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Download or read book Investigation of Lane-keeping and Lane-changing Characteristics in Fog Using the SHRP2 Naturalistic Driving Study Data written by Anik Das and published by . This book was released on 2018 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driving in foggy weather conditions has been recognized as a major safety concern for many years. Driver behavior and performance can be negatively affected by foggy weather conditions due to limited visibility and shorter available perception-reaction time. In addition, random and unusual patterns of fog affect driver behavior greatly. A number of previous studies focused on driver performance and behavior in simulated environments. However, very few studies have examined the impact of foggy weather conditions on specific driver behavior in naturalistic settings. The second Strategic Highway Research Program (SHRP2) has conducted the largest Naturalistic Driving Study (NDS) between 2010 and 2013 on six US states to observe drivers performance and their interactions with roadway features, traffic, and other environmental conditions. The study conducted in this thesis utilized the SHRP2 NDS dataset to evaluate driver lane-keeping behavior in clear and foggy weather conditions. A total of 62 drivers involved in 124 trips in fog with their corresponding 248 matching trips in clear weather were selected for investigating lane-keeping behavior. Preliminary descriptive analysis was performed and a lane-keeping model was developed using ordered logistic regression approach to achieve the study goals. Individual variables such as visibility, traffic conditions, occurrence of lane-changing maneuver, driver marital status, geometric characteristics, among other variables, as well as some interaction terms (i.e., weather and gender, surface condition and driving experience, speed limit and mileage last year) have been found to significantly affect lane-keeping ability. An important finding of this study illustrated that affected visibility caused by foggy weather conditions decreases lane-keeping ability significantly. More specifically, drivers in affected visibility conditions showed 1.37 times higher Standard Deviation of Lane Position (SDLP) in comparison with drivers who were driving in unaffected visibility conditions. The outcome of this research may provide a better understanding of driver lane-keeping behavior and their perception of foggy weather conditions. This thesis also provided valuable insights into lane-changing characteristics based on driver behavior in fog and clear weather conditions. While a few studies focused on lane-changing maneuvers based on driver type, the impact of adverse weather conditions (especially in fog) was not addressed. This thesis examined lane-changing maneuvers in fog and clear weather conditions using the SHRP2 NDS dataset. A total of 125 drivers involved in 214 trips in fog with their corresponding 214 trips in clear weather were selected for analyzing the lane-changing characteristics. These participants performed 92 lane changes in heavy fog, 445 in distant fog, and 1,163 in clear weather conditions. The study tested several hypotheses to identify significant differences in number of lane-changing events per mile and lane-changing durations in fog and clear weather in different traffic conditions. In addition, different distributions of lane-changing durations were fitted to identify common trends. Using K-means cluster analysis technique and based on lane-changing behaviors, drivers were classified into two categories, conservative and aggressive. It was found that in heavy fog the mean lane-changing durations were significantly higher than clear weather in mixed-flow conditions. The cluster analysis results revealed that both conservative and aggressive drivers in heavy fog conditions had longer lane-changing durations than in clear weather. The comparison between the SHRP2 administrated survey questionnaires and the cluster analysis suggested that drivers’ responses related to foggy weather were more consistent with survey questionnaires compared to their responses in clear weather during free-flow conditions. The findings of this study have several practical implications. The result of lane-keeping behavior might be used to improve Lane Departure Warning (LDW) systems algorithm considering affected visibility by fog. The outcomes of lane-changing analysis could be used to classify drivers in real-time based on their lane-changing behaviors in a connected vehicle (CV) environment. The results might also be used in microsimulation model calibration and validation related to lane change in reduced visibility due to fog and various traffic conditions.