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 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 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 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.

This synthesis will be of interest to traffic and safety engineers; highway and bridge designers; police, fire, and emergency personnel; toll authorities; and other officials concerned with roadway safety operations. It describes the forecasting and detection of fog, as well as countermeasures that are used by state and local transportation agencies to dissipate fog and to warn motorists during reduced visibility conditions. The presence of fog has often resulted in serious collisions due to reduced visibility. This publication of the Transportation Research Board presents a generalized background on the causes of fog, fog forecasting and detection methods, and how fog affects the roadway and the driver. The various countermeasures practiced by transportation agencies that are described herein range from techniques to dissipate fog to a variety of warning systems to alert drivers to adverse conditions. The National Transportation Safety Board (NTSB) has held hearings and made recommendations for fog countermeasures, which are described in this synthesis.

Peter Rothe's absorbing volume ex-amines one of the most important areas of modern life, the culture of the automobile. Rothe takes a problem central to everyday life--auto safety-- and reconstructs it into a means of revealing the human condition. His goal is to motivate the reader to think differently about traffic safety, and to suspend all inherited epidemiological, engineering, and psychological beliefs. Because traffic arises from the interac-tion between people, he argues that traffic safety is a social process, one that is created, formed, and changed by human interaction. Beyond Traffic Safety presents con-troversial critiques and provocative positions. It stimulates insight into the question of why traffic safety issues have become so important today. Rothe explores new social boundaries and crosses old ones. He demonstrates that interlinking social factors in a motorist's behavior reveal traffic safety as a significant facet of social behavior worthy of in-depth exploration. This may well be the first work of fundamen-tal theory in an area thus far dominated by crude empiricism. Beyond Traffic Safety describes responsibilities of drivers and ex-amines how basic trust in traffic routines sustains an orderly traffic flow. It shows how physical risks are negotiated to accommodate social ex-pectations. Part of the text is devoted to the role played by the driver's license as a form of social control, emphasiz-ing the way in which various images of licensing convey different ideas about traffic safety. Rothe focuses on the development of traffic laws and how laws affect driver behavior. He also traces the roles that discretion and tolerance play in police work. In par-ticular, the dominant traffic violation, speeding, is analyzed. Rothe looks at traffic safety in a new way by presenting it as part of a social scientific framework. He provides a basis for future exploration of this kind. Beyond Traffic Safety is an im-portant and insightful analysis for road users, traffic safety educators, policymakers, psychologists, and sociologists.

This comprehensive 2nd edition covers the key issues that relate human behavior to traffic safety. In particular it covers the increasing roles that pedestrians and cyclists have in the traffic system; the role of infotainment in driver distraction; and the increasing role of driver assistance systems in changing the driver-vehicle interaction.

Further research into how the warning message is presented or worded could provide additional insight into the impact it can have on driver behavior. Since it is observed that drivers acknowledge the 'reduce speed' advisement, it is likely that specifying a specific speed limit could also warrant driver obedience. Additional testing and observation of driver reaction to larger traffic volumes and situations within the fog would also allow for further analysis of driver behavior under reduced visibility and the impact the early warning system has on their behavior.