Abstract: The difficulty with manually analyzing and processing data is that it takes too long, prone to degradation and other problems. Government agencies cannot reduce cases due to a lack of sufficient knowledge and analysis to determine the fundamental trend of RTI cases. Besides, the general public is utterly uninformed of road situations. Roadcast, a Vehicle Incident Management System, is a practical approach to minimizing road incidents. This study presents the automation of the manual process of collecting and analyzing incident data and transparency with the general public. The output of the study will be a web application system that will produce descriptive analytics, hot spots, and forecasting (7MA), supporting the PNP’s preparation for the projected future incident cases in the following week and determining the hazardous area in Pasig City through the hot spots. The general public will be informed of the number of road incidents in a particular barangay, entire cases in Pasig City, and other data visualizations integrated into the dashboard. Scrum Methodology was used to construct the system, and numerous users with varying responsibilities and administrator permissions were necessary to access and control the system. Alpha and Beta Testing examined the system’s functionality, usability, reliability, performance, and supportability. The researchers used purposive sampling to survey (11) technical and (49) non-technical respondents. The researchers received a score of 4.74, which translates to “Strongly Agree.” The system received positive feedback from both technical and non-technical respondents.