This study investigates the location and allocation problem of auxiliary rescue drone landing points for hazardous material transportation accidents. First, a drone landing points location and allocation model was established under deterministic conditions. Second, considering the uncertainty of potential road risk, a drone landing points location and allocation model was constructed based on distributionally robust optimization method. Third, an approximation method was employed to formulate the original distributionally robust optimization model as an integer programming model under the zero-mean bounded perturbation ambiguous set, which was further solved using the branch and cut algorithm based on Benders decomposition. Finally, the effectiveness of the proposed model and algorithm were verified using numerical examples. It is found that the results of the distributional robust optimization model are relatively conservative but present strong robustness, which can significantly enhance the reliability of emergency rescue systems. The coverage effect of drone landing points increases with the number of planned landing points, but its marginal benefit is diminishing. When there is a lack of information about the probability distribution of uncertain parameters, the distributionally robust optimization method outperforms the stochastic programming method. Compared to the traditional robust optimization method, the distributionally robust optimization method can avoid overly conservative location-allocation solutions by incorporating partial probability distribution information.