The impacts of heavy rain on speed and headway Behaviors: An investigation using the SHRP2 naturalistic driving study data

doi:10.1016/j.trc.2018.04.012

Transportation Research Part C: Emerging Technologies

Volume 91, June 2018, Pages 371-384

https://doi.org/10.1016/j.trc.2018.04.012 Get rights and content

Highlights

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Naturalistic driving data used in this research are extracted from the SHRP2 project.
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Speed and headway selection behaviors were compared in clear and rainy weather.
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Utilized wiper status as a novel extraction process to identify trips in rain.
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Results will pave the way in setting more realistic VSL in adverse weather conditions.
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Chucking approach in this study can help to create more homogeneous segments.
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Partial Proportional Odds model utilized to relax the parallel-line assumptions.
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Driving above the speed limits is less common on segments with higher speed limits.

Abstract

Adverse weather conditions can significantly impact roadways by influencing roadway conditions, vehicle performance and driver behavior. Vehicle user characteristics and behavior can be considered as the most important factors affecting the driving task. The ability to see objects in motion, so called “dynamic visual acuity”, and the proper reaction process, such as headway and speed selection, are imperative factors for safe driving. In this study, data from the SHRP2 naturalistic driving study (NDS) are used to provide better understanding of driver speed and headway selection behaviors in clear and rainy weather conditions. A unique procedure to identify rain-related trips from the massive SHRP2 database was introduced in this study. In addition, roadway information database (RID) and NDS were utilized to compare driver behavior in clear and heavy rain conditions using matching trips. Matching trips were defined as trips with same driver, same vehicle, and same traversed routes. Preliminary descriptive statistics, partial proportional odds model, as well as geographical information system analyses showed significant differences between driver behavior and performance in clear and rainy weather conditions. One interesting finding of this research is that drivers were less likely to drive above the speed limits on road segments with higher posted speed limits. In addition, it was found that the probability of reducing speed more than 5 kph below the speed limits were 23% and 29% higher in light rain and heavy rain, respectively. Not only will the findings of the study help in providing better insights on drivers’ behavior and performance in rainy weather conditions, but it will also serve as a foundation for further studies to investigate driver behavioral factors in other weather conditions using naturalistic driving 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). $P (y_{i} > j) = \frac{\exp (α_{j} - x_{i}^{'} β)}{1 + \exp (α_{j} - x_{i}^{'} β)}, j = 1, 2, \dots,k - 1$ 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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Citation Excerpt :
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The impacts of heavy rain on speed and headway Behaviors: An investigation using the SHRP2 naturalistic driving study data

Highlights

Abstract

Introduction

Section snippets

Literature review

Data source

Data acquisition and preparation

Trips used in this study

Descriptive analysis

Methodology: Partial proportional odds model

Conclusions

Acknowledgments

The influence of winter weather on high-crash days in Southern Ontario

Assessment of interaction of crash occurrence, mountainous freeway geometry, real-time weather, and traffic data

Transp. Res. Rec. J. Transp. Res. Board

Weather as a chronic hazard for road transportation in Canadian cities

Nat. Hazards

The relationship between weather and road safety: past and future research directions

Climatol. Bull.

Assessing safety critical braking events in naturalistic driving studies

Transp. Res. Part F Traffic Psychol. Behav.

Evaluation of Automated Bridge Deck Anti-Icing System

Assessing proportionality in the proportional odds model for ordinal logistic regression

Biometrics

Risk of a road accident in rainy weather

Accid. Anal. Prev.

Speed choice and driving performance in simulated foggy conditions

Accid. Anal. Prev.

Local feedback-based mainstream traffic flow control on motorways using variable speed limits

Intell. Transp. Syst. IEEE Trans.

Optimal motorway traffic flow control involving variable speed limits and ramp metering

Transp. Sci.

Towards a general theory of driver behaviour

Accid. Anal. Prev.

Drivers’ lane-keeping ability in heavy rain: preliminary investigation using SHRP 2 naturalistic driving study data

Transp. Res. Rec. J. Transp. Res. Board

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: non-parametric MARS and parametric logistic regression modeling approaches

Transp. Res. Part C: Emerg. Technol.

Projected implications of climate change for road safety in Greater Vancouver

Canada. Clim. Change