Analysis of the Impact of Fog-Related Reduced Visibility on Traffic Parameters
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 144, Issue 2
Abstract
There are few research studies that addressed the impact of reduced visibility due to fog using real-time data. It is thus meaningful to conduct further investigation that can clearly describe the changes in driving behavior and traffic parameters under foggy conditions using real-time traffic and weather data. Field traffic and weather data were collected in this research and fog cases were selected and analyzed by comparing them with clear cases to identify the differences in traffic characteristics under the two different situations. Moreover, vehicles were classified into two types (i.e., passenger cars and trucks) to identify whether the impact of reduced visibility due to fog on traffic varies depending on vehicle types. Afterward, the traffic parameters under different visibility classes and the effects of reduced visibility on different lanes were analyzed using ANOVA. Finally, a matched case–control logistic regression model was applied to further confirm the relationship between traffic parameters and reduced visibility due to fog. It was concluded that the impact of fog on traffic varies by vehicle types and lanes. The impact was also different by visibility classes. The impact of reduced visibility on passenger cars is more significant compared with that on trucks. The effect of reduced visibility on traffic parameters is more significant on inner lanes than outer lanes. Under these weather conditions, drivers should pay more attention to the traffic because higher headway variance is more likely to result in the crash occurrence. The matched case–control logistic regression modeling results indicate that larger average headway, speed variance, headway variance, and occupancy were related to the increase of the likelihood of a reduced visibility. The results would be helpful to understand the change of traffic status and investigate the potential factors for higher crash frequency under foggy conditions.
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Acknowledgments
The authors wish to thank the Florida Department of Transportation (FDOT) for providing the data that were used in this study, and for funding this research. Praxsoft Company is a team member with UCF in this study. The authors also thank Dr. Amr Oloufa of UCF for his contributions to this study. The authors wish to thank the National Center for Transportation Systems Productivity and Management (NCTSPM) for partly funding this research. NCTSPM is a RITA-funded UTC Center lead by GTech. This study was also sponsored by the Chinese National Science Foundation (71601143 and 51608386). All opinions and results are solely those of the authors.
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Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Mar 19, 2016
Accepted: Jun 9, 2017
Published online: Nov 30, 2017
Published in print: Feb 1, 2018
Discussion open until: Apr 30, 2018
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