Smart City Gnosys

Smart city article details

Title	Optimizing Smart City Planning: A Deep Reinforcement Learning Framework
ID_Doc	40882
Authors	Park J.; Baek J.; Song Y.
Year	2025
Published	ICT Express, 11, 1
DOI	http://dx.doi.org/10.1016/j.icte.2024.11.005
Abstract	We introduce a deep reinforcement learning-based approach for smart city planning, designed to determine the optimal timing for constructing various smart city components such as apartments, base stations, and hospitals over a specified development period. Utilizing the Dueling Deep Q-Network (DQN), the proposed method aims to maximize the city's population while maintaining a predetermined happiness level of residents in the smart city. This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios. © 2024 The Authors
Author Keywords	Deep reinforcement learning; Markov decision process; Smart city planning

Similar Articles

Id	Similarity	Authors	Title	Published
31738	0.887	Glass A.; Noennig J.R.; Bek B.; Glass R.; Menges E.K.; Okhrin I.; Baddam P.; Sanchez M.R.; Senthil G.; Jäkel R.	Innovative Urban Design Simulation: Utilizing Agent-Based Modelling Through Reinforcement Learning	ACM International Conference Proceeding Series (2023)
47094	0.883	Zhao L.; Wang J.; Liu J.; Kato N.	Routing For Crowd Management In Smart Cities: A Deep Reinforcement Learning Perspective	IEEE Communications Magazine, 57, 4 (2019)
11516	0.88	Giannini F.; Franze G.; Pupo F.; Fortino G.	Autonomous Vehicles In Smart Cities: A Deep Reinforcement Learning Solution	Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022 (2022)
10071	0.877	Ullah Z.; Al-Turjman F.; Mostarda L.; Gagliardi R.	Applications Of Artificial Intelligence And Machine Learning In Smart Cities	Computer Communications, 154 (2020)
5173	0.871	Zheng Y.; Hao Q.; Wang J.; Gao C.; Chen J.; Jin D.; Li Y.	A Survey Of Machine Learning For Urban Decision Making: Applications In Planning, Transportation, And Healthcare	ACM Computing Surveys, 57, 4 (2024)
35886	0.87	Bhowmik T.; Bhadwaj A.; Kumar A.; Bhushan B.	Machine Learning And Deep Learning Models For Privacy Management And Data Analysis In Smart Cites	Intelligent Systems Reference Library, 215 (2022)
6943	0.868	Cakir L.V.; Duran K.; Thomson C.; Broadbent M.; Canberk B.	Ai In Energy Digital Twining: A Reinforcement Learning-Based Adaptive Digital Twin Model For Green Cities	IEEE International Conference on Communications (2024)
35914	0.866	Mehta S.; Bhushan B.; Kumar R.	Machine Learning Approaches For Smart City Applications: Emergence, Challenges And Opportunities	Intelligent Systems Reference Library, 215 (2022)
35062	0.864	Kazmi S.M.A.; Khan Z.; Khan A.; Mazzara M.; Khattak A.M.	Leveraging Deep Reinforcement Learning And Healthcare Devices For Active Travelling In Smart Cities	IEEE Transactions on Consumer Electronics (2024)
1391	0.863	Giannini F.; Franzè G.; Fortino G.; Pupo F.	A Deep Reinforcement Learning Strategy For Intelligent Transportation Systems	Internet of Things, Part F1719 (2024)