Abstract
The existing crash modeling techniques for expressway tunnels must overcome the following difficulties: 1) The collected risk factors contributing to the tunnel crashes include narrow ranges, especially the pavement conditions and weather conditions of the tunnels are rarely taken into account. 2) Most researchers ignored the estimation deviation caused by the excess zero observations of tunnel crash datasets. 3) No existing tunnel crash model can combine the random-parameters approach and spatial-temporal approach to solve the estimation deviation caused by the inter-samples and spatial-temporal heterogeneity. To address these problems, this study presents an investigation of the safety effects of risk factors of tunnel design features, traffic conditions, pavement conditions and weather conditions utilizing a 12-quarter period (3 years) of data as well as five crash frequency models: 1) a fixed parameters negative binomial model (FPNB), 2) a random parameters negative binomial model (RPNB), 3) a random parameters negative binomial Lindley model (RPNBL), 4) a spatial and random parameters negative binomial Lindley model (SP-RPNBL), and 5) a spatial-temporal and random parameters negative binomial Lindley model (ST-RPNBL). The results showed that the ST-RPNBL model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity by introducing both the random parameters and spatial-temporal parameters that provided better goodness of fit and offered more insights into the factors that contribute to tunnel safety. Furthermore, the ST-RPNBL model detected 16 variables that were significantly correlated with tunnel crash frequency, of which 12 variables were associated with a higher crash frequency and four variables were associated with a lower crash frequency. The random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and the skidding resistance index (SRI) were identified, and the influence of each significant variable on the crash frequency was analyzed.
Highlights
Applying new methods to model crash frequency to analyze how safety performance is affected by traffic, geometric design, environment, weather and other factors has always been a research hotspot in the field of traffic safetyThe associate editor coordinating the review of this manuscript and approving it for publication was Michail Makridis.analysis [1]–[3]
This study presents an investigation of the safety effects of risk factors of tunnel design features, traffic conditions, pavement conditions and weather conditions utilizing a 12-quarter period (3 years) of data as well as five crash frequency models: 1) a fixed parameters negative binomial model (FPNB), 2) a random parameters negative binomial model (RPNB), 3) a random parameters negative binomial Lindley model (RPNBL), 4) a spatial and random parameters negative binomial Lindley model (SP-RPNBL), and 5) a spatial-temporal and random parameters negative binomial Lindley model (ST-RPNBL)
This study addresses the properties of excess zero observations, inter-samples heterogeneity, and spatial-temporal heterogeneity prevalent in a tunnel crash panel dataset by developing a spatial-temporal and random parameters negative binomial Lindley (ST-RPNBL) model to explore the effects of tunnel design features, traffic conditions, pavement conditions and weather conditions on the safety of expressway tunnels in China
Summary
Applying new methods to model crash frequency (i.e., crash counts of specific road stations in a specific period) to analyze how safety performance is affected by traffic, geometric design, environment, weather and other factors has always been a research hotspot in the field of traffic safetyThe associate editor coordinating the review of this manuscript and approving it for publication was Michail Makridis.analysis [1]–[3]. Applying new methods to model crash frequency (i.e., crash counts of specific road stations in a specific period) to analyze how safety performance is affected by traffic, geometric design, environment, weather and other factors has always been a research hotspot in the field of traffic safety. Most research objects of this type of method are open roads (such as basic expressway segments, urban intersections, etc.), while the safety research of expressway tunnels has been comparatively ignored [4]. Alignment and environment of expressway tunnels are greatly different from those of open roads, there are differences in the impact laws and degrees of risk factors for crash frequency [5]. F. Tang et al.: Investigation of Factors Influencing Crash Frequency in Expressway Tunnels to highway tunnel crash frequency by establishing reliable safety performance functions (SPFs) between traffic crashes and various influencing factors
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call