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Many crashes occur every year that involve drivers who are unlicensed and underage. One of the main challenges for traffic safety authorities is the lack of detailed information about this alarming phenomenon. This study aims to investigate the rate of underage driving among high school teenagers and thoroughly examine their awareness, perception, and use of the seat belt as one of the main in-vehicle safety features. Data was obtained from a self-report questionnaire. The results indicated that 59% of the participants drive without having a driver's license, and 47% reported that they use seat belts as a driver or a passenger. The results of the logistic regression model indicated that four significant factors affected high school teenagers' use of seat belts: (1) driving a car to school or “being a driver,” (2) personal perception of the role of seat belts in saving lives, (3) keen to require back-seat passengers to buckle up, and (4) previous attendance in a safety campaign that emphasized the importance of using seat belts. [ABSTRACT FROM PUBLISHER]

Speeding is a key contributing factor in roadway crashes in the United Arab Emirates (UAE) and elsewhere. Understanding how drivers behave towards speed management devices (i.e., speed cameras, radars, speed limits and speed warning signs) as well as factors affecting drivers’ involvement in speed-related crashes might help in improving traffic safety. This study aims to identify and quantify the factors that affect drivers’ compliance with speed enforcement and management devices as well as drivers’ involvement in at-fault speed-related crashes in the Emirate of Abu Dhabi (AD), UAE. Two different datasets were collected from the same drivers’ population in AD to provide different valuable information regarding the speeding problem. The first dataset was obtained from crashes’ reports while, the second dataset was obtained from a self-reported questionnaire survey that was carried out among a total of 442 drivers in AD. Three logistic regression models were developed to identify the significant variables that affect (1) the occurrence of speed related crash (using crashes reports data), (2) drivers’ compliance with speed limits (using questionnaire data), and (3) involvement in at-fault speed related crashes (using questionnaire data). The findings revealed that drivers’ factors (gender, age, and nationality), vehicle factor (vehicle type), roads and environment factors (weather, road type and speed limit) were the significant factors that affect the occurrence of speed-related crashes in AD. The questionnaire findings revealed that running late, low values of posted speed limits and no sufficient police enforcement were the three main reasons that make motorists drive over the speed limits. In addition, the results indicated that drivers’ characteristics (i.e., gender, education and income), drivers’ responses to speed enforcement and management devices, and drivers’ awareness about the importance of such devices in improving traffic safety were the main factors that affecting both drivers’ compliance with speed enforcement devices and drivers’ involvement in at-fault speed-related crashes. A comparison between the analysis results of traffic crashes and questionnaire datasets as well as a comparison between the findings of this study and existing literature are also provided. [ABSTRACT FROM AUTHOR]

Numerous studies have demonstrated that the human factor is the main contributing factor leading to traffic accidents, followed by vehicle and road factors. In addition, many earlier studies have indicated that young drivers are at a high risk of being killed in traffic accidents around the world. Compared to other age groups, young drivers tend to be over-represented in severe and fatal crashes considering their percentage of the population. One way to improve traffic safety on highways is to improve the driving behavior of drivers, especially teenager drivers. There is a lack of studies that strived to thoroughly examine the driving behavior of young drivers in the Kingdom of Saudi Arabia (KSA). Thus, this study aims at identifying and quantifying the significant factors associated with the involvement of young Saudi drivers in at-fault crashes. Another objective of this study is to provide in-depth insights concerning the types of traffic violations that young Saudi drivers commit while driving and the main reason(s) that they are involved in risky driving behaviors. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 242 young drivers in Riyadh, the capital of the KSA. To achieve these goals, the structural equation modeling (SEM) approach was adopted using SAS software package. The results indicated that exceeding the speed limit was the main reason for obtaining traffic citations by young Saudi drivers (stated by approximately 73% of participants). Additionally, running late was the main reason for being involved in risky driving behaviors while driving (62%), followed by testing the performance of the car or showing off (18%). Moreover, the results of the SEM model indicated that aggressive violations (pressing the brakes and gas pedal at the same time while driving) was the most significant factor affecting young Saudi drivers' involvement in at-fault crashes between ages 18 and 24, followed by ordinary violations (i.e., driving so close to the car in front that it would be difficult to stop in an emergency) and attitudes toward speeding. Practical suggestions on how to improve the driving behavior of young Saudi drivers and how to reduce their crash risk, along with recommendations for future studies, are also discussed. [ABSTRACT FROM AUTHOR]

Recently, the severity of injuries resulting from traffic crashes has been extensively investigated in numerous studies. However, the number of studies that addressed the severity of the run-off-road (ROR) crashes is relatively low. In the Emirate of Abu Dhabi (AD), approximately 22% of the total serious crashes and fatalities that occurred from 2007 to 2013 were ROR crashes. Despite these facts and the uniqueness of the composition of licensed drivers in AD (approximately 87% of them are non-Emiratis), the factors affecting the occurrence and severity of ROR crashes in AD have not been explicitly addressed in any prior studies. Therefore, this study aims to investigate the characteristics of at-fault drivers involved in ROR crashes in AD, the nature and main causes of those crashes. In this regard, conditional distribution and two-way contingency tables were developed. In addition, this study aims to identify and quantify the factors affecting the severity of ROR crashes such as driver, road, vehicle and environment factors. To achieve this goal, ordered probit model approach was employed. Crash data for a total of 3819 ROR crashes that occurred in AD were employed in the analysis. The results indicated that driver factors (carelessness, speeding, and nationality), vehicle characteristics (vehicle type), and road and environment factors (road type, crash location and road surface condition) were the significant factors influencing the severity of ROR crashes in AD. Countermeasures to improve traffic safety and reduce numbers and severity of ROR crashes in AD were discussed. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: Recently, growing concern has been shifting toward the necessity of improving traffic safety in the Kingdom of Saudi Arabia (KSA). KSA has a unique traffic safety problem in that: (a) it can be classified as a developed country in terms of the magnitude and quality of the roadway networks available and its compatibility with international standards; however, (b) it can also be considered a developing country as the rate of increase in the number of road crashes is substantial compared with relevant figures of other developing countries and other countries of the Gulf region. Hence, more research efforts are still needed. Objectives: This paper examines the nature and causes of fatal and serious traffic crashes in KSA so that solutions and/or future studies can be suggested. Method: Data from 11,545 reported fatal and injury traffic crashes that occurred in Riyadh (the capital of KSA) during the period 2004–2011 were analyzed by alternative and complementary methods. A logistic regression model was estimated and the results revealed that crash reason (speeding), damages in public property, day of the week, crash location (non-intersection location), and point of collision (head-on) were the significant variables affecting the binary target variable (fatal and non-fatal crashes). Additionally, the structural equation modeling approach was developed to identify and quantify the impacts of significant variables influencing crash size (e.g., no. of injuries, no. of vehicles involved in the crash). Crash size is one of the important indices that measure the level of safety of transportation facilities. Results: The results showed that road factor was the most significant factor affecting the size of the crash followed by the driver and environment factors. Impact on Industry: Considering the results of this study, practical suggestions on how to improve traffic safety in KSA are also presented and discussed. [Copyright &y& Elsevier]

Abstract: The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers’ involvement in at-fault crashes or traffic citations at the ages of 16–17 (while having the Operational License) and 18–24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 680 young drivers in Central Florida. To achieve these goals, the structural equation modeling approach was adopted. The results revealed that aggressive violations, in-vehicle distractions and demographic characteristics were the significant factors affecting young drivers’ involvement in at-fault crashes or traffic violations at the age of 16–17. However, in-vehicle distractions, attitudes toward speeding and demographic characteristics were the significant factors affecting young drivers’ crash risk at 18–24. Additionally, the majority of participants reported that “running late” is the main reason for taking risk while driving (i.e., speeding, accept short gaps, or drive so close to the car in front) followed by “racing other cars”. Additionally, “exceed speed limits” was the main reason for receiving traffic citations at 16–17 and 18–24 age groups. Practical suggestions on how to reduce crash risk and promote safe driving among young drivers are also discussed. [Copyright &y& Elsevier]

Abstract: More researchers started using real-time traffic surveillance data, collected from loop/radar detectors (LDs), for proactive crash risk assessment. However, there is a lack of prior studies that investigated the link between real-time traffic data and crash risk of reduced visibility related (VR) crashes. 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 are advantageous for predicting VR crashes; LDs or AVIs. Thus, this study 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 are better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression models using the historical crashes, LDs and AVI data. Regarding the model 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 5–10min 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 5–10min prior to the crash time, affected the likelihood of VR crash occurrence. The results showed that both LDs and AVI systems can be used for safety application (i.e., predicting VR crashes). It was found that up to 73% of VR crashes could be identified correctly. Argument concerning which traffic data (LDs or AVI) are better for predicting VR crashes is also provided and discussed. [Copyright &y& Elsevier]