| description abstract | Work zone safety, as an issue requiring complex solutions and tech-driven investment, has been a major concern for transportation agencies and policymakers. To help decision-makers prioritize investments, there is a need for tools enabling them to assess policies designed to reduce the cost and count of work zone crashes at the same time. Traditional statistical methods, however, are unable to consider two or more target variables when they are intrinsically different (e.g., when a mixture of continuous and dichotomous/discrete variables is of interest). As a result, the dependencies among the outputs of interest may be neglected, which potentially causes biased decision-making. In this study, we used two machine learning models, i.e., group method of data handling (GMDH) and multilayer perceptron (MLP), to simultaneously predict count and cost of work zone crashes in the state of Tennessee in the United States. We further used these tools to assess four policies that could help improve work zone safety. We compared predictability of these models in individual prediction of cost and count with the three statistical models of Poisson regression, negative binomial regression, and multivariate regression. Also, we compared the importance of different variables to evaluate the role that each input variable plays in prediction and recognized the most effective factors. Our results indicated that reduction of annual average daily traffic (AADT) and speed limit are the most effective policies to simultaneously lower work zone crash count and costs, ensued by reducing the percentage of trucks and prohibiting commercial vehicles on segments. We observed that MLP and GMDH perform almost equally in the simultaneous prediction, while these models provide significantly greater degrees of accuracy in crash count and cost prediction, compared with statistical models. The results depicted that the major predictors of cost and count prediction of work zone crashes are speed limit, illumination, involving commercial vehicles, AADT, type of road segment, and percentage of trucks. | |
