Data-Driven Genetic Algorithm for Evaluating the Emergency Response Capability of Urban Tourism Public Safety Crisis Events

The current traditional evaluation method of emergency response capability for urban tourism public safety crisis events has been criticized for providing poor evaluation results due to a lack of comprehensive analysis of evaluation elements. In order to address this issue, this study proposes a research on the evaluation method of emergency response capacity for urban tourism public safety crisis events using a data-driven genetic algorithm approach. By utilizing the CAR self-assessment tool, the evaluation elements are carefully analyzed to establish an evaluation model that incorporates a combination of fuzzy mathematical affiliation function and triangular function method to normalize the evaluation index system. Through empirical experiments, the effectiveness of the proposed evaluation method was examined and verified. The analysis of the experimental results demonstrates that when utilizing the proposed evaluation method to assess the emergency response capability of urban public safety crisis events, the evaluation indexes show minimal subordination errors and yield more accurate evaluation results.