Thesis (M.S.)--University of Central Florida, 2009. / Adviser: Mohamed Abdel-Aty. Includes bibliographical references (p. 67-71).

This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers’ behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers’ responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers’ behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers’ stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds’ ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that iv would achieve the best perceived safety and drivers’ compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that "Caution-fog ahead-reduce speed" was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers’ responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers’ compliance and drivers’ satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers’ satisfaction with VSL/CMS was the most significant factor that positively affected drivers’ compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers’ compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers’ familiarity with VSL signs and driver factors were the significant factors affecting drivers’ satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers’ behavior in such reduced visibility conditions by enhancing drivers’ compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous v studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two vi Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed.

Thesis (M.S.)--University of Wyoming, 2009. / Title from PDF title page (viewed on July 22, 2010). Includes bibliographical references (p. 137-139).

Thesis (Ph.D.)--George Mason University, 2007. / Title from PDF t.p. (viewed Oct. 29, 2007). Thesis director: Roger R. Stough. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Public Policy. Vita: p. 154. Includes bibliographical references (p. 149-153). Also issued in print.

Thesis (M.S.)--University of Wyoming, 2008. / Title from PDF title page (viewed on Dec. 23, 2009). Includes bibliographical references (p. 125-126).

Safety and congestion are ever present and increasingly severe transportation problems in urban areas throughout the nation and world. These phenomena can have wide-ranging consequences relating to safety, the economy, and the environment. Adverse weather conditions represent another significant challenge to safety and mobility on highways. Oregon is not immune from either of these global issues. Oregon Route (OR) 217, to the southwest of the downtown Portland, is one of the worst freeways for congestion in the state and is also subject to the Pacific Northwest's frequently inclement and unpredictable climate. High crash rates, severe recurrent bottlenecks and highly unreliable travel times continuously plague the corridor, making it a major headache for the thousands of commuters using it every day. In an effort to more effectively combat both congestion and adverse weather, transportation officials all over the world have been turning to increasingly technological strategies like Active Traffic Management (ATM). This can come in many forms, but among the most common are variable speed limit (VSL) systems which use real-time data to compute and display appropriate reduced speeds during congestion and/or adverse weather. After numerous studies and deliberations, Oregon Department of Transportation (ODOT) selected Oregon Route (OR) 217 as one of the first locations in the state to be implemented with an advisory VSL system, and that system began operation in the summer of 2014. This thesis seeks to evaluate the effectiveness of this VSL system through the first eight months of its operation through an in-depth and wide-ranging "before and after" analysis. Analysis of traffic flow and safety data for OR 217 from before the VSL system was implemented made clear some of the most prevalent issues which convinced ODOT to pursue VSL. Using those issues as a basis, a framework of seven specific evaluation questions relating to both performance and safety, as well as both congestion and adverse weather, was established to guide the "before and after" comparisons. Hypotheses, and measures of effectiveness for each question were developed, and data were obtained from a diverse array of sources including freeway detectors, ODOT's incident database, and the National Oceanic and Atmospheric Administration (NOAA). The results of the various "before and after" comparisons performed as a part of this thesis indicate that conditions have changed on OR 217 in a number of ways since the VSL system was activated. Many, but not all, of the findings were consistent with the initial hypotheses and with the findings from other VSL studies in the literature. Certain locations along the corridor have seen significant declines in speed variability, supporting the common notion that VSL systems have a harmonizing effect on traffic flow. Crash rates have not decreased, but crashes have become less frequent in the immediate vicinity of VSL signs. Flow distribution between adjacent lanes has been more even since VSL implementation during midday hours and the evening peak, and travel time reliability has seen widespread improvement in three of the corridor's four primary travel lanes during those same times. The drops in flow that generally occur upstream of bottlenecks once they form have had diminished magnitudes, while the drops in flow downstream of the same bottlenecks have grown. Finally, the increase in travel times that is usually brought about by adverse weather has been smaller since VSL implementation, while the decline in travel time reliability has largely disappeared.