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Research Article |

Machine Learning Technique for Predicting Location

Author(s): Madhur Arora1*, Sanjay Agrawal2 and Ravindra Patel3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 2,   Special Issue on Mobile Computing assisted by Artificial Intelligent for 5G/6G Radio Communication

Publisher : FOREX Publication

Published : 30 June 2023

e-ISSN : 2347-470X

Page(s) : 639-645

DOI: https://doi.org/10.37391/ijeer.110254

Abstract

In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data from multiple users. To achieve the most accurate predictions, regression techniques are utilized for user trajectory prediction, and ensemble algorithmic procedures, such as the random forest approach, the Adaboost method, and the XGBoost method, are employed. The primary goal is to improve prediction accuracy. The improvement accuracy of proposed ensemble method is around 21.2%decrease in errors, which is much greater than earlier systems that are equivalent. Compared to previous comparable systems, the proposed system demonstrates an approximately 15% increase in accuracy when utilizing the ensemble methodology.

Keywords: Random Forest model, XGBoost model, Adaboost model, Ensemble Technique, encoder-decoder, Location prediction, Trajectory Prediction, GPS trajectory data, Geolife Dataset, LSTM.




Madhur Arora*, Department of Computer Application, UIT, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India; Email: madhurarora1179@gmail.com

Sanjay Agrawal, Department of Computer Application, National Institute of Technical Teachers Training and Research, Bhopal, India; Email: sagrawal@nitttrbpl.ac.in

Ravindra Patel, Department of Computer Application, UIT, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India; Email: ravindra@rgtu.net

    [1] Y. Zhang, X. Shi, Z. Sheng, A. Anuj, “A XGBoost based lane change prediction on time series data using feature engineering for autopilot vehicles”, In proceedings: IEEE, transaction on intelligent transportation systems, pp: 1-14, march, 2022. [Cross Ref]
    [2] R. L. Gokul, N. Revathy, S. Megha, M. Jeena, E.J. Nithaya, “Traffic flow prediction using random forest and bellman ford for best route detection”, International Journal of engineering research and technology (IJERT), vol. 09, issue: 13, 2021.
    [3] C. Zhen, F. Wei, “A freeway travel time prediction method based on an XGBoost model”, sustainability, MDPI, 13, 2021. [Cross Ref]
    [4] G. Sbeastian, M. Christoph, B. Klaus, “truck parking occupancy prediction XGBoost -LSTM model fusion”, original research article, 2021. [Cross Ref]
    [5] Chekol, A.G., Fufa, M.S. A survey on next location prediction techniques, applications, and challenges. J Wireless Com Network 2022, 29 (2022). [Cross Ref]
    [6] B. Narsimha, Ch V Raghavendran, Pannangi Rajyalakshmi, G Kasi Reddy, M. Bhargavi and P. Naresh (2022), Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application. IJEER 10(2), 87-92. DOI: 10.37391/IJEER.100206. [Cross Ref]
    [7] Manoj Kumar, Dr Pratiksha Gautam and Dr Vijay Bhaskar (2022), Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders. IJEER 10(2), 117-121. DOI: 10.37391/IJEER.100211. [Cross Ref]
    [8] Bahra, Nasrin, and Samuel Pierre. "RNN-based user trajectory prediction using a preprocessed dataset." 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). IEEE, 2020. [Cross Ref]
    [9] Nawaz, Asif, et al. "GPS trajectory completion using end-to-end bidirectional convolutional recurrent encoder-decoder architecture with attention mechanism." Sensors 20.18 (2020): 5143. [Cross Ref]
    [10] G. Meena, D. Sharma and M. Mahrishi, "Traffic Prediction for Intelligent Transportation System using Machine Learning," 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 145-148, 2020. [Cross Ref]
    [11] Shah, Syed Aziz & Fan, Dou & Zhao, Nan & Yang, Xiao dong &Tanoli, Shujaat, “Seizure episodes detection via smart medical sensing system. Journal of Ambient Intelligence and Humanized Computing”, vol. 11, 2020. [Cross Ref]
    [12] O. Mbaabu, “Introduction to random forest in machine learning”, EngEd community, Dec 2020.
    [13] R.A. Asmara, B. Syahputro, D. Supriyanto and A.N. Handayani, "Prediction of Traffic Density Using YOLO Object Detection and Implemented in Raspberry Pi 3b + and Intel NCS 2," 2020 4th International Conference on Vocational Education and Training (ICOVET), pp. 391-395, 2020. [13] Fridrich, J., 2013. Digital image forensics. IEEE Signal Processing Magazine, 30(3), pp.150-155. [Cross Ref]
    [14] F. Xiaoliang, G. Lei, N. Han, Wang, S. Yujie, Y. Jia, Yongna, “A Deep Learning Approach for Next Location Prediction”, pp: 69-74, 2018. [Cross Ref]
    [15] R. Wu, G. Luo, J. Shao, Ling Tian, and C. Peng, “Location Prediction on Trajectory Data: A Review”, Vol. 1, pp: 108–127, issue: 2, June 2018.
    [16] R. Wu, G. Luo, Q. Yang and J. Shao, “Learning Individual Moving Preference and Social Interaction for Location Prediction”, date of current version March 15, 2018.
    [17] Fu, Zhongliang, et al. "Mining frequent route patterns based on personal trajectory abstraction." IEEE Access 5 (2017): 11352-11363. [Cross Ref]
    [18] I. Hazan and A. Shabtai, “Improving Grid-Based Location Prediction Algorithms by Speed and Direction Based boosting”, date of current version February 27, 2019. [Cross Ref]
    [19] C. Jin, Z. Lin and M. Wu, “Augmented Intention Model for Next-Location Prediction from Graphical Trajectory Context, Hindawi Wireless Communications and Mobile Computing”, Article ID 2860165, 2019. [Cross Ref]
    [20] Rao, Kavikondala&Murugan, Tamilarasan, “An Efficient Routing Algorithm for Software Defined Networking using Bellman Ford Algorithm”, International Journal of Online and Biomedical Engineering (iJOE), 2019. [Cross Ref]
    [21] C. Zhang and P. Patras, “Long-term mobile traffic forecasting using deep spatiotemporal neural networks,'' In Proceedings: MOBIHOC, Los Angeles, CA, USA, pp. 231-240, 2018.
    [22] Y. Liu and H. Wu, "Prediction of Road Traffic Congestion Based on Random Forest," 2017 10th International Symposium on Computational Intelligence and Design (ISCID), pp. 361-364, 2017. [Cross Ref]
    [23] Forsberg SKG, Bloom JS, Sadhu MJ, Kruglyak L, Carlborg Ö, “Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast”, Nature Generation. 2017. [Cross Ref]
    [24] M. Pritesh, N. Daniel, T. Paul and D.V. Jonathan, “Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology”, in soft computing, springer, August, 2016. [Cross Ref]
    [25] C. Serdar, L. Antonio, and M. C. González, "Understanding congested travel in urban areas", Nature Communications, vol. 7, 2016. [Cross Ref]
    [26] Y. Han, G. Wang, and J. Yu. "A Service-Based Approach to Traffic Sensor data integration and analysis to support community-wide green commute in China," IEEE Transactions on Intelligent Transportation Systems 17.9, pp. 2648-2657, 2016. [Cross Ref]
    [27] X. Fan and Y. Wang, “A Deep Learning Approach for Next Location Prediction”, http://xm.lanfw.com/0826/337970.html, 2015.
    [28] M. Chen, Y. Liu, and X. Yu, "NLPMM: A Next Location Predictor with Markov Modeling," Pacific-Asia Conference on Knowledge Discovery and Data Mining Springer, Cham, pp. 186-197, 2014.T. Minh Tri Do, O. Dousse, M. Miettinen, D. Gatica-Perez, “A probabilistic kernel method for human mobility prediction with smartphones, Published by Elsevier B.V., 2014. [Cross Ref]

Madhur Arora, Sanjay Agrawal and Ravindra Patel (2023), Machine Learning Technique for Predicting Location. IJEER 11(2), 639-645. DOI: 10.37391/ijeer.110254.