Signal phase and timing (SPAT) is critical to the operation of signalized intersections. In practice, data-sharing barrier makes it challenging to acquire SPAT data, impeding traffic management applications. Existing SPAT estimation methods mainly focus on partial estimation for selected time periods, and use data from multiple days, with a potentially problematic assumption that the same signal timing plan is applied across all the days. To address these challenges, this study proposes a method to estimate SPAT information using trajectory data from radar vision integrated camera (RVIC) sensors. The method is validated against real-world traffic light state data in China. The results show that the method can make an accurate estimation of time-of-day (TOD) division, cycle length, phasing scheme, and phase duration. It outperforms previous studies in cases with a limited number of observations. Further, with a sensitivity test, we derive the minimum amount of data required for the proposed method, which is quantified explicitly using the capture rate of the actual green-start times of each movement. The method estimates SPAT information daily, supporting signal evaluation and optimization functionalities. It can also drive Artificial Intelligence - Internet of Things applications, for example, providing red or green light countdown for drivers and extending infrastructure coverage for automated and connected vehicles.