• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 40, Pages: 2085-2092

Original Article

Improved Mayfly Optimization and LightGBM Classifier for Smart City Traffic Prediction

Received Date:31 May 2022, Accepted Date:01 September 2022, Published Date:29 October 2022


Objectives: This research work focuses on predicting traffic for the Smart City. Methods: Current research methods for traffic prediction are based on machine learning (ML) model. This article presents two contributions related to it. First, it provides feature engineering that includes feature extraction and a nature inspired optimization algorithm for selecting the best features. The mayfly optimization algorithm is improved by using the mode-based ranking method to select the best feature. Second, it uses the light-weight boosting method to train the datasets for better accuracy.Findings: The proposed Improved MayFly Optimization with LightGBM (IMFO-LGBM) is experimented with popular smart city datasets which is available in Kaggle website. IMFOLGBM shows an improvement in the prediction accuracy when compared with the baseline methods. It shows 2% of increase in the overall accuracy. Novelty:Nature inspired Mayfly optimization is improved and used to find the best feature for prediction. The selected features are then trained with the light weight boosting algorithm (i.e., Light Gradient Boosting Model). The hybrid of improved mayfly optimization and light GBM outperformed well.

Keywords: IoT; Smartcity; Mayfly optimization; Machine learning and LightGBM


  1. Attila M, Nagy V, Simon. Improving traffic prediction using congestion propagation patterns in smart cities. Advanced Engineering Informatics.50.101343. Available from. 2021. Available from: https://doi.org/10.1016/j.aei.2021.101343
  2. Nagham A, Al-Madi AA, Hnaif. Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm. Computer Systems Science & Engineering. 2022(1). Available from: https://doi.org/10.32604/csse.2022.016730
  3. Hassan M, Kanwal A, Jarrah M, Pradhan M, Hussain A, Mago B. Smart City Intelligent Traffic Control for Connected Road Junction Congestion Awareness with Deep Extreme Learning Machine. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). 2022;p. 1–4. Available from: https://doi:10.1109/ICBATS54253.2022.9759073
  4. Du S, Li T, Gong X, Shi-Jinn, Horng. A hybrid method for traffic flow forecasting using multimodal deep learning. International journal of computational intelligence systems. 2020(13):85–97. Available from: https://doi.org/10.48550/arXiv.1803.02099
  5. Moses A. Vehicular Traffic analysis and prediction using Machine learning algorithms. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). 2020;2020:1–4. Available from: https://doi.org/10.1109/ic-ETITE47903.2020.279
  6. Wei W, Wu H, Ma H. An AutoEncoder and LSTM-Based Traffic Flow Prediction Method. Sensors. 2019;19(13):2946. Available from: https://doi.org/10.3390/s19132946
  7. Lilhore UK, Imoize AL, Li CT, Simaiya S, Pani SK, Goyal N, et al. Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities. Sensors. 2908;22(8):2908. Available from: https://doi.org/10.3390/s22082908
  8. Tao P, Shen H, Zhang Y, Ren P, Zhao J, Jia Y. Status Forecast and Fault Classification of Smart Meters Using LightGBM Algorithm Improved by Random Forest. Wireless Communications and Mobile Computing. 2022;2022:1–11. Available from: https://doi.org/10.1155/2022/3846637
  9. Saleem M, Abbas S, Ghazal TM, Khan MA, Sahawneh N, Ahmad M. Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal. 2022;23(3):417–426. Available from: https://doi.org/10.1016/j.eij.2022.03.003
  10. Syed AS, Sierra-Sosa D, Kumar A, Elmaghraby A. Making Cities Smarter-Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives. Constraints. Sensors. 2022;22:4380. Available from: https://doi.org/10.3390/s22124380
  11. Jian L, Li Z, Yang X, Wu W, Ahmad A, Jeon G. Combining Unmanned Aerial Vehicles With Artificial-Intelligence Technology for Traffic-Congestion Recognition: Electronic Eyes in the Skies to Spot Clogged Roads. IEEE Consumer Electronics Magazine. 2019;8(3):81–86. Available from: https://doi:10.1109/mce.2019.2892286
  12. Vetrivel. Time Series in IOT (Internet of Things. Available from: https://www.kaggle.com/vetrirah/ml-iot (accessed )
  13. Zervoudakis K, Tsafarakis S. A mayfly optimization algorithm. Computers & Industrial Engineering. 2020(145):106559. Available from: https://doi.org/10.1016/j.cie.2020.106559
  14. Jiang S, Mao H, Ding Z, Fu Y. Deep Decision Tree Transfer Boosting. IEEE Transactions on Neural Networks and Learning Systems. 2020;31(2):383–395. Available from: https://doi.org/10.1109/TNNLS.2019.2901273


© 2022 Jenifer & Priyadarsini. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.