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Smart cities directly rely on a variety of elements, including water, gas, electricity, buildings, services, transportation networks, and others. Lack of properly designed transportation networks may cause different economic and safety concerns. Highway–rail grade crossings are known to be a hazardous point in the transportation network, considering a remarkable number of accidents recorded annually between highway users and trains, and even solely between highway users at highway–rail grade crossings. Hence, safety improvement at highway–rail grade crossings is a challenging issue for smart city authorities, given limitations in monetary resources. In this study, two optimization models are developed for resource allocation among highway–rail grade crossings to minimize the overall hazard and the overall hazard severity, taking into account the available budget limitations. The optimization models are solved by CPLEX to the global optimality. Moreover, some heuristic algorithms are proposed as well. A case study focusing on the public highway–rail grade crossings in the State of Florida is performed to evaluate the effectiveness of the developed optimization models and the solution methodologies. In terms of the computational time, all the solution approaches are found to be effective decision support tools from the practical standpoint. Moreover, the results demonstrate that some of the developed heuristic algorithms can provide near-optimal solutions. Therefore, the smart city authorities can utilize the proposed heuristics as decision support tools for effective resource allocation among highway–rail grade crossings.

Development of Algorithms for Effective Resource Allocation among Highway–Rail Grade Crossings: A Case Study for the State of Florida