Smart City Gnosys

Smart city article details

Title	Optimizing Smart City Road Capacity Using Deep Learning
ID_Doc	40883
Authors	Hong H.C.; Bin R.
Year	2025
Published	Journal of Computer Information Systems
DOI	http://dx.doi.org/10.1080/08874417.2025.2515433
Abstract	A smart city road network is a group of roads and other structures in cities that are all linked together. With these technologies, traffic flow, road conditions, and transportation systems can be managed, monitored, and improved in real time. Capacity optimization allocation is the process of distributing and using road network resources. We suggested new ways to deal with these problems, like Attention-based RNNs for better-predicting traffic flow, Dynamic Graph Networks for checking how much traffic a road can handle when conditions change, and Multi-Objective Optimization (MOORA) for discovering the best options in tough decision-making situations. To measure system performance, use metrics such as R-squared (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Overall, our proposed strategy intends to improve the accuracy of traffic forecasts, lessen the amount of congestion, and boost the effectiveness of road networks in smart cities.
Author Keywords	Attention-based Recurrent Neural Networks (RNNs); capacity allocation; dynamic graph networks; Geographic Information System (GIS); Multi-Objective Optimization (MOORA); Smart city

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