ABSTRACT 
 
 
Title: IMPACT OF DYNAMIC MESSAGE SIGNS 
ON OCCURRENCE OF ROAD 
ACCIDENTS  
  
 
 
Azadeh Norouzi, M.Sc. in Civil and 
Environmental Engineering, 2012 
  
Directed By: Professor and Chair Ali Haghani, 
Department of Civil and Environmental 
Engineering  
 
 
 
 
 
Dynamic Message Signs (DMS) are key components of Advanced Traveler 
Information Systems to manage transportation networks, reduce congestion and improve 
safety through providing motorists with real-time information regarding downstream 
traffic conditions. While DMSs are intended to improve efficiency and safety of road 
networks, little has been done to study the effect of the signs on driver safety and their 
localized safety impacts. This thesis employs ground truth data as the basis to investigate 
the issue in State of Maryland in a four-year period (2007-2010). The results show no 
significant difference between the accident pattern in the proximity of DMSs and the 
onward adjacent segments. On-and-off study is also conducted on DMS operation status 
(on/off). The results converge with the previous analysis suggesting that there is no 
meaningful relationship between occurrence of accidents and presence of DMSs. Besides, 
statistical analysis on DMS characteristics and accidents in impact areas are performed.   
 
 
 
 
 
 
 
 
IMPACT OF DYNAMIC MESSAGE SIGNS ON OCCURRENCE OF ROAD 
ACCIDENTS 
 
 
 
 
By 
 
 
 
Azadeh Norouzi 
 
 
 
 
 
Thesis submitted to the Faculty of the Graduate School of the  
University of Maryland, College Park, in partial fulfillment 
of the requirements for the degree of 
Master of Science 
2012 
 
 
 
 
 
 
 
 
 
 
 
Advisory Committee:  
Professor Ali Haghani, Chair  
Professor Paul Schonfeld 
Professor Cinzia Cirillo 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
? Copyright by 
Azadeh Norouzi 
2012 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ii 
 
 
 
 
 
 
To Keivan and Jiwan 
 
 
 
iii 
 
Acknowledgement 
 
I am deeply grateful to my advisor, Professor Ali Haghani, for providing me the opportunity, 
motivation, and guidance all throughout my education. Without his instruction, assistance 
and encouragement this thesis would never have been completed. I also sincerely 
appreciate his kindness and genuine care to my family. 
I would also like to thank all of the professors and instructors who contributed their time and 
knowledge to my education. Specifically, I acknowledge my gratitude to Dr. Ali Haghani and 
the other faculty members, Dr. Paul Schonfeld and Dr. Cinzia Cirillo for their willingness to 
serve on my committee and their constructive feedback and valuable guidance, as well as 
their efforts throughout my coursework.  
My great thanks go to my colleagues within my research group, specifically Dr. Masoud 
Hamedi for helping and encouraging me throughout my research and providing great ideas 
for our shared research. I also thank him for his assistance in coding during the process of 
integrating the databases.  
My most special thanks and appreciation goes to my dear husband, Dr. Keivan Ghoseiri. I 
owe my education to him.  He has always motivated me and offered his sincere help and 
guidance throughout my studies including this research. I dedicate this thesis to him for one 
decade love and devotion and our joy of life, Jiwan. 
Finally, I want to thank my beloved parents to whom I am indebted forever for their love and 
affection. 
iv 
 
Table of Contents 
 
 
Chapter 1: Introduction ....................................................................................................... 1 
1.1. Research Motivation and Objectives ................................................................ 1 
1.2. Organization of the Thesis ....................................................................................... 3 
Chapter 2: Background and Literature Review .................................................................. 5 
2.1. Dynamic Messages Signs ................................................................................. 5 
2.2. DMS Process and Operations ........................................................................... 5 
2.3. DMS Types ....................................................................................................... 6 
2.3.1. Portable vs. Permanent Signs ...................................................................... 6 
2.3.2. Dynamic Features ....................................................................................... 7 
2.4. Message Types .................................................................................................. 7 
2.5. Danger/Warning Messages ............................................................................... 9 
2.5.1. Incident Messages ....................................................................................... 9 
2.5.2. Road and Vehicle Unpredicted Condition Warning Messages ................... 9 
2.6. Informative/Common Road Condition Messages ............................................. 9 
2.6.1. Travel Time Messages ................................................................................ 9 
2.6.2. Congestion Messages ................................................................................ 10 
2.6.3. Queue Warning Messages......................................................................... 10 
2.6.4. Weather-Related Messages ....................................................................... 11 
2.6.5. Railroad Crossing Messages ..................................................................... 11 
2.7. Regulatory/Non-Traffic Related Messages ..................................................... 12 
2.7.1. Public Service Announcement Messages ................................................. 12 
2.7.2. AMBER Alerts.......................................................................................... 12 
2.8. Inappropriate uses of DMSs ............................................................................ 13 
2.8.1. Traffic-Related Messages ......................................................................... 14 
2.8.2. Non Traffic Related Messages .................................................................. 15 
2.8.3. Sources to Disregard the DMSs ................................................................ 15 
2.9. Location of Dynamic Message Signs ............................................................. 16 
2.10. DMS Performance Metrics ............................................................................. 18 
2.11. Studies Related to Designs of DMSs .............................................................. 18 
Chapter 3: Driver Response Behavior to Messages and Localized Impact of DMSs ...... 21 
3.1. Driver?s Response to Displayed Messages ..................................................... 21 
3.1.1. Route Diversion in Response to Messages ............................................... 23 
3.1.2. Speed Reduction in Response to Messages .............................................. 26 
3.2. Effect of DMSs Design on Driver Response .................................................. 26 
3.2.1. Text-based vs. Graphic-aided Messages ................................................... 26 
3.2.2. Flashing vs. Static Messages..................................................................... 28 
3.3. Localized Impact of DMSs ............................................................................. 28 
3.3.1. Traffic Speed Slow Down for Perception of Messages ............................ 28 
3.3.2. Driver Distraction and Collision Occurrence ........................................... 31 
3.4. Summery ......................................................................................................... 32 
Chapter 4: Investigation on Possible Relationship between DMSs and Occurrence of 
Road Accidents ............................................................................................... 37 
v 
 
4.1. Problem Statement and Motivation of Research ............................................ 37 
4.2. Methodology ................................................................................................... 38 
4.2.1. Data Sources and Preparation ................................................................... 38 
4.2.2. Accident Database .................................................................................... 39 
4.2.3. DMS Database .......................................................................................... 41 
4.2.4. AADT Database ........................................................................................ 43 
4.3. Data Processing and Preparation Challenges .................................................. 44 
4.4. Defining the Impact Area of DMS .................................................................. 45 
4.5. Case Study on I-95 .......................................................................................... 45 
4.5.1. Analysis of Case Study and Preliminary Results ...................................... 47 
4.6. Weather Conditions Database ......................................................................... 58 
4.7. Log of Messages Database .............................................................................. 61 
4.8. Analysis and Results ....................................................................................... 63 
4.9. Analysis on Impact Areas and Following Segment ........................................ 64 
4.9.1. Findings..................................................................................................... 71 
4.10. On-and-off Analysis........................................................................................ 73 
4.10.1. Findings..................................................................................................... 76 
4.11. Accidents in DMS Impact Areas and Weather Conditions ............................ 78 
4.12. Accidents in DMS Impact Areas and DMS Characteristics ........................... 81 
Chapter 5: Conclusions and Directions for Further Research .......................................... 84 
5.1. Summary and Conclusions ............................................................................. 84 
5.2. Future Research .............................................................................................. 88 
References ???????????????????????.????????????90 
 
 
vi 
 
List of Tables  
 
Table  2.1. Message Categorization ..................................................................................... 8 
Table  2.2. Example Performance Indicators for Dynamic Message Signs....................... 19 
Table  3.1. Results from driver surveys (Wendelboe, 2008) ............................................. 22 
Table  3.2. Literature Summary on Driver Response to Diversion Messages ................... 33 
Table  3.3.  Literature Summary on Driver Response to Speed Reduction Messages ...... 34 
Table  3.4. Driver Distraction and Speed Slow Down for Perception of Messages .......... 35 
Table  4.1. Variables used in case study ............................................................................ 49 
Table  4.2. I-95 Case Study Samples ................................................................................. 50 
Table  4.3. Tower stations assigned to each weather region .............................................. 58 
Table  4.4. Tabulated facts of impact areas and forwarding segments .............................. 69 
Table  4.5. Tabulated facts of on and off study ................................................................. 74 
Table  4.6. Accidents in DMS areas and precipitation ...................................................... 78 
Table  4.7. DMS accidents and wind gust ......................................................................... 79 
 
vii 
 
List of Figures  
 
Figure  2.1. Fixed location vs. portable DMSs .................................................................... 7 
Figure  2.2. A Queue Warning Message ............................................................................ 11 
Figure  4.1. The databases and sources of data used in the research ................................. 38 
Figure  4.2. Study Area ...................................................................................................... 39 
Figure  4.3. First shape of accident data and pointing location of accidents on road 
network map...................................................................................................................... 40 
Figure  4.4. First shape of DMS database and projection to road map .............................. 42 
Figure  4.5. Map of accidents and DMS locations ............................................................. 43 
Figure  4.6. An example of the volume map AADT (SHA 2011) ..................................... 44 
Figure  4.7. Impact Area .................................................................................................... 46 
Figure  4.8. I-95 along with the DMSs along this highway ............................................... 46 
Figure  4.9. Accidents in I-95 ............................................................................................ 47 
Figure  4.10 . Projection of AADTs to road map............................................................... 48 
Figure  4.11. Multiple Buffers along I-95 .......................................................................... 49 
Figure  4.12. SAS outcomes of unbalanced two-way ANOVA for case study in I-95 ..... 52 
Figure  4.13. SAS outcomes of Poisson regression for case study in I-95 ........................ 54 
Figure  4.14. SAS outcomes of Negative Binomial regression for case study in I-95 ...... 56 
Figure  4.15. Weather Database Format ............................................................................ 60 
Figure  4.16. Log of Messages Database ........................................................................... 62 
Figure  4.17. Projection of Integrated Database ................................................................ 65 
Figure  4.18. Close up shot of projected map .................................................................... 66 
Figure  4.19. Accident rate for impact area of 900 feet compared to their subsequent900 
feet segment ...................................................................................................................... 68 
Figure  4.20. Difference of the accidents rates between the impact area and its subsequent 
segment ............................................................................................................................. 70 
Figure  4.21. SAS outcomes for comparison of impact areas and following section ........ 71 
Figure  4.22. Comparison of accident rates while DMS are on and while blank .............. 75 
Figure  4.23. Difference of the accidents rates in on and off study ................................... 75 
Figure  4.24. SAS outcomes for on and off study .............................................................. 76 
Figure  4.25. Frequency of accidents  in different precipitation conditions .................... 79 
Figure  4.26. DMS accidents and wind gust ...................................................................... 80 
Figure  4.27. Type of accidents in DMS area # ................................................................. 81 
Figure  4.28. Number of accidents versus Beacon status .................................................. 82 
Figure  4.29. Number of accidents for DMS message types ............................................. 83 
1 
 
1. Chapter 1: Introduction 
 
1.1. Research Motivation and Objectives 
Increasing traffic volumes over recent decades is the compelling motivation to manage 
transportation networks, increase capacity, enhance the communication capabilities of 
transportation systems, improve safety and reduce congestion. Physically increasing the 
capacity of roadways and arterials by adding lanes is not economically and 
environmentally justified most of the times and is generally seen as an ineffective 
solution in the long term. One of the most popular alternative strategies is to provide 
travelers with real time information regarding downstream traffic conditions using 
Advanced Traveler Information Systems (ATIS). Two of the main technologies 
employed in this effort are Highway Advisory Radio (HAR) and Dynamic Message Signs 
(DMS). DMSs are often regarded as the most visible form of ATIS since they are 
available equally to all road users. Some of the most popular types of messages displayed 
on DMS are weather conditions, travel time, construction information, speed limits, 
incident locations and various other public service announcements including AMBER 
alerts. While DMSs are intended to improve the efficiency and safety of road networks, 
little has been done to study the effect of the signs on driver safety. The purpose of this 
study is to determine whether or not drivers who are exposed to DMSs could be 
distracted by what the signs display and eventually be involved in an accident. 
In the State of Maryland, the State Highway Administration?s (SHA) Coordinated Highways 
Action Response Team (CHART) operates nearly 184 DMSs. The signs located on major 
highways and arterials are often used to inform motorists of delays, incidents, road closings 
and recently real-time travel times.  
2 
 
The accident and log of messages data in the study period was acquired from the Center for 
Advanced Transportation Technology (CATT) Laboratory at the University of Maryland, 
College Park and from Coordinated Highway Action Response Team (CHART) reports. 
The database was filtered and cleaned up. The DMS inventory was also provided through 
the CATT Laboratory. The DMS types in this research include permanently mounted 
overhead, roadside models and portable signs that are operated by CHART or Maryland 
Transportation Authority (MdTA). The roadway network map and AADT of roadway 
segments were obtained from Maryland Department of Transportation, State Highway 
Administration (SHA) and weather conditions databases were acquired from DOT archived 
data.  
The accidents along with DMS locations and AADT database are projected onto 
Maryland roadway map in ArcGIS 10.1. An impact area is defined to perform spot 
analysis to evaluate whether DMSs influence on drivers? operational performance.  
A case study is performed on Interstate 95 in Maryland which is regarded as a major 
highway. A sample of 70 road segments is chosen based on homogeneity in geometry. 
Regression analysis is performed based on the fact that the segment is an impact area or 
not, the segment includes interchanges or not and what the AADT of the segment is. 
Besides, an unbalanced two-way ANOVA is used to compare mean accident rate in 
impact areas and other segments.  
The study area is divided into 5 regions and the nearest central weather tower station in 
each region is assigned to represent the weather condition in each region.  The weather 
database is accumulated for the four-year study period and then joined to the main 
database based on closest weather tower station to the time and location of accident. The 
matching process is performed using SQL queries coded in C++. 
3 
 
The message log database is imported in SQL server along with the main database. For 
each accident if it is located in impact area, the assigned DMS is matched with the 
message displayed at the time of occurrence of accident. Likewise, the matching process 
is conducted using SQL queries coded in C++.  
The integrated database was analyzed in several aspects. To determine the effects of 
DMSs on occurrence of accidents, accident rates in DMS impact areas and adjacent 
segment were compared using paired t-tests. 
An on-and-off study is conducted to compare the results for the previous study. The 
difference in accident rates is tested on two DMS operation status, when they display 
messages and while they are blank, using one-way ANOVA with pairwise comparison 
test.  
Ultimately statistical analyses on DMS characteristics, message types, weather conditions 
and accidents in impact area are performed.    
The finding and methods of this research could be applicable for state officials and 
transportation and ITS agencies to analyze, evaluate and improve their DMS operations. This 
thesis focused on DMS operation in the state of Maryland and the methods employed for 
evaluation are extendable to other locations. 
1.2. Organization of the Thesis 
This thesis comes in five chapters. Chapter 2 reviews the literature on DMS operation, 
design and type of the messages that are displayed on DMSs. Chapter 3 is on the driver 
behavior, response to messages and localized safety impacts of these signs. It provides a 
comprehensive review on study methods and research to evaluate effectiveness and 
safety impacts of DMSs. Chapter 4 investigates the possible relationship between DMSs 
4 
 
and occurrence of road accidents, and describes the motivation and methodology of this 
thesis along with all analysis and results. The results of the study and suggested future 
research are given in Chapter 5. 
5 
 
2. Chapter 2: Background and Literature Review  
2.1. Dynamic Messages Signs 
Maryland Manual on Uniform Traffic Control Devices defines Dynamic Message Signs 
as ?A sign that is capable of displaying more than one message, changeable manually, by 
remote control or by automatic control. These signs are called Dynamic Message Signs in 
the National Intelligent Transportation Systems (ITS) Architecture?. Dynamic Message 
Signs (DMS), also known as Variable Message Signs (VMS) or Changeable Message 
Signs (CMS), can be used by transportation authorities and operating agencies to 
disseminate travel information on a near real-time basis.     
DMSs are valuable instruments and according to Deployment Tracking Database of 
Federal Highway Administration, it is estimated that more than $330 million has been 
spent in the deployment of DMSs in the United States (Dudek, 2008). The main goal of 
DMSs is to enhance motorist safety and provide real-time traffic information to motorists 
allowing them to make intelligent travel decisions ahead.  
2.2. DMS Process and Operations 
The information displayed on DMSs is gathered from a variety of traffic monitoring and 
surveillance systems and means including video detection systems, loop detectors, 
automatic vehicle identification transponders and toll tags and is reported to Traffic 
Management Centers (TMC). Travel time messages is derived by applying an algorithm 
which calculates the distance covered to determine the estimated travel times from a 
DMS to specific destination. The destination is usually considered as a major intersection 
or interchange. In most jurisdictions the travel time information is posted during morning 
and evening peak travel times and the system is generally timed to begin and end at a 
6 
 
certain time of day. The TMC operator is responsible for monitoring, interpretation and 
decision making for posting the messages.  
2.3. DMS Types 
Dynamic Message Signs can be divided into permanent and portable with respect to 
installation. They also can be equipped with beacon and/or can have flashing messages. 
2.3.1. Portable vs. Permanent Signs 
DMSs can be fixed (overhead or roadside) or portable. Either fixed location or portable 
DMSs are used to support incident management and informative functions. Fixed DMSs 
can be deployed above the arterials and highways, bridges, tunnels or toll plazas. The 
portable truck or trailer mounted DMSs are sometimes dispatched by highway agencies 
to warn drivers of incidents such as accidents or work zones in the areas where 
permanent DMSs are not available or nearly enough to inform motorists to reduce speed 
and prevent secondary accidents. Trailer-mounted DMSs are used to alter traffic patterns 
near work zones and to manage traffic in special occasions such as sporting events, 
natural disasters and other temporary changes in normal traffic patterns. Most of 
manufacturers produce trailers that comply with the National Transportation 
Communications for Intelligent Transportation System Protocol (NTCIP) to allow the 
portable trailer to be integrated with an intelligent transportation system. Trailer-mounted 
DMS signs can be equipped with radar, cameras and other sensing devices as part of a 
smart work zone deployment. Figure 2.1 shows fixed location and portable DMS signs. 
7 
 
 
Figure 2.1. Fixed location vs. portable DMSs 
  
 
2.3.2. Dynamic Features 
DMSs can be equipped with flashing beacons, which are typically installed on top of the 
message panel. They are usually yellow in color and should meet the requirements as 
commanded by NTCIP communications protocol in size and shape. The displayed 
messages on DMSs can also be flashing or blinking especially in the school zones, but 
since flashing line messages might have an adverse effect on comprehension of messages 
(Dudek, 2005), these types of messages are not very common.   
 
2.4. Message Types 
DMSs warn motorists regarding different situations and provide real time information on 
traffic, roadway and environmental conditions, location and expected duration of incident 
related delays, alternate routes for a roadway closure, redirected routes for diverted 
drivers and traversable shoulders in the event of a major incident to restore the traffic 
flow safely (Farradyne, 2000). 
They are primarily used to display the following messages (Dudek, 2008): 
8 
 
- Random and unpredictable situations such as crashes, stalled vehicles, spilled loads 
- Temporary and preplanned activities such as construction, maintenance or utility 
operations 
- Adverse environmental situations such as fog, floods, ice and snow, etc. 
- Special events such as road closures because of sport games and parades 
- Traffic flow operational initiatives such as high occupancy, reversible, exclusive or 
contraflow lanes. 
- Certain design features such as drawbridges, tunnels and ferry services. 
- Travel-time information 
- AMBER (America?s Missing: Broadcast Emergency Response) alerts to help locate 
missing people 
Ridgeway categorizes messages into three types. The types are as follows: Danger/ 
Warning Messages, Informative/Common Road Conditions and Regulatory/Non-Traffic 
Related.  Table 2.1 shows type and example of messages in this classification. 
 
 
Table 2.1. Message Categorization 
 
 
Message Category Examples of Displayed Messages 
Type 1: Danger/Warning Incidents, Disabled Vehicles, Non-recurring Slow-
 Downs, Roadway Debris, Unplanned Lane/Tunnel/ 
Bridge Closures 
Type 2: Informative/Common 
Road Condition 
Roadwork Closures, Major & Minor Delays, Congestion, 
Travel Time, Other travel related messages (Fog, Ice, 
Snow Plowing, Major Events) 
Type 3: Regulatory/Non-Traffic 
Related 
Work Zone Speeds, Seatbelt Use, Cell Phone Regulations, 
Motorcycle Awareness, Amber Alerts, Homeland 
Security Messages 
9 
 
2.5. Danger/Warning Messages 
2.5.1. Incident Messages 
One of the main functions of DMSs is to alert motorists of lane closures due to traffic 
incidents and accidents as unexpected situation to reduce roadway capacity. The message 
can be displayed due to any traffic incident; however no message should be displayed if 
the sign is at such a distance from the affected area that full capacity will be restored 
before motorists reading the sign would be impacted.  Conversely, if the incident is 
confined to an adjoining route such that motorists in that route would be affected, a 
message should be displayed.  Depending on the location, severity and duration of the 
incident, messages may be displayed up to several hundred miles in advance of the 
incident. If a situation arises whereby multiple incidents are downstream from a sign, 
DMS shall alert motorists to the closest incident unless conditions warrant otherwise 
(NJDOT, 2008). 
2.5.2. Road and Vehicle Unpredicted Condition Warning Messages 
These types of messages inform drivers of special issues with respect to road and vehicle 
conditions including changes in roadway alignment or surface conditions, disabled 
vehicle, vehicle restrictions and advance notice of new traffic control device installation 
(Walton et al, 2001).  
2.6. Informative/Common Road Condition Messages 
2.6.1. Travel Time Messages  
These types of messages inform drivers in five ways:  
1. Travel time on freeways which is the time in minutes required to traverse from one 
specified location to another 
10 
 
2. Comparative travel times on the freeway and alternate route 
3. Time saved by taking an alternate route 
4. Delay on the freeway 
5. Delay avoided by taking the alternate route 
2.6.2. Congestion Messages 
DMSs are used to present information on traffic conditions when the freeway becomes 
congested. The problem regarding these messages involves the large continuum of 
possible traffic operational conditions that are difficult to describe on DMSs. In 
jurisdictions where quantitative travel time information is not available, terms such as 
?Heavy Delay? and ?Major Delay? are often used. Little information or guidance exists on 
how these terms are defined. However, according to the Dynamic Message Sign Message 
Design and Display Manual, the average motorist in Texas interprets ?Heavy Delay? as being 
between 25 and 45 minutes while a ?Major Delay? is interpreted as a delay greater than 45 
minutes. Similarly, a study in England to determine driver response to Dynamic Message 
Signs found that ?Long Delays? were interpreted as delays between 35 and 47 minutes, while 
?Delays Likely? indicated a 10 to 31 minute delay. In contrast, the Minnesota Department of 
Transportation?s Guidelines for Changeable Message Sign (CMS) Use specifies that a 
?Major Delay? is not indicative of an amount of time but rather an incident causing more 
than 2 miles of traffic backup. These conflicting definitions alone demonstrate the need for 
high quality evaluation of DMS messages and the conditions to which they correspond (Fish 
et al, 2012). 
2.6.3. Queue Warning Messages 
Queue warning messages have been employed in Germany on several motorways. The 
queue warning messages vary in appearance, scope and complexity. A queue warning 
11 
 
system uses a small roadside DMS with flashers to indicate the length and location of the 
queue. Germany Transportation Policy strongly emphasizes on comprehensive 
communication of the queue warning on the message signs using minimal wording and 
simple imagery. Benefits gathered from the German queue warning system include fewer 
incidents, reduced incident severity, closer headways, greater uniformity on all driver 
speeds and a slight increase in capacity (Bolte, 2006). Figure 2.2 depicts a dynamic queue 
warning message sign. 
 
Figure 2.2. A Queue Warning Message 
 
2.6.4. Weather-Related Messages 
One of most common uses of DMSs is to display weather information that affects traffic. 
DMSs are used to advise motorists of severe weather or environmental conditions in the 
area, especially the situations which requires a change in the driving behavior of 
motorists (NCDOT, 1996) and (ORDOT, 2000).   
2.6.5. Railroad Crossing Messages  
One of the applications of DMSs is where roadway and railroad meet. According to 
Finely et al. (2001), since traffic conditions can also be affected by rail systems, railroad 
grade crossing information can be available via DMSs. An example of application of 
12 
 
DMSs in railroad crossing area is in San Antonio, where displaying the real time 
information on these messages allows drivers to alter their routes to avoid a lengthy wait 
for a crossing train. 
2.7. Regulatory/Non-Traffic Related Messages 
2.7.1. Public Service Announcement Messages 
The use of DMSs for Public Service Announcement (PSA) is accepted by some agencies; 
however the type of messages that are permitted depends on each jurisdiction. PSAs 
include brief messages that do not require an immediate response but encourage drivers 
to alter a future driving behavior. Since PSAs do not provide drivers with real-time safety 
or travel efficiency information and usually are not associated with any urgent response, 
these messages are generally given low priority. PSAs provide motorists with information 
that can be given more effectively through other methods such as media campaigns or 
pamphlets (NCHRP, 2008). Another argument in support of not displaying public service 
announcement messages is the concern that motorists who continually travel a specific 
route will become accustomed to them and then begin to ignore the DMSs. For example, 
in State of Oregon Department of Transportation the very lowest priority is given to 
PSAs and they are displayed only in off-peak periods for a maximum of 5 hours a day 
and 5 days a month. In addition, these messages are generally restricted to permanent 
DMSs and not permitted on portable DMSs (ORDOT, 2000). 
2.7.2. AMBER Alerts 
AMBER alerts are notification programs to help locate missing children believed to have 
been abducted. The Emergency Alert System (formerly known as the Emergency 
Broadcast System) is used to alert the public by means of television and radio in the event 
13 
 
of an AMBER alert (NCHRP, 2008). America?s AMBER Plan Program through which 
emergency alerts are issued to notify the public about potential abductions of children is 
voluntary.  Federal Highway Administration notes that DMS signs are not always the 
most effective or safest method to disseminate information related to child abductions 
and just a limited amount of information can be conveyed on them. When there is a need 
to provide extensive information to motorists, FHWA states that it is critical that other 
types of traveler information media such as 511, HAR, informative websites and 
commercial radio be used and DMSs play a supplementary role besides these media. 
2.8. Inappropriate uses of DMSs 
A national policy on DMS use and message design does not currently exist and 
transportation authorities are responsible to create and implement their own guidelines on 
the use, location, operation and evaluation of DMSs in their area. Mounce et al. (2007) 
assessed current DMS applications and practices based on a National literature reviews 
and agency surveys and found that majority of respondents in the survey believed that 
one of the major benefits of DMS is to provide timely and important information about 
the travel routes. The survey revealed that although most DMS applications are 
considered effective, there are some sources for concerns among the respondents, 
including information overload, adverse traffic impacts and lost motorist confidence. The 
results of the survey also indicated that although DMS evaluations are generally 
conducted in conjunction with an entire ITS evaluation,  very little has been done 
regarding the evaluation of DMS?s and special considerations should be given regarding 
the unique ability of DMSs as well as the message content, location and evaluation of 
DMSs to aid in creating successful DMS systems.  
14 
 
According to Mounce et al. (2007) all messages are prioritized by the following order:  
1. Safety related: messages that are directly related to safety are given first priority for 
display. Examples of this type of messages include winter traction device requirements, 
mountain pass information or flammable restrictions. 
2. Roadway closures: DMSs are used to display road or ramp closures, regardless of the 
reason for the closures (accident, construction, weather, etc.). 
3. Minor traffic impacts: DMSs are used to display information about minor traffic 
impacts, such as construction lane closures, blocking incidents and delay information. 
4. Public text messages: as mentioned in the previous section, the least priority messages 
displayed on DMSs are transportation related Public Service Messages. These messages 
do not directly impact motorists and therefore are not critical to the safe and efficient 
operation of the transportation system. Examples of these messages are Click It or Ticket, 
Rideshare information or announcements about traveler information phone numbers like 
511. 
5. Test messages: these types of messages are used to perform sign operation or 
maintenance checks and to ensure proper operation of new DMSs. 
 
2.8.1. Traffic-Related Messages 
The Kentucky Transportation Center notes several inappropriate usages of DMS (Walton 
et al., 2001). A particular inappropriate application of DMS is the use of DMS messages 
to restate or replace required permanent signage. This could result in serious problems of 
information overload and driver inattention to DMS. Specifically, DMS messages should 
15 
 
not replace static signs, regulatory signs, pavement markings, standard traffic control 
devices, conventional warnings or guide signs. 
2.8.2. Non Traffic Related Messages 
Policies regarding the display of non-traffic-related messages on DMS are not consistent. 
The Manual on Uniform Traffic Control Devices states that DMSs should not be used to 
display information other than regulatory, warning and guidance information related to 
traffic control. Some policies state that messages displayed on DMSs must require 
motorists to take an action or alter their driving behavior (NCDOT, 1996) and (Johnson, 
2001). There is a consensus that DMS should not be used to advertise commercial events 
or entities. Additionally, tourist information should not be provided via DMS (NCDOT, 
1996; ORDOT, 2000; Walton, 2001; and Jones et al., 2003). 
2.8.3.  Sources to Disregard the DMSs 
Dudek (2008) further specifies DMS problems that lose the motorists? confidence:  
- Displaying inaccurate or unreliable information 
- Displaying information too late for drivers to make an appropriate response 
- Displaying messages that drivers do not understand 
- Displaying messages that are too long for drivers to read 
- Not informing drivers of major incidents 
- Informing drivers of something they already know 
- Displaying information not related to environmental, roadway or traffic conditions 
or routing, and 
- Displaying garbled messages 
16 
 
If any of these errors are committed by DMS operators, motorists are likely to disregard 
these signs. Influencing the decisions of motorists is necessary for a DMS to be effective. 
2.9. Location of Dynamic Message Signs 
DMS locations are generally established through prior experience with the local traffic 
problems. Recently researchers have experimented with computer programs that can 
more precisely locate signs. These methods have not yet been implemented by any local 
traffic management agency responding to the survey. The locations of DMSs are often 
determined through unwritten current practice and general policies. Agencies seldom 
implement methods to ensure that specific DMS locations are optimal. Two applicable 
methods for optimizing DMS locations include genetic algorithms and integer 
programming. Abbas and McCoy (1999) have researched the use of genetic algorithms 
for this purpose. They indicated that their decision to implement genetic algorithms was 
based on several factors including the fact that Genetic algorithms give several solutions, 
not just one ?best? solution and additionally, the constraints required in genetic 
algorithms are less than those necessary to find an integer programming solution (Abbas 
et al., 1999). 
Chiu et al. (2001) researched the use of integer programming to optimize DMS locations. 
With a given number of DMSs, possible locations were determined and analyzed. 
Optimal locations were chosen so that the long-run expectation of benefits was satisfied 
under stochastically occurring incident scenarios. They stated that the main benefit of 
correctly locating DMSs was the reduction in total user travel time. Implementation of 
the programming required numerous inputs to describe geometry and traffic patterns of 
the highway network. The problem was simulated using a dynamic traffic assignment 
17 
 
algorithm, which aided in determining the effectiveness of DMS locations. It was 
necessary that each location had a high probability of capturing the randomly occurring 
incidents and then could effectively divert traffic. The final solution generated by the 
integer-programming model determined the optimal location for all incident scenarios on 
the system. The solution might not be optimal for an individual incident (Chiu et al., 
2001).  
Chiu and Huynh (2007) combined a mesoscopic dynamic traffic assignment simulation 
with a tabu search heuristic to optimally locate DMSs. Incidents were randomly 
generated using a Monte Carlo scheme and some drivers would switch routes if their path 
encounters an incident and a DMS sign; based on the resulting flow patterns, a set of 
DMS locations was determined to optimize some measure of effectiveness (Chiu et al., 
2007).  
Huynh et al. (2003) used a similar analysis framework to find the optimal locations of 
portable DMSs in a real-time framework using the G-D heuristic. Although the 
simulation approach allowed a rich set of traffic and behavioral impacts to be modeled, 
the computational burden associated with many simulation runs on a large network could 
be troublesome. This limitation was realized by Henderson (2004), who adopted a static 
equilibrium framework for DMS location, together with a discrete choice model to 
determine the proportion of drivers who switch routes in response to learning of an 
incident. Henderson (2004) developed and compared several heuristic techniques 
including a genetic algorithm and a greedy approach based on sequential location. While 
computationally faster, the approaches implicitly assumed that drivers did not anticipate 
receiving information which means their initial route choice was not affected by the DMS 
18 
 
locations, so links with a DMS did not "attract" drivers who anticipate benefitting from 
that information, for instance (Hendeson, 2004). Although this distinction may seem 
subtle, this anticipation effect could lead to radically different route choices for rational 
drivers, even from the origin (Boyles, 2006).  
2.10. DMS Performance Metrics 
Tarry (1996) defined performance indicators expressly for evaluation of DMSs. Table 2.2 
presents examples of performance indicators for DMSs.  To produce appropriate driver 
response, the messages displayed on DMSs must be meaningful, accurate, timely and 
useful. According to Dudek (2006), if the messages displayed on DMSs do not have 
adhered to the guidelines of Dynamic Message Sign Message Design and Display 
Manual, operator?s credibility is lost. 
 
2.11. Studies Related to Designs of DMSs  
Extensive human factors and traffic operations research has been previously conducted to 
develop fundamental principles and guidelines for DMS message design including 
alphanumeric messages, graphics and symbols. Using these fundamental principles, 
guidelines for effective message design and display for TxDOT have been published in 
Report 0-4023-P3 Dynamic Message Sign Message Design and Display Manual (Dudek, 
2006). The use of graphics or symbols on DMSs has been employed in many European 
countries such as Germany and Spain but has not yet gained widespread popularity in the 
United States.  
Nyg?rdhs (2011) reviewed the literature of Dynamic Message Signs focusing on a large 
number of studies done from 2006 to 2009. This literature review reached the following 
findings about design of DMSs:  
19 
 
Table 2.2. Example Performance Indicators for Dynamic Message Signs 
 
Evaluation Category  Indicators  
Technical Analysis  ? Reliability and correctness of information displayed  
? Appropriateness of plans  
? Operator interface usability 
? Sensitivity to errors in inputs 
? Level of operator intervention needed  
Impact Analysis  ? Degree of diversion at nodes  
? Reduction in delays and extent of queuing  
? Change in travel time on individual routes  
? Change in total travel times and journey distances in the network  
? Reduction in the duration of congestion  
? Reduction in emissions  
? Driver response to: range of information types, travel cost differences on alternative 
routes and driver familiarity with the network  
? Reduction in traffic diversion through urban areas or on the undesirable routes  
? Number of accidents  
Socioeconomic Analysis  ? User cost-benefit analysis of performance network  
? Impact on non-road users   
Legal/Institutional Analysis  ? Legal/institutional conflicts  
Public Acceptance Analysis  ? User attitudes to DMSs  
? Non-user attitudes to DMSs  
 
1. Graphic-aided messages are significantly better than text-only messages in terms 
of preference, response time and accuracy and should be used as much as possible. 
2. Red color is not recommended for DMS messages. 
3. Older drivers? performances were significantly improved by graphic-aided 
messages. 
20 
 
4. Graphic-aided DMS messages enhanced message comprehension time for non-
 native English speakers. 
5. More research is required to find out the proper specifications and design 
guidelines of these graphical images to be used on DMS messages. 
6. The number of lines on DMS should be kept to a minimum. 
7. Bilingual signs should only be used when absolutely necessary. 
8. If bilingual signs are used, different colors or type fonts should separate the 
languages. 
9. The number of information units may be better correlated to DMS reading time 
than the number of lines displayed. 
10. A blank ?off-screen? with short duration may enhance information processing 
when successive DMS frames are used. 
11. Right-justified text on DMS should be avoided. 
12. Abbreviations could decrease understanding of DMS if they are not very 
commonly known. 
13. Luminance class L3 is preferable for symbols on DMS. 
14. A three diode symbol thickness leads to better legibility than one or two diodes 
thickness. 
21 
 
3. Chapter 3: Driver Response Behavior to Messages and Localized Impact 
of DMSs 
3.1. Driver?s Response to Displayed Messages 
The existing studies for evaluating driver response to DMS messages mainly focus on 
DMS objectives of route choice guidance and improving road network performance and 
speed slowdown in correspondence to messages. From the literature review, it is evident 
that the acceptance of DMS is associated with the travelers? perception and their subjective 
attitudes towards information and its presentation. Most of the studies have found that 
demographic and socio-economic characteristics are important factors in assessing the 
satisfaction of the travelers towards a novel traveler information technology like the DMS 
signs. However, travelers also have specific preferences about the formats and contents of 
messages and information posted on the DMS. While most of the studies show that the 
travelers adopt DMSs for their traveler information needs, DMS do not necessarily change 
their travel behavior. Network familiarity, proactive information and advisory information 
have been found to have different effects at different locations of the study (Rogers, 2005). 
Multinomial and binomial logit models have been predominantly used to model the diversion 
behavior under traveler information scenarios with DMSs. The effect of DMSs has been 
found to vary in different study sites.  
Wendelboe (2008) performed a research on driver response to DMS messages in 2008 
based on driver surveys. Table 3.1 shows the results and conclusions of the surveys.  
The literature review conducted by Nyg?rdhs (2011) concluded the following findings 
about the issue of DMSs and driver reaction to messages:  
 
22 
 
Table 3.1. Results from driver surveys (Wendelboe, 2008) 
 
Respondents who:  
 
Percent 
Understood variable speed limits (VSL) correctly  82%   
Perceived queue information correctly    88%   
Perceived queue information correctly when information about 
distance to the rear end of the queue was added   
 61%   
Had a generally positive attitude to VSL    84%   
Thought VSL had a positive effect on traffic flow    58%   
Thought VSL had a negative effect on traffic flow    12%   
Thought VSL had a positive effect on traffic safety    33%   
Thought VSL had a negative effect on traffic safety    3%   
Had a generally positive attitude to queue information    86%   
Had a generally negative attitude to queue information    5%   
 
1. DMSs are effective in rerouting traffic. 
2. Supplementary information of DMSs may not enhance behavior concerning 
compliance. 
3. Reading and processing text messages on DMS leads to speed reductions. 
4. Displayed delay times on DMS are correlated to diversion patterns.  
5. Factors correlated to unwillingness to divert from the freeway are driving employer-
 provided cars, frequency of driving on the freeway and being middle-age. 
There is some concern that more frequent use of non-incident and non-roadwork 
transportation-related messages can compromise the credibility of the DMS?s. If DMS?s 
distract drivers from more critical tasks while traveling at prevailing speeds or if the 
23 
 
messages are erroneous or outdated, then driver acceptance can be compromised. In 
addition, if the messages are too long, complex and/or confusing to read and comprehend, 
drivers may reduce speed to read the messages and this could result in a potential safety 
problem (Dudek, 2008). 
3.1.1. Route Diversion in Response to Messages 
Many researchers have studied drivers' attentions and responses to DMSs. To evaluate the 
effectiveness of DMSs for route choice guidance, some researchers have tried to estimate 
a route choice model for predicting how drivers respond to the information provided by 
DMS and whether the drivers will divert to avoid an incident or congestion on road. 
Many researchers used surveys or simulations to gather the data regarding the behavior of 
motorists in response to DMS messages. The surveys used revealed preference or stated 
preference questionnaires of hypothetical situations (Khattak et al., 1993, Wardman et al., 
1998, Abdel-Aty, 2000, and Hao et al, 1999). Fish (2012) presented empirical evaluations 
of the quality and effectiveness of highway DMSs and introduced Bluetooth sensor 
technology as a new method for evaluating messages posted on DMS for both the 
accuracy of the content as well as the influence they may have on travel behavior.  The 
results showed that diversion messages are effective in route choice decisions of 
motorists.  
The study of incident impacts on driver behaviors have focused on changes at the 
strategic behavior level, particularly changes in the route choice behavior. Incident 
messages include accident, lane closures and traffic merge messages. Several researchers 
have used the stated preference approach in an attempt to determine the percentage of 
travelers changing trip decisions in response to information disseminated by ATIS 
24 
 
devices such as DMSs. The studies concluded based on this type of surveys that the 
disseminated information can result in up to 60-70 percent of the freeway traffic exiting 
the freeway ahead of a bottleneck, like an incident location and as a result 30 to 40 
percent reduction in congestion (Barfield et al., 1989, Benson, 1996, Madanat et al., 
1995, and Chatterjee et al., 2002). However, limited information is available about the 
actual diversion due to traveler information as reflected by revealed preference or field 
measurements. Several European field studies have found that DMS compliance rates 
range between 27-44% (Tarry et al., 1995). Knopp et al. (2009) found that for major 
incidents, up to 50% of the travelers take another route. Schroeder et al. (2010) 
investigated the impacts of existing message strategies to determine messages that 
maximize diversion for specific circumstances and to develop new messages for future 
deployment.  
Ullman et al. (2005) evaluated DMS messages to determine which displayed message drivers 
found the most effective in an emergency situation. The study concluded that during 
emergencies, DMS messages should provide meaningful and straightforward messages that 
can be read and responded to quickly because their impact on drivers can be huge.  
In a questionnaire survey, Benson (1996) investigated whether drivers noticed and thus 
responded to DMSs. The author found that about 20% out of 500 subjects ignored active 
DMSs while driving. Interview surveys conducted by Bonsall (1993) in Paris revealed that 
97% of the drivers knew that DMSs existed, 84% identified DMSs as providing very useful 
information and 46% had at least once detoured accordingly.  
Peng et al. (2004) conducted a similar study in Wisconsin. The results indicated that 62% of 
the drivers responded to DMS messages more than once per week and 66% of them changed 
their route at least once per month due to the posted message.  
25 
 
Khattak (1993) suggested that diversion behavior was influenced by the accuracy and 
detail of information, including travel times and alternate choices and knowledge of 
nature of the event and actions to clear it in case of incidents.  
The study done by Roshandeh and Puan (2009) attempted to utilize archived traffic data 
from a freeway area in Kuala Lumpur to assess the accuracy with which DMS display 
travel time estimates and driver response to display messages of varying lengths and 
formatting. Results showed that usage of DMSs reduce the average travel times during 
the duration of the incident until the clearing of the resulting congestion by a significant 
amount.  
Levinson and Huo (2003) conducted a on and off study using data from inductive loop 
detectors placed on different networks located in the Twin Cities of Minneapolis and St. 
Paul, Minnesota. The purpose of this study was to measure the effectiveness of the 
DMSs. Using the traffic flow and occupancy data, a discrete choice model was developed 
to forecast the percentage of vehicles that diverted to alternative route based on the 
message displayed. Results showed that drivers? diversion increased when a warning 
message about the traffic conditions was displayed and that DMSs can reduce the total 
delay.  
Peeta (1991) found that the location of an incident and its duration also affected route 
choice. In a survey conducted in Virginia, it was found that drivers? characteristics such 
as age, education, income and sex have no significant influence on their attitude towards 
DMS messages (United States Department of Transportation, 2002). In Dallas, 71-85% 
of surveyed drivers used the recommended route. The factors having influence on 
diversion include traffic conditions on the alternate routes, familiarity with the alternate 
26 
 
route, and confidence in the information (United States Department of Transportation, 
2002).  
Yang (1993) also found that the route choice behavior was affected by the characteristics 
of the alternative routes. The results of this study which was based on loop detector data, 
indicated that DMS could affect vehicle diversion significantly, especially during 
congested times. DMS?s had more influence on drivers during morning peak hours than 
during evening peak hours. According to a survey conducted by Huo and Levinson 
(2002), drivers are more willing to divert if there are fewer traffic stops on the alternate 
routes and if they are familiar with the alternate routes. Their study also showed that 
young, male and unmarried drivers were more likely to divert.   
3.1.2. Speed Reduction in Response to Messages 
Benekohal and Shu (1992) performed research in university of Illinois to evaluate driver 
behavior responses to speed reduction messages in construction work zone areas. They 
employed statistical analysis techniques for treatment/control conditions when DMS sign 
is turned on and off. They found that displaying the speed limits on DMS was effective in 
reducing the average speed. Their study showed that displaying messages reduced speed 
of cars immediately after passing the sign, but not at a point far from DMS. Cars and 
Trucks reduced their speed by as much as 5 and 4 mph respectively near the DMS. 
3.2. Effect of DMSs Design on Driver Response 
Studies show that DMSs with different format and design could have different effect on 
driver behaviora. This section reviews the research which compared driver response to 
text versus graphic and flashing versus static messages. 
3.2.1. Text-based vs. Graphic-aided Messages 
27 
 
Wang et al. (2007) conducted a study on the use of graphics on DMS and found that most 
drivers preferred graphics over text and responded faster to graphic-aided messages than 
text-only messages. Due to these findings, it is suggested to use graphics in some 
advisory signs to help enhance drivers? understanding and responses to messages and 
improve the effectiveness of these signs. 
In another similar research, Bai et al. (2011) suggested that the traditional text-based 
messages have several limitations such as confusing drivers and delaying their responses 
during driving, being difficult to read for older drivers and non-English-speaking drivers 
and having a short range of legibility. Bai et al. (2011) state that use of graphic-aided and 
graphic messages on portable DMSs have many advantages over text-based ones based 
on a number of previous laboratory simulation experiments. They used field experiments 
and driver surveys to determine the effectiveness of a graphic-aided and graphic portable 
DMSs on reducing vehicle speed in the upstream of a one-lane two-way rural highway 
work zone and compared the effectiveness of text, graphic aided and graphic portable 
DMSs on reducing vehicle speed in a highway work zone in Kansas based on regression 
models of the relationship between mean vehicle speed and distance under the three 
conditions. The findings showed that: 
1. Text, graphic-aided and graphic portable DMSs resulted in a mean vehicle speed 
reduction of 13%, 10% and 17%, respectively. 
2. Graphic-aided portable DMS reduced mean vehicle speed more effectively than the 
text one from 1,475 feet to 1,000 feet in the upstream of a work zone. 
3. The majority of drivers understood the work zone and flagger graphics and believed 
the graphics drew their attention more to the work zone traffic conditions. 
28 
 
4. Most of drivers preferred the information to be presented in the graphic-aided format. 
3.2.2. Flashing vs. Static Messages  
Based on the research performed by Dudek (2005), average reading times for flashing 
messages were not higher than for static messages. However, the results indicate that 
flashing messages may have an adverse effect on message comprehension for unfamiliar 
drivers. Average reading times for flashing line messages and two-phase messages with 
alternating lines were significantly longer than the alternative messages. In addition, 
message comprehension was negatively affected by flashing line messages. 
3.3. Localized Impact of DMSs 
3.3.1. Traffic Speed Slow Down for Perception of Messages 
Oh, Hong and Park (2009) conducted a study with the aim of investigating drivers? 
(about 20-30 years old) behavioral responses to DMSs when reading and processing the 
messages in a DMS influence zone. Individual vehicle trajectories were studied via 
differential global positioning system (DGPS) and thereby speed and acceleration rates 
were used as surrogate measurements to represent driver behavior. The DMS influence 
zone was divided into five sections of 100 meters long. Results from ANOVA tests 
showed that the average speed and acceleration were statistically different in each 
section. It was found that drivers tend to reduce their travel speed while reading and 
processing DMS messages and increase speeds again after they finish reading the 
messages. 
Rama and Kulmala (2000) investigated the effects of two DMSs on drivers? car-
 following behavior. Results showed that a sign for slippery road conditions reduced the 
29 
 
mean speed by 1-2 km/hour in addition to the decrease caused by the adverse road 
conditions.  
In study performed by Wang et al (2007), the effects of DMS messages on traffic 
approaching and passing the signs were investigated. Traffic data gathered by several 
Mobility Technology Units (MTUs) near DMSs along I-95 in Rhode Island were 
analyzed. The purpose of the research was to understand the effects of various DMS 
messages on the speed variations on traffic approaching and passing the signs through 
traffic data analysis. With a positive correlation found between certain posted DMS 
messages and traffic slow-downs, the study next explored means to better the design and 
display on DMSs. A questionnaire survey was developed to find the general and specific 
causes of slow-downs. Survey results indicated that DMS was among the top few that 
caused drivers to slow down while danger warning messages attracted the most attention 
from drivers. It also showed that the majority of drivers reduced their speeds when 
approaching active DMSs while lengthy, complex or abbreviated messages caused 
further slowdowns. Their study also employed a computer based questionnaire survey 
and a driving simulation experiment to measure drivers? preferences and responses to 
various DMS displays and formats. The results showed that elder drivers exhibit a higher 
tendency to slow down.  
In a recent study, Fish et al (2012) investigated 2,268 cases of message activation, 
removal and switching using RTMS speed data to determine whether DMS messages 
cause speed slowdown. The study confirmed that in some cases traffic streams decrease 
speed in response to message activation.  
In a study conducted by Harder et al. (2003) a computer based driving simulation was used to 
test various message types to see whether a slow-down effect was evident. The results 
30 
 
showed that 21.7% of participants slowed their speed by 13.9 mph as ?AMBER? alert DMS 
messages were approached. Alternatively, when a ?Crash? alert DMS message was 
displayed, 13.3% of participants slowed their speed by 12.7 mph.  
In another study, Boyle and Mannering (2004) used a driving simulation to determine the 
impact of DMSs on drivers? speed. While it was found that drivers did slow down when 
approaching active DMSs, the study also showed that drivers speed up to compensate for 
their speed reduction after passing DMSs. Furthermore, the study demonstrated that when 
drivers encountered a new DMS message, they were more likely to have a larger deviation in 
speed. This can mean that when a new message is presented on a DMS, drivers tend to notice 
the change in message and as a result more time is needed to process the information. 
Moreover, when a DMS is displaying the same message for a long period of time, drivers 
become familiar with it and thus less time is needed to read it.  
It has been shown in several studies that the use of graphics to convey meaning on roadway 
signs provided many advantages over text-only messages. Graphic aided messages could be 
more easily and quickly identified compared to text-only messages from a further distance.  
The fact that graphically presented information allowed faster responses than information 
presented by words was found by many studies (Bruce et al., 2000; Hanowski and Kantowitz, 
1997; Staplin et al., 1990). Wang et al. (2007) conducted a study on the use of graphics on 
DMSs and found that most drivers preferred graphics over text and responded faster to 
graphic-aided messages than text-only messages. The use of graphics or symbols on traffic 
signs has been widely employed in European countries such as Germany and Spain to 
influence derivers? route choices. All of these studies and practices indicated that by adding 
graphics, it might help enhance drivers? understanding of and responses to DMSs and ease 
the slow-downs. Adding graphics to DMS messages could help enhance drivers? 
31 
 
understanding of and responses to those messages and reduce their speed variation while 
reading DMSs and might help eventually ease the slow-downs. 
3.3.2. Driver Distraction and Collision Occurrence 
Driver distraction plays a significant role in traffic safety. Driver distraction is a factor in 
one in four car crashes and of those crashes involving driver distraction; one in four 
involves distractions outside the vehicle (NHTSA, 2009). Few studies have been 
conducted on accident rates due to distractions associated with DMSs. Part of the reason 
is that unless there is a clear accident trend prior to a DMS, a definite accident rate 
formulation would be hard to determine. According to the Kiewit Center for 
Infrastructure and Transportation (2003) accident rates for a section of road can be 
determined by a ratio of accidents per million vehicle miles of travel. The normalized 
formula would allow comparing various accidents with respect to the rates of other 
stretches of roads that are not necessarily of the same length.  
Many studies focus on the impacts of DMS on driver behavior and the potential benefit 
of using DMS to reduce downstream accidents. Chamberlain (1995) demonstrated that 
the use of DMS associated with a queue detecting system could reduce accidents for 
upstream drivers who otherwise would be unprepared for queues downstream. According 
to NHTSA?s Distraction initiative, 20% of all accidents are related to some kind of 
distraction (2010). Many studies indicated that DMSs have attracted drivers' attentions 
from their driving (Wang et al., 2007). Since drivers are expecting useful information 
from active DMSs, they are slowing down to gain extra time to read and comprehend the 
messages. To compensate for their speed reduction, drivers speed up after passing DMSs. 
32 
 
Crashes are highly correlated to driving speed and this speed variation could pose a threat 
to other vehicles in the traffic and lead to crashes.  
Erke et al (2007) conducted a field test and video observation study. In their research 
messages were set on and off to observe and compare driver behavior including route 
choice, speed and braking behavior between vehicles approaching the DMSs while they 
displayed messages and while they were left blank without message. Two DMSs were 
used in this study, which displayed road closure and recommendations for alternative 
routes. Speed measurements of 3342 vehicles showed large speed reductions and video 
observations showed that large proportions of vehicles braked while approaching the 
DMSs. This research states that speed reductions and braking maneuvers can partly be 
attributed to attention overload or distraction due to the information on the DMSs. 
Besides, a proportion of the speed reductions was due to chain reactions where one 
vehicle braked and forced the following vehicles to brake or change lanes in order to 
avoid collisions. Safety problems may result directly from distraction or indirectly from 
the reactions of the drivers to the distraction. 
3.4. Summery  
Many methods have been utilized in an effort to determine the driver response when 
approaching the DMSs. Surveys, simulators, video observation and loop detector data 
have been the most common of these methods in the past and have shown some 
promising results. Table 3.2 and Table 3.3 present a summary table for previous studies 
on driver response in correspondence to diversion and speed reduction messages, while 
Table 3.4 summarizes the reviewed literature on the localized impacts of the signs. This 
33 
 
thesis uses the ground truth data integrated database to evaluate the impact of the signs on 
occurrence of road accidents.  
 
Table 3.2. Literature Summary on Driver Response to Diversion Messages 
 
Author Source Year Country Study 
Approach 
Results 
Fish 
et al. 
TRB 2012 US,  
Maryland 
Field Test/ 
Bluetooth 
sensors 
? diversion messages are 
effective in route choice 
decisions. 
Chen et al. IWMSO 
2008 
2008 China, 
Beijing,  
SP survey  ? diversion increases as the 
traffic speed decreases. (<20 
km/h). 
? 21.45% of drivers divert 
Foo & 
Abdullahi 
TRB 2008 Canada, 
Ontario 
Field 
Test/Loop 
detector 
? occurrence of a message 
change plays a vital role in 
influencing downstream 
diversion  
Cheng & 
Firmin  
12th IEE 
Int. Conf. 
2004 UK, 
London 
SP survey ? more exposure to DMS 
increases  appreciation of the 
information displayed. 
Peng  
et al. 
Trans. 
Res. 
Rec. 
2004 US, 
Wisconsin 
RP survey 
combined 
with  
? 75% are positive with 
usefulness of VMS. 
? 16% don?t trust VMS 
information and don?t 
34 
 
Author Source Year Country Study 
Approach 
Results 
logit model change their route. 
Levinson & 
Huo 
TRB 2003 US, 
Minnesota 
Field Test/ 
Loop 
Detector 
? a probit model to estimate 
diversion as a function of 
message content. 
? ahead warning is effective 
for diversion. 
Chatterjee et 
al. 
Trans. 
Res.  
Part C 
2000 UK, Leeds Survey, 
Logistic 
Regression 
? location of incident and 
message content influence 
the probability of diversion. 
 
Table 3.3.  Literature Summary on Driver Response to Speed Reduction Messages 
 
Author Source Year Country Study 
Approach 
Results 
Alm & 
Nilsson 
Trans. 
Human 
Factors 
2000 Sweden Simulation  ? all participants reduced their 
speed in response to incident 
warning messages  
Luoma  
et al. 
Trans Res. 
? Part F 
2000 Finland Simulation ? drivers reduced speed 1-2 
km/h in response to a DMS 
warning of slippery condition  
35 
 
Author Source Year Country Study 
Approach 
Results 
Benekohal& 
Shu 
Civil Eng. 
Studies 
1992 US, 
Illinoise 
Treatment 
control 
(DMS on 
and off)/  
statistical 
analysis 
? displaying the speed limits is 
effective in reducing the 
speed. 
? speed of cars reduces 
immediately after passing the 
DMS, but not at a point far 
from DMS.  
? cars and trucks reduced their 
speed by as much as 5 and 4 
mph respectively near the 
DMS. 
 
Table 3.4. Driver Distraction and Speed Slow Down for Perception of Messages 
 
Author Source Year Country Study 
Approach 
Results 
Wang  
et al. 
TRB 2009 US, 
Rhode 
Island 
Survey ? DMS cause slowdown (specially 
danger warning messages).  
? lengthy, complex or abbreviated 
messages caused further slowdowns. 
? elder drivers exhibit a higher 
tendency to slow down  
36 
 
Author Source Year Country Study 
Approach 
Results 
Erke 
et al. 
Trans. 
Res.  
Part F 
2007 Norway, 
Oslo 
Field 
Test/video 
observation 
(messages 
on/off) 
? most of vehicles braked approaching 
the DMS.  
? messages causes distraction and leads 
to speed reduction and chain 
collisions and safety problem. 
37 
 
4. Chapter 4: Investigation on Possible Relationship between DMSs and 
Occurrence of Road Accidents  
4.1. Problem Statement and Motivation of Research 
While DMSs are intended to improve the efficiency and safety of road networks, as it 
was mentioned in the literature review, little research has been done to study the effect of 
these devices on driver safety. In spite of all advantages of DMSs, some issues regarding 
the disadvantages of real-time travel signs have emerged. The news in WTOP and NBC 
are examples of the opposing side which claim besides the fact that these devices are very 
expensive, they have adverse impact on drivers? distraction and speed slow down which 
may consecutively lead in occurrence of road crashes (HSM, 2010). The purpose of this 
research is to investigate the problem and determine if there is any meaningful 
relationship between occurrence of accidents and presence of DMSs in proximity to 
them. 
For this study, accident data and DMS locations in the state of Maryland for a 
time period of 4 years from 2007 to 2010 are mapped in ArcGIS to determine accident 
pattern on the state highway network. Although general public acceptance to these 
messages is positive, some users and media outlets have raised concerns that DMSs cause 
vehicles to slow down and distract drivers which may result in congestion and safety 
issues. In order to investigate the claims, All 184 highway DMSs in State of Maryland 
are studied to evaluate the accident patterns in their proximity. The purpose of this study 
is to determine whether DMSs in Maryland highways produce significant localized safety 
issues. The data used and methods of research are described in detail in the following 
sections. 
38 
 
4.2. Methodology 
4.2.1. Data Sources and Preparation 
The data used to complete this research are collected from the Center for Advanced 
Transportation Technology (CATT) Laboratory in the Department of Civil and 
Environmental Engineering at the University of Maryland at College Park, Coordinated 
Highway Action Response Team (CHART) reports for regions within the District of 
Columbia in Maryland, and Maryland Department of Transportation, State Highway 
Administration (SHA) and DOT archived data. Figure 4.1 shows the databases and 
sources that are used in the research.  
 
Figure 4.1. The databases and sources of data used in the research 
 
The study area is set roadway network in State of Maryland. Figure 4.2 depicts the study 
area in the research.  
39 
 
 
Figure 4.2. Study Area 
 
4.2.2. Accident Database 
The accident database included 38,718 records. A data cleansing process was conducted 
to remove data gap and outliers which resulted in a data set of 23,842 accident records for 
the four-year period of 2007 to 2010 in the entire State of Maryland. The data set consists 
of accident type (property damage, personal injury and fatality), geographical location, 
jurisdiction, time of accident and other related information. Due to confidentiality 
concerns, access to police records and accident causes was not possible.  
Locations of accidents are pinpointed on road network map for further analysis. 
 
 
Figure 4.3 shows the first shape of accident database and the locations of accidents 
projected on the road map.
40 
 
 
 
 
 
 
 
 
 
 
Figure 4.3. First shape of accident data and pointing location of accidents on road network map
41 
 
4.2.3. DMS Database 
The DMS inventory is acquired from CATT Laboratory. The DMS inventory includes all 
types of signs including permanently mounted overhead, roadside models and portable 
signs that are operated by CHART or Maryland Transportation Authority (MdTA). The 
DMS database with 184 records includes identification number, longitude and latitude, 
address location and type for all the 184 DMSs in the state of Maryland. Figure 4.4 shows 
the first shape of DMS data and its projection onto the road network map. 
As mentioned earlier, associated with each accident is a geographic longitude and latitude 
which are used to join the accident and DMS databases. Likewise, each DMS is projected 
onto the same road network map from SHA database using their longitudes and latitudes. 
As shown in Figure 4.5, a network system is created with the three overlaid layers. 
An impact area of 900 feet is defined for each DMS and in each DMS impact area, the 
sign was assigned to accidents within 900 feet of the DMS. The details on impact area 
definition will be provided in next section.  Accidents in 900 feet proximity to DMSs 
were accounted as occurring in the impact area based on location field, visual judgment 
and direction of DMSs to mitigate GPS errors. 
42 
 
 
 
 
 
 
 
Figure 4.4. First shape of DMS database and projection to road map 
 
43 
 
 
 
Figure 4.5. Map of accidents and DMS locations 
 
4.2.4. AADT Database 
Highway Safety Manual (2010) defines traffic flow as one of the most important 
contributing factors to occurrence of crashes. This research uses Average Annual Daily 
Traffic (AADT) of the road segments as an index for traffic flow. The AADT data are 
retrieved from Maryland?s State Highway Administration volume maps for the four year 
period of study. The AADTs are collected from more than 3,000 Program Count Stations 
and 79 Automatic Traffic Recorders (ATRs) located throughout Maryland. The shape file 
of AADT layer is projected onto the road map along with the accidents and DMSs. An 
example of the map is shown in Figure 4.6. 
44 
 
 
Figure 4.6. An example of the volume map AADT (SHA 2011) 
 
4.3. Data Processing and Preparation Challenges 
This study is a new approach to the problem dealing with several huge databases with 
different data structure and coordination systems. The need to acquire data from different 
sources was another challenge for the research. Besides, some parts of police accident 
report such as causes of the accidents are not accessible due to confidentiality concerns. 
Apart from the difficulties in obtaining the data, another issue confronting the research 
was processing of data sets with more than ten thousands of records that was resolved by 
the use of a data cleansing process with filtering and removing the outliers. The need to 
be joining the databases with two dimensions of time and location was another challenge 
45 
 
that was resolved by pinpointing the locations through GIS tools and matching the time 
of events through coding in SQL environment. 
4.4. Defining the Impact Area of DMS 
The methodology used in this study is to pinpoint the locations of accidents to count the 
number of accidents within the 900 feet radius distance. When both the DMSs and 
accident locations are projected on ArcGIS, the goal was to determine the distance within 
which DMS might affect the occurrence of accidents. Size of characters of electronic 
signs is the important factor determining the maximum viewing distance. In order to 
define the distance within which DMS may affect occurrence of accidents, the visibility 
distance from DMS needs to be determined. According to Maryland Manual on Uniform 
Traffic Control Devices (MUTCD), the minimum character size of DMS fonts in major 
roads (55 mph speed limit) is 18 inches. Based on the information provided by 
International Sign Association the maximum viewing distance for 18 inches character 
size sign is 900 feet. Figure 4.7 illustrates the impact area for research. 
4.5. Case Study on I-95 
Interstate 95 in Maryland is a major highway that runs diagonally from northeast to 
southwest, from Maryland's border with Delaware, to the Woodrow Wilson Bridge, 
briefly entering the District of Columbia before reaching Virginia. The reason for 
choosing this freeway is that the route is one of the most heavily traveled Interstate 
Highways in Maryland, especially between Baltimore and Washington, D.C.  
Figure 4.8 shows I-95 and the DMSs located on this highway. The light blue pushpins are 
DMSs on northbound and the dark one are the signs located on southbound.  
 
46 
 
 
 
 
 
Figure 4.7. Impact Area 
 
 
 
 
 
 
Figure 4.8. I-95 along with the DMSs along this highway 
47 
 
The accidents along I-95 are projected onto the map. Figure 4.9 gives a perspective of the 
accidents in I-95 and northbound and southbound DMSs.  
 
Figure 4.9. Accidents in I-95 
Figure 4.10  shows the projected AADTs to road map. 
Since the impact area of DMSs is determined as 900 feet, multiple ring buffer zones with 
radius of 900 multiplier feet (900, 1800, 2700, etc) radius were performed for each DMS 
sign along I-95. This is shown in Figure 4.11.. 
4.5.1. Analysis of Case Study and Preliminary Results 
In this step, a sample of 70 geometrically homogenous segments with 900 feet length 
along I-95 highway is selected. . For each segment, accidents are counted and the 
accumulated number of crashes in each segment are tabulated and used for regression 
analysis considering that segment is an impact area or not as well as the existence of 
48 
 
interchange and AADT in the segment. Table 4.1 shows the variable used for the case 
study. 
Table 4.2 shows the 70 segments with their accumulated number of crashes, and 
existence of DMS and interchanges in the impact areas.  To analyze the data, first an 
unbalanced two way ANOVA is performed using SAS software. The results show that P-
 value strongly rejects the hypothesis that Interchanges have no impact on the occurrence 
of accidents. The significance level for the impact of DMS is not high and shows that 
DMSs are not significantly contributing in occurrence of accidents. The results are shown 
in Figure 4.12. 
 
 
Figure 4.10 . Projection of AADTs to road map 
 
 
49 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4.11. Multiple Buffers along I-95 
 
 
 
Table 4.1. Variables used in case study 
 
  
In addition, Poisson regression analysis is conducted to predict the number of crashes 
within 900 feet segments considering existence of DMSs, interchanges and AADT of the 
route. The test strongly rejects the hypothesis that interchanges and AADT do not have 
significant impact on the occurrence of accidents. Regression analysis also shows that 
DMSs are not significant contributors for crash occurrence. Figure 4.12 and Figure 4.13 
50 
 
show the outcome of ANOVA and Poisson regression analysis respectively. The results 
of both methods converge to the point that interchanges and AADT are important factors 
on accidents, but do not show any relationship between presence of DMSs and 
occurrence of accidents. 
Table 4.2. I-95 Case Study Samples 
 
BufferID NumberCrash ImpactArea Interchange AADTVMT SouthORNorthBound 
10 1 1 0 147581 S 
20 1 1 0 147130 N 
30 7 1 0 177981 S 
40 1 1 0 206880 N 
50 0 1 0 213841 N 
60 0 1 0 213841 S 
70 1 1 0 205142 N 
80 28 1 0 205142 S 
90 0 1 0 212261 N 
100 0 1 0 188601 S 
110 0 1 0 183961 S 
120 3 1 0 188671 N 
130 0 1 0 194069 N 
140 0 1 0 192871 S 
150 0 1 0 182473 N 
160 40 1 0 182478 S 
170 4 1 0 123232 S 
180 0 1 0 129021 S 
190 3 1 0 119161 N 
200 2 1 0 165104 S 
210 0 1 0 147341 N 
220 1 1 0 147341 S 
230 1 1 0 121581 N 
240 0 1 0 121581 S 
250 0 1 0 96951 N 
260 0 1 0 96951 S 
270 1 1 0 98941 N 
280 2 1 0 98941 S 
290 0 1 0 84721 N 
300 0 1 0 91711 S 
310 0 1 0 91711 N 
320 43 0 1 191981 N 
330 57 0 1 147581 S 
51 
 
340 2 0 1 147581 N 
350 19 0 1 147130 N 
360 10 0 1 213841 N 
370 81 0 1 213841 S 
380 8 0 1 231801 S 
390 5 0 1 205142 N 
400 15 0 1 221521 N 
410 4 0 1 188671 N 
420 15 0 1 182473 N 
430 3 0 1 123232 N 
440 16 0 1 147341 N 
450 9 0 1 98941 N 
460 5 0 1 80571 N 
640 3 1 1 187501 N 
650 38 1 1 174051 S 
960 18 0 0 147130 S 
970 1 0 0 177981 N 
980 9 0 0 177981 S 
990 1 0 0 177981 N 
1000 1 0 0 213841 N 
1010 1 0 0 213841 S 
1020 1 0 0 205142 S 
1030 2 0 0 205142 S 
1040 2 0 0 212261 N 
1050 2 0 0 183961 N 
1060 0 0 0 194069 S 
1070 11 0 0 192871 N 
1080 1 0 0 192871 N 
1090 4 0 0 175027 N 
1100 9 0 0 129021 N 
1110 17 0 0 129021 S 
1120 5 0 0 119151 N 
1130 0 0 0 165104 S 
1140 1 0 0 161521 S 
1150 1 0 0 96951 S 
1160 0 0 0 9651 N 
1170 0 0 0 98841 N 
 
 
52 
 
 
 
 
Figure 4.12. SAS outcomes of unbalanced two-way ANOVA for case study in I-95 
 
 
 
 
 
53 
 
Recent studies in the literature raised some concerns regarding the usage of a Poisson 
distribution for accident frequency regression models. They state that one characteristic 
of crash-frequency data could be the probability that the variance exceeds the mean of the 
crash counts (Dominique et al, 2010) and since a property of Poisson distribution is that 
the mean and variance are equal, this could be problematic. To ensure verification of 
results, a Negative Binomial regression was also performed.  The results of Negative 
Binomial regression also converges Poisson regression. P-value of 0.0006 strongly rejects 
the hypothesis that interchanges are not significant contributors but P-value of 0.34 
suggests that DMSs are not contributing factors to occurrence of accidents. The result for 
Negative Binomial regression analysis agrees with the Poisson regression analysis in 
favor of the fact that DMSs do not affect causing accidents. The coding and outcomes are 
presented in Figure 4.13 and Figure 4.14. 
 
 
 
54 
 
 
 
Figure 4.13. SAS outcomes of Poisson regression for case study in I-95 
 
55 
 
 
 
Figure 4.13 (Continued). SAS outcomes of Poisson regression for case study in I-95 
 
 
 
56 
 
 
 
Figure 4.14. SAS outcomes of Negative Binomial regression for case study in I-95 
 
 
57 
 
 
 
Figure 4.14 (Continued). SAS outcomes of Negative Binomial regression for case study in I-95 
 
 
58 
 
4.6. Weather Conditions Database 
An important factor in causing driver distraction is visibility while driver tries to read the 
messages. Since precipitation, wind gust and severe weather conditions could have 
adverse impact on visibility of messages, another factor that contributes in occurrence of 
accidents and should be accounted for is climate status. The factors that are analyzed for 
weather conditions include precipitation, gust and visibility factors.  The weather data for 
this research are retrieved from DOT archived databases. The initial format of the 
database was in the shape of month to month archived data collected from 49 weather 
tower stations and contained the following data fields: date and time, air temperature, 
humidity, average wind speed, wind gust, wind direction, precipitation type, precipitation 
intensity (light, medium, heavy), precipitation accumulation, rate (rate per hour in 
inches), visibility (miles) and surface temperature. 
For simplicity, the area of research is divided into 5 regions of north, south, west, east 
and Washington, DC. The nearest central weather tower station in each region is assigned 
to represent the weather condition in that region. Table 4.3 shows these regions.  
Table 4.3. Tower stations assigned to each weather region 
 
Weather Station Region Latitude Longitude 
I-68 @ Cumberland West 39.70302 
-78.63177 
US 50 Kent Narrow Bridge East 38.97203 
-76.25391 
I-895 @ Levering Ave North 39.21854 
-76.71071 
US-301 at Potomac River South 38.36366 
-76.983 
I-270 @ I-370 Washington, DC 39.11946 
-77.19593 
 
The data set is accumulated for the four-year period of study (2007-2010).  Figure 4.15 
shows the format of weather database. As mentioned earlier, if an accident in the database 
lies within 900 feet sight distance of an onward DMS, the accident is joined with that DMS 
59 
 
and AADT of the roadway on which that accident has occurred. In this step, the main 
database is integrated with the weather stations data sets. The weather database is joined to 
the main database based on proximity to the closest weather tower station and occurrence 
time of accident.  
For integrating the weather database and the main database, the weather database was 
imported into SQL server and each accident was matched with the closest weather tower 
station and the weather condition at the time of accident. The matching process is 
performed using SQL queries coded in C++. 
 
 
 
60 
 
 
 
 
Figure 4.15. Weather Database Format 
61 
 
4.7. Log of Messages Database 
The database for log of messages as mentioned before was acquired from the CATT 
Laboratory in the Department of Civil and Environmental Engineering at the University 
of Maryland, College Park. This database contains whole messages displayed on all of 
the DMSs in State of Maryland during the time period of 2007 to 2010. This database is a 
huge data sheet which includes 1,047,586 records of messages and consists of 
identification number of DMSs, time of displaying the messages, the messages and 
beacon data fields. The beacon data field shows that if the beacon has been on or off. 
Figure 4.16 show the log of messages database. 
The syntax for message data field is based on the definitions in National Transportation 
Communications for ITS Protocol, Object Definitions for Dynamic Message Signs 
Version 02 (2007). The number of panes can be determined by interpreting the system of 
coding that comes along with each message. The main codes of messages are: 
- [PT##O#]: This code is interpreted as Panel Time, ## in tenths of seconds on, # in 
tenths of seconds off (normally this # is 0, otherwise the panel would be flashing) 
-  [JL#]: This code is for text justification. The number corresponds to various 
justifications (i.e. 2 left, 3 center, 4 right)  
- [NL] - New Line 
- [NP] - New Pane 
 
62 
 
 
Figure 4.16. Log of Messages Database 
63 
 
The following example illustrates the message syntax: 
 
[PT25O0][JL3]ACCIDENT AHEAD[NL][JL3][NL][JL3]PAST EXIT 51[NP][PT25O0][JL3] 2 
LEFT LANES BLOCKED[NL][JL3][NL][JL3] EXPECT DELAYS 
 
This message has 2 panes, alternating appearances for 2.5 seconds, all lines center 
justified. It would appear as: 
PANE 1: 
ACCIDENT AHEAD 
 
PAST EXIT 51 
 
PANE 2: 
2 LEFT LANES BLOCKED 
 
EXPECT DELAYS 
 
The message log database is imported into SQL server along with the main database. For 
each accident if it was located in impact area, the assigned DMS is matched with the 
message displayed at the occurrence time of accident. Likewise the weather data sets, the 
matching process was conducted using SQL queries coded in C++. 
4.8. Analysis and Results 
The integrated database consists of the integrated data for each accident. Every record of 
an accident contains the following information: time and date of accident, location and 
longitude and latitude of accident, type of accident, AADT of the roadway, weather 
condition at the time of accident (including air temperature, humidity, average wind 
speed, wind gust, wind direction, precipitation type and rate and visibility). If the 
accident occurred in the impact area, the following information are also present: the 
assigned DMS and the message that DMS had displayed at the time of accident.  
64 
 
Figure 4.17 depicts the projection of integrated database for the entire study area in 
ArcGIS.  
Figure 4.18 illustrates the close up shot of the projected map. 
The integrated database consists of 23,842 records for accident during the time period of 
2007 to 2010. There are 298 accidents located in 900 feet vicinity of DMSs. From whole 
accidents in impact areas, there are 50 accidents exposed to the active DMSs with 
displaying messages. For the rest of accidents, the DMSs were inactive at the time of 
accidents. As the following sections present, multiple approaches are employed to 
analyze different aspects of the data. A paired t-test analysis at 95% significant level is 
conducted to compare accident rate in impact areas with their onward 900 feet segment. 
In addition, an on-and-off study is conducted to compare accident rates of 15 DMSs with 
on and off displaying messages. Statistical analyses to investigate the effects of weather 
conditions, visibility and type of messages on accident in impact areas are presented in 
the subsequent sections. 
4.9. Analysis on Impact Areas and Following Segment 
 To investigate the effects of DMSs on occurrence of road accidents, a paired t-tests 
statistical analysis at 95% confidence level is used to compare accident rates for the 50 
accidents in impact areas of active DMSs with displaying messages with their subsequent 
900 feet segment.  
The null hypothesis states that the difference in mean accident rate between two 
consecutive 900 feet segments is equal to zero. On the other hand, the alternative 
hypothesis suggests that difference between the means is not equal to zero: 
0 2 1
 1 2 1
 : 0
 : 0
 H
 H
 ? ?
 ? ?
 ? =
 ? ?
  
65 
 
 
 
Figure 4.17. Projection of Integrated Database 
 
66 
 
 
 
Figure 4.18. Close up shot of projected map 
67 
 
The data was compiled and the total number of accidents in each impact area and its 
subsequent 900 feet along with AADT of the segment were tabulated. The accident rates 
for both segments were calculated using spot accident rate formulation recommended by 
FHWA Safety Program guidance and Kiewit Center at Oregon State University (2003). 
According to the formulation, accident rate for a spot of a road is calculated by a ratio of 
accidents per million vehicles. A spot location is generally defined as a location about 0.3 
miles or less in length. Since the segments compared in this study are 900 feet length, 
equal to 0.17 mile, this formulation is used to calculate the accident rate. The normalized 
formula would allow comparing various accidents rates with respect to the rates of the 
subsequent segments. The equation for computing accident rate for a spot location is as 
follows: 
Rsp = A/Exposure [million entering vehicles]                             (Equation 1) 
or 
Rsp = (C) (1,000,000)/AADT (365)(N) 
Where: 
Rsp = Accident rate at a spot in accidents per million vehicles, 
C = Number of crashes for the study period, 
N = Period of study (years or fraction of years), 
AADT = Average Annual Daily Traffic (AADT) during the study period.  
For this formulation of accident rates, a segment of less than 0.3 miles would not 
be appropriate to be treated as a section and should be considered a spot rather than a 
segment. (Kiewit 2003)  
68 
 
Table 4.4 shows the tabulated facts for the accident rates in both segments compiled in a 
table including DMS identification number, AADT of segment, number of accidents in 
segment and accident rates in segments. Figure 4.19 shows the accident rates for impact 
areas compared to their subsequent 900 feet segment. 
 
Figure 4.19. Accident rate for impact area of 900 feet compared to their subsequent900 
feet segment 
 
The graph shows that for the majority of impact areas, rate of accidents is lower 
than their onward adjacent segment. Figure 4.20 shows the difference of the accidents 
rates for the two segments,  
 
 
69 
 
Table 4.4. Tabulated facts of impact areas and forwarding segments 
Impact 
Area DMS_id AADT 
# 
accidents 
in 900 
feet 
900ft 
Accident 
Rate 
# 
accidents 
in 
Forward 
900 feet  
Forward 
900ft 
Accident 
Rate DMS 
2 CHART_01010528004f00820047f02c76235daa 13974 7 0.343102945 5 0.245073532 0.098029 
12 CHART_0c011090002d0067003f062c3d235daa 2364 1 0.28973414 2 0.57946828 -0.28973 
29 CHART_1901170900050002003d242c3b235daa 61273 2 0.022356715 0 0 0.022357 
32 CHART_1b010c38005200820047f02c76235daa 65821 2 0.020811945 1 0.010405972 0.010406 
33 CHART_1b01212600da0008003d242c3b235daa 187920 1 0.003644804 27 0.098409699 -0.09476 
34 CHART_1c000b26004c00820047e22c9e235daa 145780 1 0.004698391 2 0.009396783 -0.0047 
41 CHART_1e01133800d90008003d242c3b235daa 57512 4 0.047637467 2 0.023818734 0.023819 
46 CHART_2c00083a004b00820047e22c9e235daa 444336 5 0.00770736 7 0.010790304 -0.00308 
55 CHART_39010a59005100820047f02c76235daa 88882 1 0.007706077 0 0 0.007706 
64 CHART_40ff12d400c200820047e32c96235daa 8282 1 0.08270122 3 0.248103661 -0.1654 
68 CHART_46010ade0036005a0039fc442f1f5daa 190391 1 0.003597499 3 0.010792498 -0.00719 
70 CHART_4701165e00d90008003d242c3b235daa 74887 2 0.018292401 25 0.228655009 -0.21036 
88 CHART_5f00077a004600820047e32c96235daa 245421 1 0.002790843 5 0.013954216 -0.01116 
92 CHART_62000ff900a300e0003e062c3d235daa 121581 1 0.005633541 1 0.005633541 0 
95 CHART_650113d6003d0067003f062c3d235daa 65214 2 0.021005659 2 0.021005659 0 
104 CHART_6dff058b004500820047e32c96235daa 255882 1 0.002676748 1 0.002676748 0 
105 CHART_6e00069600af0054003afc442f1f5daa 23726 8 0.230947149 4 0.115473574 0.115474 
113 CHART_74000733009000d3003e062c3d235daa 98941 1 0.006922626 0 0 0.006923 
124 CHART_89000cab00d80008003d242c3b235daa 147130 3 0.013965843 0 0 0.013966 
137 CHART_aa01033e000c00630045152cea235d0a 23741 1 0.028850154 0 0 0.02885 
139 CHART_ac0064d1002f00ae003ac7442f1f5daa 66761 1 0.010259455 1 0.010259455 0 
162 CHART_d8ff030400b800c60047832c33235daa 153481 1 0.004462647 2 0.008925294 -0.00446 
182 CHART_fdff03d9008000c80040062c3d235daa 8600 2 0.159286397 0 0 0.159286 
      50         
70 
 
 
Figure 4.20. Difference of the accidents rates between the impact area and its subsequent 
segment 
 
The analysis of difference between the accident rates show that 70% of the impact 
areas has lower or equal accident rates compared to their subsequent 900 feet segments 
which means DMSs do not have significant influence on increasing the accident rate. The 
remaining 30%, or 7 impact areas of the study, show a positive difference between the 
accident rates. As the results of the case study in I-95 supported the fact that interchanges 
are contributing factor to accidents, a simple qualitative analysis of the locations of the 
DMS with the highest accidents rates showed that they tended to occur within short 
distances of interchanges and those with lower rates tended to occur further away from 
interchanges, so the reason for positive accident rates could be attributed to external 
factors such as existence of interchanges in DMS buffer zones and roadway geometry 
that would increase the accident rates in these segments.  
71 
 
4.9.1. Findings 
A paired t-test statistical analysis on the accident rates is performed to compare the 
accident rates in the two segments. The p-value of 0.5245 associated with t statistic of      
-0.65 suggest that DMSs do not increase occurrence of the accidents. The mean 
difference of the two accident rates is -0.013.  The coding in SAS software and the results 
are presented in Figure 4.21. 
 
 
Figure 4.21. SAS outcomes for comparison of impact areas and following section 
 
72 
 
 
 
 
Figure 4.21 (Continued). SAS outcomes for comparison of impact areas and following 
section 
 
73 
 
 
4.10. On-and-off Analysis  
An on-and-off study was conducted to compare the results obtained from the previous 
section. The data were compiled into a table. Total numbers of accidents for 15 signs 
were accumulated while DMSs were displaying messages and when they were blank. The 
accident rates for both situations were calculated using the same formulation used for the 
previous section. To conduct the analysis, a one-way ANOVA with pairwise comparison 
test was performed to assess accident rates in impact area while DMSs were on and when 
they were off. The null hypothesis states that the difference in mean accident rate 
between two conditions is equal to zero. On the other side, the alternative hypothesis 
suggests that the difference between the means is not equal to zero and mean accident 
rate is different with or without presence of messages. Table 4.5 shows the tabulated facts 
of on-and-off study including DMS identification number, number of accidents in impact 
areas, AADT of segment and accident rates in segments. 
Figure 4.22 depicts the comparison of accident rates when messages are displaying on 
DMSs and when these signs are blank.  
To better determine how different the accidents rates are for on and off DMSs, the graph 
of the difference between the rates of the two conditions is shown in Figure 4.23. As this 
graph shows, in all DMS impact areas the accident rate is lower when the sign shows a 
message.
74 
 
Table 4.5. Tabulated facts of on and off study 
Impact 
Area DMS_id 
# accidents 
in impact 
area 
With DMS 
Message Accident 
Rate AADT 
DMS blank 
before  
DMS Blank 
Accident Rate DMS Effect 
2 CHART_01010528004f00820047f02c76235daa 7 0.343102945 13974 11 0.53916177 -0.196059 
29 CHART_1901170900050002003d242c3b235daa 2 0.022356715 61273 7 0.078248503 -0.055892 
32 CHART_1b010c38005200820047f02c76235daa 2 0.020811945 65821 8 0.083247779 -0.062436 
33 CHART_1b01212600da0008003d242c3b235daa 1 0.003644804 187920 4 0.014579215 -0.010934 
34 CHART_1c000b26004c00820047e22c9e235daa 1 0.004698391 145780 1 0.004698391 0 
41 CHART_1e01133800d90008003d242c3b235daa 4 0.047637467 57512 19 0.22627797 -0.178641 
46 CHART_2c00083a004b00820047e22c9e235daa 5 0.00770736 444336 16 0.024663552 -0.016956 
55 CHART_39010a59005100820047f02c76235daa 1 0.007706077 88882 1 0.007706077 0 
64 CHART_40ff12d400c200820047e32c96235daa 1 0.08270122 8282 6 0.496207322 -0.413506 
68 CHART_46010ade0036005a0039fc442f1f5daa 1 0.003597499 190391 2 0.007194999 -0.003597 
70 CHART_4701165e00d90008003d242c3b235daa 2 0.018292401 74887 7 0.064023403 -0.045731 
105 CHART_6e00069600af0054003afc442f1f5daa 8 0.230947149 23726 21 0.606236266 -0.375289 
113 CHART_74000733009000d3003e062c3d235daa 1 0.006922626 98941 1 0.006922626 0 
124 CHART_89000cab00d80008003d242c3b235daa 3 0.013965843 147130 8 0.037242249 -0.023276 
162 CHART_d8ff030400b800c60047832c33235daa 1 0.004462647 153481 1 0.004462647 0 
 
75 
 
 
Figure 4.22. Comparison of accident rates while DMS are on and while blank 
 
 
Figure 4.23. Difference of the accidents rates in on and off study 
 
The results show that accident rates for DMSs that are displaying messages are 
less than or equal to the blank DMSs for all cases under study. The results of this on-and-
76 
 
off study support the outcomes of the previous sections and the fact that DMSs are not 
contributing factors in causing accidents.  
4.10.1. Findings 
A Repeated Measures Analysis of Variance is conducted to compare the mean 
accident rates in two conditions. The F-value of 6.73 and P(F < 6.73) of 0.0212 for the 
one-way ANOVA with paired comparison suggests that null hypothesis is rejected with 
98% level of confidence in favor of supporting the fact that the mean accident rate for 
active DMSs is lower than the rate of accidents for inactive DMSs. The SAS coding and 
the outcomes are presented in Figure 4.24. 
 
 
 
 
Figure 4.24. SAS outcomes for on and off study 
 
77 
 
 
 
 
 
 
 
Figure 4.24 (Continued). SAS outcomes for on and off study 
 
 
78 
 
4.11. Accidents in DMS Impact Areas and Weather Conditions 
The purpose of this section is to summarize and categorize accident characteristics in 
DMS areas. As mentioned before, weather conditions can induce accidents through 
reducing drivers? visibility. According to FHWA Road Weather Management Program, 
visibility impairments, precipitation, high winds and temperature extremes affect driver 
capabilities and operational decisions, traffic flow and crash risk. Considering the fact 
that this research concerns driver response to DMS messages, which is known to be an 
environmental factor, it would be necessary to investigate the accident in conjunction 
with weather conditions at the time of accident for active DMS. As Table 4.6 and 
Figure 4.25 show, there are only 4 accidents in the entire set of accidents within the 
impact area that happened in rainy and snowy conditions. 
 
Table 4.6. Accidents in DMS areas and precipitation 
 
Precipitation  Accidents in Impact Area # 
Rain 2 
Snow 2 
None 45 
other 1 
Total 50 
 
  
 
79 
 
 
 
Figure 4.25. Frequency of accidents  in different precipitation conditions 
 
In spite of the concerns regarding lack of visibility of messages during wind gust 
condition, as shown in Table 4.7 and Figure 4.26, the statistical analysis regarding 43 
accidents in impact area indicates there is not significant number of accidents in this condition.  
 
Table 4.7. DMS accidents and wind gust 
 
 
Wind Gust 
(mph) Accidents in Impact Area # 
0-10 32 
 10-20 9 
20-30 2 
Total 43 
 
 
80 
 
 
 
Figure 4.26. DMS accidents and wind gust 
 
81 
 
4.12. Accidents in DMS Impact Areas and DMS Characteristics 
This section concerns statistical analysis of accident types in DMS  impact areas in 
conjunction with type of messages and beacon operational status (on and off) of DMSs. 
Figure 4.27 shows that among 50 accidents in DMS impact areas, 35 collisions are 
property damage and 15 are personal injury. 
There are some concerns that flashing beacons could distract drivers and eventually affect 
the driving performance. As Figure 4.28 shows, 10 accidents have happened while 
beacons were on which accounts for one fifth of the total number of accidents in the 
impact areas. 
 
 
Figure 4.27. Type of accidents in DMS area # 
 
 
82 
 
 
Figure 4.28. Number of accidents versus Beacon status 
 
Analysis on displayed messages shows that 11 accidents occurred while danger/warning 
messages were displayed on DMSs. This amount for informative/common road condition 
messages and regulatory/non-traffic related messages are 22 and 17, respectively. 
Although some concerns exist that accident warning messages attract more attention from 
drivers (Wang et al, 2007), the least number of accidents in DMS impact areas belong to 
danger and incident warning messages category (see Figure 4.29).  
83 
 
 
Figure 4.29. Number of accidents for DMS message types  
 
 
 
 
84 
 
5. Chapter 5: Conclusions and Directions for Further Research 
5.1. Summary and Conclusions 
This thesis evaluated localized safety impacts of highway Dynamic Message Signs 
(DMS). The accident data from 2007 to 2010 served as the ground-base for the analysis 
of road collisions in entire State of Maryland. The accident and log of messages data in 
study period was collected from the Center for Advanced Transportation Technology 
(CATT) Laboratory in the Department of Civil and Environmental Engineering at the 
University of Maryland, College Park and Coordinated Highway Action Response Team 
(CHART) reports for regions within the District of Columbia in Maryland. The roadway 
network map and AADT of roadway segments were obtained from Maryland Department 
of Transportation, State Highway Administration (SHA) and weather conditions 
databases were gathered from DOT archived data. To conduct this research we needed to 
acquire data from a variety of different sources.  Dealing with huge and several databases 
with different data structure and coordination systems, confidentiality of police accident 
reports, processing of huge databases with tens of thousands of records and joining the 
databases based on two dimensions of time and location, were among the challenges 
which were successfully overcome in this research.  
The accident database included 38,718 records, which were filtered, cleaned up, and from 
which data gap and outliers were removed. After data processing, number of accidents 
decreased to 23,842 records for the four-year study period. The accident database 
consisted of accident type (property damage, personal injury and fatality), address 
location and county, time and date of occurrence of accident and coordinates of accident 
85 
 
location. Due to confidentiality concerns, access to police records and accident causes 
was not possible.  
The DMS inventory was also provided by the CATT Laboratory. The DMS types in this 
research include permanently mounted overhead, roadside models and portable signs that 
are operated by CHART or Maryland Transportation Authority (MdTA). The DMS 
database with 184 records included DMS ID, longitude and latitude, address location and 
DMS type fields.  
Since another important contributing factor to occurrence of crashes is traffic flow, 
AADT of the road segments was another factor that was taken into account for analysis. 
The AADT data was retrieved from Maryland?s State Highway Administration volume maps 
of the state of Maryland for study period.  
The accidents along with DMS locations and AADT database were projected to 
Maryland roadway map to perform spot analysis and to evaluate DMS influence on 
drivers? operational performance. An impact area of 900 feet was defined for each DMS 
based on the average size of electronic signs character and maximum visibility distance 
for the signs. A DMS was assigned to accidents within 900 feet of each DMS based on 
location and direction of DMS.  
A case study was performed on Interstate 95 in Maryland which is a major highway. 70 
samples of 900 feet segments along I-95 highway were chosen based on homogeneity in 
geometry. the number of accidents were counted for each segment and accumulated 
number of crashes in each segment was tabulated and used for regression analysis based 
on the fact that the segment is impact area or not, existence of interchange in the segment 
and AADT of the segment. The results of unbalanced two-way ANOVA revealed that P-
 value strongly rejects the hypothesis for lack of impact of Interchanges and showed that 
86 
 
they actually do affect occurrence of accidents, while significance level for DMS impact 
was not high and made it clear that DMSs are not contributing factors in occurrence of 
accidents. The outcome of Poisson regression supported these results, too. The results for 
both methods converged to the point that interchanges and AADT are important factors 
on accidents, but do not show any relationship between occurrence of presence of DMSs 
and occurrence of accidents. 
Another main factor in causing accidents is lack of visibility due to adverse climate 
situation. Since precipitation, wind gust and severe weather conditions could have 
negative impact on visibility of messages while driver tries to read the messages, 
statistical analysis was performed regarding this factor. For simplicity, the area of 
research was divided into 5 regions of north, south, west, east and Washington, DC. The 
nearest central weather tower station in each region was assigned to represent the weather 
condition in each region.  The database was accumulated for four-year study period 
(2007-2010). Each accident in database was joined with weather stations database. The 
weather database was joined to the main database based on the proximity of the closest 
weather tower station to the time and location of each accident. The matching process was 
performed using SQL queries coded in C++. 
The database for log of messages was acquired from the CATT laboratory. This database 
contained the entire messages that were displayed on all of the DMSs in State of 
Maryland during the time period of 2007 to 2010  including 1,047,586 records of 
messages and consisted of DMS ID, time of displaying the message, the message and 
beacon data fields.  
The message log database was imported in SQL server along with the main database. For 
each accident if it was located in impact area, the assigned DMS was matched with the 
87 
 
message displayed at the time of occurrence of accident. Likewise the weather data, the 
matching process was conducted using SQL queries coded in C++. The integrated 
database consisted of 23,842 records for accident during study time period. There were 
298 accidents located in 900 feet proximity to DMSs. From all accidents in impact area, 
there were 50 accidents during which, the dynamic message signs were displaying 
messages. For the remaining accidents, the DMSs were blank. The data were analyzed in 
several aspects.  
The paired t-test analysis at 95% confidence level for difference of mean accident rates 
on DMS impact areas and their subsequent 900 feet segment of buffer zones with t-
 statistic of -0.65 and p-value of 0.5245 showed that DMSs do not increase accident 
occurrence.  The mean of the difference of the two accident rates was -0.013. 
The one-way ANOVA analysis with pairwise comparison test in on-and-off study for 15 
DMSs to compare accident rates of active and inactive DMSs with F-value of 6.73 and 
P(F < 6.73) of 0.9995 showed that the mean accident rate of active DMS is lower than the 
inactive DMSs at 98% level of confidence.  
The statistical analysis of accidents in conjunctions with weather conditions showed that, 
there are only 4 accidents in the entire accidents of impact areas that fall in rainy and 
snowy conditions. 32 out of 43 accidents were in wind gust with 0-10 mph condition, 9 
out of 43 were in wind gust with 10-20 mph condition and 2 of the 43 accidents were in 
wind gust with 20-30 mph speed condition.  
The statistical analysis of accidents in conjunction with DMS characteristics revealed that 
among 50 accidents in DMS area, 35 collisions are property damage and 15 are personal 
injury.  
88 
 
10 accidents occurred while beacons were on which accounts for one fifth of the 
accidents. Analysis on displayed messages showed that 11 accidents occurred while 
danger/warning messages were displayed on DMSs. This number for 
informative/common road condition messages and regulatory/non-traffic related 
messages was 22 and 11, respectively. Although some concerns exist that accident 
warning messages attract more attention from drivers, the least number of accidents in 
DMS areas belonged to danger and incident warning messages.  
In summary, the findings from all evaluations converge and indicate that DMS could be a 
safe tool for disseminating real-time travel information to motorists and these signs do 
not have significant adverse effects on driver?s operation and causing accidents. This 
thesis focused on DMS operations in the state of Maryland and the methods employed for 
evaluation are extendable to other locations if the data are available. 
5.2. Future Research 
The broad range of subjects for future study provides opportunities and challenges for 
researchers. The research could be further completed if study area encompasses several 
states. Future research in this area may be improved through investigating the issue 
through simulation and site human factor analysis. Also it would be of interest to improve 
DMS design (such as message design, size, color, lengths and number of panes and speed of 
switching between messages) to provide better driver?s understanding of messages specially 
elder or bilingual drivers. Investigation on the impacts of displaying messages on newly 
installed DMSs as well as the impact analysis of DMSs on road curvatures could be other 
topics for future research. Beside it would be of interest to investigate the impact differences 
in daylight and-nightlight situations. Another direction for future research in the extension of 
89 
 
this thesis is to investigate the impact of the incident messages and to provide motorists with 
information ahead on tailgating and secondary accidents close to the incident location. 
Moreover, the integrated database could be used to investigate the impact of weather 
conditions on occurrence of road accidents. 
Finally, optimization of displayed messages and DMS location considering traffic flow, 
geometry of the roadway, proximity to interchanges and reducing drivers? mental 
processing time to perceive environmental factors and speed up drivers? response could 
be another interesting topic for future study. Moreover, a cost and benefit analysis on 
installing DMSs on roadways clarify the concerns regarding expenses and values of the 
signs. These directions for future studies would help transportation engineers and 
planners improve DMS operations and eventually improve transportation network 
management with smoother traffic flow conditions. 
90 
 
6. References 
 
1. Abbas, M.M. and McCoy, P.T. ?Optimizing Variable Message Sign Locations on 
Freeways Using Genetic Algorithms.? Presented at the 78th Annual Meeting of the 
Transportation Research Board, Washington, D.C., 1999.  
 
2. Abdel-Aty Mohamed A., Using ordered probit modeling to study the effect of ATIS 
on transit ridership. Transpn. Res.-C 9, pp 265-277, 2000. 
 
3. Adler J., Recker W. and McNally M. G., A conflict model and Interactive Simulator 
(FASTCARS) for predicting enroute driver behavior in response to real-time traffic 
condition information, Transportation 20, pp 83-106, 1993.  
 
4. Bai, Y., Huang, Y., Schrock, S.D. and Li, Y., Determining the Effectiveness of 
Graphic-aided Dynamic Message Signs in Work Zone, Smart Work Zone 
Deployment Initiative/IDOT/FHWA, 2011. 
 
5. Barfield, W., Conquest, L., Spyridakis, J. and Haselkorn, M. ?Information 
Requirements for Real-Time Motorist Information Systems.? Proceedings of the 
Vehicle Navigation and Information Systems Conference (VNIS), New York, 
IEEE, 1989, pp. 101-112. 
 
6. Benekohal, R.F., Shu, J., Speed Reduction Effects of Changeable Message Signs in 
a Construction Zone, Civil Engineering Studies, Transportation Engineeing, No. 72, 
Illinois  Cooperative Highway Research Program, Series No. 239, 1992. 
 
7. Bonsell P. W., MERRALL A.C., Analyzing and Modeling the Influence of 
Roadside Variable Message Displays on Drivers' Route Choice, 7
 th 
WCTR, Sydney, 
1995. 
 
8. Benson, B.G. Motorist Attitudes about Content of Variable-Message Signs. 
Transportation Research Record, No. 1550, pp. 48-57, 1996. 
 
9. Bolte, F. 2006. ?Transport Policy Objectives: Traffc Management as Suitable 
Tool,? Federal Highway Research Institute (BASt), Bergisch-Gladbach, Germany, 
Presentation to Planning for Congestion Management Scan Team. 
10. Borello, C. and Ornitz, S. E., Dynamic Message Sign Influence on Driver 
Performance, ITE 2010 Annual Meeting and Exhibit, 2010. 
91 
 
 
11. Boyles, S. D. (2006). Reliable routing with recourse in stochastic, time-dependent 
transportation networks. Master's thesis, The University of Texas at Austin. 
 
12.  Changeable Message Sign (CMS) Usage Procedure, New Jersey Department of 
Transportation, 2008. 
 
13. Chatterjee, K., Hounsell, N.B., Firmin, P. E. and Bonsall, P. W. Driver Response 
to Variable Message Sign Information in London. Transportation Research Part C, 
Vol. 10, No. 2, 2002, pp. 149-169. 
 
14. Chien-Jung Lai, Effects of color scheme and message lines of variable message 
signs on driver performance, Accident Analysis and Prevention 42 (2010) 1003?
 1008, 2010. 
 
15. Chiu, Y.-C. and N. Huynh, Location configuration design for dynamic message 
signs under stochastic incident scenarios. Transportation Research Part C 15 (1), 
33-50, 2007. 
 
16. Chiu, Y.C., Huynh, N. and Mahmassani, H.S. ?Determining Optimal Locations for 
Variable Message Signs under Stochastic Incident Scenarios.? Presented at the 80th 
Annual Meeting of the Transportation Research Board, Washington, D.C., 2001. 
 
17. Dominique L. and Mannering, F., ?The statistical analysis of crash-frequency data: 
A review and assessment of methodological alternatives?, Transportation Research 
Part A 44, 291?305, 2010.  
 
18.  Dos Santos, C., ?Assessment of the Safety Benefits of VMS and VSL using the 
UCF Driving Simulator?.  Masters Thesis, University of Central Florida orlando, 
2006. 
 
19. Dudek CL, Changeable message sign displays during non-incident, non-
 roadwork periods. NCHRP Synthesis 383. Transportation Research Board, 
Washington, DC, 2008. 
 
20. Dudek, C.L. Dynamic Message Sign Message Design and Display Manual. Report 
No. FHWA/TX-04/0-4023-P3. Texas Transportation Institute, The Texas A&M 
University System, College Station, TX, May 2006. 
92 
 
21. Dudek, C.L., S.D. Schrock and G.L. Ullman. Impacts of Using Dynamic Features 
to Display Messages on Changeable Message Signs. Report No. FHWA-HOP-05-
 069. FHWA, U.S. Department of Transportation, Washington, D.C., August 2005. 
 
22. Dudek, C.L. Changeable Message Signs?Synthesis of Highway Practice 61. 
National Cooperative Highway Research Program, 1979. 
 
23. Erke, A., Sagberg, F., Hagman, R., Effects of route guidance variable message 
signs (VMS) on driver behavior, Institute of Transport Economics, Gaustadalleen 
21, NO-0349 Oslo, Norway, 2007. 
 
24. Farradyne, P.B., Traffic Incident Management Handbook, Prepared for Federal 
Highway Administration, Office of Travel Management, 2000. 
 
25. Finley, M.D., Durkop, B.R., Wiles, P.B., Carvell, J.D. and Ullman, G.L. Practices, 
Technologies and Usage of Incident Management and Traveler Information 
Exchange and Sharing in Texas. Report 4951-1, Texas Transportation Institute, 
December 2001. 
 
26. Fish, R. L., Haghani, A. Hamedi, M., Analysis of Traffic Speed Response to 
Display of Dynamic Message Sign Messages , Empirical Analysis Of The Quality, 
Effectiveness and Localized Impacts Of Highway Dynamic Message Sign, TRB 
Annual Meeting, 2012. 
 
27. Guidelines for the Operation of Variable Message Signs on State Highways oregon 
Department of Transportation, 2000. 
 
28. Hao E., Taniguchi M., Sugie Y., Kuwahara M. and Morita H. (1999), 
Incorporating an information acquisition process into a route choice model with 
multiple information sources, Transport. Res.-C 7, pp 109-129, 1999. 
 
29. Henderson, J. M. A planning model for optimizing locations of changeable 
message signs. Master's thesis, University of Waterloo, 2004. 
 
30. Highway Safety Manual (HSM), 1st edition, ASHTO, 2010. 
 
31. HUO, H., Evaluation of Variable Message Signs Using Empirical Loop Detector 
Data, Thesis UNIVERSITY OF MINNESOTA, 2002. 
 
93 
 
 
32. Huynh, N., Y.-C. Chiu and H. S. Mahmassani, Finding near-optimal locations for 
variable message signs for real-time network traffic management. Transportation 
Research Record 1856, 34-53, 2003. 
 
33. Johnson, C.M. Use of Changeable Message Signs (CMS). FHWA Policy 
Memorandums?Manual on Uniform Traffic Control Devices, January 19, 2001. 
 
34. Jones, S.L., Jr. and Thompson, M.W. State of the Practice for Displaying Non-
 traffic Related Messages on Dynamic Message Signs. Report No. 03413, 
University Transportation Center for Alabama, August 2003. 
 
35. Kiewit Center for Infrastructure and Transportation, Oregon State University 
(2003), Analysis of Accident Statistics. 
 
36. Khattak A.J., Schofer J. L. and Koppelman F. (1993), Commuters' Enroute 
Diversion and Return Decisions: Analysis and Implications for Advanced Traveler 
Information Systems, Transpn. Res.-A, Vol. 27A, No.2, pp.101-111. 
 
37. Knoop, V.L., Hoogendoorn, S.P. and Van Zuylen, H.J. Route Choice Under 
Exceptional Traffic Conditions. International Conference on Evacuation 
Management, 23-25 September 2009, The Hague, the Netherlands. 
 
38. Kraan M., Zijpp B., Tutert B., Vonk T. and Megen D. V., Evaluating Networkwide 
Effects of Variable Message Signs in the Netherlands, Transportation Research 
Record 1689, Paper No. 99-1126, 1999.  
 
39. Levison, D. and Huo, H. (2003) ?Effectiveness of Variable Message Signs.? 
Presented at the  82nd Annual Meeting of the Transportation Research Board, 
Washington, D.C. 
 
40. Madanat, S., Yang, C.Y. and Yen, Y.M. Analysis of Stated Route Diversion 
Intentions under Advanced Traveler Information Systems Using Latent Variable 
Modeling. Transportation Research Record, No. 1485, 1995, pp. 10-17. 
 
41. Manual on Uniform Traffic Control Devices (MUTCD). Federal Highway 
Administration, U.S. Department of Transportation, Washington, D.C., 2000. 
 
94 
 
42. Molino, J., Wachtel, J., Farbry, J., Hermosillo, M., & Granda, T. The Effects of 
Commercial Electronic Variable Message Signs (CEVMS) on Driver Attention 
and Distraction: An Update (FHWA-HRT-09-018). Washington, DC: Federal 
Highway Administration, 2009. 
43. Mounce, J.M., Ullman, G., Pesti, G. and Pezoldt, V. Guidelines for the Evaluation 
of Dynamic Message Sign Performance. Texas Transportation Institute, College 
Station, 2007. 
 
44. Operational Guidelines for the Use of Changeable Message Signs. North Carolina 
Department of Transportation, 1996. 
 
45. Peng, Z., Guequierre, N., Blakeman, J.C.,  Motorist Response to Arterial Variable 
Message Signs Transportation Research Record: Journal of the Transportation 
Research Board, No. 1899, TRB, National Research Council, Washington, D.C., , 
pp. 55?63, 2004. 
 
46. Rama, P. & Kulmala, R.,Effects of variable message signs for slippery road 
conditions on driving speed and headways. Transportation Research F, 3(2), 85-
 94, 2000. 
 
47. Ridgeway, R. Framework for harmonized Implementation of Variable Message 
Signs in Europe, Rijkswaterstaat, Netherlands, 2003. 
 
48. Rogers, John, ?Evaluating the Impact of OOCEA?s Dynamic Message Signs 
(DMS) on Travelers Experience Using the Pre-Deployment Survey.? University of 
Central Florida, 2007. 
 
49. Roshandeh, A.M. and Puan, A. C., "Assessment of impact of variable message 
signs on traffic surveillance in Kuala Lumpur," in EEE International Conference 
on Intelligence and Security Informatics, ISI 2009, p. 223-225. 
 
50. Sara Nyg?rdhs, Literature review on variable message signs (VMS) 2006?2009, 
VTI notat 15A-2011. 
 
51. Schroeder, J. L. and Demetsky, M. J., Evaluation of Driver Reactions for Effective 
Use of Dynamic Message Signs in Richmond, Virginia, Virginia Transportation 
Research Council, 2010. 
 
95 
 
52. Sperry, R., McDonald, T., Nambisan, S. and Pettit, R., Effectiveness of Dynamic 
Messaging on Driver Behavior for Late Merge Lane Road Closures, The Iowa 
Department of Transportation, 2009. 
53. Song, M., Maier-Speredelozzi, V., Wang, J-H, Cheung, S., Akdemir, M. K., 
Assessing The Slow-Down Effects Caused By Active Dynamic Message Signs 
And Exploring Means To Ease The Slow-Down, available at: 
www.onlinepubs.trb.org/onlinepubs/conferences/2011/RSS/2/Song,M.pdf.  
 
54. SRF Consulting Group Inc. "Dynamic" signage: Research related to driver 
distraction and ordinance recommendations (Report prepared for the City of 
Minnetonka). Minnetonka, M, 2007. 
 
55. Tarry, S. ?A Framework for Assessing the Benefits of ITS.? Traffic Technology 
International, Aug./Sept. 1996, pp. 25-30. 
 
56. Tarry, S. and Graham, A.. The Role of Evaluation in ATT Development. Traffic 
Engineering and Control. Vol. 36, No. 12, London, England, 1995, pp. 688- 693. 
 
57. United States Department of Transportation, ITS Benefits and Unit Costs 
Database, Access time: March, 2002: 
 http://www.benefitcost.its.dot.gov/its/benecost.nsf. 
 
58. Walton, J.R., Barrett, M.L. and Crabtree, J.D. Management and Effective Use of 
Changeable Message Signs. Report KTC-01-14/SPR233-00-1F, Kentucky 
Transportation Center, 2001. 
 
59. Wang, J.H., Keceli, M. and Maier-Speredelozzi, V., Effect of Dynamic Message 
Sign Messages on Traffic Slow Downs, TRB 2009 Annual Meeting. 
 
60. Wang, J-H,Collyer, C. E. and Clark A., Assisting Elder Drivers? Comprehension 
of Dynamic Message Signs, University of Rhode Island Transportation Center, 
2007. 
 
61. Wang, J-H, Hesar, S. and Collyer, C. E.. ?Adding Graphics to Dynamic Message 
Sign Messages.? Transportation Research Record 2018, (2007): 63-71. 
 
62. Wardman M., Bonsall P. and Shires J., Driver Response to Variable Message 
Signs: A Stated Preference Investigation, Transpn. Res.-C, Vol.5, No.6, 1998, 
pp389-405, 1998. 
96 
 
 
63. Wendelboe, J., Toft., When Under Construction: Copenhagen's Motorway 
Experience. Traffic Technology International, 2008, p. 74?76, 2008. 
 
64. WTOP, Real-time travel signs become real pain for drivers (2010) , retrieved from: 
 
           http://www.wtop.com/?nid=&sid=1913047