Big Data: Hidden Challenges for a Fair Mobility Planning

Abstract

Big data provides unprecedented opportunities for understanding and planning urban mobility. Big data makes huge amounts of information available with a high level of detail, potentially precisely depicting overall macrotrends while also detecting micropractices that elude traditional investigative approaches. Despite this unprecedented insight on mobility issues, big data is not the ultimate solution for dealing with urban mobility, especially when considering social dimensions. The chapter intends to discuss at what conditions big data may contribute to mobility planning and policy approaches that may effectively enable individuals and their opportunities. After introducing the concept of big data and its increasing relevance, the significance for urban policy of such a knowledge source is briefly presented. The discussion then moves to four critical issues that question the contribution of big data to enabling mobilities. These include the representativeness of the information provided by big data; the interpretative issues associated with understanding mobilities through manifold digital technologies; the differentiated individual ability to produce information through portable devices and emerging actors who operate through big data and influence urban mobility dynamics in unforeseen ways. These dimensions highlight three forms of partiality that affect the completeness, the neutrality and the usability of big data in relation to mobility issues, leading to a call for a critical usage of big data. This approach can contribute to enabling mobilities thanks to the enriched information it provides, without delegating to it the responsibility of defining urban problems and solutions.