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A new freeway work zone traffic delay and cost optimization model is presented in terms of two variables: the length of the work zone segment, and the starting time of the work zone using average hourly traffic data. The total work zone cost defined as the sum of user delay, accident, and maintenance costs is minimized and the number of lane closures, darkness factor, and seasonal variation travel demand normally ignored in prior research are included. To find the global optimum solution, a Boltzmann-simulated annealing neural network is developed to solve the resulting mixed real variable–integer cost optimization problem for short-term work zones. The new model can be used as an intelligent decision support system to (1) find the optimum work zone segment length and optimum starting time; (2) study the impact of various factors such as number of lane closures and darkness; and (3) quickly observe the relation between the total work zone cost and the work zone segment length and starting time in a quantitative and rational way.
Jiang et al. (Mon,) studied this question.