The Role of Artificial Intelligence in Smart Cities: Systematic Literature Review

Dominiković, Ivana; Ćukušić, Maja; Jadrić, Mario

doi:10.1007/978-3-030-77417-2_5

Cited by 6 publications

(2 citation statements)

References 93 publications

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“…At the same time, the interest in using AI for urban innovation continues to grow [ 25 ]. The role of AI-based solutions and their potential in the process of shaping smart cities has been widely discussed [ 3 , 4 , 26 ]. It is no longer understood simply as collecting data and building from its comprehensive knowledge of the complex operation of a city.…”

Section: Literature Reviewmentioning

confidence: 99%

Greencoin as an AI-Based Solution Shaping Climate Awareness

Obracht-Prondzyńska

Duda

Anacka

et al. 2022

IJERPH

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Our research aim was to define possible AI-based solutions to be embedded in the Greencoin project, designed as a supportive tool for smart cities to achieve climate neutrality. We used Kamrowska-Załuska’s approach for evaluating AI-based solutions’ potential in urban planning. We narrowed down the research to the educational and economic aspects of smart cities. Furthermore, we used a systematic literature review. We propose solutions supporting the implementation process of net zero policies benefiting from single actions of urban dwellers based on the Greencoin project developed by us. By following smart city sectors, the paper introduces AI-based solutions which can enrich Greencoin by addressing the following needs: (1) shaping pro-environmental behaviors, (2) introducing instruments to reinforce the urban management process, (3) supporting bottom-up initiatives allowing to shape urban resilience, (4) enhancing smart mobility, (5) shaping local economies supporting urban circularity, and (6) allowing better communication with residents. Our research fills the gap in the limited group of studies focused on shaping climate awareness, enhancing smart governance, and supporting social participation and inclusion. It proves that AI-based educational tools can be supportive when implementing adaptation policies toward climate neutrality based on our proposed AI-based model shaping climate awareness.

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Section: Literature Reviewmentioning

confidence: 99%

Greencoin as an AI-Based Solution Shaping Climate Awareness

Obracht-Prondzyńska

Duda

Anacka

et al. 2022

IJERPH

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“…The features extracted with the help of thin methods have a good correlation. These features will be used to train the network [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of short-time traffic prediction in intelligent transport system based on machine learning algorithms

2024

MJET

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Intelligent traffic management systems, urban planning, and the reduction of traffic congestion all depend on traffic flow prediction. In this research, a method based on machine learning based on neural network combination and feature selection based on genetic algorithm is presented for predicting short-time traffic flow. The genetic algorithm-based approach seeks to find a model's optimal parameters globally. Inner-city traffic constantly changes and can be unpredictable. This is because traffic patterns repeat over time (have periodic characteristics) but also swing wildly from moment to moment (high fluctuations). As a result, it's very hard to guess what traffic will be like in the future. Thin operators have been used duo to it good performance for short-time traffic prediction in neural networks system. In Isfahan gathered traffic data to see how well a new model, called LSTM, predicts traffic flow. We compared LSTM's performance against other established methods like wavelet neural networks (WNN) and multilayer perceptron (MLP). the proposed neural network prediction model and genetic algorithm results have %97 accuracy, %97 correlation coefficient, 14.67 less average absolute error, higher signal-to-noise ratio, 0.97 entropy value, and 3.95 standard deviation. Compared to other methods, it has shown its superiority, such as ordinary neural networks. This model excels at finding the best solution quickly and accurately, even with noisy or complex data.

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