Skip to main content
SpringerLink
Log in

Evaluating the Performance of a Hybrid Model for Classification of Bicycle Crash Severity and Identification of Associated Risk Factors

  • Conference paper
  • First Online:
Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 735))

  • 201 Accesses

Abstract

This study conducted an exploratory analysis of bicycle crash data from Great Britain with the aim of identifying the key variables that influence the classification of such incidents. It also analysed data on a range of factors that may contribute to bicycle crashes, including the age of the cyclist, lighting conditions, weather conditions, road types, road conditions, and speed limits. Results indicated that these variables are among the most significant predictors of bicycle crashes, with road conditions, time of day, and lighting conditions being particularly vital factors. In addition, the study sought to compare the efficacy of different machine learning and deep learning models in predicting the severity of such incidents. Results indicated that these models demonstrated poor performance in predicting the severity of bicycle crashes. As a result, a hybrid model that combines the K-Nearest Neighbor and eXtreme Gradient Boosting algorithms was developed to improve accuracy. The hybrid model outperformed all other models, achieving an accuracy rate of 83.56%. The study, additionally, has put forward several recommendations, including the mandatory use of reflective clothing and the installation of Intelligent Transportation Systems (ITS) to enhance the safety of cyclists.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Oja, P., et al.: Health benefits of cycling: a systematic review. Scand. J. Med. Sci. Sport. 21(4), 496–509 (2011). https://doi.org/10.1111/j.1600-0838.2011.01299.x.

  2. Leyland, L.-A., et al.: The effect of cycling on cognitive function and well-being in older adults. PLoS ONE 14(2) (2019). https://doi.org/10.1371/journal.pone.0211779

  3. Luo, J., et al.: Intentional weight loss and obesity-related cancer risk. JNCI Cancer Spectr. 3(4) (2019). https://doi.org/10.1093/jncics/pkz054

  4. Julian, V., et al.: Eccentric cycling is more efficient in reducing fat mass than concentric cycling in adolescents with obesity. Scand. J. Med. Sci. Sport. 29(1), 4–15. https://doi.org/10.1111/sms.13301

  5. Rasmussen, M.G., et al.: Associations between recreational and commuter cycling, changes in cycling, and type 2 diabetes risk: a cohort study of Danish men and women. PLOS Med. 13(7) (2016). https://doi.org/10.1371/journal.pmed.1002076

  6. Huang, H., et al.: County-level crash risk analysis in Florida: Bayesian spatial modeling. Transp. Res. Rec.: J. Transp. Res. Board 2148(1), 27–37 (2010). https://doi.org/10.3141/2148-04

  7. Oh, J., et al.: Assessing critical factors associated with bicycle collisions at urban signalized intersections. TRID (2007). https://trid.trb.org/view/847696. Accessed 13 Mar 2023

  8. Wu, S., et al.: Analyzing accident injury severity via an extreme gradient boosting (XGBoost) model. J. Adv. Transp. 2021, 1–11 (2021). https://doi.org/10.1155/2021/3771640

  9. Prati, G., et al.: Using data mining techniques to predict the severity of bicycle crashes. Accid. Anal. Prev. 101, 44–54 (2017). https://doi.org/10.1016/j.aap.2017.01.008

  10. Dash, I., et al.: Factors impacting bike crash severity in urban areas. J. Saf. Res. 83, 128–138 (2022). https://doi.org/10.1016/j.jsr.2022.08.010

  11. Asgarzadeh, M., et al.: The impact of weather, road surface, time-of-day, and light conditions on the severity of bicycle-motor vehicle crash injuries. Am. J. Ind. Med. 61(7), 556–565 (2018). https://doi.org/10.1002/ajim.22849

  12. Kim, J.-K., et al.: Bicyclist injury severities in bicycle–motor vehicle accidents. Accid. Anal. Prev. 39(2), 238–251 (2007). https://doi.org/10.1016/j.aap.2006.07.002

  13. Myhrmann, M.S., et al.: Factors influencing the injury severity of single-bicycle crashes. Accid. Anal. Prev. 149, 105875 (2021). https://doi.org/10.1016/j.aap.2020.105875

  14. Eriksson, J., et al.: Injured cyclists with focus on single-bicycle crashes and differences in injury severity in Sweden. Accid. Anal. Prev. 165, 106510 (2022). https://doi.org/10.1016/j.aap.2021.106510

  15. Utriainen, R.: Characteristics of commuters’ single-bicycle crashes in insurance data. Safety 6(1), 13 (2020). https://doi.org/10.3390/safety6010013

  16. Prati, G., et al.: Gender differences in cyclists’ crashes: an analysis of routinely recorded crash data. Int. J. Inj. Control Saf. Promot. 26(4), 391–398 (2019). https://doi.org/10.1080/17457300.2019.1653930

  17. Cobey, K.D., et al.: Sex differences in risk taking behavior among Dutch cyclists. Evol. Psychol. 11(2), 147470491301100 (2013). https://doi.org/10.1177/147470491301100206

  18. Li, Y., et al.: Collaborative filtering recommendation algorithm based on KNN and XGBoost hybrid. J. Phys.: Conf. Ser. 1748(3), 032041 (2021). https://doi.org/10.1088/1742-6596/1748/3/032041

  19. Rusland, N.F., et al.: (2017) Analysis of naïve Bayes algorithm for email spam filtering across multiple datasets. In: IOP Conf. Ser.: Mater. Sci. Eng. 226, 012091. https://doi.org/10.1088/1757-899x/226/1/012091

  20. Wang, S., et al.: Adapting naive Bayes tree for text classification. Knowl. Inf. Syst. 44(1), 77–89 (2014). https://doi.org/10.1007/s10115-014-0746-y

  21. Troussas, C., et al.: Sentiment analysis of Facebook statuses using naive Bayes classifier for language learning. IISA 2013 (2013) [Preprint]. https://doi.org/10.1109/iisa.2013.6623713

  22. Azar, A.T., et al.: A random forest classifier for lymph diseases. Comput. Methods Programs Biomed. 113(2), 465–473 (2014). https://doi.org/10.1016/j.cmpb.2013.11.004

  23. Alam, M.S., Vuong, S.T.: Random forest classification for detecting android malware. In: 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing [Preprint] (2013). https://doi.org/10.1109/greencom-ithings-cpscom.2013.122

  24. Harshith, J.: Bicycle Accidents in Great Britain (1979 to 2018), Kaggle (2021). https://www.kaggle.com/datasets/johnharshith/bicycle-accidents-in-great-britain-1979-to-2018. Accessed 24 Feb 2023

  25. Liu, X., et al.: Analysis of bicycle accidents and recommended countermeasures in Beijing, China. Transp. Res. Rec. 1487, 75–83 (1995)

    Google Scholar 

  26. Dozza, M.: Crash risk: how cycling flow can help explain crash data. Accid. Anal. Prev. 105, 21–29 (2017). https://doi.org/10.1016/j.aap.2016.04.033

  27. Rodgers, G.B.: Factors associated with the crash risk of adult bicyclists. J. Saf. Res. 28(4), 233–241 (1997). https://doi.org/10.1016/s0022-4375(97)00009-1

  28. Ekman, R., et al.: Bicycle-related injuries among the elderly—a new epidemic? Public Health 115(1), 38–43 (2001). https://doi.org/10.1038/sj.ph.1900713

  29. Stone, M., Broughton, J.: Getting off your bike: cycling accidents in Great Britain in 1990–1999. Accid. Anal. Prev. 35(4), 549–556 (2003). https://doi.org/10.1016/s0001-4575(02)00032-5

  30. Eluru, N., et al.: A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accid. Anal. Prev. 40(3), 1033–1054. https://doi.org/10.1016/j.aap.2007.11.010

  31. Yan, X., et al.: Motor vehicle–bicycle crashes in Beijing: irregular manoeuvres, crash patterns, and injury severity. Accid. Anal. Prev. 43(5), 1751–1758 (2011). https://doi.org/10.1016/j.aap.2011.04.006

Download references

Author information

Authors and Affiliations

  1. School of Creative Technologies, University of Bolton, Bolton, BL3 5AB, UK

    Maruf Ahmed & Pradeep Hewage

Authors
  1. Maruf Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeep Hewage
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pradeep Hewage .

Editor information

Editors and Affiliations

  1. University of Bolton, Bolton, UK

    Celestine Iwendi

  2. Hassan II University Casablanca, El Mansouria, Morocco

    Zakaria Boulouard

  3. Department of Information Systems, Comenius University, Bratislava, Slovakia

    Natalia Kryvinska

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ahmed, M., Hewage, P. (2023). Evaluating the Performance of a Hybrid Model for Classification of Bicycle Crash Severity and Identification of Associated Risk Factors. In: Iwendi, C., Boulouard, Z., Kryvinska, N. (eds) Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering. ICACTCE 2023. Lecture Notes in Networks and Systems, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-031-37164-6_44

Download citation

Publish with us

Policies and ethics

Search

Navigation