AbstractGap acceptance, the fundamental part of pedestrian crossing behavior, represents a pedestrian’s assessment of how safe an existing gap in traffic flow is to cross the path. This assessment is an essential factor affecting pedestrian safety. However, the complex decision problem of “how pedestrians assess the available gap as safe?” is still ambiguous. The proposed paper addresses this with an integrated analytical hierarchical process-fuzzy logic (AHP-FL) approach in line with a realistic target of vision zero. In this approach, FL stood out in the analysis of the uncertainty of gap acceptance, while AHP supported FL to mitigate the expert judgment effect. Thus, the practical application of an innovative methodology based on the combined use of machine learning and a statistical approach was realized. The data collected at three semicontrolled pedestrian crossings were used. AHP questionnaires were conducted on pedestrians and were divided according to age groups and pedestrian driving habits. The effects of age and driving habits on gap acceptance behavior were examined in-depth with the FL models revised with six different weight sets obtained from AHP. The best model performance was achieved with 0.79R2 and 25.19% MAPE. The results revealed that the most critical explanatory factors are vehicle speed and item carriage. The highest prediction error was observed in the elderly pedestrian group. Results revealed that the AHP-FL approach would be useful to better understand the complex decision structure of pedestrian crossing behavior.