameters during normal conditions. Their procedure is summarized in Section 3.2.3. As 

mentioned in the data preparation section, initial origin-destination matrices could be obtained by 

updating historical OD matrices. The update typically implements a gravity model with observed 

traffic counts. Optimization can then be used to check to see that the OD matrices are 

convergent. These new OD matrices are then run through the simulation to replace the original 

OD matrices. The resulting matrices are once again optimized for convergence and run through 

the simulation again. This process can be repeated as many times as necessary until the resulting 

matrices are well calibrated and accurately reflect the real network being simulated. 

3.2.4. Calibration for Weather Impacts

As discussed, understanding the impact of weather on a transportation system could greatly 

improve transportation management, overall mobility, and efficiency. In order to include the 

impacts of weather in a traffic simulation, the supply and/or demand parameters are adjusted to 
18 

better reflect either 1) what the roadway can offer for capacity or travel speed or 2) the number 

of travelers commuting between origins and destinations and via which routes. 

3.2.4.1. Supply Parameters

Determining the supply parameters for traffic analysis during inclement weather is a two step 

process. First, Weather Adjustment Factors (WAFs) must be determined and then they must be 

calibrated. 

Weather Adjustment Factors (WAFs) are used to reduce supply parameters to a level that is more 

appropriate for the inclement weather conditions being observed. The three weather parameters 

used to determine WAFs are visibility, rain intensity, and snow intensity. Visibility is measured 

in miles and both rain and snow intensities are measured in inches per hour. A WAF then needs 

to be calculated for each supply parameter in the traffic flow model for each weather condition. 

For each of the parameters, a weather adjustment factor (WAF) was calculated. WAF is 

calculated as: 

F v r s v r v s i   0  1    2   3    4     5   (3-3) 

Where, 

Fi = WAF for parameter i 

v = visibility (miles) 

r = precipitation intensity of rain 

s = precipitation intensity of snow 

β0, β1, β2, β3, β4, β5 = coefficients 

Under inclement weather, the supply parameters are calculated as follows: 

F  Fo  Fi

' (3-4) 

Where, 

F’ = weather adjusted parameter 

Fo = normal weather parameter 

 Fi = weather adjustment factor 

In order to calibrate the WAFs the following calculation should be done and the results should be 

analyzed using linear regression analysis. 

o

i F

F F

'

 (3-5) 

Where, 

F’ = weather adjusted parameter 

Fo = normal weather parameter 

 Fi = weather adjustment factor 

3.2.4.2. Demand Parameters

The demand parameters are origins, destinations, and demand volumes. If weather causes drivers 

to change their destination, change the time at which they make their trip, or results in a change 

in the number of drivers using a particular route in order to complete their travel, then the 
19 

demand parameters for that particular weather event would be unlike the normal condition. 

Understanding these decisions would require understanding the behavior of every driver on the 

road. For the purposes of calibrating the simulation, historic driver and weather data can be 

paired up and calibrated to generate factors similar to the Weather Adjustment Factors used in 

the supply parameter calibration section. 

One method for determining and calibrating demand parameters is outlined by Samba and Park 

(2009). This study is one of the first attempts to analyze the impact of inclement weather on 

demand parameters. In their study, they proposed a probabilistic approach to determine the 

percent average reduction of traffic demand under rainy and snowy conditions for seven sites 

surrounding major central business districts in Minnesota and Virginia. Factors including timeof-day and varying precipitation intensity were incorporated in the analysis. Weather data for 

Minnesota and Virginia were collected from the National Oceanic and Atmospheric 

Administration hourly precipitation report for the years 2006 and 2007. Traffic data was grouped 

into 1-hour intervals to be consistent with the weather data format. They separated traffic data by 

each month and parsed out weekends, holidays, and any other non-inclement weather days that 

produced atypical Average Daily Traffic (ADT) values or hourly volume curves (e.g., atypical 

ADT resulting from construction). Then a spreadsheet of the inclement weather days was 

developed in the analysis. The non-inclement weather days were studied so that the mean 

volume and standard deviation could be produced for a typical dry day for each month. The 

yielded mean was used as a baseline to compute the percent difference of inclement weather 

volume for each hour. Equation 3-6 presents the percent difference equation that was used in the 

analysis. 

Dry Baseline Average Volume

Inclement Weather Volume Dry Baseline Average Volume Percent Difference (  )  (3-6) 

A 95 percent confidence value can be determined with the use of the yielded standard deviation. 

The 95 percent confidence value is used in analyses to calculate a threshold value that is above 

and below the mean that is expected 95 percent of the time. If the absolute percent difference of 

a precipitation day’s hourly volume exceeds the threshold value then it can be stated with 95 

percent confidence that the volume falls outside the expected range and can be attributed to the 

impact of inclement weather. 

The percent difference can be used to predict the changes to traffic demand that are caused by 

inclement weather conditions. Users can follow the proposed procedure: 

Traffic Volume Reduction Procedure 

Step 1: Determine precipitation impact probability. Depending on the amount of rain or snow, V 

percent can be determined from the maximum statistically significant volume. 

Step 2: Determine a median percent reduction in traffic demand at a given hour PM-HR. The 

median percent reduction can be obtained using Equation 3-2. 

Step 3: Determine traffic volume reduction as: Step 1.1 Categorize the traffic data (speed and 

occupancy), for each location. 

Volume Reduction (Δ) = V percent × PM-HR 

 Where, V percent = Probability of significant volume reduction, and 

 PM-HR = the median percent reduction from the mean at a specific hour 

(Source: Samba and Park, 2009) 
20 

This procedure can be best explained with an example: 

Volume Reduction Example Problem 

Step 1: For light snow event, the “V percent” value is 76 percent. 

Step 2: From using Equation 3-2, the median percent of volume reduction due to snow event 

between 4 pm and 5 pm is 32 percent. 

Step 3: A traffic volume reduction of 24.3 percent as obtained from 76 percent × 32 percent. 

A traffic engineer can use multiple simulation runs to consider day-to-day variability in traffic 

demands. Let’s assume a total 50 replications will be made. As seen above, Step 1 determined V 

percent of 76 percent. Thus, traffic volume will significantly change with a 76 percent probability. 

Consequently, 38 cases out of 50 total runs will have reduced traffic demand volumes. 

Furthermore, Step 2 shows 32 percent reduction in traffic demand. Thus, the 38 cases will be 

evaluated with the 32 percent reduced traffic demand, while the remaining 12 runs will be made 

with the original traffic demand. Inclement weather impact can be estimated from these 50 runs 

using a distribution of selected measures of effectiveness (e.g., average speed or average travel 

time). 

(Source: Samba and Park, 2009) 

3.2.5. Performance Measures

To ensure that the model is working properly and providing reliable results, it needs to be 

validated against real data from the network being modeled. This is done by comparing the 

mesoscopic model outputs and field measurements. The following parameters can be established 

as performance measures for mesoscopic traffic analyses: 

 Travel Time; 

 Speed; 

 Delay; 

 Queue; 

 Stops; and 

 Density. 

It is important that a performance measure be appropriate (i.e., it can provide an adequate 

representation of at least one objective established in the analysis) and measurable. 

3.2.6. Weather Model Implementation and Analysis

This section presents mesoscopic model implementation and analysis under inclement weather 

conditions. 

3.2.6.1. Supply Parameters Calibration  

The two methods for determining and calibrating supply parameters are (1) obtaining inclement 

weather data and analyzing it by fitting it to the traffic flow models or (2) applying Weather 

Adjustment Factors (WAFs). 

The direct curve fitting method estimates parameters of a traffic flow model directly using flow 

and speed data observed during inclement weather conditions. Figure 3-4 shows the steps for this 

process. First the data needs to be sorted by weather condition. A speed-flow curve can be 

plotted and the traffic flow model parameters can be determined. It is noted that Kjam represents 

jam density and alpha and beta are calibration parameters of the mesoscopic traffic model. 
21 

Figure 3-4 Supply Parameter Calibration Procedure 

(Source: Modified from Field Evaluation of DynaMIT in Hampton Roads. Park et al., 2004) 

Table 3-1 shows the supply parameters for inclement weather conditions that were estimated for 

the DynaMIT program using Hampton Roads data by following the supply parameter calibration 

procedure shown in Figure 3-4. 
22 

Table 3-1 Calibrated Supply Parameters 

Segment Type vf (mph) kjam

(vplvm)   Capacity (vphpl) k0 (v/lm) 

1 lane basic freeway 61 160 5.21 1.68 2000 15 

3 and 3+ lane basic 

freeway

66 160 5.33 1.65 2100 15 

2 lane merging area 61 160 4.91 1.68 2150 15 

3 and 3+ lane merging area 64 160 5.16 1.71 2100 15 

2 lane diverging area 59 160 11.17 2.20 2000 15 

3 and 3+ lane diverging 

area (I-64 and I-564) 61 160 11.18 2.23 2100 15 

3 and 3+ lane diverging 

area (I-264) 56 160 5.16 1.85 2100 15 

Weaving area (I-64 and 

I-564) 61 160 9.62 2.05 2000 15 

Weaving area (I-264) 56 160 8.52 2.07 1900 15 

Ramps 46 150 1.82 1.52 1900 10 

The WAF method can be used to calibrate the supply parameter for inclement weather 

conditions. The method is already discussed under Section 3.2.4. The WAF method is a 

regression model capturing impacts of different weather conditions (e.g., normal, light rain, 

moderate rain, and light snow). The regression model is developed on the basis of speed and 

density relationship functions estimated for various weather conditions. Table 3-2 presents 

calibrated coefficients of the weather adjustment factors. 
23 

Table 3-2 Weather Adjustment Factor Coefficients 

Input 

Data Traffic Properties β0 β1 β2 β3 β4 β5 

Traffic 

Flow 

Model 

1. Speed-intercept, (mph) 0.91 0.009 -0.404 -1.455 0 0 

2. Minimal speed, (mph) 1 0 0 0 0 0 

3. Density break point, 

(pcpmpl) 0.83 0.017 -0.555 -3.785 0 0 

4. Jam density, (pcpmpl) 1 0 0 0 0 0 

5. Shape term alpha 1 0 0 0 0 0 

Link 

6. Maximum service flow 

rate, (pcphpl or vphpl) 0.85 0.015 -0.505 -3.932 0 0 

7. Saturation flow rate, 

(vphpl) 0.91 0.009 -0.404 -1.455 0 0 

8. Posted speed limit 

adjustment margin, (mph) 0.91 0.009 -0.404 -1.455 0 0 

Left-Turn 

Capacity 

9. g/c ratio 0.91 0.009 -0.404 -1.455 0 0 

2-way 

Stop Sign 

Capacity 

10. Saturation flow rate for 

left-turn vehicles 0.91 0.009 -0.404 -1.455 0 0 

11. Saturation flow rate for 

through vehicles 0.91 0.009 -0.404 -1.455 0 0 

12. Saturation flow rate for 

right-turn vehicles 0.91 0.009 -0.404 -1.455 0 0 

4-way 

Stop Sign 

Capacity 

13. Discharge rate for leftturn vehicles 0.91 0.009 -0.404 -1.455 0 0 

14. Discharge rate for 

through vehicles 0.91 0.009 -0.404 -1.455 0 0 

15. Discharge rate for rightturn vehicles 0.91 0.009 -0.404 -1.455 0 0 

Yield Sign 

Capacity 

16. Saturation flow rate for 

left-turn vehicles 0.91 0.009 -0.404 -1.455 0 0 

17. Saturation flow rate for 

through vehicles 0.91 0.009 -0.404 -1.455 0 0 

18. Saturation flow rate for 

right-turn vehicles 0.91 0.009 -0.404 -1.455 0 0 

(Source: Incorporating Adverse Weather Impacts in Dynamic Traffic Simulation-Assignment Models: Methodology and 

Application, Dong et al., 2010) 

The results of implementing weather adjustment factor were illustrated by Dong et al. (2010) 

when they simulated a stretch the of I-95 corridor and nearby arterial networks in Maryland from 

Washington D.C. to Baltimore. The three scenarios simulated were a clear and normal weather 

day, a moderate rain event, and a heavy rain event. Moderate rain is defined as visibility of 1 

mile and rain intensity of 0.2 inch/hour, while heavy rain is defined as visibility of 0.5 miles and 

rain intensity of 0.5 inch/hour. Supply parameters for these weather conditions were adjusted 

using WAFs from Table 3-3. As shown in Figure 3-5, the time-varying network travel times 

became longer as weather conditions became worse. This is due to rain impacts on reduced 

capacity and saturation flow rates. 
24 

Figure 3-5 Travel Time Comparisons under Three Weather Conditions 

(Source: Incorporating Adverse Weather Impacts in Dynamic Traffic Simulation-Assignment 

Models: Methodology and Application, Dong et al., 2010) 

3.2.6.2. Demand Calibration  

The estimation of demand parameters can be approached two ways: (1) by adjusting demand 

“on-the-fly” or (2) by developing a mathematical representation of how demand may change. 

The first method calibrates demand (i.e., OD matrix) by reactively updating OD demand based 

on real-time sensor counts during inclement weather conditions. This makes sense because 

studies have shown few changes during morning peak hours but some reductions during nonpeak hours under inclement weather conditions. An example of the real-time operation of the 

DynaMIT program’s application is in the Hampton Roads area of Virginia (Park et al., 2004). 

While the real-time implementation was not conducted during inclement weather conditions, 

dynamic OD estimation was implemented based on real-time sensor counts. Thus, demand (i.e., 

the OD matrix) was updated using observed traffic counts to reflect changes in network travel 

behaviors, including weather, recurrent, and non-recurrent events. 

The second method is less clear because there is no one model available that perfectly describes 

the factors that determine whether or not a person will decide to make a trip during various 

weather conditions. It is not clear what impact weather has on the driver’s decision to make a 

trip. This method uses probabilistic demand adjustment factor based on empirical data observed 

during inclement weather conditions. The method is already described earlier in Section 3.2.4.2 

Samba and Park (2009) applied this method using traffic and weather data from several locations 

in Virginia and Minnesota. The resulting probabilities of significant volume change under 

inclement weather as well as averages and ranges of volume changes are shown in Table 3-3. 

Note that the probability of significant volume change is determined when volume reduction is 

bigger than normal traffic volume variations at a given location. 
25 

Table 3-3 Demand Adjustment due to Inclement Weather Conditions 

Weather Condition Probability of 

Weather Impacted 

Volumes 

Volume Reduction 

Change 

Average Volume 

Reduction 

Light Rain 16.6% 1.0% to 6.3% 2.32% 

Heavy Rain 31.3% 3.1% to 4.4% 3.75% 

Light Snow 76.0% 10.6% to 56.2% 28.80% 

Heavy Snow 42.9% 4.7% to 30.4% 13.30% 

(Source: Probabilistic Modeling of Inclement Weather Impacts on Traffic Volume, Samba and Park, 2009) 

It should be noted that these estimated demand adjustment values may not be suitable for traffic 

operators conducting their own analyses because these values correlate to a specific study area 

(i.e., using these demand adjustment values will not adequately reflect the change in demand due 

to inclement weather). Traffic operators could follow the procedure presented in Section 3.2.4.2 

to obtain their own inclement weather-based regionalized demand adjustment values. 

3.2.6.3. Implementation and Analysis  

A mesoscopic model under inclement weather conditions has not been implemented in the real 

world. Instead, a simulation study was implemented to demonstrate the impact of weather and 

control strategies (Mahmassani et al., 2009). 

In the study, the calibrated supply parameters were input to the I-95 corridor and adjacent arterial 

networks. The supply parameters were further adjusted for inclement weather conditions 

including a rain event and a rain plus VMS event. Thus, three scenarios, including normal 

condition, are (1) a clear and normal day, (2) a rain event, and (3) rain plus VMS event. The 

third event includes driver information to help change the route. 

In the implementation, Dong et al. (2010) assumed the following weather event, in Figure 3-6, 

during the morning peak hours. 

Figure 3-6 Weather Events during Peak Hour 

(Source: Incorporating Adverse Weather Impacts in Dynamic Traffic Simulation-Assignment 

Models: Methodology and Application, Dong et al., 2010) 
26 

The results of these three simulations are seen in Figure 3-7. It is very clear that the rain scenario 

had the worst performance, as indicated by the low travel speeds. The rain with VMS case did 

not perform as well as the clear day base case, but did come close to the same speed at some 

points in the simulation. 

Figure 3-7 Link Speed Comparisons 

(Source: Incorporating Adverse Weather Impacts in Dynamic Traffic Simulation-Assignment 

Models: Methodology and Application, Dong et al., 2010) 

Similar studies could be performed for other weather responsive transportation management 

strategies that were listed previously to determine what impacts those measures have on traffic 

flow. Additionally, because every weather event and every roadway is unique, these simulations 

could be run during an event to help determine what actions should be taken at that time for that 

specific event. 

3.3. Microscopic Analysis

Of the three types of analysis discussed in this module, microscopic analysis, based on individual 

vehicle movement, is the finest representation of the transportation system. Microscopic 

simulation models simulate the movement of individual vehicles, which can be done by using 

car-following models, lane-changing models, and gap acceptance models. Utilizing microscopic 

simulation, users can input detailed traffic data into the analysis, thereby creating an opportunity 

to incorporate diversity in vehicles and driver characteristics, enabling accurate simulation of 

real-world traffic. 

3.3.1. Microscopic Traffic Simulation Model Set Up

The overall procedure of developing and applying microscopic traffic simulation modeling to a 

traffic analysis consists of seven steps, four of which are part of the model set up. FHWA 

constructed a flow chart of the model set up for microscopic analysis, which is depicted in Figure 

3-1 in Section 3.2.1. 
27 

3.3.1.1. Project Scope

The project scope consists of five tasks: 1) Define project purpose, 2) Identify influence areas, 3) 

Select approach, 4) Select model, and 5) Estimate staff time (Holm et al., 2007). It is appropriate 

to present questions in this section that would help identify the study breadth – influence areas. 

Questions that can be asked pertaining to inclement weather are: 

 How large is the study area that is being analyzed for inclement weather impacts? 

 What weather-related resources are available to the analyst? 

 What measure of effectiveness (MOEs) will be required to analyze the inclement weather 

impacts? 

The criteria used for selecting the analytical tool are tied to the analytical approach. FHWA 

states that key criteria for choosing a modeling tool include technical capabilities, input/output 

interfaces, user training/support, and ongoing software enhancements (Dowling et al., 2004). To 

gain accuracy in simulating driver behavior during inclement weather conditions, it is necessary 

to choose a microscopic modeling tool such as CORSIM, VISSIM, PARAMICS, 

INTEGRATION and AIMSUN2. 

The final task within the project scope is establishing the staff time for the project. Performing 

this function provides certainty that the project can be completed with the chosen approach in the 

allotted time. Creating a schedule for key project milestones is one approach to accomplishing 

this task. 

3.3.1.2. Data Collection

Required input data for microscopic simulations vary based on the analytical tool and modeling 

application. In most microscopic modeling applications, required input data include the 

following: 

 Road geometry (lanes, lengths, curvature); 

 Traffic controls (signs, signal timing); 

 Demands (entry volumes, turning volumes, O-D table); and 

 Calibration data (traffic counts and performance data such as speed, queues). 

For many transportation microscopic analyses, detailed data on vehicle and driver characteristics 

(e.g., vehicle length and driver aggressiveness) also need to be included in modeling 

applications. Since field data of such caliber are difficult to collect, they are established as 

default values in most microscopic traffic analytical tools. 

When possible, data collection should be managed in such a manner that there is consistency in 

the datasets (Road and Traffic Authority, 2009). For instance, travel time and queue length 

should be recorded at the same time period as traffic count data. 

3.3.1.3. Base Model Development

Once the required data is collected, one can proceed to develop a microscopic simulation model. 

A successful model entails the development of a model blueprint (the link-node diagram). The 

link-node diagram is a visual representation of the study area in terms of links and nodes. It can 

be created within the microscopic simulation tool or it can be created by other tools such as CAD 

programs. Once the blueprint is established, the model can be built in the following sequence: 

coding links and nodes, establishing link geometries, adding traffic control data at appropriate 
28 

nodes, coding travel demand data, adding driver behavior data, and selecting control parameters 

that will be used to run the model (Dowling et al., 2004). 

3.3.1.4. Error Checking

Before any model runs are made it is both essential and beneficial to perform error checking so 

that the calibration process does not output distorted results. Calibrating model parameters relies 

on the assurance that major errors in demand and network coding are found and removed before 

proceeding in the model set up (Dowling et al., 2004). Error checking is carried out in three 

primary stages: 1) software error checking, 2) input coding error checking, and 3) animation 

review to find obscure input errors (Holm et al., 2007). 

3.3.2. Data Preparation

Conducting data preparation allows for quality assurance in the input data of the study. It is made 

up of review, error checking, and the reduction of the data collected in the field. The main 

purpose is to check for any discrepancies in the data (e.g., breaks in geometric continuity, 

unexplained large gains or losses in traffic volume, and unrealistic speeds for roadway segments) 

before proceeding with the analysis. Failure to do so can result in false outputs generated in the 

model. Please refer to FHWA’s Traffic Analysis Toolbox Volume III for detailed data verification 

and validation checks. 

3.3.3. Traffic Model Calibration for Normal Conditions

Traffic model calibration is the process of fine-tuning the data inputs that represent 

characteristics of the vehicle and driver. This is executed by comparing and adjusting absolute 

measures (clear, definitive, and measurable parameters) such as flow rate and mean speed. 

Figure 3-8 presents a flow chart of the overall calibration and validation process for microscopic 

traffic simulation modeling. 
29 

Figure 3-8 Calibration and Validation Process Flow Chart 

(Source: Park and Won, 2006) 

3.3.3.1. Simulation Model Set Up

The first part of the calibration and validation process involves the set up of the simulation 

model. It involves tasks identical to those in the microscopic traffic simulation set up previously 

discussed. Please refer to that section for the model set up procedure. 

3.3.3.2. Initial Evaluation

The default parameter set (i.e., uncalibrated parameters) is the focus of this stage of the 

calibration process. A feasibility test is required at this stage. This is a test that is conducted to 

ensure that the field data is well represented by the distribution of the simulation results. If the 

default parameter set produces acceptable results (results that accurately reproduce field 

conditions), the calibration and validation procedure may be skipped and further analysis can be 

conducted with the default parameter set. 
30 

Two steps are involved in the feasibility test. First, the user needs to perform multiple runs of the 

simulation model using the default parameter set. Then consecutive comparisons to calibration 

data should be made. Figure 3-9 presents the initial evaluation process. 

Figure 3-9 Initial Evaluation Process 

(Source: Park and Won, 2006) 

Multiple runs should be performed because simulation does not output the exact same results for 

each run. The randomly generated seed number is the cause of simulation resulting in similar but 

not exact outputs. It is important to include the randomly generated seed number in microscopic 

simulation models because its purpose is to be the decision-maker. It decides the speeds that 

vehicles are traveling at, the type of vehicles that are included in the simulation, and the paths 

that the vehicles will take. Without the randomly generated seed number, the simulation loses the 

stochastic nature of real world conditions. 

Conducting multiple runs is a must, but how many runs should there be? Too many runs are not 

necessarily bad, but too many runs means time lost in performing additional simulation runs. Too 

few runs raise questions as to whether field conditions are well represented in the simulation runs 

that have been conducted. The user can perform the following procedure to estimate the 

minimum number of simulation runs: 

1. Execute a few simulation repetitions; 

2. Estimate the sample standard deviation; 

3. Select an appropriate confidence level; and 

4. Calculate minimum number of simulation runs. 

Typically, the user should perform four simulation runs in the first step of the procedure and then 

analyze the calibration data from these runs. This is done so that the user can get a feel for what 

the distribution of simulation results will be and can make an educated guess as to how many 

simulation runs are required for the analysis. 

The user can estimate the sample standard deviation using the calibration data from Step 1 and 

the following equation: 

ܵଶ ൌ ∑ሺݔെݔҧሻଶ

ܰെ1 (3-7) 

Where, 

 x = output value for each simulation repetition 

ݔҧ = average value of all simulation repetitions 

 N = number of simulation repetitions 

 S = standard deviation 

One can get a sense of the difference between a set of data and the average value by calculating 

the sample standard deviation. 
31 

Typically, a 95 percent confidence level is used in model simulation. Performing this task allows 

the user to decide the accuracy of results. What can be said about a 95 percent confidence level is 

that the user is 95 percent confident that field conditions are within the range set by the 

simulation outcomes for each repetition. 

In finding the minimum number of simulation repetitions, the user can use Table 3-4, which 

consists of confidence levels ranging from 90 percent to 99 percent and the minimum number of 

repetitions corresponding to those confidence levels. 

Table 3-4 Minimum Number of Simulation Repetitions 

C1-/S Selected Confidence Level Minimum Number of Repetitions 

0.5 

99% 130 

95% 83 

90% 64 

1.0 

99% 36 

95% 23 

90% 18 

1.5 

99% 18 

95% 12 

90% 9 

2.0 

99% 12 

95% 8 

90% 6 

(Source: Park and Won, 2006) 

To use Table 3-4, a user must calculate the ratio of confidence level to standard deviation, C1-α/S. 

α is the selected confidence level and C = 1 – α. If the confidence level is 95 percent then C 

equals 0.05. 

So a user now has an estimated the minimum number of simulation runs that are required in the 

analysis and has conducted them. Now, the user has to determine the validity of the default 

parameter set. This can be done with a histogram or X-Y plot. Both are visual aids to the user to 

determine the worth of the default parameter set. 

The histogram shows the frequency of data points and presents the modeler to view the 

distribution of simulation output data. For a feasible default parameter set, the field data must fall 

within the distribution set by the simulation output data. 

Unlike a histogram, the frequency of a particular value is not required to create an X-Y plot. The 

level or location of each simulation output data is what is required. On an X-Y plot, each data 

point shows the location of where two variables, in this case two performance measures, 

intersect. An example showing how a user can determine the feasibility of the default parameter 

set may be helpful here. Let’s assume that the field-collected data for performance measures 1 

and 2 range from 62 to 86 and 8.3 to 15.2, which is outlined in the dark-shaded box seen in 

Figure 3-10. 
32 

Figure 3-10 Example of X-Y plot with Acceptable Results 

(Source: Park and Won, 2006) 

From Figure 3-10, the light-shaded box represents the 90 percent confidence interval region of 

simulation output data. If the two boxes overlap one another, then it can be concluded that the 

default parameter set is feasible. However, if they do not overlap then the default parameter set is 

unfeasible. 

3.3.3.3. Initial Calibration

Initial calibration consists of three steps: 1) identifying calibration parameters, 2) sampling 

different cases within a determined range, and 3) verifying whether the determined ranges are 

appropriate. Calibration parameters vary depending on the modeling applications. 

The calibration parameters are values that the user places in the simulation model so that the 

simulation conditions accurately represent field conditions. The calibration parameters vary 

based on the simulation model. In CORSIM, calibration parameters range from mean value of 

lost start-up time to minimum deceleration for lane change to desired free flow speed. 

Once the calibration parameters are identified, a user should begin sampling from the 

combinations of a parameter set. The combinations of the parameters can be quite large, to the 

point where they exceed millions. If this is the case, then it would be unpractical to sample these 

combinations because such a process would last years. To avoid this obstacle, a user can use an 

algorithm called Latin Hypercube Design (LHD) which reduces the number of combinations that 

need to be analyzed while still maximizing the parameter cover. 

Like the previous step of determining the feasibility of the default parameter set, a user needs to 

conduct multiple runs using the selected parameter sets. Doing so will allow the user to simulate 

the stochastic nature of field conditions. After conducting multiple runs, the user needs to 

determine if the parameter sets provide acceptable ranges. Procedures used to validate the default 

parameter set can be used here. Whichever procedure is used, histogram or X-Y plot, the field 

conditions must fall within the 90 percent confidence level in order for the parameter set range to 

be considered acceptable. 
33 

3.3.3.4. Feasibility Test and Adjustment

If the parameter set range from the initial calibration was determined unacceptable (field 

measures did not fall within the 90 percent distribution of the range), then conducting a 

feasibility test is necessary in order to find an appropriate parameter set range to use in the main 

calibration process. This can be conducted by two methods: X-Y plots or a statistical method 

known as Analysis of Variance (ANOVA). These two methods allow the user to identify the key 

calibration parameters. When using the X-Y plots, the user should look for a relationship that 

exists between the calibration parameter and the measure that goes along with it. If the data 

points on the plot show a relationship, then that X-Y plot is one of a key parameter. If the data 

points are scattered and a relationship cannot be made, then the calibration parameter is not be 

considered a key parameter. Like the X-Y plots, the user should look for a relationship among 

the calibration parameter and the corresponding measure when using ANOVA. This method 

provides statistical outputs such as Sums of Squares, F-statistic, and P-value. For the purposes of 

identifying key parameters, the p-value is the most important statistic in ANOVA. If the p-value 

of a parameter is less than the confidence interval, then that parameter is a key calibration 

parameter. 

Once the key parameters are identified, the user can adjust the calibration parameter ranges so 

that they reflect field conditions. This can be done by merely shifting the parameter range of the 

X-Y plot used for the key parameter identification step. Remember to shift the parameter range 

so that data from the field is well represented. A feasibility test needs to be conducted again once 

the parameter ranges are adjusted. 

3.3.3.5. Parameter Calibration

Once an acceptable parameter range is determined, the next step is to select a parameter set that 

best represents data collected from the field. An optimization method, such as the Genetic 

Algorithm (GA), is used to complete this task. The GA uses a specific number of digits, known 

as the chromosome. These digits, which are generated at random, correspond to calibration 

parameter values. When the digits are generated, the parameter values are generated as well. A 

randomized simulation run can be completed afterwards. 

3.3.3.6. Evaluation of Parameter Set

For this step, the user should make an assessment of the performance of the calibrated parameter 

when it is set to another parameter set. This allows the user to confirm that the calibrated 

parameter will output more accurate results than the other parameter set. Comparing the default 

parameter set to the calibrated parameter set could be completed in the evaluation step. As 

conducted in previous steps, the user should conduct multiples runs for each model using a 

different parameter set and check the feasibility for each model using a histogram. 

3.3.3.7. Model Validation

Validation of the untried data is the last step of the model calibration and validation procedure. It 

is imperative to conduct this step because, if successful, it will show that the calibrated parameter 

set is versatile. In the validation process, the model needs to be tested with the untried data. 

Multiple runs are conducted with the random seeded numbers and the calibrated parameter set. 

The user needs to create a histogram of the simulation output data so that there is a visual 

representation of it. The field data must fall within the 90 percent confidence interval region 

(acceptable region) in order to validate the fine-tune process of calibration applied to the 

parameter set. Figure 3-11 shows the histogram format used for model validation. 
34 

Figure 3-11 Concept of Acceptable Region 

(Source: Park and Won, 2006) 

For additional information on the calibration and validation procedure, please refer to 

Microscopic Simulation Model Calibration and Validation Handbook (Park and Won, 2006) and 

Traffic Analysis Toolbox Volume IV: Guidelines for Applying CORSIM Microsimulation 

Modeling Software (Holm et al., 2007). 

3.3.4. Calibration for Weather Impacts

Calibration for weather impacts is no easy feat but it is possible to do in microscopic traffic 

simulation modeling. This section discusses the methods that others have used to calibrate supply 

and demand parameters for inclement weather in existing microscopic traffic simulation models. 

3.3.4.1. Supply Parameters

Supply parameters are defined as the conditions that the roadway can offer to drivers such as 

roadway capacity. This parameter has been the focus of several past and recent studies on the 

effect of inclement weather on the transportation system. 

Based on the study conducted by FHWA (2009), it was recommended that longitudinal models 

(i.e., car-following, deceleration, and acceleration models) and other models be used to reflect 

field conditions during inclement weather. To calibrate for weather impacts in microscopic 

traffic analyses, weather conditions are used as the basis for the adjustment of macroscopic 

traffic stream parameters. These weather conditions are represented by Weather Adjustment 

Factors (WAFs). The macroscopic traffic stream parameters that are adjusted by these WAFs 

include the following: free-flow speed (uf), speed at capacity (uc), and saturation flow at 

capacity (qc). Weather Adjustment Factors are a function of precipitation type, intensity level, 

and visibility level as seen in Equation 3-8: 

 (3-8) ݒ݅ · ଺ߙ ൅ ଶݒ · ହߙ൅ݒ· ସߙ ൅ ଶ݅ · ଷߙ൅݅· ଶߙ ൅ ଵߙ ൌ ܨܣܹ

Where, 

i = Precipitation intensity (cm/h) 

v = Visibility level (km) 

iv = Interaction between precipitation and visibility 

1, 2, 3, 4, 5, 6 = Calibrated model parameters 

A user can determine the microscopic traffic simulation parameters after estimating the 

macroscopic traffic stream parameters that were adjusted using WAFs. 

The calibration of traffic stream of longitudinal motion can be accomplished in the microscopic 

simulation analysis through steady-state modeling and nonsteady-state modeling. Steady-state 
35 

(stationary) conditions occur when the traffic remains relatively constant over a short period of 

time and distance. For nonsteady-state behavior, analysis involves the movement of vehicles 

from one state to another. When calibrating for nonsteady-state behavior, the user can simulate 

capacity reduction, which is a result of traffic conditions such as congestion and capacity loss 

during lost start-up time. Calibration of steady-state and nonsteady-state behaviors can be 

performed with the use of car-following models. 

Car-following models explain the behavior of drivers in vehicles that follow the lead vehicle. 

Currently, several car-following models exist in various microsimulation software packages. 

These include: 

 The Pitt model (CORSIM); 

 The Wiedemann74 and 99 models (VISSIM); 

 The Gipps’ model (AIMSUN2); 

 The Fritzsche’s model (PARAMICS); and 

 The Van Aerde model (INTEGRATION). 

One can calibrate car-following models with the use of the adjusted macroscopic traffic stream 

parameters: free-flow speed (uf), speed at capacity (uc), saturation flow at capacity (qc), and jam 

density (kj). Remember, these macroscopic traffic stream parameters are influenced by 

inclement weather. So using these parameters will allow users to calibrate the car-following 

models under inclement weather conditions. 

The calibration procedure for steady-state behavior in these car-following models is as follows 

(Hranac et al., 2006): 

1. Define the functional form to be calibrated; 

2. Identify the dependent and the independent variables; 

3. Define the optimum set of parameters; and 

4. Develop an optimization technique to compute the set of parameter values. 

Additional methods of calibrating for weather impacts exist. These methods include: deceleration 

modeling, acceleration modeling, gap acceptance modeling, and lane-changing modeling. For 

more information on the feasibility of incorporating weather-related factors into these analyses, 

please refer to the FHWA report (2009) Microscopic Analysis of Traffic Flow in Inclement 

Weather at: http://ntl.bts.gov/lib/32000/32500/32539/tfiw_final.pdf. 

3.3.4.2. Demand Parameters

Very few studies have been successful in modeling the impacts of inclement weather on demand 

(e.g., traffic volume) because of the random nature of driver behavior. Studies that analyzed the 

impact of inclement weather on traffic conditions typically assumed that demand remains at 

volumes observed under normal, dry conditions. This approach quantifies the effect of inclement 

weather, but the accuracy of traffic demand behavior suffers. 

A probabilistic approach, as discussed in Section 3.2.4.2, can be used to determine the percent of 

average reduction in traffic demand under inclement weather, such as rainy and snowy 

conditions. Please refer to Section 3.2.4.2 to obtain the procedure that was used to calibrate 

demand parameters for inclement weather. 
36 

3.3.4.3. Driver Behavior Parameters

Driver behaviors influence the supply and demand parameters. For instance, a driver who 

decides not to travel affects traffic demand, and a driver who slows during inclement weather 

conditions will have lower speed, longer saturation headway, and lost startup time. Saturation 

headway and lost startup time are considered driver behavior parameters because they are 

developed in response to supply and demand traffic parameters. Calibrating for these two 

parameters can be accomplished, but the procedure will differ based on the analysis. Users can 

obtain such driver behavior parameters (e.g., saturation headway and/or lost startup time) from 

the field during inclement weather conditions. 

3.3.5. Performance Measures

The purpose of conducting simulation models is to recreate field conditions and to evaluate the 

impacts of untried strategies over the base case. In order to adequately assess the performance of 

a transportation facility, a user should select performance measures for their analyses. These 

measures give insight into the performance of a project’s traffic operations objectives. The 

performances measures used in mesoscopic traffic analyses (see Section 3.2.5) can also be used 

in microscopic traffic analyses. Such performance measures include travel time, speed, delay, 

queue, stops, and density. The user may also establish a reliability measure (e.g., travel time 

variance) as a performance measure for microscopic traffic analyses. Travel time variance would 

be easier to obtain for microscopic traffic analysis rather than mesoscopic analysis because 

microscopic traffic analysis has the capability to model individual movement of vehicles. 

When choosing performance measures they should, of course, be measureable, and have some 

relation to the objective of the project. 

3.3.6. Model Implementation and Analysis

Performing model implementation involves model development. This includes: model setup, 

data preparation, and calibration and validation. Please refer back to the procedure for simulation 

model setup discussed in Section 3.3.1. 

Because the focus of this discussion is on weather model implementation, it is imperative to 

discuss the procedures that others have adopted to incorporate the effects of inclement weather in 

their analyses. Since a universally “correct” method of modeling to determine inclement weather 

impacts is nonexistent, procedures will vary. 

3.3.6.1. Data Preparation

Modeling the impact of inclement weather in traffic analyses is a complex task. Therefore, users 

should reduce data to prevent increasing the complexity of the analysis. This can be 

accomplished by simply setting limitations on what will be analyzed, such as restricting the 

number of lanes or datasets in the analysis. Use the objectives and goals of the analysis to decide 

which values are important and should remain in the analysis 

3.3.6.2. Calibration

To calibrate for weather impacts, a user can use macroscopic traffic stream parameters that have 

been adjusted to reflect inclement weather conditions in microscopic simulation modeling. 

Weather Adjustment Factors (WAFs) can be used to adjust the key macroscopic traffic stream 

parameters: free-flow speed, speed at capacity, and saturation flow at capacity. Please refer to 

Section 3.3.4 for more information on the calibration for weather impacts. 
37 

3.3.6.3. Model Implementation of Weather Impacts

Studies that have attempted to model the impact of inclement weather on the transportation 

system do not all use the same approach or method for model implementation. Some studies 

incorporate weather impacts by making assumptions as to how traffic conditions react to weather 

while other studies use actual field observations. 

This section presents a model implementation of weather impacts by using updated microscopic 

simulation parameters reflecting traffic conditions under inclement weather conditions. These 

parameters include: 

 Free-flow speeds on roadways (mph); 

 Maximum acceleration and deceleration rates (ft/s2); 

 Gap acceptance for car-following, lane changing, and turning(s); 

 Queue discharge headways at intersection(s); and 

 Lost startup time at intersection(s). 

For example, Lieu and Lin (2004) modified these microscopic traffic parameters by assuming 

reductions/augmentations to account for inclement weather conditions. The weather case, which 

consisted of wet and slushy weather/road surface conditions, had free-flow speed reduced by 20 

percent and had queue discharge headway and lost startup time increased by 20 percent from 

those of the base condition. Three scenarios were analyzed in the study: 1) base case, 2) weather 

case, 3) weather case with signal retiming. After one hour of simulation, it was seen that speed 

reduced significantly in the weather base case. If average speed was 25 mph for the major-street 

demand of 1,450 vph in the base case then the average speed was 16 mph for a reduced majorstreet demand of 1,230 vph. Figure 3-12 shows the relationship of traffic speed and demand 

volume for 1 hour of simulation for the “weather without signal retiming” case and the “weather 

with signal retiming” case. 

Figure 3-12 Relationship between Speed and Volume for Base and Weather Cases 

(Source: Lieu and Lin, 2004) 
38 

Based on the analysis, implementing signal retiming specifically for inclement weather 

conditions improve traffic conditions if demand volumes on the major street are between 1100 

vph and 1700 vph. As seen from Figure 3-13, demand volume of 1,230 vph coincides with a 

travel speed of 19 mph when signal retiming is put into effect for inclement weather conditions. 

For this demand volume, travel speed is 16 mph when signal retiming is not implemented. 

A user can develop optimal signal timing plans for inclement weather conditions and evaluate 

those plans using microscopic models that reflect inclement weather conditions. For more 

information on this method please refer to Case Study 2. 

 
39 

4.0 Data Sources for Weather and Traffic Analysis

Using proper data sources can generate accurate and reliable results from a traffic analysis. For 

traffic analyses that focus on the impact of inclement weather conditions on the transportation 

system, weather and traffic data sources are required to complete the analysis. This section 

discusses the weather data sources, traffic data sources and Intelligent Transportation Systems 

(ITS) data sources available for weather-related traffic analysis. 

4.1. Weather Data Sources

When conducting studies on the impact of inclement weather on traffic operations, collecting the 

proper weather-related data is essential. The most common weather-related data that is used in 

the evaluation of weather impacts on transportation are precipitation type, precipitation intensity, 

and visibility level. Weather Adjustment Factors (WAFs) are a function of these weather-related 

parameters. Agencies can use these WAFs within the weather module of DYNASMART-P and 

within microscopic modeling tools to assess the weather impacts on transportation. The 

advancement in technology has allowed weather-related data, necessary for impact assessment, 

to be available on the Web. This section presents examples of existing weather data sources that 

can be accessed online. 

Clarus is an excellent source for accessing weather-related data for use by the transportation 

system. Clarus is designed to display current and forecasted weather-related data for a particular 

region Sensors that are used to collect traffic data vary among the different State DOTs. 

Therefore, the quality and format of the traffic data varies as well. Fortunately, Clarus provides 

users with data that share a common format. Convenience is a key characteristic of Clarus. 

Current users can access weather-related data for 38 States in the United States and 3 provinces 

in Canada, as shown in Figure 4-1. 

Figure 4-1 Snapshot of Clarus Portal 

(Source: http://www.clarus-system.com/) 

Users can obtain detailed weather data such as air temperature, average wind direction, average 

wind speed, precipitation type, and precipitation rate from Clarus in either metric or English 

units. Incorporating the weather-related data available at Clarus can make data collection less 

time consuming and less costly as well. 
40 

In addition to Clarus, weather data is available online at the National Climatic Data Center 

(NCDC) website. This organization offers climate and weather information including 

temperature, precipitation, extreme weather events (e.g., hurricanes and tornados), and snow 

extremes. A snapshot of NCDC’s webpage is shown in Figure 4-2. 

Figure 4-2 National Climatic Data Center Website 

(Source: http://www.ncdc.noaa.gov/oa/climateresearch.html) 

From the NCDC website, users can access heavy rainfall frequencies provided by the National 

Oceanic and Atmospheric Administration’s National Weather Service Hydrometeorological 

Design Studies Center. This weather data source allows user to be specific in their search for 

heavy rainfall frequency by providing options for data description (e.g., data type, units, and time 

series type) and location within a State. Weather data from NCDC can be obtained from the 

following link. 

NCDC’s Weather/Climate Data and Products 

Available at: 

http://www.ncdc.noaa.gov/oa/climateresearch.html#dandp 

Developed by the Department of Atmospheric, Oceanic and Space Sciences at the University of 

Michigan, UM Weather, shown in Figure 4-3, provides the public with up-to-date weather data 

on the Web. 
41 

Figure 4-3 UM Weather Homepage 

(Source: http://cirrus.sprl.umich.edu/wxnet/) 

Users of UM Weather can obtain city-by-city forecasts, conditions, warnings, and weather 

graphics for the United States. UM Weather allows users to receive live and daily pictures of 

weather conditions at over 700 locations in the United States and Canada. Users have access to 

over 150 weather sites (e.g., National Oceanic Atmospheric Administration, National Snow and 

Ice Data Center, and Midwestern Regional Climate Center) through UM Weather. Users can 

collect weather-related data from UM Weather at the link below. 

UM Weather Data 

Available at: 

http://cirrus.sprl.umich.edu/wxnet/ 

4.2. Traffic Data Sources

Transportation-related data is quite accessible currently because multiple traffic data sources are 

available through the Internet. This section discusses some Web-based data sources that can 

provide information on traffic data (e.g., speed and volume) commonly used in traffic analyses. 

It should be noted that analysts are not limited to these data sources, and that traffic data exists 

for different locations around the county. The purpose here, however, is to show examples of the 

types of Web-based data sources that are available. 

Data collection through the Internet can begin with search engines such as Google. A user can do 

a search for “Highway Performance Monitoring System.” Doing this will provide the link for the 

FHWA’s Highway Performance Monitoring System (HPMS) website. The HPMS database 
42 

provides traffic data for statewide and nationwide highway systems. As part of HPMS, each 

State has permanent count stations, established in selected locations, that collect continuous 15-

minute aggregated traffic counts. These traffic counts can be used in transportation planning 

models in order to obtain OD data for traffic analysis. Please refer to the HPMS website to 

access the available links to state HPMS websites. Link is provided. 

Highway Performance Monitoring System 

Available at: 

http://www.fhwa.dot.gov/policy/ohpi/hpms/index.cfm 

Traffic engineers and researchers conducting weather-related traffic analyses involving the state 

of Virginia can collect traffic data from the Archived Data Management System (ADMS). 

ADMS provides data for such cases as planning and mobility performance measurement, 

decision support, and enhanced operational effectiveness (Center for Transportation Studies, 

2010). A snapshot of the ADMS website is presented in Figure 4-4. 

Figure 4-4 Archived Data Management System (ADMS) Homepage 

(Source: http://adms.vdot.virginia.gov/ADMSVirginia/) 

Authorized ADMS users can access traffic count and speed data in 24 hour periods from 

Northern Virginia and the Hampton Roads region of Virginia. ADMS uses permanent count 

stations to obtain traffic count data. These permanent count stations archive traffic counts by 

speed bins for a particular time interval (e.g., from 6:00 to 6:15, 85 vehicles counted for 55-60 

mph bin). ADMS also uses sensors (e.g., loop detectors) that are embedded in the road to collect 

averaged speed data for a specific time interval. This Web-based system allows users to specify 

routes and dates when collecting traffic information. 

In addition to traffic information, weather data can be easily accessed and downloaded online 

from ADMS. Users can specify weather conditions when collecting ADMS weather information. 

Available weather conditions include drizzle, rain, fog, haze, snow, thunderstorm, mist and 

others (Archived Data Management System 2010). ADMS can be accessed by the following 

link. 
43 

Archived Data Management System (ADMS) 

Available at: 

http://adms.vdot.virginia.gov/ADMSVirginia/ 

NOTE: Users must obtain an account to use this site. 

Analysts in California can access transportation-related data from the internet through the 

California Department of Transportation Performance Measurement System (PeMS). From this 

website, seen in Figure 4-5, users can access historic traffic data which were collected from a 

time period spanning over 10 years. From the 25,000 detectors, users can access real-time traffic 

data for volume, speed, and delay. 

Figure 4-5 Performance Measurement System (PeMS) Homepage 

(Source: http://pems.dot.ca.gov/?redirect=%2F%3Fdnode%3DState) 

PeMS allows users to retrieve a wide range of transportation-related information from the 

California Department of Transportation and from local agencies, all in one stop. Such 

information includes: 1) incidents, 2) toll tags, 3) weight-in-motion, 4) vehicle classification, 5) 

census traffic counts, 6) lane closures, and 7) roadway inventory (California Department of 

Transportation, 2010). PeMS can be accessed by the link below. 

Performance Measurement System (PeMS) 

Available at: 

http://pems.dot.ca.gov/?redirect=%2F%3Fdnode%3DState 

NOTE: Users must apply for an account in order to obtain traffic data 

from PeMS. 

A useful traffic data source for analysis in the Pacific Northwest is PORTAL which stands for 

Portland Oregon Regional Transportation Archive Listing. The PORTAL system contains a wide 

range of archived transportation-related data including the freeway loop detector data from 

Portland, OR to Vancouver, WA metropolitan area, incident data, freight data, transit data, and 

weather data (PORTAL, 2010). Figure 4-6 presents the homepage of PORTAL. 
44 

Figure 4-6 PORTAL Homepage 

(Source: http://portal.its.pdx.edu/home/) 

This data source provides daily statistics (e.g., total vehicle miles traveled, average travel time, 

and average travel speed) of highways in and around Portland, Oregon. An example of these 

daily statistics is shown in Figure 4-7 for I-5 North. 

Figure 4-7 Daily Statistics for I-5 North 

(Source: http://portal.its.pdx.edu/portal2.1/index.php/highways) 

In conducting a microscopic analysis of traffic flow, users can use the live camera images from 

PORTAL to make visual observations of traffic flows on highways. Doing so could help users 

decide what needs to be accomplished to complete a successful analysis. For instance, users can 

conduct visual observations of headways during normal dry conditions and during inclement 

weather conditions. If they believe that headways significantly increase for inclement weather 

conditions from the visual observations then they can choose to calculate the actual changes in 

headways for their analysis. Data from PORTAL can be accessed at the following link. 
45 

PORTAL Traffic Data 

Available at: 

http://portal.its.pdx.edu/home/ 

Transportation-related analyses should be conducted using data sources with an abundance of 

information, such as that made available by the Minnesota Department of Transportation 

(Mn/DOT). This State DOT provides traffic volume information consisting of annual average 

daily traffic (AADT) and heavy commercial average daily traffic (HCADT) (Minnesota 

Department of Transportation, 2010). Figure 4-8 presents a snapshot of the Mn/DOT traffic data 

webpage. 

Figure 4-8 Minnesota Department of Transportation Traffic Data Webpage 

(Source: http://www.dot.state.mn.us/traffic/data/atr/atr.html) 

Minnesota also provides data obtained from continuous traffic counting sites that are located on 

interstates, municipal state-aid streets, and county-aid highways at different areas throughout the 

State. From these continuous traffic counting sites, users have access to data on volume, vehicle 

class, vehicle weight, and speed. Users can access the Minnesota Department of Transportation 

traffic data from the following link. 

Minnesota Department of Transportation Traffic Data 

Available at: 

http://www.dot.state.mn.us/traffic/data/atr/atr.html 

In some cases, using archived databases from State DOTs is not sufficient to conduct 

transportation-related analyses. This may be due to the location that is being studied. The State 

DOT may not contain essential traffic information that would be relevant to those traffic 

analyses. Therefore in those cases, the collection of field data may be a suitable option. 

4.3. Intelligent Transportation Systems Data Sources

Intelligent Transportation Systems (ITS) allows for traffic data collection through the use of 

wireless and wired electronic devices. This section discusses those ITS technologies that can 

provide users with traffic data, such as travel time, traffic speed, and vehicle location. 
46 

We are in a time where technology is constantly advancing, and ITS takes traffic data sources 

and weather data sources to a new technological level. Connected vehicles, a US Department of 

Transportation initiative, is designed to create a network that connects vehicles to infrastructure 

and wireless devices. It is proposed that connecting these three elements will enhance safety and 

mobility and reduce the negative effects of transportation on the environment. 

The mobility applications for connected vehicles have the potential to provide detailed, real-time 

traffic data about traffic conditions to transportation managers, allowing them to optimize 

transportation system performance. Such optimization includes adjusting traffic signals and 

sending out maintenance personnel in the event of an emergency. Along with providing traffic 

data, connected vehicles will provide information on current weather and road conditions in 

small coverage areas such as mile-by-mile or block-by-block (Row, 2010). 

Some short-term traffic analyses may be performed using real-time travel data acquired from 

ITS. These analyses may provide a better assessment of current traffic operations because they 

were conducted with consistent traffic data (i.e., data being collected for the same time period). 

Therefore, these analyses will not have to rely heavily on historic traffic data. 

By using ITS-generated data, traffic management agencies may have better weather coverage for 

a particular location. These agencies may be able to estimate the precipitation intensity through 

the use of wiper sensors on vehicles. The frequency of these wipers is key here. If wipers are set 

at a fast rate to clear the fallen precipitation from windshields of the initiative’s probe vehicles, 

then agencies may to able to assume that vehicles in the study area are experiencing severe 

inclement weather (e.g., heavy rain or snow). 

 For more information on the connected vehicles initiative, please refer to the following link. 

Connected Vehicles 

Available at: 

http://www.its.dot.gov/research/v2i.htm 

Transportation agencies can obtain traffic information from INRIX. However, this data is not 

free of charge. INRIX, shown in Figure 4-9, is a traffic services company that provides 

transportation-related information to businesses and individuals located in North America and 

Europe. 

Figure 4-9 INRIX Homepage 

(Source: http://www.inrix.com) 
47 

Such information includes real-time and predictive traffic speed and travel times for major 

roadways (i.e., freeways and highways) and secondary roadways (i.e., arterials and side streets) 

(INRIX, 2010). Transportation-related information can be obtained for those who operate GPSequipped vehicles (e.g., long-haul trucks and fleet vehicles) and who own consumer GPS-based 

devices (e.g., iPhone, iPad, Blackberry phones, and Android phones). For more information 

about the services provided by INRIX please refer to the following link. 

When applicable, traffic information may be obtained from automatic vehicle location (AVL) 

technologies. This advanced traffic monitoring technology collects data from vehicles equipped 

with electronic tags (Tanikella et al., 2007). AVL allows for continuous collection of travel time 

data for each day of the year. Collecting travel time data in 24 hour periods is possible because 

AVL does not require manual recording of field data. Although AVL is heavily dependent on 

AVL devices (e.g., roadside antennas and roadside readers), it is a reliable source for travel time 

data collection. 

Unlike AVL, Global Positioning System (GPS) involves locating objects that are not quite within 

reasonable reach. Transportation-related information (i.e., location and travel time) is collected 

from GPS-equipped vehicles that receive signals sent from the 24 satellites located in space 

(Turner et al., 1998). The collected data is sent to a storage computer located in a control center 

once the data collection process is completed. This method does not require making phone calls 

at checkpoints or manually recording information. Therefore, human error is smaller for data 

collection techniques that use GPS. 

Travel time information collected from these ITS data sources can be essential in comparing the 

performance of a transportation facility during normal dry conditions and inclement weather 

conditions for weather-related transportation analyses. For additional information on AVL and 

GPS, please refer the Travel Time Data Collection Handbook shown below. 

Travel Time Data Collection Handbook (1998) 

Available at: 

http://www.fhwa.dot.gov/ohim/tvtw/natmec/00020.pdf 

Engineers from Indiana Department of Transportation have raised the bar for the methods used 

to collect travel time data. Wasson, Sturdevant, and Bullock (2008) created a method that uses 

Bluetooth technology from cellular phones and other wireless devices to collect travel time data. 

Each wireless device has a unique digital signature which can be tracked by detectors located 

along the road. This method allows agencies to collect travel time for analysis purposes while 

also providing travel time information to motorists. For additional information on travel time 

data collection via Bluetooth please refer to Real-Time Travel Time Estimates Using Media 

Access Control Address Matching from the June 2008 issue of ITE Journal. 

Real-Time Travel Time Estimates Using Media Access Control 

Address Matching 

Available at: 

http://www.ite.org/membersonly/itejournal/pdf/2008/JB08F.pdf 
48 

5.0 Weather Responsive Traffic Operations and Management

Strategies

Because the ideal weather condition (i.e., dry roadway, good visibility, no precipitation, and low 

winds) does not occur every day of the year, appropriate measures should be taken. Weather 

responsive traffic operations and management strategies have been developed to mitigate the 

impacts of inclement weather conditions. This section identifies existing traffic management 

strategies that would benefit from weather impact analysis. 

A general concept of operations for weather responsive traffic management strategies consists of 

1) basic operational objectives, 2) information gathering and impact assessment, 3) operational 

strategies, and 4) transportation outcomes. A flow chart of this Concept of Operations is 

presented in Figure 5-1. 

Figure 5-1 WRTM Concept of Operations Flow Chart 

(Source: Cambridge Systematics, Inc., 2003) 
49 

Traffic management is regulated by basic operational objectives to ensure that safety, mobility, 

and agency productivity remain or improve to an acceptable condition. Gathering essential 

information, such as level of severity, area of impact, time of day, and event duration, is 

necessary in order to accurately assess weather impacts on safety, mobility, and productivity. By 

analyzing these impacts, traffic managers will have a better understanding of the influence of 

weather events on traffic operations and will be able to create appropriate mitigation strategies. 

Such strategies include treatment strategies, control strategies, and advisory strategies. 

5.1. Advisory Strategies

Results of weather impact analyses can easily influence traffic advisory strategies. Let us say that 

a traffic analysis was conducted to determine the impact of rain on a road segment that includes a 

sharp curve. From the results, it was observed that the occurrence of crashes on this particular 

curve jumps up whenever there is heavy rain. Advisory strategies can be used to counteract the 

effect of inclement weather for that section of the road. These strategies are intended to provide 

pre-trip and en-route alerts and warnings which exist in the forms of passive warning systems, 

active warning systems, pre-trip road condition systems, and en-route weather alerts. 

Travel time and delay information that are obtained from weather-sensitive traffic analysis can 

be used to improve existing traffic advisory strategies. Because of these weather-related 

analyses, the quality of the information that is shared to motorists through pre-trip and en-route 

alerts is higher. Their decisions on the aspects of the trip (e.g., trip value and route) can be made 

with higher confidence because they were given accurate travel time and delay information. 

5.2. Control Strategies

In control strategies, roadway devices are altered to accommodate the change in traffic, weather, 

and pavement conditions. Examples of control strategies include speed management (e.g., 

variable speed limits, traffic signal control at signalized intersections, and ramp metering). 

As part of speed management, variable speed limits are enforced on roadways in response to 

inclement weather conditions such as high winds and falling snow and rain. These variable speed 

limits will vary depending on weather, roadway, and traffic conditions. 

Many weather-sensitive traffic analyses have shown that a significant number of motorists 

reduce their traveling speeds if they come upon severe inclement weather such as heavy rain or 

snow. A reduction in speeds can be due to poor visibility and/or decreased driver comfort levels 

when traveling in inclement weather. Traffic management agencies can incorporate this common 

trend acquired from weather-sensitive traffic analyses into the modification of their speed 

management strategies. If these agencies know that such a weather event as heavy rain will cause 

poor visibility and a reduction in the surface friction between the tires of a vehicle and the road 

then they can enforce variable speed limits suitable for motorists via dynamic message signs 

(DMS) and variable speed advisory signs. Because of inclement weather conditions, these 

enforceable speed limits may be significantly lower than the normal speed limit of a road, 

thereby causing a reduction in flow. This may not be a desired outcome from enforcing slower 

speed limits, but there would be larger reductions to flow if a motorist lost control of his vehicle 

when speeding on an icy road and crashed into surrounding vehicles. A traffic incident like this 

could cause a shutdown of lanes in that direction and ultimately cause a major disruption in the 

flow of traffic. 
50 

The information that can be taken from weather-sensitive traffic analyses can be beneficial to 

control strategies that involve signal timing. As previously discussed, weather-sensitive traffic 

analyses show that motorists reduce speeds when traveling in inclement weather, causing a 

reduction in flow. A reduction in flow is clearly seen at signalized intersections through 

saturation flow rate. This is defined as the number of vehicles per lane that can travel through a 

signalized intersection in an hour. When a significant amount of snow covers the roadway 

surface at a signalized intersection, vehicles stall in their initial movement before they can get 

through the signalized intersection. Due to reduced speed and this stalled initial movement, 

fewer vehicles can travel through the signalized intersection in an hour. With this knowledge, 

traffic management agencies can customize the traffic signal timing plans for inclement weather 

conditions. Doing so could allow more vehicles to get through the signalized intersection, 

thereby increasing saturation flow rate during inclement weather conditions. 

Ramp control signals (also known as ramp metering) are used to regulate the traffic flow at 

freeway entrance ramps. Ramp meters detect when there are breaks in the platoons of vehicles. 

When there are adequate openings in these platoons, the ramp control signals allow vehicles to 

enter the freeway. Like signalized intersections, ramp control signals may be optimized if results 

from weather-sensitive traffic analyses are implemented in the ramp signal timing plans. 

Depending on traffic flow and inclement weather conditions, longer or short green times may be 

implemented into the ramp control signals. 

5.3. Treatment Strategies

In treatment strategies, roadways are treated in order to reduce the effect inclement weather has 

on them. Strategies of this type often involve the interaction and cooperation of traffic 

management agencies and emergency agencies. Examples of treatment strategies include winter 

road maintenance (e.g., snow plowing to clear snow from roads and de-icing pavements) 

(Cambridge Systematics, 2003). 

When weather-sensitive traffic analyses show that components of traffic operations, such as 

speed and flow, decline due to the accumulation of ice or snow on roads, traffic managers may 

be inclined to use the Maintenance Decision Support System (MDSS). MDSS is a tool developed 

by FHWA to help traffic managers make appropriate decisions on winter road maintenance. 

Such decisions are intended to improve treatment strategies with less financial strain. For 

example, Indiana DOT showed a savings of $12,108,910 in salt usage for the 2008-2009 winter 

season by using a MDSS (McClellan et al., 2009). 

Along with MDSS, results from weather-sensitive traffic analyses can be an aid to traffic 

managers for their roadway treatment strategies. Some analyses may show that winter road 

conditions (e.g., icy roads and snow-covered roads) stall the initial movement of vehicles in a 

queue at a signalized intersection. This stall in movement is due to the reduction in surface 

friction caused by the winter road conditions. Results of these traffic analyses can support the 

treatment strategies (e.g., deicing roads with sand, salt, or other chemical de-icing agents) 

implemented by traffic management agencies. In other words, these traffic analysis results 

validate the purpose of having winter road maintenance strategies. 

From weather-sensitive traffic analyses, transportation management agencies will be better 

informed about weather impacts on traffic operations. Some of these analyses show that queue 

length and delay increase because vehicles reduce their speeds significantly when traveling on 

icy or snow-covered roads. Using this information, traffic management agencies can be better 
51 

prepared in handling such weather events. They will be able to estimate the manpower and labor 

that is required to treat roads so that the flow of traffic does not worsen significantly. 
52 

6.0 Case Study  

6.1. An Implementation of DYNASMART in Hampton Roads, VA. – Mesoscopic

Simulation  

6.1.1. Case Study Summary

Recent research has shown that inclement weather may negatively affect many traffic parameters 

(e.g., traveling speeds, travel time, and roadway capacities), roadway safety, and vehicle 

operability. The extent to which weather affects these parameters has not been well quantified. 

Inclement weather is sporadic and often has a sudden onset; if sensors are not already in place, it 

is difficult to capture the effects of inclement weather. To this end steps have been taken to 

quantify and model the effects of weather on traffic operations. 

This study was conducted to develop models in DYNASMART-P that would estimate and 

predict the impact of inclement weather on traffic operations. DYNASMART-P, a mesoscopic 

traffic simulation tool, was selected for the study because it consists of a recently developed 

weather module. The required inputs consisted of data for the traffic parameters speed and 

density, data for segment types (e.g., ramp, merge, diverge, and weave), and weather-related data 

(e.g., precipitation type, precipitation intensity, and visibility). Results of this study showed that 

using Weather Adjustment Factors (WAFs) would provide smaller overestimations of speed in 

inclement weather. 

6.1.2. Description of the Study

Hampton Roads, Virginia, located in Southeastern Virginia, is comprised of 16 diverse localities 

and is home to 1.6 million people. In 2006, 26 million hours of delay were caused by vehicles 

slowed or stopped in Hampton Roads traffic due to congestion and lack of capacity. Inclement 

weather can greatly exacerbate the safety problems and congestion delay of the region, which 

makes it a prime location to assess the potentially negative effects of inclement weather. 

It is noted that the primary focus of the DYNASMART-P weather module was realizing the 

effect that weather has on supply parameters and user response to weather-informed control 

devices (e.g., traffic signals, VMS, etc.) and weather information. As such, this case study does 

not cover demand adjustment. For more information on how weather might affect demand 

parameters and how users can modify their demands to reflect the effect of weather, refer to 

Section 3.2.4.2. 

The study network is composed of approximately 197 miles of freeway segments that service 

Hampton Roads on I-64, I-564, I-264, and I-664. 

The network was coded into DYNASMART-P using coordinates and geometries already 

collected for a previous study using DynaMIT. The DYNSMART-P Hampton Roads Network is 

depicted in Figure 6-1. 
53 

Figure 6-1 Hampton Roads DYNASMART-P Network 

DYNASMART-P uses a macroscopic relationship between speed and density to simulate vehicle 

movement. The single regime model is applicable to arterial models and the dual regime model 

is applicable to freeway models due to capacity differences. For this case study, the dual regime 

model is used. The mathematical representations of this relationship are as follows: 

Dual Regime Model: 

 (6-1) ௧௕௥௞௣௡݇௜ ൑ ݇൑0௙ ݑ ൌ௜ ݒ

ݒ ௜െ ݒ ଴ൌ ൫ݒ ௙െ ݒ଴൯ · ቈ1 െ

݇௜

݇௝௔௠቉

ఈ

݇௕௥௞௣௡௧ ൑ ݇௜ ൑ ݇௝௔௠ (6-2) 

Where 

vi = speed on link i 

vf = speed-intercept 

uf = free-flow speed on link i 

v0 = minimum speed on link i 

ki = density on link i 

kjam = jam density on link i 

α = power term 

kbrkpnt = breakpoint density 

To initiate use of the weather module, a user must first create the weather.dat file in their project 

directory. This file describes either an across-the-board (network-wide) or a link-specific 

weather condition (link-specific weather information will take precedence over network-wide 

weather information). The general format of the weather.dat file and the weather.dat file of the 

Hampton Roads test case are shown in Figure 6-2. 
54 

Figure 6-2 Description of Weather Data in DYNASMART-P 

Table 6-1 Weather Sensitive Demand Parameters 

Input Data Traffic Properties Notes 

Traffic Flow 

Model 

1. Speed-intercept, (mph) Italic properties are 

for dual regime model 

only 

2. Minimal speed, (mph) 

3. Density break point, (pcpmpl) 

4. Jam density, (pcpmpl) 

5. Shape term alpha 

Link 6. Maximum service flow rate 

7. Saturation flow rate 

8. Posted speed limit adjustment margin 

Signal Control Cycle length, offset, green, amber, max green, min green All same units, (sec) 

Left-turn Capacity 9. g/c ratio 

2-way Stop Sign 

Capacity 

10. Saturation flow rate for left-turn vehicles All same units, (vph) 

11. Saturation flow rate for through vehicles 

12. Saturation flow rate for right-turn vehicles 

4-way Stop Sign 

Capacity 

13. Discharge rate for left-turn vehicles All same units, (vph) 

14. Discharge rate for through vehicles 

15. Discharge rate for right-turn vehicles 

Yield Sign 

Capacity 

16. Saturation flow rate for left-turn vehicles All same units, (vph) 

17. Saturation flow rate for through vehicles 

18. Saturation flow rate for right-turn vehicles 

Once the weather conditions are specified, the users are able to implement the weather module to 

calculate the effects of inclement weather. Weather module effects take the form of a weather 

adjustment factor (WAF). Normal weather parameters are multiplied by a WAF in order to 

calculate inclement weather parameters. Northwestern University identified 18 supply and 

operational parameters present in the DYNASMART-P framework that could be affected by 

inclement weather. These parameters are described in Table 6-1. 

1 (Network wide weather condition exist, (0 otherwise)) 

0.5 0.1 0 0 120 (Network Weather Conditions: visibility=.5mile, rainfall=0.1 in/hr, snowfall = 0 in/hr, duration = first 

2 hours (0 to 120 minutes)) 

3 (3 link specific weather conditions exists) 

1 4042 4087 2 (link condition 1: on link connecting node 4042 to node 4087 has 2 weather conditions)

10 40 1.0 0.1 0 (between minutes 10 and 40, visibility = 1.0mile, rainfall =0.1in/hr, snowfall=0in/hr.) 

41 60 0.5 0.2 0 (between minutes 41 and 60, visibility = 0.5mile, rainfall =0.2in/hr, snowfall=0in/hr.)

2 4084 4042 1 (link condition 2: on link connecting node 4084 to node 4042 has 1 weather condition)

10 80 0.5 0 0.1 (between minutes 10 and 40, visibility = 0.5mile, rainfall =0in/hr, snowfall=0.1in/hr 

3 3826 581 1 (link condition 3: on link connecting node 3826 to node 581 has 1 weather condition)

0 20 1.0 0 0 (between minutes 0 and 20, visibility = 1.0mile, rainfall =0in/hr, snowfall=0.0in/hr 

1 

0.5 0.02 0 90 150 
55 

For each of the parameters above, a weather adjustment factor (WAF) was calculated. WAF is 

calculated as: 

F v r s v r v s i   0  1    2    3    4     5   (6-3) 

Where, 

Fi = WAF for parameter i 

 v = visibility (miles) 

 r = precipitation intensity of rain 

 s = precipitation intensity of snow 

 β1, β2, β3, β4, β5 = coefficients 

Under inclement weather, the supply parameters are calculated as follows: 

F  Fo  Fi

' (6-4) 

Where, 

 F’ = weather adjusted parameter 

 Fo = normal weather parameter 

 Fi = weather adjustment factor 

The coefficients for the above equations are described in the WAF.dat control file. The general 

format of this file and the Hampton Roads WAD.dat file are presented in Figure 6-3. Note that 

because the Hampton roads network is a freeway system containing no traffic control devices, 

many of the supply/operational parameters are not applicable, and so weather would not have 

any effect on them. The WAF.dat table allows the user to implement the weather module either 

generally or specifically. For example, to use the module, generally the user would need to 

calculate the coefficients of each supply parameter by observing a wide variety of weather 

conditions and performing some regression analysis. Therefore, no matter what amount of 

precipitation (rain and snow) and level of visibility the user plugs into the weather.dat table, the 

calculated coefficients would still be applicable. If the user is only interested in observing the 

effects of a very specific weather condition, that user could collect traffic information for just 

that weather condition. Using this newly collected data, the user could calculate the weather 

adjustment factor for each parameter as: 

o

i F

F F

'

 (6-5) 

These WAFs can then be plugged into the WAF.dat table in the β0β0 position for each parameter 

and the other coefficients can be set to zero which would result in Fi = β0.β0. The caveat to this 

approach is that the WAFs calculated are only good for the specific weather situation from which 

they were derived. 
56 

(a) General WAF.dat file 

(b) Hampton Roads WAF.dat file 

Figure 6-3 Snapshots of WAF.dat files 

From Figure 6-3, rows 1 to 18 represent the weather-sensitive demand parameters shown in 

Table 6-1. The order in which these parameters are presented in Figure 6-3 and Table 6-1 are the 

1 0.910 0.009 -0.404 -1.455 0 0 

2 1 0 0 0 0 0 

3 0.830 0.017 -0.555 -3.785 0 0 

4 1 0 0 0 0 0 

5 1.005 0 0 0 0 0 

6 0.850 0.015 -0.505 -3.932 0 0 

7 0.910 0.009 -0.404 -1.455 0 0 

8 0.910 0.009 -0.404 -1.455 0 0 

9 1 0 0 0 0 0 

10 1 0 0 0 0 0 

11 1 0 0 0 0 0 

12 1 0 0 0 0 0 

13 1 0 0 0 0 0 

14 1 0 0 0 0 0 

15 1 0 0 0 0 0 

16 1 0 0 0 0 0 

17 1 0 0 0 0 0 

18 1 0 0 0 0 0 

1 0.91 0.009 -0.404 -1.455 0 0 (speed intercept weather adjustment factor: 

β0ൌ.91,β1ൌ0.009, β2ൌ‐.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

2 1 0 0 0 0 0 (minimal speed weather adjustment factor: β0ൌ0, β1ൌ0, β2ൌ0, β3ൌ0, β4ൌ0, 

β5ൌ0ሻ

3 .83 0.017 -0.555 -3.785 0 0 (Density Breakpoint weather adjustment factor: 

β0ൌ.83,β1ൌ0.017, β2ൌ‐.555, β3ൌ‐3.785, β4ൌ0, β5ൌ0ሻ

4 1 0 0 0 0 0 (Jam Density weather adjustment factor: β0ൌ0, β1ൌ0, β2ൌ0, β3ൌ0, β4ൌ0, 

β5ൌ0ሻ 

5 1 0 0 0 0 0 (Shape Term weather adjustment factor: β0ൌ0, β1ൌ0, β2ൌ0, β3ൌ0, β4ൌ0, β5ൌ0ሻ

6 0.85 0.015 -0.505 -3.932 0 0 (Maximum Service Flow Rate weather adjustment 

factor: β0ൌ0.85, β1ൌ0.015, β2ൌ‐0.505, β3ൌ‐3.932, β4ൌ0, β5ൌ0ሻ

7 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate weather adjustment factor: 

β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

8 0.91 0.009 -0.404 -1.455 0 0 (Posted Speed Limit Adjustment Margin weather 

adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

9 0.91 0.009 -0.404 -1.455 0 0 (g/c ratio weather adjustment factor: β0ൌ0.91, 

β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

10 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for left turn 2- way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

11 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for thru 2- way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

12 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for right turn 2-way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

13 0.91 0.009 -0.404 -1.455 0 0 (Discharge Flow Rate for left turn weather 

adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

14 0.91 0.009 -0.404 -1.455 0 0 (Discharge Flow Rate for thru weather 

adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

15 0.91 0.009 -0.404 -1.455 0 0 (Discharge Flow Rate for right turn weather 

adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

16 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for left turn 4-way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

17 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for thru 4-way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ

18 0.91 0.009 -0.404 -1.455 0 0 (Saturation Flow Rate for right turn 4-way stop 

weather adjustment factor: β0ൌ0.91, β1ൌ0.009, β2ൌ‐0.404, β3ൌ‐1.455, β4ൌ0, β5ൌ0ሻ
57 

same. From both, row 1 is the speed intercept, row 2 is the minimal speed, and so on. Each row 

from Figure 6-3 consists of six columns that fall after the first column on the far left. These 

columns represent the coefficients in the following order: β0, β1, β2, β3, β4, and β5. 

Once the weather files are created, the user can execute DYNSMART-P for a specified planning 

horizon. As shown in Figure 6-4, the program simulates the movements of vehicles using the 

speed-density relationships and link-based constraints (speed limits, saturation flow rates, etc.) 

using any weather information that has been specified by the user. 

Figure 6-4 DYNSMART-P Program 

The outputs of the simulation can be evaluated to determine the relative effects of weather. For 

example, users can compare the speeds, densities, volumes, etc. of normal weather and inclement 

weather simulations (keeping all other factors such as planning horizon, OD, traveler population 

characteristics constant). The relative difference in performance measures between the normal 

weather and inclement weather simulations would reflect how much the simulated weather 

condition affects traffic flow and behavior. 

6.1.3. Demand Adjustment

An example simulation was performed for Hampton Roads. A demand-based analysis was also 

used in order to compare the effectiveness of the supply-based adjustments versus demand-based 

adjustments. The University of Virginia has taken a critical look at the effects of weather on 

traffic demand. A study entitled “Probabilistic Modeling of Inclement Weather Impacts on 

Traffic Volume” observed the relative effects of weather, particularly rain and snow, on traffic 

volumes (Samba and Park, 2009). The study analyzed traffic volumes that occurred under 

inclement weather, comparing to average volumes that could be expected under normal weather 

conditions. From this analysis, it was observed that the likelihood of traffic volumes declining as 

a result of inclement weather increased as precipitation intensity increased and that the 

magnitude of volume reduction was time-dependent. These observations resulted in a 

probabilistic method for estimating traffic demand reductions at a given hour of the day based on 

precipitation intensity categories (light, less than .25 in/Hr, versus heavy, greater than .25 in/Hr) 
58 

for both rain and snowfall conditions. Please refer back to Section 3.2.4.2 for the traffic volume 

reduction procedure and example problem. 

A summary of the weather-related impacts on traffic demand is provided in Table 6-2. 

Table 6-2 Weather Impacts on Traffic Demand 

Weather Condition Probability of 

Weather Impacted 

Volumes 

Volume Reduction 

Change 

Average Volume 

Reduction 

Light Rain 16.6% 1.0% to 6.3% 2.32% 

Heavy Rain 31.3% 3.1% to 4.4% 3.75% 

Light Snow 76.0% 10.6% to 56.2% 28.80% 

Heavy Snow 42.9% 4.7% to 30.4% 13.30% 

(Source: Probabilistic Modeling of Inclement Weather Impacts on Traffic Volume, Samba and Park, 2009) 

It should be noted that the demand-based method has not yet been validated for its ability to 

reflect real-world traffic behaviors such as travel speed and volumes. 

6.1.4. Analysis and Results

The evaluation process for the DYNASMART-P weather module is as follows. First, real world 

speed data for light rain and heavy rain days were gathered for 10 traffic stations located to the 

north, south, and west of the I-264 interchange. Then, three simulations were made for each of 

these weather types (Figure 6-5). 

Figure 6-5 Hampton Roads DYNASMART-P Program Snapshot 

The lower right window in Figure 6-5 shows a curve representing the number of vehicles in the 

network (y-axis) for a duration period of 150 minutes (x-axis). The maximum number of 

vehicles present in the network is approximately 150,000. The right toolbar consist of network 

attributes (e.g., zones and signals) and traffic attributes (e.g., density, speed, queue length, and 

vehicles). 
59 

The simulation modified those normal weather parameters using the weather adjustment factors 

of the DYNASMART-P weather module. For this simulation, an across-the-board weather 

scenario that occurred for the entire simulation horizon was specified. The primary supply 

parameters that are applicable to this network and were adjusted for weather impacts are the 

components of the traffic flow model (speed-intercept, minimal speed, and posted speed limit 

adjustment margin). Additional link-specific parameters like maximum service flow rate and 

posted speed limit adjustment margin were also modified with the weather adjustment factors. 

The last simulation reduced the historical OD demands using the probabilistic traffic demand 

reduction method based on the precipitation category and time of day. All simulation and realworld data were for the hours of 2:00 p.m. to 5:00 p.m. The root mean square errors (RMSE) of 

speeds were used to assess the ability of each simulation type to reflect real-world data 

accurately. RMSE can be calculated as: 

 

n

x x n

i  i i   1

2

1, 2,

(6-6) 

Where, 

x1,i = actual value for time i 

x2,i = simulated value for time i 

n = number of observations

Table 6-3 presents calculated RMSE for the observed and simulated speeds. Both light and 

heavy rain days are represented. The WAF module consistently shows the least error in 

simulated speeds for both light and heavy rain days with RMSE of 14.2 and 14.7 for the two 

scenarios, respectively. The average normal weather parameter case had RMSE of 15.7 and 17.1 

for light and heavy rain days. Adjusting demand creates minor increases in the error of simulated 

mobility measures relative to using normal parameters. The reduction in error due to WAF is 

greater in the heavy rain case than in the light rain case; we can expect that weather would have 

the greatest perceived (by drivers) and observed (in terms of supply parameter changes) effect at 

greater intensities. This suggests that the more intense (heavier precipitation) or extreme (snow 

versus rain, for example), the less the variation in the magnitude of individual errors when using 

the WAF module. 

Table 6-3 RMSE between Observed and Simulated Speeds 

Weather Condition Average Normal 

Weather 

Parameter Speed 

Average Demand 

Adjusted Speeds 

Average WAF 

Module 

Light Rain 15.7 15.9 14.2 

Heavy Rain 17.1 18.2 14.7 
60 

(a) Light Rain 

(b) Heavy Rain 

Figure 6-6 Simulated and Observed Speeds 

In terms of the simulated speeds, on average, using normal parameters, or the demand-adjusted 

case, to assess a light rain day would result in overestimation of speeds by approximately 2 miles 

per hour, whereas using the weather adjustment factor would lead to speed overestimations by 

approximately 1 mile per hour. For heavy rain, simulations with weather adjustment factors 

reduced the error between simulated and observed speeds by approximately 33 percent compared 

to the errors of the normal parameter case. Figure 6-6 shows that for light rain conditions, the 

WAF module is more accurate in representing observed values than normal weather parameters. 

For heavy rain conditions, the WAF module is again shown to be better than using normal 

parameters, especially at lower speeds. It should be noted that at higher speeds, the WAF 

module is less precise, suggesting that more calibration could increase the accuracy of the model 

when simulating heavier precipitation. 

6.1.5. Conclusions

The following presents the conclusions discovered in the case study. 
61 

Hampton Roads Case Study – Conclusions 

 Smaller overestimations of speeds in light rain were seen with the use of 

weather adjustment factors (WAFs). WAFs overestimated speeds by 

approximately 1 mile per hour while using normal parameters or the demand 

adjusted case overestimated speeds by approximately 2 miles per hour. 

 When compared to the errors observed in the normal parameter case, 

simulations conducted with the use of WAFs for heavy rain conditions reduced 

the error between the simulated and actual speeds by about 33 percent. 

 At higher speeds the WAF module is less precise. This suggests that 

simulating heavy precipitation with higher accuracy could be performed. 

6.1.6. Guidance

Those who are interested in conducting their own weather-related transportation study, similar to 

the Hampton Roads case study, can follow the procedure presented in this section. A review of 

this procedure follows. Users can begin their study by collecting all the necessary input data. 

This includes traffic data (e.g., speed and density), segment data, and weather-related data (e.g., 

precipitation type, precipitation intensity, and visibility). Assuming that data calibration can be 

performed within the modeling tool, input data should be calibrated so that simulation results do 

not include any major errors. Doing so allows users to assume and ensure that traffic and weather 

data are valid. Users can then create a weather.dat file and specify the weather types that are 

involved in their analysis. Afterwards, users can begin the process to calculate the impacts of 

inclement weather using the weather module in DYNASMART-P. 

In the analysis, three simulation runs are conducted for each precipitation type. For the first 

simulation run, users should convert the calculated normal weather parameters to the 

DYNASMART-P format. In the second simulation run, users can adjust the normal weather 

parameters by multiplying them with the Weather Adjustment Factors (WAFs) from the 

DYNASMART-P weather module. For the third simulation, users can use the probabilistic 

traffic demand reduction method to estimate the reduced OD demands, although this method has 

not been validated in terms of its ability to reflect with accuracy the reduced demand caused by 

inclement weather. 

Using the weather module in DYNASMART-P, agencies could have more accurate estimations 

and predictions of real-world traffic parameters during inclement weather conditions. However, 

further calibration and validation of the WAFs might be required to ensure accurate traffic 

estimation and prediction under inclement weather conditions. It is expected this would be 

achieved in the near future by analyzing archived weather data from the Clarus initiative and/or 

the connected vehicles initiative, and conducting more case studies incorporating inclement 

weather. 

6.2. Traffic Signal Operation under Inclement Weather – Signal Optimization

and Microscopic Traffic Simulation

6.2.1. Case Study Summary

Traffic flow at signalized intersections is influenced by time-dependent factors such as time of 

day and day of week. Generally, traffic flow is at its busiest during peak hours of the day. Traffic 

conditions can worsen when an additional variable such as inclement weather is added to mix. 

Depending on the severity of the inclement weather condition, traveling speeds can decrease, 
62 

causing saturation headway to increase and ultimately causing an increase in saturation flow 

(Perrin et al., 2001). These traffic conditions are known to occur at signalized intersections 

during inclement weather, and yet the timing of the signals is often operated under normal 

weather and traffic behavior. 

Macroscopic and microscopic modeling tools (TRANSYT-7F, SYNCHRO, CORSIM, and 

SimTraffic) were used to develop and evaluate weather-specific signal timing plans for four 

corridors in New England. The following parameters were required for calibration: saturation 

headway, saturation flow rate, lost startup time, free flow speed, and headway factor. The study 

shows that weather-specific signal timing plans will provide operational benefits when inclement 

weather last for longer periods (e.g., 1 hour and 2 hours). 

6.2.2. Description of the Study

Sadek and Agbolosu-Amison conducted a case study with two main objectives: 1) analyze the 

impact of inclement weather at signalized intersections, and 2) quantify the likely operational 

benefits of signal retiming specifically tailored for inclement weather conditions. The latter is the 

focus of this discussion. In the second half of the case study, a total of four corridors (two from 

Vermont and two from Connecticut) were analyzed. Each corridor selected needed to have at 

least 3 but no more than 10 intersections. The w corridors from Vermont (Dorset Street and 

Vermont Route 15) have 10 intersections each while the 2 corridors from Connecticut (Storrs 

Road and Hale Road) has 5 intersections each. 

Figures 6-7 and 6-8 display the locations of the intersections for the two Vermont corridors. Both 

corridors are located from one end of Arrow A to one end of Arrow B. Dorset Street, which has a 

total length of 1.725 kilometers (1.08 miles), consists of eight signalized intersections and two 

unsignalized intersections. The signalized intersections are being operated by actuated 

controllers. Dorset Street provides access to nearby businesses and allows the through traffic 

movement to continue through the City of South Burlington The segment that was selected from 

Vermont Route 15, located in Chittenden County, Vermont, is 5.836 kilometers (3.63 miles) long 

and is made up of 10 signalized intersections; all of which are operated by actuated controllers. 

Vermont Route 15 provides access to businesses in the area and accommodates the traffic 

movement through Colchester. 
63 

Figure 6-7 Dorset Street 

Notes: Intersections 1 and 10 are isolated, fully-actuated intersections. 

Intersections 2, 3, 4, 5, 8 and 9 are semi-actuated and coordinated. 

Intersections 6 and 7 are unsignalized. 

(Source: Sadek and Agbolosu-Amison, 2004) 
64 

Figure 6-8 Vermont Route 15 

Intersections 1 to 10 are actuated signalized intersections. 

Intersections 1 to 8 are coordinated. 

Intersections 9 to 10 are uncoordinated. 

(Source: Sadek and Agbolosu-Amison, 2004) 

In order to assess the likely benefits of implementing “special” signal timing plans that take 

account of inclement weather conditions, Sadek and Agbolosu-Amison (2004) used two 

modeling tools in the case study. The first tool was used to develop the signal timing plans, and 

the second tool was used to evaluate the feasibility of incorporating these plans into action. Two 

macroscopic traffic signal timing optimization models (TRANSYT-7F and SYNCHRO) and two 

microscopic simulation models (CORSIM and SimTraffic) were used in the study to meet the 

objectives. The microscopic simulation models were used to evaluate the signal plans. 

6.2.3. Analysis and Results

Data were required for the development and calibration of these simulation models. The case 

study consisted of traffic data that were collected during the PM peak hour period between 4 pm 

and 5 pm for the study’s six different weather conditions (dry, wet, wet and snowy, wet and 

slushy, slushy in wheelpaths, and snowy and sticky) (Sadek and Agbolosu-Amison, 2004). The 

required data model development and calibration included the following: 
65 

Required Data for Model Development and Calibration 

 Traffic Flow Data: This includes data on total volumes, turning movements at 

the intersections, and traffic speed information. These data were collected 

between the hours of 4:00 p.m. and 5:00 p.m. for each intersection using a 

digital video camera recorder and a manual traffic counter. 

 Geometric Data: This included information on the number of lanes, spacing 

between intersections and driveways, and lane channelization. 

 Traffic Control: This refers to information about the type of traffic control at 

each intersection, traffic signal timing, and coordination plans. These data 

were collected using a stopwatch. For the signal timing parameters for 

actuated controllers, averages of 10 signal cycles were computed. 

 Saturation Headway and Lost Startup Time: Saturation headways and lost 

startup times were collected from the project site and used in developing and 

calibrating the simulation models for the “dry” weather condition. 

(Source: Sadek and Agbolosu-Amison, 2004) 

Specific performance measures were incorporated in the analysis for the evaluation of the 

“special” signal timing plans. These include: 1) total travel time, 2) maximum queue length 

(MQL), and 3) average maximum back of the queue (AMBQ). The AMBQ measures the average 

number of vehicles in a queue including the vehicles that arrive at the back of the queue during 

the green time of a cycle. 

Developing a base model is necessary for proper comparison of the traffic conditions for the six 

different weather conditions. This case study set the “dry” weather condition as the base 

condition. A total of 16 base models were developed using TRANSYT-7F and SYNCHRO. 

After developing the base models, the next step in the procedure was calibrating the model 

parameters so that the model output matched field conditions. Table 6-4 presents the adjusted 

parameters for the four simulation models. Please refer to the following links for additional 

information on the four models used in the study: 

TRANSYT-7F, SYNCHRO, CORSIM, and SimTraffic Information 

Available at: 

TRANSYT-7F and CORSIM 

http://mctrans.ce.ufl.edu/ 

SYNCHRO and SimTraffic 

http://www.trafficware.com/simtraffic7.html 

Table 6-4 Parameters Adjusted During Calibration of Simulation Models 

Parameter CORSIM SimTraffic TRANSYT-7F SYNCHRO 

Saturation Headway • 

Saturation Flow Rate • • 

Lost Startup Time • • • • 

Free Flow Speed • • • • 

Headway Factor • 

(Source: Sadek and Agbolosu-Amison, 2004) 
66 

In CORSIM, multiple runs were generated with the use of different random seed numbers, which 

were automatically developed with a random number generator called CORSEED (Sadek and 

Agbolosu-Amison, 2004). This utility program was created by the Advanced Traffic Analysis 

Center (ATAC) at North Dakota State University. A total of 10 simulation runs were generated 

using 10 different random seed numbers. The results of these simulation runs were then 

averaged. From the field data, the total travel time was computed as the average of seven probe 

vehicle runs, which were conducted by driving from one end of the corridor to the other end. The 

MQL data of 10 cycles was obtained from a portion of signalized intersections in a corridor 

(Sadek and Agbolosu-Amison, 2004). Calibration parameters were adjusted until simulation 

results closely modeled field conditions. In the calibration procedure, initial parameters were 

modified and then used to perform a simulation run. If inadequate results were developed, then 

the calibration parameters were further adjusted and incorporated into another simulation run. 

This process continued until field conditions were accurately modeled in the simulation runs. 

In the development of weather-specific signal timing plans, saturation flow rate for each 

weather/road surface condition was coded using reduction factors that were found from the first 

half of the case study. As for free-flow speeds, they were coded using the reduction factors from 

the Salt Lake City study conducted by Perrin et al. (2001). Table 6-5 presents the reduction 

factors for saturation flow rate and speed that were used in this study. 

Table 6-5 Reduction Factors for Saturation Flow Rate and Free Flow Speed 

Weather/Road Surface Condition % 

Reduction in Sat. 

Flow Rate % 

Reduction in Free 

Dry 0% 0% 

Wet 2% 10% 

Wet and Snowy 4% 13% 

Wet and Slushy 15% 25% 

Slushy in Wheel Paths 21% 30% 

Snowy and Sticky 16% 35% 

(Source: Sadek and Agbolosu-Amison, 2004) 

In order to evaluate the benefits of weather-specific signal timing plans properly, the 

performance of optimal weather-specific plans must be compared to the optimal plan that is 

created for the “dry” condition. The following procedure was performed to develop weatherspecific signal timing plans. 
67 

Development of Weather-Specific Signal Timing Plans - Procedure 

The first step was to develop optimal plans for the six different weather/road surface 

conditions, including the “dry” condition. For TRANSYT-7F, the Genetic Algorithm 

(GA) optimization routine was used to optimize the cycle length, splits, and offsets. 

The GA was preferred over the traditional hill climbing optimization routine because it 

allows the model to escape out of local optima. For the GA, the crossover rate was set 

to 30 percent, and the mutation rate was equal to 1 percent. A population size equal to 

20 was used, and the GA was run for 700 generations to make sure a “good” signal 

plan was obtained. The objective function selected for optimization was the function 

designed to minimize the Disutility index (DI), which represents a combination of 

delays and stops. 

For SYNCHRO, the following optimization steps recommended by the SYNCHRO 

users’ manual were followed: first, the individual intersection cycle lengths were 

optimized followed by optimization of the splits for each individual intersection. After 

this, the network wide cycle length was optimized and the network was partitioned into 

zones. Finally, the signal offsets were optimized using SYNCHRO’s quasi-exhaustive 

search optimization algorithm. For both TRANSYT-7F and SYNCHRO, the optimal 

plan developed for the “dry” condition was used as a starting point for the search 

procedure when developing optimal plans for the remaining five weather/road surface 

conditions. 

(Source: Sadek and Agbolosu-Amison, 2004) 

To evaluate the likely operational benefits of implementing “special” signal timing plans for 

each weather and road surface condition, Sadek and Agbolosu-Amison first quantified the traffic 

conditions from each signal timing plan using the performance measures shown in Table 6-6. 

Table 6-6 Performance Measures used for Benefit Assessment 

Performance 

Measures 

Traffic Simulation Models 

TRANSYT-7F SYNCHRO CORSIM SimTraffic 

Control/Signal Delay • • • 

Average Delay Time • • 

Total Travel Time • • • • 

Average Speed • • • • 

Total Stops • • 

Fuel Consumption • • 

(Source: Sadek and Agbolosu-Amison, 2004) 

Then the traffic conditions from the weather/pavement conditions were compared to the traffic 

conditions from the “dry” base signal timing plan. The percent gain of the performance measures 

were calculated for each weather condition. This was done first using the macroscopic models, 

TRANSYT-7F and SYNCHRO. Then the enhanced signal timing plans developed in 

TRANSYT-7F were analyzed in CORSIM, and the signal timing plans developed in SYNCHRO 

were analyzed in SimTraffic. This was executed so that a more detailed evaluation of the signal 

timing plans could be performed. In order to capture the stochastic nature of field conditions, 

five runs were performed with random seed numbers for each weather/pavement condition in 

CORSIM and SimTraffic (Sadek and Agbolosu-Amison, 2004). The same random seed numbers 

were used for all of the inclement weather cases. 
68 

Table 6-7 Benefits of Special Timing Plans – CORSIM Results for Dorset Street 

Weather 

Condition 

Timing Plans Control 

Delay 

(min/veh) 

Avg. 

Delay 

Time 

(min/veh) 

Total 

Travel 

Time 

(veh-hrs) 

Avg. 

Speed 

(mph) 

2 – Wet Under Dry Optimal Plan 0.42 0.71 100.95 23.87 

Weather-Specific Optimal Plan 0.41 0.70 100.66 23.96 

3 – Wet and 

Snowy 

Under Dry Optimal Plan 0.42 0.71 104.59 23.02 

Weather-Specific Optimal Plan 0.41 0.70 104.38 23.11 

4 – Wet and 

Slushy 

Under Dry Optimal Plan 0.46 0.79 116.54 20.52 

Weather-Specific Optimal Plan 0.44 0.76 116.04 20.65 

5 – Slushy in 

Wheel Paths 

Under Dry Optimal Plan 0.47 0.80 123.53 19.22 

Weather-Specific Optimal Plan 0.48 0.80 124.37 19.18 

6 – Snowy 

and Sticky 

Under Dry Optimal Plan 0.45 0.78 125.13 18.08 

Weather-Specific Optimal Plan 0.47 0.80 132.78 17.92 

(Source: Sadek and Agbolosu-Amison, 2004) 

Table 6-8 Benefits of Special Timing Plans – CORSIM Results for Vermont Route 15 

Weather 

Condition 

Timing Plans Control 

Delay 

(min/veh) 

Avg. 

Delay 

Time 

(min/veh) 

Total 

Travel 

Time 

(veh-hrs) 

Avg. 

Speed 

(mph) 

2 – Wet Under Dry Optimal Plan 0.86 1.23 137.49 21.36 

Weather-Specific Optimal Plan 0.88 1.24 138.55 21.26 

3 – Wet and 

Snowy 

Under Dry Optimal Plan 0.88 1.25 141.74 20.60 

Weather-Specific Optimal Plan 0.89 1.27 135.37 20.54 

4 – Wet and 

Slushy 

Under Dry Optimal Plan 1.11 1.51 160.87 17.69 

Weather-Specific Optimal Plan 1.05 1.45 159.01 18.03 

5 – Slushy in 

Wheel Paths 

Under Dry Optimal Plan 1.21 1.58 168.91 16.47 

Weather-Specific Optimal Plan 1.09 1.50 165.74 16.93 

6 – Snowy 

and Sticky 

Under Dry Optimal Plan 1.09 1.52 174.52 15.89 

Weather-Specific Optimal Plan 1.07 1.49 173.29 16.08 

(Source: Sadek and Agbolosu-Amison, 2004) 

The results from the 10 simulation runs for the microscopic analysis (i.e., 5 runs using CORSIM 

and 5 runs using SimTraffic) were averaged for each weather condition case. Table 6-7 and 

Table 6-8 present the percent gain of the CORSIM performance measures (i.e., control delay 

(min/veh), average delay time (min/veh), total travel time (veh-hrs), and average speed (mph)) 

for Dorset Street and Vermont Route 15. A simulation time of 15 minutes was used for the 

microscopic analysis. 

As seen, the percent gains for most cases do exist but they appear marginal. Keep in mind that 

these are the results of microscopic analyses that consist of data for traffic conditions pertaining 

to individual vehicles. Models of this type of analysis usually require much more detailed data 

than macroscopic simulation models. Therefore, microscopic simulation model users will gain 

higher accuracy when modeling field conditions than they would have using macroscopic 

simulation models. 
69 

Sadek and Agbolosu-Amison took the case study to another level by comparing the operational 

benefits of the signal timing plans for durations of 15 minutes, 1 hour, and 2 hours. Figure 6-9 

and Figure 6-10 present the Dorset Street and Vermont Route 15 operational benefits of the 

signal timing plans for weather/road surface conditions 4 through 6 (i.e., wet and slushy, slushy 

in wheel paths, and snowy and sticky) for the three duration levels. 

Figure 6-9 Impact of Inclement Weather Duration on Operational Benefits (Dorset Street) 

(Source: Sadek and Agbolosu-Amison, 2004) 

Figure 6-10 Impact of Inclement Weather Duration on Operational Benefits (Route 15) 

(Source: Sadek and Agbolosu-Amison, 2004) 

Both figures show an increasing trend in operational benefits, which are the percentage gains for 

control delay (veh/min), as the inclement weather duration increases. For example, the 

operational benefits on Dorset Street for condition 4 (i.e., wet and slushy) are around 4.1 percent 

for the 15-minute occurrence, but these operational benefits increase to a whopping 38.4 percent 
70 

for the 2-hour occurrence (Sadek and Agbolosu-Amison, 2004). It should be noted that the 

special signal timing plans on Dorset Street for conditions 5 and 6 (i.e., slushy in wheel paths and 

snowy and sticky) do not produce any operational benefits for the 15-minute case. Because of 

this, it can be deemed that the signal timing plans for the Dorset Street intersections will gain 

better benefits for the 15-minute case if they are operated under normal, dry weather conditions. 

These figures show that significant operational benefits can occur when the duration of the 

inclement weather event is longer. 

6.2.4. Conclusions and Recommendations

The following presents the conclusions from the results of the case study. These conclusions 

pertain to the development and implementation of “special” signal timing plans for inclement 

weather conditions. 

Implementing Weather-Specific Signal Timing Plans - Conclusions 

 The implementation of special signal timing plans for inclement weather 

resulted in significant operational benefits for both corridors, especially once 

slushy conditions developed or snow started sticking to the ground; 

 The operational benefits of inclement weather special timing plans estimated 

using stochastic, microscopic simulation models tend to be less than those 

estimated using deterministic, macroscopic models. This is especially true 

when the time period simulated is short. When the length of the microscopic 

simulation is increased to one hour, the benefits from microscopic models tend 

to get closer to those estimated from macroscopic models; and 

 The duration of the inclement weather event has a significant impact on the 

benefits realized from inclement weather special timing plans. A significant, 

consistently increasing trend in operational savings can be observed for 

increasing durations of the inclement weather event. 

(Source: Sadek and Agbolosu-Amison, 2004) 

This case study did not take into account the change in traffic demand due to inclement weather 

conditions. As is known, inclement weather does cause traffic demand volumes to decrease when 

conditions have reached a severe intensity. For future studies, it is recommended that reductions 

to traffic demand be quantified and used to develop weather-specific signal timing plans (Sadek 

and Agbolosu-Amison, 2004). 

The operational benefits that were observed from the microscopic model assessment were far 

fewer than those obtained from the macroscopic models. This is due to the fact that the 

microscopic models included the stochastic nature of driving conditions. For future research it 

may be a good idea to make the comparison of benefits that were collected from the stochastic 

optimization techniques, as utilized in the microscopic simulation models, with the traditional 

optimization techniques found in macroscopic simulation models such as TRANSYT-7F and 

SYNCHRO (Sadek and Agbolosu-Amison, 2004). 

6.2.5. Guidance

Those who are interested in performing their own analysis to observe the operational benefits of 

implementing weather-specific signal timing plans may incorporate the steps that were taken in 

this case study. An agency can collect traffic data during inclement weather conditions, which 

can be directly input into macroscopic models, such as TRANSYT-7F and SYNCHRO, which 

have the capabilities to develop optimal signal timing plans. In order to capture the random 
71 

nature of driving conditions, microscopic models should be used to evaluate the operational 

benefits of weather-specific signal timing plans. 
72 

References

Agbolosu-Amison, S. J., A. W. Sadek, and W. El Dessouki. Inclement Weather and Traffic Flow 

at Signalized Intersections: Case Study from Northern New England. Transportation Research 

Record 1867, 2004. 

Alexiadis, V., K. Jeannotte, and A. Chandra. Traffic Analysis Toolbox Volume I: Traffic Analysis 

Tools Primer. 2004. 

Battelle, McFarland Management, LLC & Texas Transportation Institute. Developments in 

Weather Responsive Traffic Management (WRTM) Strategies, State of the Practice Review: 

Technical Memorandum, 2010. 

California Department of Transportation. Performance Measurement System. 

http://pems.dot.ca.gov/?redirect=%2F%3Fdnode%3DState. Last accessed September 2010. 

Cambridge Systematics, Inc. Weather-Responsive Traffic Management – Concept of Operations. 

2003. 

Chiu, Y-C et al. DynusT User’s Manual Version 2.0.1. 

http://dynust.net/wikibin/doku.php?id=start. Last Updated: July 2010. Accessed September, 

2010. 

Chiu, Y-C, H. S. Mahmassani, H. Sbayti. DYNASMART-P Users Guide V1.3, FHWA, 2007. 

“Clarus Initiative.” http://www.clarusinitiative.org/background.htm 

“Clarus Initiative.” http://www.clarusinitiative.org/documents/clarus_trb_2007flyer_rev.pdf 

Datla, S. and S. Sharma. “Impact of Cold and Snow on Temporal and Spatial Variations of 

Highway Traffic Volumes.” Journal of Transport Geography, Volume 16, Issue 5, pp. 358-372, 

2008. 

Department of Atmospheric, Oceanic and Space Sciences. UM Weather. 

http://cirrus.sprl.umich.edu/wxnet/. Last accessed September 2010. 

Dong, J., H. S. Mahmassani, and R. Alfelor. Incorporating Adverse Weather Impacts in Dynamic 

Traffic Simulation-Assignment Models: Methodology and Application. Transportation Research 

Record, 2009. 

Dowling, R. Traffic Analysis Toolbox Volume IV: Definition, Interpretation, and Calculation of 

Traffic Analysis Tools Measures of Effectiveness. FHWA-HOP-08-054. 2007. 

Dowling, R., A. Skabardonis, and V. Alexiadis. Traffic Analysis Toolbox Volume III: Guidelines 

for Applying Microsimulation Software. FHWA-HRT-04-040, 2004. 

DYNAMIT-R’s User Guide Version 2.0. Massachusetts Institute of Technology. 2003. 

DYNASMART-P – Priority, Market-Ready technologies and Innovations List. 

http://www.fhwa.dot.gov/crt/lifecycle/dynasmart.cfm 

Federal Highway Administration and Research & Innovative Technology Administration. 

“Clarus System.” http://www.clarus-system.com/. Last accessed November 2010. 
73 

FHWA Road Weather Management. http://ops.fhwa.dot.gov/Weather/ 

Goodwin, L. C. Weather Impacts on Arterial Traffic Flow. FHWA. 2002. 

http://ops.fhwa.dot.gov/Weather/resources/publications.htm 

Hardy, M., and K. Wunderlich. Traffic Analysis Toolbox Volume VIII: Work Zone Modeling and 

Simulation – A Guide for Decision-Makers, FHWA-HOP-08-029, 2008. 

Holm, P., D. Tomich, J. Sloboden, and C. Lowrance. Traffic Analysis Toolbox Volume IV: 

Guidelines for Applying CORSIM Microsimulation Modeling Software. FHWA- HOP-07-079, 

2007. 

Hranac, R., E. Sterzin, D. Krechmer, H. Rakha, and M. Farzaneh. Empirical Studies on Traffic 

Flow in Inclement Weather. FHWA-HOP-07-073, 2006. 

INRIX Traffic. http://www.inrix.com. U.S. Last accessed November 2010. 

Intelligent Transportation Systems Program. Massachusetts Institute of Technology. Accessed: 

September 2010. http://mit.edu/its/jRNE.html 

Jeannotte, K. A. Chandra, V. Alexiadis, and A. Skabardonis. Traffic Analysis Toolbox Volume 

II: Decision Support Methodology for Selecting Traffic Analysis Tools. FHWA- FHWA-HRT04-039, 2004. 

Kilpeläinen, M., and H. Summala. "Effects of Weather and Weather Forecasts on Driver 

Behaviour." Transportation Research Part F: Traffic Psychology and Behaviour 10.4. pp. 288-99. 

2007. 

Kumar, M., and C. Strong. Comparative Evaluation of Automated Wind Warning Systems. 2006. 

Kundé, K.K., Calibration of Mesoscopic Traffic Models for Dynamic Assignment. Massachusetts 

Institute of Technology, 2002. 

Kyte, M., Z. Khatib, P. Shannon, and F. Kitchener. Effect of Weather on Free-Flow Speed, in 

Transportation Research Board, Volume 1776, 2001. 

Lam, W. H. K., and H. Shao, et al. Modeling Impacts of Adverse Weather Conditions on a Road 

Network with Uncertainties in Demand and Supply. TRB. 2007. 

Liang, W.L., M. Kyte, F. Kitchener, and P. Shannon. “The Effect of Environmental Factors on 

Driver Speed: A Case Study.” In Transportation Research Record 1635, TRB, 

Lieu, H. and S. Lin. Benefit Assessment of Implementing Weather-Specific Signal Timing Plans 

by Using CORSIM. Transportation Research Board of the National Academies. Vol. 1867, 

2004. 

Mahmassani, H. S., H. Sbayti. DYNASMART-P Users Guide V1.6. U.S. Northwestern 

University. FHWA. 2009. 

Mahmassani, H. S., J. Dong, J. Kim, R. B. Chen and B. Park. Incorporating Weather Impacts in 

Traffic Estimation and Prediction Systems. Contract No.: DTFH61-06-D-00005. 2009. 
74 

Mahmassani, H.S., J. Dong, J. Kim, R.B. Chen, and B. Park. Incorporating Weather Impacts in 

Traffic Estimation and Prediction Systems. Contract: DTFH61-06-D-00005 Task No: 01-0262-

71-2007-077, 2009. 

Maze, T., M. Agarwal, and G. Burchett. (2005). Whether Weather Matters to Traffic Demand 

and Traffic Flow, Center for Transportation Research and Education, 2005. 

McClellan, T., P. Boone, and M. A. Coleman. Maintenance Decision Support System (MDSS). 

Indiana Department of Transportation. 2009. 

Mctrans. http://mctrans.ce.ufl.edu/featured/dynasmart 

Minnesota Department of Transportation. http://www.dot.state.mn.us/traffic/data/atr/atr.html. 

Last accessed September 2010. 

National Climatic Data Center. Weather/Climate Events. 

http://www.ncdc.noaa.gov/oa/climateresearch.html. Last accessed September 2010. 

Park, B, J. Lee, D. M. Pampati, and B.L. Smith. Online Implementation of DynaMIT: A 

Prototype Traffic Estimation and Prediction Program. KSCE Journal of Civil Engineering 12(2), 

March 2008. 

Park, B., and H. Qi. Development and Evaluation of a Calibration and Validation Procedure for 

Microscopic Simulation Models. FHWA/VTRC 05-CR1, Virginia Transportation Research 

Council, Charlottesville, 2004. 

Park, B., and J. Won. Microscopic Simulation Model Calibration and Validation Handbook. 

FHWA/VTRC 07-CR6, Virginia Transportation Research Council, Charlottesville, 2006. 

Park, B., B. L. Smith, D. Pampati, J. Cao, Y. Qi, M. Ben-Akiva, and R. Balakrishnan. Online 

Implementation of DYNAMIT in Hampton Roads, Virginia, Phase II. Contract No. DTFH61-03-

C-00107. 2006. 

Park, B., B. L. Smith, J. Lee, D. Pampati, M. Ben-Akiva, and R. Balakrishnan. Field Evaluation 

of DYNAMTI in Hampton Roads. Virginia, Phase I. FHWA. Contract No. DTFH61-03-00107. 

2004. 

Perrin, J. H., P. T. Martin, and B. G. Hansen. Modifying Signal Timing During Inclement 

Weather. In Transportation Research Record: Journal of the Transportation Research Board, No. 

1748, TRB, National Research Council, Washington, D.C., 2001, pp. 66-71. 

Pisano, P. A., L. C. Goodwin, and M. A. Rossetti. U.S. Highway Crashes in Adverse Road 

Weather Conditions, 2009. 

Pisano, P. A., P. J. Kennedy, B. C. Boyce, and A. D. Stern. Regional Demonstrations of the 

FHWA Clarus System. FHWA. 2008. 

http://ops.fhwa.dot.gov/Weather/resources/publications.htm 

Pisano, P., R. M. Alfelor, J. S. Pol, L. C. Goodwin, and A. D. Stern. Clarus – The Nationwide 

Surface Transportation Weather Observing and Forecasting System. 2005. 
75 

Portland State University, Oregon Department of Transportation, Federal Highway 

Administration, and National Science Foundation. PORTAL. http://portal.its.pdx.edu/home/. 

Last accessed September 2010. 

Prevedourous, P. D. and K. Chang. Potential Effects of Wet Conditions on Signalized 

Intersection LOS, 2004. 

Rakha, H., D. Krechmer, G. Cordahi, I. Zohdy, S. Sadek, and M. Arafeh. Microscopic Analysis 

of Traffic Flow in Inclement Weather. FHWA-JPO-09-066, 2009. 

Rakha, H., M. Farzaneh, M. Arafeh, and E. Sterzin. Inclement Weather Impacts on Freeway 

Traffic Stream Behavior. In Transportation Research Record: Journal of the Transportation 

Research Record, No. 2071, Transportation Research Board of the National Academies, 

Washington, D.C., 2008, pp.8-18. 

Road and Traffic Authority. Paramics Microsimulation Modeling – RTA Manual, RTA/Pub 

09.166, 2009. 

Row, S. J. IntelliDriveSM: Safer. Smarter. Greener. FHWA-HRT-10-004, Vol 74, 1. 2010. 

http://www.fhwa.dot.gov/publications/publicroads/10julaug/04.cfm 

Sakek, A., and S. J. Agbolosu-Amison. Validating Traffic Simulation Models to Inclement 

Weather Travel Conditions with Applications to Arterial Coordinated Signal Systems. NETCR 

47, 2004. 

Samba, D., and B. Park. Probabilistic Modeling of Inclement Weather Impacts on Traffic 

Volume, TRB. For presentation at the 89 Annual Transportation Research Board Meeting. 

Compendium of Papers DVD. 2009. 

Stern, A. D., V. Shah, L. Goodwin, and P. A. Pisano. Analysis of Weather Impacts on Traffic 

Flow in Metropolitan Washington, D.C. FHWA. 2003. 

http://ops.fhwa.dot.gov/Weather/resources/publications.htm 

Tanikella, H., B. Smith, G. Zhang, B. Park, and W. Scherer. Simulating VII-Enabled Operations 

Applications – Traffic Monitoring Case Study, Transportation Research Board, TRB 2007 

Annual Meeting CD-ROM, 2007. 

Turner, S. M., W. L. Eisele, R. J. Benz, and D. J. Holdener. Travel Time Data Collection 

Handbook. FHWA-PL-98-035. 1998. 

US Department of Transportation and Federal Highway Administration. Highway Performance 

Monitoring System. http://www.fhwa.dot.gov/policy/ohpi/hpms/index.cfm. Last accessed 

November 2010. 

US Department of Transportation. IntelliDriveSM. http://www.intellidriveusa.org/. Last accessed 

September 2010. 

Virginia Department of Transportation, Virginia Transportation Research Council, and Center 

for Transportation Studies. Archived Data Management System Virginia. 

http://adms.vdot.virginia.gov/ADMSVirginia/. Last accessed September 2010. 
76 

Wasson, J. S., J. R. Sturdevant, and D. M. Bullock. Real-Time Travel Time Estimates Using 

Media Access Control Address Matching. ITE Journal, June 2008, p. 20. 

http://www.ite.org/membersonly/itejournal/pdf/2008/JB08F.pdf. Last accessed November 2010. 

Zhang, L., P. Holm, and J. Colyar. Identifying and Assessing Key Weather- Related Parameters 

and Their Impacts on Traffic Operations Using Simulation, FHWA-HRT-04-131, September 

2004. 
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