Ali Ghasemzadeh

Drivers’ Lane-Keeping Ability in Heavy Rain: Preliminary Investigation Using SHRP 2 Naturalistic Driving Study Data

There is a lack of studies that have examined the impact of weather conditions on drivers’ lane-keeping performance. Many driver behavior studies have been conducted in simulated environments. However, no studies have examined the impact of heavy rain on lane-keeping ability in naturalistic settings. A study used data from the SHRP 2 Naturalistic Driving Study to provide better insights into driver behavior and performance in clear and rainy weather conditions. In particular, a lane-keeping model was developed using logistic regression to better understand factors affecting drivers’ lane-keeping ability in different weather conditions. One interesting finding of this research is that heavy rain can significantly increase the standard deviation of lane position, which is a widely used method for analyzing lane-keeping ability. More specifically, drivers in heavy rain are 3.8 times more likely to show a higher standard deviation of lane position than in clear weather condition. An additional interesting finding is that drivers have better lane-keeping abilities in roadways with higher posted speed. Results from this study could provide a better understanding of the complex effects of weather conditions on drivers’ lane-keeping ability and how drivers perceive and react in different weather conditions. Results from this study may also provide insights into automating the activation and deactivation of lane departure warning systems.

Parametric Ordinal Logistic Regression and Non-Parametric Decision Tree Approaches for Assessing the Impact of Weather Conditions on Driver Speed Selection Using Naturalistic Driving Data

The impact of adverse weather conditions on transportation operation and safety is the focus of many studies; however, comprehensive research detailing the differences in driving behavior and performance during adverse conditions is limited. Many previous studies utilized aggregate traffic and weather data (e.g., average speed, headway, and global weather information) to formulate conclusions about the impact of weather on network operation and safety; however, research into specific factors associated with driver performance and behavior are notably absent. A novel approach, presented in this paper, fills this gap by considering disaggregate trajectory-level data available through the SHRP2 Naturalistic Driving Study and Roadway Information Database. Parametric ordinal logistic regression and non-parametric classification tree modeling were utilized to better understand speed selection behavior in adverse weather conditions. The results indicate that the most important factors impacting driver speed selection are weather conditions, traffic conditions, and the posted speed limit. Moreover, it was found that drivers are more likely to significantly reduce their speed in snowy weather conditions, as compared with other adverse weather conditions (such as rain and fog). The purpose of this study was to gather insights into driver speed preferences in different weather conditions, such that efficient logic can be introduced for a realistic variable speed limit system—aimed at maximizing speed compliance and reducing speed variations. This study provides valuable information related to drivers’ interaction with real-time changes in roadway and weather conditions, leading to a better understanding of the effectiveness of operational countermeasures.

Investigating the Impact of Fog on Freeway Speed Selection using the SHRP2 Naturalistic Driving Study Data

The negative effect of reduced visibility on driver performance has been recognized as one of the main causes of motor vehicle crashes in fog. Although many studies have concentrated on driver behavior during foggy weather in a simulated environment, there is a lack of studies that have addressed the impact of fog on driver behavior and performance in naturalistic settings. This paper utilized the data from the SHRP2 Naturalistic Driving Study (NDS) database to understand driver behavior in general and speed selection in particular during clear and foggy weather conditions. In this study, a comparative preliminary analysis and an ordered logit model were developed to evaluate driver speed behavior in fog and clear weather conditions. Results from the preliminary analysis showed 10% and 3% reduction in speed because of near fog and distant fog, respectively. In addition, results from the speed selection model showed that the odds of reducing speed were 1.31 and 1.28 times higher for drivers traveling in near fog and distant fog, respectively, compared with drivers who were driving in clear weather conditions. However, there is an over-representation of young drivers in the SHRP2 NDS database, which was reflected in the dataset used in this study. Therefore, a more representative sample of age groups might provide different results. The results from this study could provide a better insight into driver speed selection during foggy weather conditions, which can be utilized to improve various safety strategies including variable speed limits.