Skip to main content
SpringerLink
Log in

WKNN-FDCNN method for big data driven traffic flow prediction in ITS

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Traffic prediction is a vital paradigm in intelligent transport system (ITS) due to the increase in traffic flow. The big data traffic flow prediction faces heterogeneity and complexity in data samples due to the huge number of data samples. The proposed WKNN-FDCNN method simplifies the big data handling process by utilizing the Weighted K Nearest Neighbour (WKNN) algorithm for data mining and a Fuzzy based Deep Convolutional Neural Network (FDCNN) for prediction. The spatio-temporal characteristics of traffic flow data are modeled as a weight function in WKNN, which helps in handling heterogeneity and complexity in data samples. The fuzzy logic incorporates uncertain information from real traffic flow data to improve the prediction performance. Finally, a DCNN approach is designed to predict the traffic flow using spatio-temporal features, traffic state information mined using the WKNN, and fuzzy traffic rules. The WKNN-FDCNN outperforms the conventional approaches in terms of Root Mean Squared Error (RMSE= 13.27), Mean Absolute Error (MAE= 10.34), R-square (0.98), and Mean Absolute Percentage Error (MAPE= 0.92) in the PeMSD4 dataset. The proposed method contributes to the development of intelligent transportation systems and provides a promising solution to handle big data challenges in traffic flow prediction.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The data that support the findings of this study are openly available at [https://github.com/liangzhehan/DMSTGCN], reference number [41].

References

  1. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp 2019

  2. Zheng Z, Yang Y, Liu J, Dai HN, Zhang Y (2019) Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Trans Intell Transp Syst 20(10):3927–3939

    Article  Google Scholar 

  3. Liu Y, James JQ, Kang J, Niyato D, Zhang S (2020) Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet Things J 7(8):7751–7763

    Article  Google Scholar 

  4. Zhou F, Li L, Zhang K, Trajcevski G (2021) Urban flow prediction with spatial–temporal neural ODEs. Transp Res Part C: Emerg Technol 124:102912

    Article  Google Scholar 

  5. Du S, Li T, Gong X, Horng SJ (2018) A hybrid method for traffic flow forecasting using multimodal deep learning. arXiv preprint arXiv:1803.02099

  6. Ryu U, Wang J, Kim T, Kwak S, Juhyok U (2018) Construction of traffic state vector using mutual information for short-term traffic flow prediction. Transp Res Part C: Emerg Technol 96:55–71

    Article  Google Scholar 

  7. Liu Q, Cai Y, Jiang H, Chen X, Lu J (2018) Traffic state spatial-temporal characteristic analysis and short-term forecasting based on manifold similarity. IEEE Access 6:9690–9702

    Article  Google Scholar 

  8. Miglani A, Kumar N (2019) Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Veh Commun 20:100184

    Google Scholar 

  9. Suthaharan S (2014) Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM Sigmet Perform Eval Rev 41(4):70–73

    Article  Google Scholar 

  10. Yang J, Li Y, Liu Q, Li L, Feng A, Wang T, Lyu J (2020) Brief introduction of medical database and data mining technology in big data era. J Evid-Based Med 13(1):57–69

    Article  PubMed  PubMed Central  Google Scholar 

  11. Katal A, Wazid M, Goudar RH (2013) Big data: issues, challenges, tools and good practices. In: 2013 6th International conference on contemporary computing (IC3), IEEE, pp 404–409 

  12. Djenouri Y, Belhadi A, Lin JCW, Cano A (2019) Adapted k-nearest neighbors for detecting anomalies on spatio–temporal traffic flow. IEEE Access 7:10015–10027

    Article  Google Scholar 

  13. Tang J, Li L, Hu Z, Liu F (2019) Short-term traffic flow prediction considering spatio-temporal correlation: A hybrid model combing type-2 fuzzy C-means and artificial neural network. IEEE Access 7:101009–101018

    Article  Google Scholar 

  14. Duan P, Mao G, Liang W, Zhang D (2018) A unified spatio-temporal model for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 20(9):3212–3223

    Article  Google Scholar 

  15. Chen X, Chen H, Yang Y, Wu H, Zhang W, Zhao J, Xiong Y (2021) Traffic flow prediction by an ensemble framework with data denoising and deep learning model. Physica A: Stat Mech Applic 565:125574

    Article  Google Scholar 

  16. Zhang Y, Xin D (2020) Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction. IEEE Access 8:91510–91520

    Article  Google Scholar 

  17. Wei W, Wu H, Ma H (2019) An autoencoder and LSTM-based traffic flow prediction method. Sensors 19(13):2946

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C: Emerg Technol 90:166–180

    Article  Google Scholar 

  19. Kong F, Li J, Jiang B, Zhang T, Song H (2019) Big data-driven machine learning-enabled traffic flow prediction. Trans Emerg Telecommun Technol 30(9):e3482

    Article  Google Scholar 

  20. Xia D, Li H, Wang B, Li Y, Zhang Z (2016) A map reduce-based nearest neighbor approach for big-data-driven traffic flow prediction. IEEE Access 4:2920–2934

    Article  Google Scholar 

  21. Lu H, Ge Z, Song Y, Jiang D, Zhou T, Qin J (2021) A temporal-aware lstm enhanced by loss-switch mechanism for traffic flow forecasting. Neurocomputing 427:169–178

    Article  Google Scholar 

  22. Zheng L, Yang J, Chen L, Sun D, Liu W (2020) Dynamic spatial-temporal feature optimization with ERI big data for Short-term traffic flow prediction. Neurocomputing 412:339–350

    Article  Google Scholar 

  23. Chen L, Zheng L, Yang J, Xia D, Liu W (2020) Short-term traffic flow prediction: From the perspective of traffic flow decomposition. Neurocomputing 413:444–456

    Article  Google Scholar 

  24. Boukerche A, Wang J (2020) A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model. Ad Hoc Netw 106:102224

    Article  Google Scholar 

  25. Lu S, Zhang Q, Chen G, Seng D (2021) A combined method for short-term traffic flow prediction based on recurrent neural network. Alex Eng J 60(1):87–94

    Article  Google Scholar 

  26. Kong F, Li J, Jiang B, Song H (2019) Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network. Futur Gener Comput Syst 93:460–472

    Article  Google Scholar 

  27. Hou Q, Leng J, Ma G, Liu W, Cheng Y (2019) An adaptive hybrid model for short-term urban traffic flow prediction. Physica A: Stat Mech Applic 527:121065

    Article  Google Scholar 

  28. Zheng H, Lin F, Feng X, Chen Y (2020) A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):6910–20

  29. Liu L, Zhen J, Li G, Zhan G, He Z, Du B, Lin L (2020) Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):7169–83

  30. Yang B, Sun S, Li J, Lin X, Tian Y (2019) Traffic flow prediction using LSTM with feature enhancement. Neurocomputing 332:320–327

    Article  Google Scholar 

  31. Dai X, Fu R, Zhao E, Zhang Z, Lin Y, Wang FY, Li L (2019) DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending. Transp Res Part C: Emerg Technol 103:142–157

    Article  Google Scholar 

  32. Li Y, Chai S, Ma Z, Wang G (2021) A hybrid deep learning framework for long-term traffic flow prediction. IEEE Access 9:11264–71

    Article  Google Scholar 

  33. Wang K, Ma C, Qiao Y, Lu X, Hao W, Dong S (2021) A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction. Physica A: Stat Mech Applic 583:126293

    Article  Google Scholar 

  34. Ma C, Dai G, Zhou J (2021) Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method. IEEE Trans Intell Transp Syst 23(6):5615–24

  35. Li W, Wang X, Zhang Y, Wu Q (2021) Traffic flow prediction over muti-sensor data correlation with graph convolution network. Neurocomputing. 427:50–63

    Article  Google Scholar 

  36. Boukerche A, Tao Y, Sun P (2020) Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Comput Netw 182:107484

    Article  Google Scholar 

  37. Duan P, Mao G, Zhang C, Wang S (2016) STARIMA-based traffic prediction with time-varying lags. In 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), IEEE, pp 1610–1615

  38. Zhou Z, Matteson DS (2015) Predicting ambulance demand: A spatio-temporal kernel approach. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2297–2303

  39. George S, Santra AK (2020) Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators. Big Data 8(4):291–307

    Article  PubMed  Google Scholar 

  40. An J, Fu L, Hu M, Chen W, Zhan J (2019) A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. IEEE Access 7:20708–20722

    Article  Google Scholar 

  41. Dataset: https://pems.dot.ca.gov/?dnode=Clearinghouse. Accessed 20 June 2021

Download references

Funding

There is no funding for this study.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bhilai Institute of Technology, Bhilai, Chhattisgarh, 491001, India

    Ravikant Soni

  2. Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, Chhattisgarh, India

    Partha Roy

  3. Department of CSE, SoS E&T, Guru Ghasidas Vishwavidyalaya, Bilaspur, India

    Kapil Kumar Nagwanshi

Authors
  1. Ravikant Soni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Partha Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kapil Kumar Nagwanshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript. Conceptualization: Ravikant Soni; Methodology: Ravikant Soni, Partha Roy; Formal analysis and investigation: Ravikant Soni, Sunita Soni; Writing - original draft preparation: Ravikant Soni, Kapil Kumar Nagwanshi; Writing - review and editing: Sunita Soni, Kapil Kumar Nagwanshi; Supervision: Sunita Soni.

Corresponding author

Correspondence to Ravikant Soni.

Ethics declarations

Conflict of interest

Authors declares that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants and/or animals performed by any of the authors.

Informed consent

There is no informed consent for this study.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soni, R., Roy, P. & Nagwanshi, K.K. WKNN-FDCNN method for big data driven traffic flow prediction in ITS. Multimed Tools Appl 83, 25261–25286 (2024). https://doi.org/10.1007/s11042-023-16591-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16591-4

Keywords

Search

Navigation