A Surrogate-Assisted Genetic Algorithm for the Selection and Design of Highway Safety and Travel Time Improvement Projects

Measures to improve highway safety can affect travel times, just like measures to reduce travel times can affect highway safety. For this reason, the models used in the process of allocating funds across different highway improvement projects should simultaneously consider the safety and travel time effects of the project alternatives. In this paper, an optimization model is presented for the joint selection and design of highway safety and travel time improvement projects. The model is formulated as a bi-objective, mixed-integer optimization problem with constraints on project costs and on the types of improvement combinations admissible at project sites. By incorporating travel behavior models within the optimization process, the model accounts for the potential network-level effects of highway improvement schemes. Given that the model systems needed in this process are time-consuming, a genetic algorithm is proposed that utilizes surrogate models to accelerate the discovery of good solutions to the presented optimization model. In this algorithm, the surrogate models are used to generate computationally inexpensive approximations to computationally expensive functions that quantify a decision-maker’s safety and travel time objectives. Like the problem formulation, the proposed heuristic can be employed in conjunction with computer-based travel demand models commonly used by transportation planning agencies. An illustrative application of the model and its solution heuristic is presented using a hypothetical planning scenario in Southwest Puerto Rico. Besides illustrating the application of the model, the example was used to test the surrogates’ predictive accuracy and the impact of different parameter values on the algorithm’s performance.


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  • Accession Number: 01663439
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 21 2018 10:13AM