Designing a Transport Data Analytics Platform for the Lagos Metropolitan Area Transport Authority

Lagos operates one of the most complex urban transport systems in Africa, and a professional data analytics platform would enable LAMATA to optimise routes, manage congestion, and improve service delivery based on evidence. This study designed a transport data analytics platform for the Lagos Metropolitan Area Transport Authority. A professional design methodology was applied, drawing on structured consultations with 18 LAMATA planners and transport engineers, data infrastructure assessment of existing BRT GPS and ticketing systems, and benchmarking against transport data platforms deployed by Cape Town, Nairobi, and Singapore transport authorities. The platform designed specifies five data pipelines: BRT GPS and occupancy, ride-sharing application data via partner API integration, traffic camera feeds for congestion detection, ticketing system transaction data, and citizen transport complaints from social media and 311 channels. An origin-destination demand estimation module using machine learning on ticketing data is detailed. A dynamic service scheduling optimisation component using historical demand patterns is specified. Expert review by ten transport planning and data engineering specialists confirmed the platform's technical and operational adequacy. The study recommends a phased implementation starting with GPS and ticketing data integration in Phase 1, followed by congestion detection integration in Phase 2, and demand forecasting in Phase 3, with a dedicated LAMATA data science unit established before Phase 2 commencement.

Keywords: transport analytics, LAMATA, Lagos, BRT, urban mobility data platform