research-article

Artificial intelligence-enabled smart city construction

Authors:

Yifang GaoAuthors Info & Claims

The Journal of Supercomputing, Volume 78, Issue 18

Pages 19501 - 19521

https://doi.org/10.1007/s11227-022-04638-6

Published: 01 December 2022 Publication History

Abstract

This work aims to promote smart city construction and smart city management. Firstly, this work analyzes the relevant theories and processing methods of short-term traffic flow prediction. Secondly, the random forest regression (RFR) theory in machine learning is discussed to realize the short-term traffic flow prediction model (STTPM). Meanwhile, STTPM data are processed by k-nearest neighbors (KNN) and optimized by Complete Ensemble Empirical Mode Decomposition (CEEMD) and RFR method. Finally, the KNN-CEEMD-RFR model is proposed, and the performance of the model is evaluated. The results show that the proposed KNN-CEEMD-RFR model has better traffic prediction effect than support vector regression, RFR model, and CEEMD-RFR model. The prediction of support vector regression model is the worst, followed by RFR model. The mean square error of CEEMD-RFR is about 2% lower than that of RFR without data preprocessing. The mean square error of KNN-CEEMD-RFR model is 4% smaller than that of CEEMD-RFR model. Finally, the prediction accuracy of the proposed KNN-CEEMD-RFR model is more than 92%, which has a very ideal prediction effect. This work provides specific ideas for the application of artificial intelligence in smart city construction and smart city management. The proposed KNN-CEEMD-RFR model for smart city has made an important contribution to the development of traffic management in smart city management.

References

[1]

Li Y et al. Urbanization for rural sustainability–Rethinking China's urbanization strategy J Clean Prod 2018 178 580-586

[2]

Wu H, Hao Yu, and Weng J-H How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models Energy Policy 2019 127 24-38

[3]

Wang J et al. Land-use changes and land policies evolution in China's urbanization processes Land Use Policy 2018 75 375-387

[4]

Song C et al. The impact of China's urbanization on economic growth and pollutant emissions: an empirical study based on input-output analysis J clean prod 2018 198 1289-1301

[5]

Lang W et al. Reinvestigating China's urbanization through the lens of allometric scaling Physica A: Stat Mech Appl 2019 525 1429-1439

[6]

Camero A and Alba E Smart City and information technology: a review Cities 2019 93 84-94

[7]

Gascó-Hernandez M Building a smart city: lessons from Barcelona Commun ACM 2018 61 4 50-57

[8]

Caragliu A and Del Bo CF Smart innovative cities: the impact of Smart City policies on urban innovation Technol Forecast Soc Change 2019 142 373-383

[9]

Allam Z and Newman P Redefining the smart city: culture, metabolism and governance Smart Cities 2018 1 1 4-25

[10]

Komninos N et al. Smart city ontologies: improving the effectiveness of smart city applications J Smart Cities 2019 1 1 31-46

[11]

Laufs J, Borrion H, and Bradford B Security and the smart city: a systematic review Sustain Cities Soc 2020 55 102023

[12]

Ingwersen P and Serrano-López AE Smart city research 1990–2016 Scientometrics 2018 117 2 1205-1236

[13]

Ndip-Agbor E et al. Prediction of rigid body motion in multi-pass single point incremental forming J Mater Process Technol 2019 269 117-127

[14]

Xu L, Xuedong Du, and Wang B Short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm Int J Pattern Recognit Artif Intell 2018 32 12 1850041

[15]

Duo, Mei, et al. A short-term traffic flow prediction model based on EMD and GPSO-SVM. 2017 IEEE 2nd Advanced Information Technology, electronic and automation control conference (IAEAC). IEEE. 22 (14).14–23 (2017)

[16]

Liu F, Gao J, and Liu H The feature extraction and diagnosis of rolling bearing based on CEEMD and LDWPSO-PNN IEEE Access 2020 8 19810-19819

[17]

Zhu S et al. PM2. 5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors Atmos Environ 2018 183 20-32

[18]

Lu Y, Xie R, and Liang SY CEEMD-assisted bearing degradation assessment using tight clustering Int J Adv Manuf Technol 2019 104 1 1259-1267

[19]

Brokamp C et al. Predicting daily urban fine particulate matter concentrations using a random forest model Environm Sci Technol 2018 52 7 4173-4179

[20]

Araki S, Shima M, and Yamamoto K Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan Sci Total Environ 2018 634 1269-1277

[21]

Kang K and Ryu H Predicting types of occupational accidents at construction sites in Korea using random forest model Saf Sci 2019 120 226-236

[22]

Liu X et al. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model Remote Sensing Environ 2019 231 110772

[23]

Zhao C et al. High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region Atmos Environ 2019 203 70-78

[24]

Su H-Y and Liu T-Y Enhanced-online-random-forest model for static voltage stability assessment using wide area measurements IEEE Trans Power Syst 2018 33 6 6696-6704

[25]

Zhao C et al. Estimating the daily PM2. 5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01°× 0.01° spatial resolution Environ Int 2020 134 105297

[26]

Zhang S et al. A novel kNN algorithm with data-driven k parameter computation Pattern Recognit Lett 2018 109 44-54

[27]

Shi B, Han L, and Yan H Adaptive clustering algorithm based on kNN and density Pattern Recogn Lett 2018 104 37-44

[28]

Saçlı B et al. Microwave dielectric property based classification of renal calculi: application of a kNN algorithm Comput Biol Med 2019 112 103366

[29]

Wang B et al. A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning IEEE Access. 2020 8 30591-30602

[30]

Larijani MR et al. Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means Food Sci Nutrition. 2019 7 12 3922-3930

[31]

Chen Y et al. Fast density peak clustering for large scale data based on kNN Knowl-Based Syst 2020 187 104824

[32]

Falamarzi A, Moridpour S, and Nazem M Development of a tram track degradation prediction model based on the acceleration data Struct Infrastruct Eng 2019 15 10 1308-1318

[33]

HargrovesSeppelt S et al. Compare and Contrast of Options to Collect Freight Vehicle Data in Order to Inform Traffic Management Systems Civil Eng Construct: English Version. 2021 15 8 15

[34]

Wang B, Wang J, Zhu Y, et al. Study on Short-term Traffic Volume Prediction Model Based on ARMA-SVR J Highway and Trans Res Develop 2021 38 11 126-133

[35]

Zheng C, Fan X, Wang C, et al. Gman: A graph multi-attention network for traffic prediction Proceed AAAI Conf Artificial Intell 2020 34 01 1234-1241

Cited By

Duan HZhou Q(2024)A novel partial grey prediction model based on traffic flow wave equation and its applicationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108142133:PBOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108142

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Artificial intelligence-enabled smart city construction

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Information & Contributors

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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 78, Issue 18

Dec 2022

354 pages

ISSN:0920-8542

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2022

Accepted: 30 May 2022

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Cited By

Duan HZhou Q(2024)A novel partial grey prediction model based on traffic flow wave equation and its applicationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108142133:PBOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108142

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