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Smart city article details
| Title | A Dashboard Framework For Decision Support In Smart Cities |
|---|---|
| ID_Doc | 1218 |
| Authors | Kameswari Y.L.; Kumar S.; Moram V.; Kumar M.; Shah K.B. |
| Year | 2024 |
| Published | Digital Twins for Smart Cities and Villages |
| DOI | http://dx.doi.org/10.1016/B978-0-443-28884-5.00011-7 |
| Abstract | Effective decision support systems are more essential as smart cities continue to develop. Decision-makers can use the suggested framework as a flexible platform for integrating, analyzing and visualizing complex data streams to help with resource management, policy formation, and urban planning. The use of data from many sources, such as sensors, IoT devices and user-generated content, is what defines smart cities. The framework enables a comprehensive understanding of urban dynamics by combining data from various domains, including transportation, energy, environment, and social services. The architecture of the dashboard includes elements for data gathering, processing, analysis, and visualization. The framework incorporates cutting-edge data analytics methods such as machine learning and predictive modeling to extract insights from the data. As a result of these findings, proactive interventions might be made in response to new urban concerns. Customizable dashboards, real-time data updates, interactive visualizations, and scenario modeling tools are some of the dashboard framework's key features. The dashboard can be customized by decision-makers to meet their unique requirements, tracking pertinent KPIs, observing trends, and evaluating the effects of changing policies. Real-time data updates guarantee that decisions are made using the most up-to-date information, and interactive visualizations make it easy to explore intricate urban data in a natural way. The usefulness of the framework is shown through case studies that highlight its use in various smart city scenarios. These case studies cover emergency response coordination, energy usage optimization, traffic management, and air quality monitoring. The chapter emphasizes how the dashboard structure helps decision-makers to react quickly to altering circumstances, make educated decisions, and efficiently allocate resources. The dashboard framework implementation has difficulties with data quality control, interoperability, and closing the digital divide. The discussion includes methods to guarantee that all demographic groups have equal access to the dashboard's insights as well as strategies for data validation, standardization, and integration. The dashboard framework suggested in this chapter is a critical tool for decision support in smart cities, to sum up. The framework helps cities to proactively solve difficulties, optimize resource allocation, and improve the general quality of life for people by giving decision-makers access to a consolidated view of urban data, predictive analytics, and scenario modeling capabilities. In order to create more resilient and liveable urban settings, continued research and collaboration will enhance and diversify the capabilities of dashboard frameworks as smart cities continue to develop. This chapter describes the design and creation of a dashboard architecture that combines diverse data and makes it easily understandable and useable. This chapter offers a thorough dashboard framework designed specifically for the opportunities and difficulties faced by smart cities. © 2025 Elsevier Inc. All rights reserved. |
| Author Keywords | Data visualization for decision making; Decision support framework; Smart cities; Traffic management; User-friendly dashboard design; Visualizing mobility dynamics |
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