Skip to main content
SpringerLink
Log in

Driving behaviors analysis based on feature selection and statistical approach: a preliminary study

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Due to the prevalence of IoV technology, big data has increasingly been promoted as a revolutionary development in a variety of applications. Indeed, the received big data from IoV is valuable particularly for those involved in analyzing driver’s behaviors. For instance, in the fleet management domain, fleet administrators are interested in fine-grained information about fleet usage, which is influenced by different driver usage patterns. In the vehicle insurance market, usage-based insurance or pay-as-you-drive schemes aim to adapt the insurance premium to individual driver behavior or even to provide various value-added services to policy holders. These applications can be expected to improve and to make safer the driving style of various individuals. Nowadays, big data analysis is becoming indispensable for automatic discovering of intelligence that is involved in the frequently occurring patterns and hidden rules. It is essential and necessary to study how to utilize these large-scale data. Regarding driving behaviors analysis, this paper presents a preliminary study based on feature selection and statistical approach. Feature selection is one of the important and frequently used techniques in data preprocessing for big data mining. Feature selection, as a dimensionality reduction technique, focuses on choosing a small subset of the significant features from the original data by removing irrelevant or redundant features. According to selection process, the most significant feature is vehicle speed for the collected vehicular data. Afterward, the statistical approach calculates skewness and dispersion in speed distribution as the statistical features for driving behaviors analysis. Finally, the established classification rules not only provide data-driven services and big data analytics but also offer training data samples for supervised machine learning algorithms. To validate the feasibility of the proposed method, over 150 drivers and more than 200,000 trips are verified in the simulation. As expected, experimental results are well matched with our observations.

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Ahmed E, Yaqoob I, Hashem IAT, Khan I, Ahmed AIA, Imran M, Vasilakos AV (2017) The role of big data analytics in Internet of Things. Comput Netw 129:459–471

    Article  Google Scholar 

  2. Kerang C, Lee H, Jung H (2017) Task management system according to changes in the situation based on IoT. J Inf Process Syst 13(6):1459–1466

    Google Scholar 

  3. Suryani V, Sulistyo S, Widyawan W (2017) Internet of Things (IoT) framework for granting trust among objects. J Inf Process Syst 13(6):1613–1627. https://doi.org/10.3745/JIPS.03.0088

    Article  Google Scholar 

  4. Lee E-J, Kim C-H, Jung IY (2014) An intelligent green service in internet of things. J Converg 5(3):4–8

    Google Scholar 

  5. Kim B (2017) A distributed coexistence mitigation scheme for IoT-based smart medical systems. J Inf Process Syst 13(6):1602–1612

    Google Scholar 

  6. Kumar N, Kaur K, Jindal A, Rodrigues JJPC (2015) Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment. Digit Commun Netw 1(3):191–203

    Article  Google Scholar 

  7. Li C-S, Franke H, Parris C, Abali B, Kesavan M, Chang V (2017) Composable architecture for rack scale big data computing. Future Gener Comput Syst 67:180–193

    Article  Google Scholar 

  8. Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: opportunities and challenges. Neurocomputing 37(10):350–361

    Article  Google Scholar 

  9. Sabah Mohammed and Tai Hoon Kim (2016) Big data applications for healthcare: preface to special issue. J Supercomput 72(10):3675–3676

    Article  Google Scholar 

  10. Hyun-Jeong Y, Shin-Kyung L, Oh-Cheon K (2011) Vehicle-generated data exchange protocol for remote OBD inspection and maintenance. In: 6th International Computer Sciences and Convergence Information Technology, pp 81–84

  11. Händel P, Ohlsson J, Ohlsson M, Skog I, Nygren E (2014) Smartphone-based measurement systems for road vehicle traffic monitoring and usage-based insurance. IEEE Syst J 8(4):1238–1248

    Article  Google Scholar 

  12. Tselentis DI, Yannis G, Vlahogianni EI (2016) Innovative insurance schemes: pay as/how you drive. Transp Res Procedia 14:362–371

    Article  Google Scholar 

  13. Willke TL, Tientrakool P, Maxemchuk NF (2009) A survey of inter-vehicle communication protocols and their applications. IEEE Commun Surv Tutor 11(2):3–20

    Article  Google Scholar 

  14. Jaco Prinsloo RM (2016) Accurate vehicle location system using RFID, an internet of things approach. Ad Hoc Netw 16:1–24

    Google Scholar 

  15. Zhu X, Zhang H, Cao D, Fang Z (2015) Robust control of integrated motor-transmission powertrain system over controller area network for automotive applications. Mech Syst Signal Process 58–59:15–28

    Article  Google Scholar 

  16. Kumar A, Mallik RK, Schober R (2014) A probabilistic approach to modeling users’ network selection in the presence of heterogeneous wireless networks. IEEE Trans Veh Technol 63(7):3331–3341

    Article  Google Scholar 

  17. Cayci A, Menasalvas E, Saygin Y, Eibe S (2013) Self-configuring data mining for ubiquitous computing. Inf Sci 246:83–99

    Article  Google Scholar 

  18. Kim Y, Kim W, Kim U (2010) Mining frequent itemsets with normalized weight in continuous data streams. J Inf Process Syst 6(1):79–90

    Article  Google Scholar 

  19. Lee M, Park Y-S, Kim M-H, Lee J-W (2016) A convergence data model for medical information related to acute myocardial infarction. Hum Centric Comput Inf Sci 6:15

    Article  Google Scholar 

  20. Choi JH, Shin HS, Nasridinov A (2016) A comparative study on data mining classification techniques for military applications. J Converg 7(1):15–22

    Google Scholar 

  21. Donoho DL et al (2000) High-dimensional data analysis: the curses and blessings of dimensionality. AMS Math Challenges Lecture, pp 1–32

  22. Li J, Chen Z, Wei L, Xu W, Kou G (2007) Feature selection via least squares support feature machine. Int J Inf Technol Dec Mak 6(04):671–686

    Article  MATH  Google Scholar 

  23. Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165

    Article  Google Scholar 

  24. He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, vol 18, pp 507–514

  25. Zhang D, Chen S, Zhou Z-H (2008) Constraint score: a new filter method for feature selection with pairwise constraints. Pattern Recognit 41(5):1440–1451

    Article  MATH  Google Scholar 

  26. He X, Niyogi P (2003) Locality preserving projections. In: Advances in Neural Information Processing Systems, vol 16, pp 585–591

  27. Bonato M (2011) Robust estimation of skewness and kurtosis in distributions with infinite higher moments. Finance Res Lett 8(2):77–87

    Article  Google Scholar 

  28. Washington SP, Karlaftis MG, Mannering F (2010) Statistical and econometric methods for transportation data analysis, 2nd edn. CRC Press, Boca Raton

    MATH  Google Scholar 

  29. Horswill MS, McKenna FP (2004) Drivers’ hazard perception ability: situation awareness on the road. In: Banbury S, Tremblay S (eds) A cognitive approach to situation awareness. Ashgate, Aldershot, pp 155–175

    Google Scholar 

  30. Lu J, Filev D, Prakah-Asante K, Tseng F, Kolmanovsky I (2009) From vehicle stability control to intelligent personal minder: Realtime vehicle handling limit warning and driver style characterization. In: IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems. CIVVS’09, pp 43–50

  31. Chen M-S (2015) Neuro-fuzzy approach for online message scheduling. Eng Appl Artif Intell 38:59–69

    Article  Google Scholar 

Download references

Acknowledgements

This study is conducted under the “Project for 4G Advanced Business and video services platform” of the Institute for Information Industry (III) which is subsidized by the Ministry of Economic Affairs of the Republic of China.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Da Yeh University, 510, Changhua, Taiwan

    Mu-Song Chen

  2. Department of Electronic Engineering, National Changhua University of Education, 510, Changhua, Taiwan

    Chi-Pan Hwang

  3. Department of Electrical Engineering, Feng Chia University, 100 WenHwa Road, 40724, Taichung, Taiwan

    Tze-Yee Ho

  4. Department of Electrical and Energy Technology, Chung Chou University of Science and Technology, 510, Changhua, Taiwan

    Hsuan-Fu Wang

  5. Institute for Information Industry, Taipei, Taiwan

    Chih-Min Shih, Hsing-Yu Chen & Wen Kai Liu

Authors
  1. Mu-Song Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chi-Pan Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tze-Yee Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hsuan-Fu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chih-Min Shih
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hsing-Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wen Kai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mu-Song Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, MS., Hwang, CP., Ho, TY. et al. Driving behaviors analysis based on feature selection and statistical approach: a preliminary study. J Supercomput 75, 2007–2026 (2019). https://doi.org/10.1007/s11227-018-2618-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2618-9

Keywords

Search

Navigation