Design and implementation of a data sharing API for supporting urban governance schemes in environmental and traffic...

This paper presents the design and implementation of a novel, unified data-sharing API that integrates real-time air quality, noise, and traffic monitoring data to support urban planning and governance. Unlike existing API frameworks for urban data management that often focus on static or delayed datasets, our API uniquely integrates multi-source data with real-time processing capabilities. While prior research often addresses environmental monitoring or traffic analytics separately, our approach provides a single, comprehensive platform, enhancing usability for urban planners. The API provides a unified platform for collecting, processing, and sharing environmental and traffic data from diverse sources, including sensors for air quality, noise levels, and traffic conditions. The system facilitates real-time data integration, enabling urban planners, transport authorities, and policymakers to make informed decisions based on accurate environmental and traffic data. The API supports four key governance schemes in urban planning: User Demand Planning, Transport Planning, Freight and logistics Planning, and City Infrastructure Planning. By integrating environmental and traffic monitoring through a robust API architecture, this project connects technical innovation with real-world applications, supporting smarter urban management. This paper discusses the architecture, functionality, and impact of the API, demonstrating its ability to enhance data-driven decision-making for sustainable and efficient cities.•Developed a unified API that integrates real-time air quality, noise, and traffic data from diverse sensors to support comprehensive urban data-sharing.•Enables four key urban governance schemes-User Demand, Transport, Freight and logistics, and City Infrastructure Planning-through accurate, integrated environmental and traffic data.•Bridges technical innovation and practical application, enhancing data-driven decision-making for sustainable, efficient urban management.