Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures. (January 2017)

• Record Type: Journal Article
• Authors: Peng, Yichuan; Abdel-Aty, Mohamed; Shi, Qi; Yu, Rongjie
• Is Part Of: Transportation research. Volume 74(2017)
• Page Start: 295
• Page End: 305
• Publication Date: 2017-01
• Subjects: Reduced visibility -- Visibility and traffic detection system -- Surrogate measures of safety -- Speed variance -- Headway variance -- Time to collision -- Log-Inverse Gaussian regression model; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.11.022
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Investigated the effect of reduced visibility using real-time microscopic data. Log-Inverse Gaussian regression modeling applied to analyze time to collision and visibility. Identified the impact of reduced visibility varies depending on vehicle types and lanes. 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.