Skip to main content

Research on Quantitative Method of Traffic Safety Credit Score Based on Ridge-Logistic Regression

  • Conference paper
  • First Online:
Man-Machine-Environment System Engineering: Proceedings of the 21st International Conference on MMESE (MMESE 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 800))

Included in the following conference series:

  • 1600 Accesses

Abstract

In order to quantitatively analyze the driver’s traffic credit score, a quantitative analysis method based on Ridge-Logistic regression was proposed from four aspects: selecting drivers’ characteristics, defining good and bad individuals, determining the weight coefficients of different values of each characteristic, and improving the interpretability of the algorithm output results. And the output result of the algorithm was converted into a standard score table form through a score formula. The research results show that the model is highly interpretable, and the score table results are generally in good condition. According to the data test, the accuracy, precision, recall and Area under Curve (AUC) of the model are 98.31%, 97.36%, 99.33% and 0.99, respectively, which means that the model can correctly classify bad individuals and also has a good recognition effect on good individuals. The research results can be used to quantify the actual traffic credit and help to establish a reasonable traffic credit scoring mechanism.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shi, Q.Y., Jin, Y.H.: The comparative analysis of the application of several scoring models of consumer credit in China. Stat. Res. 6, 43–47 (2004)

    Google Scholar 

  2. Koh, H.C., Tan, W.C., Goh, C.P.: Credit scoring using data mining techniques. Singap. Manag. Rev. 26(2), 25–47 (2004)

    Google Scholar 

  3. Yan, Y.Y., Jiang, H.B.: An comparison study on the consumer credit scoring models: based on the data of self-employed. J. Stat. Inf. 25(5), 30–35 (2010)

    Google Scholar 

  4. Shao, H.P., Yin, J., Yu, W.H., et al.: Aberrant driving behaviours on risk involvement among drivers in China. J. Adv. Transp. (2020). https://doi.org/10.1155/2020/8878711

    Article  Google Scholar 

  5. Hu, L., Bao, X., Wu, H., et al.: A study on correlation of traffic accident tendency with driver characters using in-depth traffic accident data. J. Adv. Transp. (2020). https://doi.org/10.1155/2020/9084245

    Article  Google Scholar 

  6. Zhang, Z., Zhang, X., Ji, N., et al.: A study on the differences in driving skills of Chinese bus and taxi drivers. J. Adv. Transp. (2019). https://doi.org/10.1155/2019/8675318

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jingsheng Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, B., Wang, J., Wang, B., Wang, R., Xue, X. (2022). Research on Quantitative Method of Traffic Safety Credit Score Based on Ridge-Logistic Regression. In: Long, S., Dhillon, B.S. (eds) Man-Machine-Environment System Engineering: Proceedings of the 21st International Conference on MMESE. MMESE 2021. Lecture Notes in Electrical Engineering, vol 800. Springer, Singapore. https://doi.org/10.1007/978-981-16-5963-8_101

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-5963-8_101

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5962-1

  • Online ISBN: 978-981-16-5963-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics