Secondary crashes are generally understood as crashes that occur as a result of primary incidents. Research is needed to determine the nature and cause of these crashes. However, to date, this research has been limited for two reasons. First, there is no uniform definition of secondary crashes. Second, the usually poor quality of incident data and the unavailability of related traffic data make any analysis of secondary crashes difficult. This paper describes a study that uses a comprehensive incident database from District 4 of the Florida Department of Transportation to identify freeway secondary crashes and their contributing factors. A method based on a cumulative arrival and departure traffic delay model was developed to estimate the maximum queue length and the associated queue recovery time for incidents with lane blockages. Descriptive statistics and logistic regression analysis were applied to understand the factors contributing to secondary crashes. The logistic regression analysis indicates that the following four factors have significant effects on the likelihood of secondary crashes: primary incident type, primary incident lane-blockage duration, time of day, and whether the incident occurred on northbound I-95.

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Smads are intracellular signaling molecules of the transforming growth factor-β (TGF-β) superfamily that play an important role in the activation of hepatic stellate cells (HSCs) and hepatic fibrosis. Excepting the regulation of Smad7, receptor-regulated Smad gene expression is still unclear. We employed rat HSCs to investigate the expression and regulation of the Smad1 gene, which is a bone morphogenetic protein (BMP) receptor-regulated Smad. We found that the expression and phosphorylation of Smad1 are increased during the activation of HSCs. Moreover, TGF-β significantly inhibits Smad1 gene expression in HSCs in a time- and dose-dependent manner. Furthermore, although both TGF-β1 and BMP2 stimulate the activation of HSCs, they have different effects on HSC proliferation. In conclusion, Smad1 expression and phosphorylation are increased during the activation of HSCs and TGF-β1 significantly inhibits the expression of the Smad1 gene.

Work zones are a high priority issue in the field of road transportation because of their impacts on traffic safety. A better understanding of work zone crashes can help to identify the contributing factors and countermeasures to enhance roadway safety. This study investigates the prediction of work zone crash severity and the contributing factors by employing a parametric approach using the mixed logit modeling framework and a non-parametric machine learning approach using the support vector machine (SVM). The mixed logit model belongs to the class of random parameter models in which the effects of flexible variables across different observations are identified, that is, data heterogeneity is taken into account. The performance of the SVM model is enhanced by applying three metaheuristic algorithms: particle swarm optimization (PSO), harmony search (HS), and the whale optimization algorithm (WOA). Empirical findings indicate that SVM provides higher prediction accuracy and outperforms the mixed logit model. Estimation results reveal key factors that increase the likelihood of severe work zone crashes. Furthermore, the analysis illustrates the ability of the three metaheuristics to enhance the SVM and the superiority of the harmony search algorithm in improving the performance of the SVM model.

Modeling the impacts of incidents on traffic flow operations is essential for the use of simulation programs to evaluate incident management alternatives. In particular, these programs must be able to model correctly the reductions in highway capacity that are due to incidents and the lane-changing behaviors of drivers ahead of incident locations. Although simulation models have been used for evaluating incident impacts, the abilities of these programs to model incidents have not been adequately assessed. Here the modeling of incidents in microscopic simulation models and the effects of calibration parameters on the simulated reductions in capacity due to incidents are examined. A basic freeway segment was simulated with three widely used microscopic simulation models: CorSim, VisSim, and AIMSUN. The three investigated simulation models allow the analyst to simulate incident blockages either explicitly or by using other events that have similar impacts on traffic operations at the incident locations. Calibration parameters of the three models were varied to determine if it is possible to calibrate the models to achieve target link capacity values for conditions both with and without incidents. The target capacity values used were those presented in the Highway Capacity Manual 2000, although any other target capacities could have been used. For all three models, there is a need to calibrate model parameters to produce acceptable reductions in capacity due to incidents. It is shown that this objective is possible with all investigated models. However, there is a need to introduce incident-specific time-variant calibration parameters in AIMSUN and VisSim. Recommendations are given regarding required improvements to the simulation programs to enhance their ability to model the reduction in capacity and the lane-changing behaviors at incident locations.