AMIGOS Big Data Platform makes urban planning more efficient

Data

Image by Dennis Kummer / Unsplash

Smart cities employ several technologies to enhance the performance of transportation services and to reach carbon neutrality, thereby increasing citizen comfort. This involves reducing costs and resource consumption while more effectively engaging with residents. One recent technology with significant potential to improve smart city services is big data analytics. As digitisation becomes a partner to daily life, data collection has led to vast amounts of data that can be applied to various beneficial domains. Effective analysis and the utilisation of big data are crucial for success in many areas, including smart cities. Big data analysis and machine learning algorithms can play a fundamental role in improving city policies and addressing urban issues.

The AMIGOS Big Data platform is a comprehensive solution that has been developed by TREE Technology as part of the AMIGOS project. It is designed to manage and analyse large-scale city data, collecting information from multiple cities to enable a holistic and collaborative approach to data management. As a cloud-based platform, it offers key benefits such as extraordinary scalability, allowing storage and processing capacity to adjust as data volumes increase without needing additional infrastructure. It also facilitates accessibility from any location, essential for collaboration between different cities and departments, especially in multi-government projects like AMIGOS. Additionally, the pay-as-you-go model of cloud services helps avoid large expenditures on hardware and maintenance, providing a cost-efficient solution. Cloud platforms also offer robust security measures and disaster recovery policies, ensuring data protection and rapid restoration, if necessary.

In AMIGOS, the different cities can send their data, such as on car traffic, bikes traffic, pedestrians, noise and air pollution data, to the Big Data platform for storage and analysis. Through this analysis, relevant Key Performance Indicators can be extracted, and predictive models created. Cities can use these insights, like predictions and pattern recognition to forecast future trends and to define specific measures/recommendations, leading to better urban planning. For instance, machine learning algorithms can identify patterns in traffic data to optimise traffic light timings, thereby reducing congestion and improving air quality. Similarly, predictive models can forecast pollution levels, allowing cities to take preemptive measures to protect public health.

The platform’s ability to scale seamlessly ensures it can handle the growing data volumes as more IoT devices are integrated into city infrastructure. Moreover, its accessibility from any location supports inter-city collaboration, enabling cities to share insights and best practices effectively. Cities can, for example, share successful strategies for managing pedestrian traffic or reducing noise pollution, and thereby benefit from collective intelligence.

In conclusion, the AMIGOS project’s Big Data platform aims to leverage big data technology for efficient and secure information processing through its cloud implementation. These benefits not only enable more effective and collaborative management between cities and improve data-driven decision-making, but also contribute to more effective urban planning and enhanced citizen services, thus opening new possibilities for analysing large volumes of data.

For more information about the Big Data Platform, please contact rita [dot] nogueira [at] treetk [dot] com or alejandro [dot] gamez [at] treelogic [dot] com from TREE Technology.

Author: Rita Nogueira

Projects

.eu web awards
covenantofmayors.eu
ELTIS / Urban Mobility Observatory LOGO
European Mobility Week
INTERREG LOGO
netzerocities logo
Smart Cities Marketplace
EU Logo

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of CINEA. Neither the European Union nor CINEA can be held responsible for them.

This website is hosted by an environmentally-friendly server provider.