The impacts of heavy rain on speed and headway Behaviors: An investigation using the SHRP2 naturalistic driving study data
Introduction
Human error has been identified as one of the main causes of traffic crashes. A previous study showed that 45% to 75% of crashes were human error-related (Hankey et al., 1999). The taxonomy of driver errors has been investigated in previous studies (Treat et al., 1977, Hankey et al., 1999, Stanton and Salmon, 2009). As an example, Treat et al. (1979) have taxonomized driver errors into three main groups including “errors of recognition”, “errors of decision”, and “errors of performance”. Despite the previous efforts into human error role in car crashes, the types of these errors that drivers make need to be more investigated, specifically in different traffic and environmental conditions. Driving in adverse weather conditions might be challenging due to the slippery roadways and reduced visibility. In addition, it might introduce complexities for drivers in selecting appropriate speeds and headways, which may have a significant impact on the safety and operation of freeways. Identifying factors affecting driver speed and headway selection behavior are important for policymakers and traffic engineers. This will help in overcoming the gap of the unpredictability of driver behavior and will help to set appropriate speeds in Variable Speed Limit (VSL) systems during various weather conditions.
Section snippets
Literature review
Inclement weather events such as fog, snow, ground blizzard, slush, rain, and strong wind, affect roadways by impacting pavement conditions, vehicle performances, visibility, and drivers’ behavior (Donnell and Mason, 2004, Peterson et al., 2008, Ahmed et al., 2015). Adverse weather conditions can result in a sudden reduction in visibility on roadways leading to an increased risk of crashes. According to the Fatality Analysis Reporting System (FARS), inclement weather of rain, snow and fog/smoke
Data source
The Naturalistic Driving Study (NDS) data used in this study were a subset of data reduced from the Second Strategic Highway Research Program (SHRP2). The NDS data is collected and maintained by the Virginia Tech Transportation Institute (VTTI). The main goal of the SHRP2 is to investigate the latent causes of highway crashes and congestion in a timely manner program of focused research. Identifying and implementing countermeasures that have significant safety benefits through a comprehensive
Data acquisition and preparation
The initial acquisition of data is crucial to the success of this study and it presented a unique challenge for researchers to develop a creative and unique method for leveraging the full extent of the provided NDS and RID data. Dealing with the NDS data could be challenging for various reasons; the size and complexity of the data, the continuous nature of the data, the difficulty of identifying events of interests, processing and reducing video data, linking NDS data with RID data, identifying
Trips used in this study
As mentioned earlier, a total of 240 trips including 80 trips in rain and additional 160 trips in clear weather conditions from the state of Florida and Washington were considered in this study (Fig. 1). Mentioned trips involve 55 drivers between 19 and 84 years old. In total, 5165 one-minute segments, which is equivalent to nearly 86 h and 6806 km of driving, were processed in this study. The speed limit data provided in the RID as well as age and gender of each specific driver, provided in
Descriptive analysis
This study investigated the distribution and variation of speeds between clear and heavy rain in free-flow speed conditions. Characterization of traffic flow became very important for various reasons: realistic traffic conditions and the appropriate distributions are needed for the calibration of the simulation models, and predictability of traffic state in various weather conditions is needed for an effective and realistic VSL system (Carlson et al., 2011, Carlson et al., 2010, Papamichail et
Methodology: Partial proportional odds model
To model driver speed and headway selection behaviors the prominent approach of discrete choice models was used. Considering the fact that speed and headway selections are ordered response variables in nature, an ordered logistic regression model (OLM) can be written in terms of probability of speed/headway selection levels as shown in Eq. (1).where the probability of occurrence of speed/headway selection level j associates to a vector of
Conclusions
Behavior and road-user characteristics are among the most important elements influencing the driving task. A driver’s reaction process to speed and headway choice, along with the dynamic visual acuity, are critically important factors for safe driving. The naturalistic driving study and roadway information datasets utilized in this study revealed that modeling drivers’ behavior in rainy weather conditions using vehicle time-series data is realizable. This paper developed an effective automated
Acknowledgments
This work was conducted through the second Strategic Highway Research Program (SHRP2), which is administrated by the Transportation Research Board (TRB) of the National Academies of Sciences, Engineering, and Medicine, and it was sponsored by the Federal Highway Administration (FHWA) in cooperation with the American Association of State Highway and Transportation Officials (AASHTO).
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