Skip to main content
SpringerLink
Log in

Hybrid artificial bee colony based neural network and dynamic threshold technique for predicting moving vehicle location and co-located objects

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Co-location pattern discovery contains processing data as well as a spatial context to observe the classes of spatial objects that are often located together. Moreover, the existing moving vehicle location prediction methodology doesn't examine the vehicles movement co-location instances. So, improving the previous method process by mining spatially co-located moving objects utilize spatial data mining (SDM) methods. To improve this, Hybrid Artificial Bee Colony based Feed Forward Back propagation Neural Network for Predicting Moving Vehicle future Location and Co-Located Objects (Hyb-ABC-FFBPNN-PMVFL-CLO) is proposed. Initially, the data comes from the Honda Egocentric View-Intersection (HEV-I) dataset. In order to provide the future location of the vehicle, it is necessary to predict the driving trajectory of vehicles traveling on the road in certain segments. For predicting moving vehicles future location, FFBPNN is used. Generally, FFBPNN does not reveal any adoption of optimization techniques for computing optimal parameters to ensure accurate prediction. Therefore, in this work, proposed an Artificial Bee Colony (ABC) algorithm is utilized for optimizing the weight parameters of Feed Forward Back propagation Neural Network (FFBPNN). Also, Co-location Pattern Mining with Dynamic Threshold Technique (CPMDTT) is proposed to predict co-located objects with spatial data mining technique. In this, an undirected graph is constructed using the information on predicted future location of vehicles. Finally, the proposed method is implemented in MATLAB. The evaluation metrics such as accuracy, number of Co locations, and run time are analyzed. Then, the proposed approach provide 14.68%, 7.142%, and 4.65% higher accuracy, 38.18%, 12.02%, and 7.59% lower running time compared with existing methods, like Ego-centric vision-based future vehicle localization for intelligent driving assistance systems, Performance of design options in automated Auto-Regressive Integrated Moving Average method construction for dynamic vehicle GPS location prediction and a deep learning method for next location prediction respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Dasanayaka N and Feng Y 2022 Analysis of Vehicle Location Prediction Errors for Safety Applications in Cooperative-Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 23: 15512–15521

    Article  Google Scholar 

  2. Xiao Y and Nian Q 2020 Vehicle location prediction based on spatiotemporal feature transformation and hybrid LSTM neural network. Information. 11: 1–23

    Article  MathSciNet  Google Scholar 

  3. Liu J, Zhang L, Zhu S, Liu B, Liang Z and Yang S 2021 Exploring Complex Dependencies for Multi-modal Semantic Trajectory Prediction. Neural Processing Letters, 1–25

  4. Szwed P 2021 Classification and feature transformation with Fuzzy Cognitive Maps. Applied Soft Computing 105: 1–36

    Article  Google Scholar 

  5. Choi S, Kim J and Yeo H 2019 Attention-based recurrent neural network for urban vehicle trajectory prediction. Proscenia Computer Science 151: 327–334

    Article  Google Scholar 

  6. Rudenko A, Palmieri L, Herman M, Kitani K M, Gavrila D M and Arras K O 2020 Human motion trajectory prediction: A survey. The International Journal of Robotics Research 39: 895–935

    Article  Google Scholar 

  7. Rathore P, Kumar D, Rajasegarar S, Palaniswami M and Bezdek J C 2019 A scalable framework for trajectory prediction. IEEE Transactions on Intelligent Transportation Systems 20: 3860–3874

    Article  Google Scholar 

  8. Han G, Long X, Zhu C, Guizani M, Bi Y and Zhang W 2019 An AUV location prediction-based data collection scheme for underwater wireless sensor networks. IEEE Transactions on Vehicular Technology 68: 6037–6049

    Article  Google Scholar 

  9. Shokrolah Shirazi M and Morris B T 2019 Trajectory prediction of vehicles turning at intersections using deep neural networks. Machine Vision and Applications 30: 1097–1109

    Article  Google Scholar 

  10. Song H, Ding W, Chen Y, Shen S, Wang M Y and Chen Q 2020 Planning-informed trajectory prediction for autonomous driving. In: European Conference on Computer Vision, Springer, Cham, pp. 598–614

  11. Abbas T, Ibran M A, Afaq M and Song W C 2020 An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter. Transactions on Emerging Telecommunications Technologies 31: 3734

    Article  Google Scholar 

  12. Wang L L, Chen Z G and Wu J 2019 Vehicle trajectory prediction algorithm in vehicular network. Wireless Networks 25: 2143–2156

    Article  Google Scholar 

  13. Baby Anitha E 2019 Prediction of road traffic using Navie Bayes Algorithm. International Journal of Engineering Research and Technology 7: 62–77

    Google Scholar 

  14. Mozaffari S, Al-Jarrah O Y, Dianati M, Jennings P and Mouzakitis A 2020 Deep learning-based vehicle behavior prediction for autonomous driving applications: A review. IEEE Transactions on Intelligent Transportation Systems 23: 33–47

    Article  Google Scholar 

  15. Li X, Ying X and Chuah M C 2019 Grip: Graph-based interaction-aware trajectory prediction. In: IEEE Intelligent Transportation Systems Conference (ITSC), pp. 3960-3966. IEEE

  16. https://usa.honda-ri.com/hevi

  17. Mokashi I, Afzal A, Khan S A, Abdullah N A, Azami M H B, Jilte R D and Samuel O D 2021 Nusselt number analysis from a battery pack cooled by different fluids and multiple back-propagation modelling using feed-forward networks. International Journal of Thermal Sciences 161: 106738

    Article  Google Scholar 

  18. Dokeroglu T, Sevinc E and Cosar A 2019 Artificial bee colony optimization for the quadratic assignment problem. Applied Soft Computing 76: 595–606

    Article  Google Scholar 

  19. Bao X and Wang L 2019 A clique-based approach for co-location pattern mining. Information Sciences 490: 244–264

    Article  Google Scholar 

  20. Srivastava V, Purwar R K and Jain A 2019 A dynamic thresholds based local mesh ternary pattern technique for biomedical image retrieval. International Journal of Imaging Systems and Technology 29: 168–179

    Article  Google Scholar 

  21. Yao Y, Xu M, Choi C, Crandall D J, Atkins E M and Dariush B 2019 Egocentric vision-based future vehicle localization for intelligent driving assistance systems. In: Robotics and Automation (ICRA), IEEE. 9711–9717

  22. Alzyout M S and Alsmirat M A 2020 Performance of design options of automated ARIMA model construction for dynamic vehicle GPS location prediction. Simulation Modelling Practice and Theory 104: 102148

    Article  Google Scholar 

  23. Fan X, Guo L, Han N, Wang Y, Shi J and Yuan Y 2019 A deep learning approach for next location prediction. In: 2018 IEEE on Computer Supported Cooperative Work in Design (CSCWD), IEEE, pp. 69–74

  24. Singh D and Srivastava R 2022 Graph Neural Network with RNNs based trajectory prediction of dynamic agents for autonomous vehicle. Applied Intelligence, 1–16

  25. Liang J, Jiang L, Murphy K, Yu T and Hauptmann A 2020 The garden of forking paths: Towards multi-future trajectory prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10508–10518

  26. Baby Anitha E 2017 A dynamic thresholding technique in spatially co located objects mining from vehicle moving data. International Journal of Intelligence and Data Mining 12: 62–77

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, K.S.R College of Engineering, Tiruchengode, India

    E Baby Anitha

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    S Sivaprakash

  3. Department of Biomedical Engineering, Dr.N.G.P Institute of Technology, Coimbatore, India

    S Velmurugan

  4. Department of Computing Technologies, School of computing, SRM institute of Science and Technology, Chennai, India

    S S Saranya

Authors
  1. E Baby Anitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S Sivaprakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S Velmurugan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S S Saranya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E Baby Anitha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baby Anitha, E., Sivaprakash, S., Velmurugan, S. et al. Hybrid artificial bee colony based neural network and dynamic threshold technique for predicting moving vehicle location and co-located objects. Sādhanā 48, 68 (2023). https://doi.org/10.1007/s12046-023-02128-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-023-02128-w

Keywords

Search

Navigation