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Adverse weather conditions are known to be one of the main contributing factors affecting traffic operation and safety. Inclement weather conditions impede drivers' ability to perceive and react to their environment, and this decrease in driver performance has a dramatic impact on network-wide operations and the predictability of traffic flow. Studies have shown that drivers may reduce their speed, maintain a larger headway, and drive more carefully in adverse conditions to compensate for reduced visibility and slippery road conditions (1). A previous study investigated the impact of rain on freeway capacity, revealing that wet pavement and decreased visibility affects drivers' speed selection and roadway capacity (2). Although this study focused on the impact of heavy rain, other studies have shown that light rain can also affect travel speed and roadway capacity (3,4). A study by Kyte et al. showed that both light rain and snow can reduce speed up to 50% (5). In addition, the study identified a 9 km/h speed reduction during wind speeds >48 km/h; however, the impact of reduced visibility on speed reduction was found to be marginal. Another study using the same test sites revealed that snow caused a speed reduction of 18 km/h (6). Ibrahim and Hall investigated the difference in traffic conditions during light rain, heavy rain, and snow compared with matching trips in clear weather conditions using the data from two rainy, two snowy, and six clear weather days. Results indicated a 3-5% speed reduction during light rain and snow, a 758035T RRXXX10.

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.

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