Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures |
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| Affiliation: | 1. Tongji University, Department of Transportation Engineering, China;2. Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States;3. Tongji University, Department of Traffic Engineering, China;1. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Engineering II-215, Orlando, FL 32816, United States;2. School of Transportation Engineering, Tongji University, 4800 Cao’an Road, 201804 Shanghai, China;1. Green Transport and Logistics Institute, Korea Railroad Research Institute, Uiwang, Republic of Korea;2. Department of Civil and Environmental Engineering, Seoul National University, Seoul, Republic of Korea;1. School of Transportation Engineering, Tongji University, 4800 Cao’an Road, 201804 Shanghai, China;2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, United States;1. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, United States;2. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States;1. School of Transportation, Southeast University, 2 Si Pai Lou, Nanjing, 210096, PR China;2. Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, Chapel Hill, NC 27599, United States;1. College of Transportation Engineering, Tongji University, 4800 Cao''an Road, 201804 Shanghai, China;2. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao''an Road, 201804 Shanghai, China;3. Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States |
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| Abstract: | Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk. |
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| Keywords: | Reduced visibility Visibility and traffic detection system Surrogate measures of safety Speed variance Headway variance Time to collision Log-Inverse Gaussian regression model |
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