A Micro-services Architecture for Anomaly Detection in Heterogeneous Urban Mobility Data
DOI:
https://doi.org/10.5753/reic.2022.2329Keywords:
urban mobility, anomaly detection, heterogeneous dataAbstract
The adoption of collaborative platforms has grown in a way that public agents are increasingly seeking partnerships with these information providers. Data from cameras, social networks, and applications can contribute to the management of smart cities, such as detecting unusual traffic events, for example. This work presents a framework that uses heterogeneous sources to detect anomalous traffic events. The framework is responsible for collecting data, filtering and clustering them, detecting and visualizing anomalies in real-time based on these clusters. In this work, we propose the use of microservices to execute each component of the framework. As a case study, the proposed architecture detects anomalies in urban mobility data from Vitória-ES, based on city hall and Twitter data.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
