Funds: DSAIPA/DS/0111/2018 by Fundação para a Ciência e Tecnologia
(2018 Call for R&D projects in Data Science and Artificial Intelligence in Public Administration)
Scope: Mobility in major European capitals, including Lisbon, is not yet sustainable. The Lisbon City Council (CML) has established notable efforts to acquire and store relevant sources of available urban data, including road traffic from fixed and mobile sensors, public transport usage data (bus, metro, train, cycling, river inland, micromobility), and the city situational context. The situational context includes public events (e.g. congresses, sport events, large-scale concerts), accidents, works on public roads, urban planning in the city, weather conditions or, among others, real-time notifications from citizens. Despite these efforts, the true potential of the knowledge contained in these urban data is still unknown.
Challenges: The ILU project proposes to address two major challenges: 1) the lack of an integrative analysis capable of combining heterogeneous and multimodal sources of urban data; and 2) the absence of situational context in the prediction and recommendation of circulation in the city.
Scientific contributions: 1) consolidate the different sources of mobility data available in the Plataforma para a Gestão Inteligente de Lisboa (PGIL) in order to allow a multimodal analysis of mobility in the city; 2) discover actionable traffic patterns from these heterogeneous sources of urban data, particularly multimodal origin-destination matrices, emerging vulnerabilities, and correlations between urban traffic and its situational context; 3) anticipate outflow problems in road traffic and public transport; and 4) support mobility decisions, such as the context-aware reinforcement of public transportation, route redisign, and intelligent traffic control.
Results: The contributions of the ILU project will be made available to CML through a decision support system that may operate from the National Infrastructure for Distributed Computing. The ILU project will promote a data-centric management of urban mobility (oriented to what is happening and emerging in the city), more dynamic, focused on the real needs of the citizen, and capable of allowing a more objective, transparent and effective coordination between authorities, municipalities, and public transport operators.
Contacts: Rui Henriques (PI)
rmch AT tecnico.ulisboa.pt
(+351) 21 310 0300
Prof. Rui Henriques, INESC-ID, Room 433
R. Alves Redol 9, 1000-029 Lisboa, Portugal
(2018 Call for R&D projects in Data Science and Artificial Intelligence in Public Administration)
Scope: Mobility in major European capitals, including Lisbon, is not yet sustainable. The Lisbon City Council (CML) has established notable efforts to acquire and store relevant sources of available urban data, including road traffic from fixed and mobile sensors, public transport usage data (bus, metro, train, cycling, river inland, micromobility), and the city situational context. The situational context includes public events (e.g. congresses, sport events, large-scale concerts), accidents, works on public roads, urban planning in the city, weather conditions or, among others, real-time notifications from citizens. Despite these efforts, the true potential of the knowledge contained in these urban data is still unknown.
Challenges: The ILU project proposes to address two major challenges: 1) the lack of an integrative analysis capable of combining heterogeneous and multimodal sources of urban data; and 2) the absence of situational context in the prediction and recommendation of circulation in the city.
Scientific contributions: 1) consolidate the different sources of mobility data available in the Plataforma para a Gestão Inteligente de Lisboa (PGIL) in order to allow a multimodal analysis of mobility in the city; 2) discover actionable traffic patterns from these heterogeneous sources of urban data, particularly multimodal origin-destination matrices, emerging vulnerabilities, and correlations between urban traffic and its situational context; 3) anticipate outflow problems in road traffic and public transport; and 4) support mobility decisions, such as the context-aware reinforcement of public transportation, route redisign, and intelligent traffic control.
Results: The contributions of the ILU project will be made available to CML through a decision support system that may operate from the National Infrastructure for Distributed Computing. The ILU project will promote a data-centric management of urban mobility (oriented to what is happening and emerging in the city), more dynamic, focused on the real needs of the citizen, and capable of allowing a more objective, transparent and effective coordination between authorities, municipalities, and public transport operators.
Contacts: Rui Henriques (PI)
rmch AT tecnico.ulisboa.pt
(+351) 21 310 0300
Prof. Rui Henriques, INESC-ID, Room 433
R. Alves Redol 9, 1000-029 Lisboa, Portugal