On how to incorporate public sources of situational context in descriptive and predictive models of traffic data

European cities are placing a larger emphasis on urban data consolidation and analysis for optimizing public transport in response to changing urban mobility dynamics. Despite the existing efforts, traffic data analysis often disregards vital situational context, including large-scale events, weather factors, traffic generation poles, social distancing norms, or traffic interdictions. Some of these sources of context data are still private, dispersed, or unavailable for the purpose of planning or managing urban mobility. Addressing this observation, the Lisbon city Council has already established efforts for gathering historic and prospective sources of situational context in standardized semi-structured repositories, triggering new opportunities for context-aware traffic data analysis. Given the above mentioned facts, the research presented in this article adds value to the current theory and practice with three major contributions. First, we propose a methodology to integrate situational context in the descriptive and predictive models of traffic data, with a focus on the three following major spatiotemporal traffic data structures: (1) georeferenced time series data; (2) origin-destination tensor data; (3) raw traffic event data. Second, we introduce additional principles for the online consolidation and labelling of heterogeneous sources of situational context from public repositories. Third, we quantify the impact produced by situational context aspects on urban mobility using different spatiotemporal traffic data structures, focusing on public passenger transport data gathered from smart card validations along the bus (CARRIS), subway (METRO) and bike sharing (GIRA) modes in the city of Lisbon. The gathered results show that the quantifiable impact of different public events, weather variables or interdictions can be used to produce correction factors for a context-sensitive modelling of origin-destination matrices, traffic demand series, or raw individual trip records along the public transportation system.