Data-Driven Urban Micromobility Management via Intelligent Monitoring
DOI:
https://doi.org/10.52575/2687-0932-2026-53-2-320-329Keywords:
development management, intelligent systems, urban mobility, IMD, video analytics, decision support, Agile, hybrid modelAbstract
The rapid growth of the micromobility market and the dominance of oligopolistic structures in the kicksharing sector create significant challenges for municipal transport management. The article addresses the problem of information asymmetry, where city authorities lack objective data to effectively regulate traffic and ensure safety. The study focuses on the specifics of managing the development of intelligent monitoring systems for individual mobility devices (IMD) under conditions of high uncertainty. The authors propose and test a hybrid management model that decomposes the value creation process into two asynchronous streams: the research stream (based on CRISP-DM methodology for hypothesis verification) and the product stream (based on Agile principles for reliable service delivery). The paper describes the evolution of the Netvision video analytics software architecture from universal computer vision models to specialized algorithms detecting specific risk scenarios. The pilot implementation results, derived from the continuous processing of massive video data streams, reveal a relatively high percentage of safety deviations, particularly the failure to dismount at crosswalks and tandem riding. The analysis indicates that a significant portion of these violations is driven by infrastructure gaps rather than user intent. The study substantiates the role of analytical dashboards as a crucial Decision Support System (DSS). It is concluded that independent monitoring tools allow municipalities to shift from reactive measures to proactive Evidence-Based Management (EBM), ensuring a balanced dialogue with operators and improving urban safety through data-driven infrastructure planning.
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