Public Safety Applications of Network Activity Data

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Proactive public safety can be facilitated by gathering data pertaining to adverse events in a city, and predictive models can use this data to recognize and mitigate patterns that lead to injuries and death. Our objective is to create low-cost, effective public safety systems by applying analytics and machine learning (ML) to anonymized network activity data to detect adverse events. As people use their apps, their smart phones interact with the cellular network and in doing so they indicate their location. Many applications depend on user mobility information extracted from this location data, typically requiring that a user opt-in to share their location data. In this project we are interested in models that use network activity data that preserves individual privacy, and that do not require user mobility data. We focus on ML prediction and risk assessment for adverse events in three use cases: Vision Zero which attempts to reduce pedestrian and bicyclist fatalities to zero; Fire and emergency response; and 911 call dispatching.The project will investigate and develop analytics and ML algorithms that process network activity data combined with relevant city and other data to predict and help respond to adverse events that occur at random times and places. We wish to predict these events over a range of geographic areas and time intervals, as well as to characterize these by type, severity, and other attributes. Thus, we are interested in predicting the occurrence and severity of pedestrian and cyclist crash events. We are also interested in predicting the occurrence of fires and their types and impacts in various districts of a city under various weather conditions. For 911 calls, our ML models learn patterns of event occurrences and their types and severity to help improve response to emergency calls by automatically selecting and providing relevant information to dispatchers and first responders. By working with data from several Canadian cities, our project will help to better public safety through improved planning, timeliness, accuracy, and quality of response.