Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach

Caliendo, Ciro; Guglielmo, Maria Luisa De; Russo, Isidoro

doi:10.1016/j.tust.2018.12.012

Cited by 44 publications

(29 citation statements)

References 17 publications

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“…Coruh et al (2015), andHou et al (2018) also find that the correlated random parameters Negative Binomial model is more appropriate than the one with uncorrelated parameters. Similar results are in Caliendo et al (2019) for a correlated random parameters Poisson model. Multivariate random parameters models provide a further performance increase in terms of goodness-of-fit of road crashes data with different severity levels, see Han et al (2018), Dong et al (2014), Guo et al (2019).…”

Section: Introduction and Related Worksupporting

confidence: 82%

A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

et al. 2021

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An analysis of crashes occurring in 252 unidirectional Italian motorway tunnels over a 4-year monitoring period is provided to identify the main causes of crashes in tunnels. In this paper, we propose a full Bayesian bivariate Poisson lognormal hierarchical model with correlated parameters for the joint analysis of crashes of two levels of severity, namely severe (including fatality and injury accidents only) and non-severe (property damage only), providing better insight on the available data with respect to an analysis based on severe and non-severe independent univariate models. In particular, the proposed model shows that for both of severity levels the crash frequency increases with some parameters: the average annual daily traffic per lane, the tunnel length, and the percentage of trucks, while the presence of the sidewalk provides a reduction in severe accidents. Also the presence of the third lane induces a reduction in severe accidents. Moreover, a reduction in the crash frequency of the two crash-types over years is present. The correlation between the parameters might offer additional insights into how some combinations can affect safety in tunnels. The results are critically discussed by highlighting strength and weakness of the proposed methodology.

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Section: Introduction and Related Worksupporting

confidence: 82%

A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

et al. 2021

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“…Literature [65] provided an analysis of severe crashes (fatal and injury accidents only) that occurred in 260 Italian road tunnels on the basis of random-parameters regression models. Furthermore, research [54], [66] consider the interactions of unobserved heterogeneity, the research [54] used a random effects negative binomial model (RENB), an uncorrelated random parameters negative binomial model (URPNB), and a correlated random parameters negative binomial model (CRPNB) to fit crash frequency of freeway tunnels in China, which showed that the CRPNB model provided better goodness-of-fit and offered more insights into the factors that contribute to tunnel safety. Similarly, The research [66] provided an analysis of crash frequency, which occurred in 226 unidirectional motorway tunnels over a fouryear monitoring period in Italy, based on the unrelated and correlated random-parameter Poisson models.…”

Section: Safety Analysis Of Tunnelsmentioning

confidence: 99%

“…The second type of research can effectively reveal the relationship between crash contribution factors and crash variables, which is also the purpose of this paper. However, the research related to this method (1) have not adequately collected the dadaset of crash contributing factors, where most of the tunnel crash contributing factors established in literature [64]- [66] are related to tunnel design features and the impacts of pavement conditions on tunnel crash are rarely reported, (2) have not considered both unobserved heterogeneity and excess zero observations, where studies [64]- [66] were all modeling only for unobserved heterogeneity.…”

Section: Safety Analysis Of Tunnelsmentioning

confidence: 99%

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Tang

Cai

et al. 2020

IEEE Access

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Highway tunnels have a higher risk of crashing than open roads, which require a systematic approach to tunnel safety. However, previous research had the following problems: (1) Studies have largely focused on open roads, with very little research on tunnels. (2) The collected crash contributing factors involve narrow ranges, with very little tunnel crash data including both tunnel design features, traffic conditions and pavement conditions. (3) None of the studies considered both excess zero observations and unobserved heterogeneity with its interactions. To address these issues, this paper first established an appropriate tunnel dataset containing 3 to 5 years of crash data from several highways in China and the influence factors of tunnel design features, traffic conditions and pavement conditions. A correlated random parameters negative binomial Lindley (CRPNB-L) model that considers both excess zero observations and unobserved heterogeneity with its interaction effects was then proposed. Compared to the uncorrelated random parameters negative binomial Lindley (URPNB-L) model, fixed parameters negative binomial Lindley (FPNB-L) model and fixed parameters negative binomial (FPNB) model, the CRPNB-L model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity with its interactions by introducing correlated random parameters. In the comparisons, the CRPNB-L model achieves the best effects in the goodness-of-fit. Furthermore, the estimated results of the CRPNB-L model showed that segment length, traffic volume, proportion of class 5 vehicle (heavy trucks and trailers), tunnel entrance and exit segments, and steep uphill and downhill segments were associated with higher crash frequency, while curvature, tunnel length, pavement damage condition index (PCI) and skid resistance index (SRI) were associated with lower crash frequency. In addition, the random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and SRI were identified and their intercorrelations were analyzed. INDEX TERMS Tunnel safety, correlated random parameters, negative binomial Lindley model, tunnel design features, traffic conditions, pavement conditions.

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“…In this area, both fixed-and random-effects models were developed in traffic safety studies. For example, Caliendo et al (2019) developed unrelated and correlated-random parameters models to investigate the crash frequency in tunnels and meant to find which one is better to account for the cross correlation among parameters. Chin and Quddus (2003) developed the random effect negative binomial model to deal with the spatial and temporal effects in data.…”

Section: Bayesian Negative Binomial Regressionmentioning

confidence: 99%

“…As for tunnels, most studies concentrated on safety performance in interior tunnel areas (Amundsen & Ranes, 2000;Caliendo, De Guglielmo, & Russo, 2019;Caliendo & De Guglielmo, 2012). Caliendo, De Guglielmo, and Guida (2013) used monitoring traffic data from 2006 to 2009 in Italy and corresponding crash data to develop a crash prediction model for road tunnels.…”

Section: Introductionmentioning

confidence: 99%

Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

Ouyang

et al. 2020

Journal of Transportation Safety & Security

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With increasing traffic demand in urban areas of metropolises, many tunnels have been constructed to improve road capacity and traffic mobility. The distance between two consecutive tunnels is relatively short which usually forms a weaving section, leading to considerable traffic conflicts. The objective of this study is to evaluate the safety performance of such inter-tunnel sections. Conflict prediction models based on negative binomial regression were developed to identify influential factors. Field data were collected at ten selected sites in Nanjing, China, and used for calibrating and validating the proposed models. Two types of inter-tunnel weaving sections (type 1 and type 2) were found in the field with distinct lane markings and operation rules. The unique lane markings in type 1 weaving sections are designed to isolate weaving traffic flows and thus reduce conflicts, but in practice, contradictory to its design intention, lead to more traffic conflicts compared with type 2 weaving sections. In addition, the length of the diverging section, merging section, and whole weaving section are found to be significant influencing factors on the conflict occurrence. The findings in the present study are expected to help engineer better design inter-tunnel sections.

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Analysis of crash frequency in motorway tunnels based on a correlated random-parameters approach

Cited by 44 publications

References 17 publications

A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

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