Artificial Intelligence in Urban Mobility: A Theoretical Account of Optimisation, Equity and Informal Transport in African Cities
DOI:
https://doi.org/10.68854/kagm5280Keywords:
urban mobility, intelligent transport systems, artificial intelligence, informal transport, paratransit, ride-hailing, transport equity, traffic management, African citiesAbstract
Artificial intelligence promises cities fluid traffic, predictable transit and seamless mobility, and the promise is now being sold to African cities whose transport is dominated by minibuses, motorcycle taxis and shared cabs operating outside formal regulation. This theoretical paper examines what AI in urban mobility can mean where most mobility is informal. Drawing on transport-planning and transport-equity theory, on the paratransit and informal-transport literatures, and on recent research on digital platforms, ride-hailing, transit data and bus rapid transit in African cities, it distinguishes three applications of mobility AI, traffic optimisation, transit planning and platform matching, and shows that the first serves private vehicles that a minority own, the second depends on data that informal operators do not produce, and the third formalises informal transport on platform terms that shift risk to drivers. The paper develops a framework in which the equity effect of mobility AI depends on whose trips are measured, whose time is valued and whose vehicles are prioritised, derives seven propositions about the conditions under which AI improves mobility for the majority who ride informally, and considers three alternative models, data cooperatives with operator associations, hybrid formalisation, and public mobility platforms, through which informal transport might be improved rather than replaced. Lagos, with its danfo minibuses, its contested bus rapid transit corridor and its history of motorcycle-taxi bans, anchors the analysis. The paper closes with implications for transport authorities, platform regulators and African transport research. It concludes that a mobility AI which cannot see the trips of the informal majority is not, in any meaningful sense, smart, and that measuring those trips, valuing their passengers' time and prioritising their vehicles is the condition for the technology to serve the many rather than the few.
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