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

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.

[1]  R. Brant Assessing proportionality in the proportional odds model for ordinal logistic regression. , 1990, Biometrics.

[2]  Ashley Martin,et al.  Speed choice and driving performance in simulated foggy conditions. , 2011, Accident; analysis and prevention.

[3]  Dominique Lord,et al.  The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.

[4]  Lu Ma,et al.  Car-Following Behavior Analyses Using BJTU Driving Simulator , 2013 .

[5]  Mohamed M. Ahmed,et al.  Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data , 2012 .

[6]  Richard Williams Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables , 2006 .

[7]  Mohamed Ahmed,et al.  Drivers’ Lane-Keeping Ability in Heavy Rain: Preliminary Investigation Using SHRP 2 Naturalistic Driving Study Data , 2017 .

[8]  S. Afrin The influence of winter weather on high-crash days in Southern Ontario , 2013 .

[9]  Carlo Giacomo Prato,et al.  Risk factors associated with bus accident severity in the United States: a generalized ordered logit model. , 2012, Journal of safety research.

[10]  Monica L. Barrett,et al.  EVALUATION OF AUTOMATED BRIDGE DECK ANTI-ICING SYSTEM , 2001 .

[11]  J. Bared,et al.  Accident Models for Two-Lane Rural Segments and Intersections , 1998 .

[12]  Markos Papageorgiou,et al.  Optimal Motorway Traffic Flow Control Involving Variable Speed Limits and Ramp Metering , 2010, Transp. Sci..

[13]  Gang-Len Chang,et al.  Exploring the Effectiveness of Variable Speed Limit Controls on Highway Work-Zone Operations , 2004, J. Intell. Transp. Syst..

[14]  Neville A. Stanton,et al.  Human error taxonomies applied to driving: A generic driver error taxonomy and its implications for intelligent transport systems , 2009 .

[15]  Monica Menendez,et al.  Application of partial proportional odds model for analyzing pedestrian crash injury severities in Switzerland , 2019 .

[16]  Christopher K Strong,et al.  Safety Effects of Winter Weather: The State of Knowledge and Remaining Challenges , 2010 .

[17]  Mohamed Ahmed,et al.  Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches , 2018 .

[18]  R. Fuller Towards a general theory of driver behaviour. , 2005, Accident; analysis and prevention.

[19]  Frank Drews,et al.  Profiles in Driver Distraction: Effects of Cell Phone Conversations on Younger and Older Drivers , 2004, Hum. Factors.

[20]  Mahdi Pour-Rouholamin,et al.  Investigating the risk factors associated with pedestrian injury severity in Illinois. , 2016, Journal of safety research.

[21]  Omar Bagdadi,et al.  Assessing safety critical braking events in naturalistic driving studies , 2013 .

[22]  A S Hakkert,et al.  Risk of a road accident in rainy weather. , 1988, Accident; analysis and prevention.

[23]  Wei Wang,et al.  Identifying crash-prone traffic conditions under different weather on freeways. , 2013, Journal of safety research.

[24]  Nicholas E Lownes,et al.  Analysis of rainfall impacts on platooned vehicle spacing and speed , 2012 .

[25]  Worku Y. Mergia,et al.  Exploring factors contributing to injury severity at freeway merging and diverging locations in Ohio. , 2013, Accident; analysis and prevention.

[26]  Lekshmi Sasidharan,et al.  Partial proportional odds model-an alternate choice for analyzing pedestrian crash injury severities. , 2014, Accident; analysis and prevention.

[27]  M. Wehner,et al.  Climate Variability and Change with Implications for Transportation , 2008 .

[28]  Markos Papageorgiou,et al.  Local Feedback-Based Mainstream Traffic Flow Control on Motorways Using Variable Speed Limits , 2011, IEEE Transactions on Intelligent Transportation Systems.

[29]  J. Andrey,et al.  Weather as a Chronic Hazard for Road Transportation in Canadian Cities , 2003 .