This project will develop a method for assessing network-level curve safety conditions safely and cost effectively by collecting and analyzing vehicles’ GPS trajectory and kinematic data using intra-agency, crowdsourced, low-cost mobile devices. Work in Stage 1iwill develop a framework for a network-level curve safety assessment and calibrating and validating the developed algorithms using a single run of mobile device data. The specification for each component, such as data type, data accuracy, mobile device, and data server will be identified and a prototype will be developed to evaluate the system capability. The developed algorithms will be critically calibrated and validated for computing curve radii, superelevation, and BBI. Vehicles and test roadways with different curves will be selected with assistance from the Georgia DOT and ground truth curve properties (i.e., radii and superelevation) will be collected. By combining the vehicle’s rolling angle measurement, roadway superelevation and BBI can be calculated. The calculated results will be compared with the ground truth data. In Stage 2, a method to improve data quality by integrating multiple-run data will be developed and validated. By using multiple-run data, errors that arise from drivers’ unexpected driving actions or other systematic bias will be identified. Unknown biases will be removed using multiple-run data collected by different drivers and the corresponding data processing. The developed method will be validated using field test routes.