![]() To this end, efforts are being established towards traffic data collection, consolidation and exploration from public and private, individual and active modes of transport. ![]() In this light, most European cities such as Lisbon in Portugal are pursuing technological solutions to face the challenge of dynamically adapting public passenger transportation systems in accordance with the real traffic dynamics. Urban mobility decarbonisation initiatives, encompassing enhanced public transport, infrastructure interventions towards active modes such as walking and cycling, are aimed to provide a better fit with individual mobility needs and the distribution of traffic generation poles. The contributions reported in this work are anchored in the empirical observations gathered along the first stage of the ILU project (see footnote 1), providing a study case of interest to be followed by other European cities.Įuropean and worldwide cities are entailing complex urban mobility changes to respond to the COVID-19 pandemic crisis and satisfy climate and environmental goals. The instantiation of the proposed methodology in the city of Lisbon, discussing the role of recent initiatives for the ongoing monitoring of relevant context data sources within semi-structured repositories, and further showing how these initiatives can be extended for the context-sensitive modelling of traffic data for descriptive and predictive ends Ī roadmap of practical illustrations quantifying impact of different context factors (including weather, traffic interdictions and public events) on different transportation modes using different spatiotemporal traffic data structures andĪ review of state-of-the-art contributions on context-enriched traffic data analysis. Overall, the research offers the following major contributions:Ī novel methodology on how to acquire, consolidate and incorporate different sources of context for the context-enriched analysis of traffic data The gathered results stress the importance of incorporating historical and prospective context data for a guided description and prediction of urban mobility dynamics, irrespective of the underlying data representation. Third, we quantify the impact produced by situational context aspects 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. Second, we introduce additional principles for the online consolidation and labelling of heterogeneous sources of situational context from public repositories. We propose a methodology anchored in data science methods 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: i) georeferenced time series data ii) origin-destination tensor data iii) raw traffic event data. ![]() The work presented in this paper aims at tackling the following main research question: How to incorporate historical and prospective sources of situational context into descriptive and predictive models of urban traffic data? Methodology Addressing the above 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. Some of these sources of context data are still private, dispersed, or unavailable for the purpose of planning or managing urban mobility. 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. European cities are placing a larger emphasis on urban data consolidation and analysis for optimizing public transport in response to changing urban mobility dynamics. ![]()
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