In the recent years, considerable efforts have been made to incorporate safety into long-term transportation plans, which is termed transportation safety planning (TSP). Although some macro-level safety studies have attempted to adopt planning data (e.g., trip generation) for estimating traffic crash frequency, no studies have developed trip and crash models simultaneously. In this study, the authors suggested an integrated modeling approach for pedestrian and bicycle trip and crash estimations. The American Housing Survey (AHS) data were collected from the U.S. Census Bureau and used for this study. The AHS is a longitudinal housing unit survey that asks questions regarding the quality of housing including but not limited to demographic, socioeconomic, walking or cycling environments, public transportation systems, and accessibility to various facilities, in major metropolitan areas. The first part of the model is a multivariate logistic regression model, which estimates the proportion of pedestrians and bicyclists’ trips among total trips in each metropolitan area. The second part is a multivariate Poisson-lognormal model, which estimates pedestrian and bicycle-involved crash counts. In the crash model, the predicted trips estimated from the first step were used as exposure variables.