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.

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.INDEX TERMS Crash modeling techniques, random parameters approach, spatial-temporal approach, Lindley distribution, tunnel design features, traffic conditions, pavement conditions, weather conditions.

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Although a substantial number of traffic videos have been accumulated via daily monitoring, deep learning is seldom utilized to process these data for multilevel traffic state detection. The application of deep learning is limited for two reasons: (1) the multilevel traffic state based on traffic images has not been defined. (2) The high noise information in traffic images and extremely similar features of adjacent traffic states hinder accurate detection. Based on this situation, A new definition of the image-based multilevel traffic state is proposed using the ratio of the vehicle areas to the road areas in a traffic image, and a standard image dataset, including various illuminations and vast scenes, are established. A deep residual network named TrafficNet, which is embedded with Squeeze-and-Excitation blocks and is learned by the improved triplet loss, is proposed for multilevel traffic state detection. The Squeeze-and-Excitation block effectively reduces the model's attention to noise information and focuses on road areas that are associated with traffic features in an image. The improved triplet loss maps the learned features to a metric space where the distance between features of inter-class is larger than that within the same class, which improves the discrimination of features between adjacent traffic states. Relevant experiments prove that the performance of TrafficNet, whose accuracy (Acc) in classifying 10 traffic states reaches 94.27% with the testing dataset, is much better than that of traditional deep classification models, which do not include Squeeze-and-Excitation blocks or the improved triplet loss. INDEX TERMS Multilevel traffic state, deep residual network, Squeeze-and-Excitation blocks, improved triplet loss.

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