Safety analysis primarily focuses on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The traditional analysis paradigm relying on observed data only allows relative comparisons between analysis methods and is unable to say how well the methods mimic the true underlying crash generation process often unobserved or known only partially with various degrees of uncertainty. To address this limitation, the researchers plan to build a high-resolution disaggregate data generation process that mimics crash occurrence on transportation facilities. Specifically, a general framework of artificial realistic data (ARD) data generation embedded with heterogeneous causal structures for data generation is developed to synthesize crashes at a trip level while considering roadway facility, driver, and vehicle factors. These artificially generated crashes can be aggregated at any spatial or temporal resolution to mimic data from the real world and carry out systematic safety analysis methods evaluation. The proposed ARD generator is developed as a stand-alone software application. The application is customizable and can be run to prepare multiple realizations of the ARD. The proposed tool will be evaluated for two case studies involving vehicle crashes and pedestrian-related crashes.