Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Recent Patents on Engineering
  4. Volume 19, Issue 2
  5. Article
Volume 19, Issue 2
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Background

The objective of this work is to analyze and predict the harmfulness in traffic accidents.

Methods

Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1].

Results

In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability.

Conclusion

Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121248202231003064459
2023-12-04
2025-02-17

  • From This Site

    /content/journals/eng/10.2174/0118722121248202231003064459
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. WiklinskaM. DevterG. Apparatus for collision avoidance.U.S. Patent 20190287408A12019
     [Google Scholar]
  2. DelashmitG. BédardH. Accidents: Causes, Analysis and Prevention.New YorkNova Science Publishers2009
     [Google Scholar]
  3. ZhangY. Severity analysis in motor vehicle crashes in the state of Iowa using multiple machine learning and data balancing techniquesProQuest Dissertation publishing20173638
     [Google Scholar]
  4. YuejingL. Xing-linZ. HaixiaZ. MingL. JieL. Research on Accident Prediction of Intersection and Identification Method of Prominent Accident Form Based on Back Propagation Neural NetworkInternational Conference on Computer Application and System Modeling (ICCASM 2010)year. 2010, pp. 434-438.
     [Google Scholar]
  5. LiuP. ChenS-H. YangM-D. Study of Signalized Intersection Crashes Using Artificial Intelligence Methods. GelbukhA. MoralesE.F. MICAI200898799710.1007/978‑3‑540‑88636‑5_93
     [Google Scholar]
  6. HuiyingW. JunL. XiaolongC. XiaohuiG. Real-time Highway Accident Prediction Based on Grey Relation Entropy Analysis and Probabilistic Neural Network.20111420142310.1109/ICETCE.2011.5775249
     [Google Scholar]
  7. HwangJ-W. LeeY-S. ChoS-B. Hierarchical Probabilistic Network-based System for Traffic Accident Detection at Intersections.Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing201021121610.1109/UIC‑ATC.2010.27
     [Google Scholar]
  8. LiS. ZhaoD. Prediction of road traffic accidents loss using improved wavelet neural network.IEEE Conf. Computers, Communications, Control and Power Engineeringyear. 2002, pp. 1526-1529.
     [Google Scholar]
  9. LvY. TangS. ZhaoH. LiS. Real-time highway accident prediction based on support vector machines.Chinese Control and Decision Conference 17-19 June 2009, Guilin, pp. 4403-4407.
     [Google Scholar]
  10. YuR. Abdel-AtyM. Investigating the different characteristics of weekday and weekend crashes.J. Safety Res.201346919710.1016/j.jsr.2013.05.00223932690
     [Google Scholar]
  11. DongN. HuangH. ZhengL. Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.Accid. Anal. Prev.20158219219810.1016/j.aap.2015.05.01826091769
     [Google Scholar]
  12. PanB. DemiryurekU. ShahabiC. Utilizing real-world transportation data for accurate traffic prediction.2012 IEEE 12th International Conference on Data Mining10-13 Dec. 2012 pp. 595‑604.
     [Google Scholar]
  13. CastroP.S. ZhangD. LiS. Urban traffic modelling and prediction using large scale taxi GPS traces. Pervasive Computing. 2012, pp. 57-72.Springer Berlin Heidelberg10.1007/978‑3‑642‑31205‑2_4
     [Google Scholar]
  14. WynterL. TragerB.M. YuY. KararianakisY. JabariS. ColdefyJ. Traffic estimation and prediction for urban road networks, application to grandlyon.ITS World Congress, Bordeaux. ERTICO. Citeseer.2015
     [Google Scholar]
  15. MinW. WynterL. Real-time road traffic prediction with spatio-temporal correlations.Transp. Res., Part C Emerg. Technol.201119460661610.1016/j.trc.2010.10.002
     [Google Scholar]
  16. XieJ. ChoiY.K. Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data.Int. J. Distrib. Sens. Netw.2017131110.1177/1550147717745009
     [Google Scholar]
  17. GangR. ZhupingZ. Traffic safety forecasting method by particle swarm optimization and support vector machine.Expert Syst. Appl.2011388104201042410.1016/j.eswa.2011.02.066
     [Google Scholar]
  18. HosseinpourM. YahayaA.S. GhadiriS.M. PrasetijoJ. Application of adaptive neuro-fuzzy inference system for road accident prediction.KSCE J. Civ. Eng.20131771761177210.1007/s12205‑013‑0036‑3
     [Google Scholar]
  19. ZhuX. Application of composite grey bp neural network forecasting model to motor vehicle fatality risk.2010 Second International Conference on Computer Modeling and Simulation 22-24 January 2010, Sanya, China, 2010, pp. 236-240,10.1109/ICCMS.2010.257
     [Google Scholar]
  20. XuX. ChenB. GanF. Traffic safety evaluations based on grey systems theory and neural network.2009 World Congress on Computer Science and Information Engineering603607200910.1109/CSIE.2009.888
     [Google Scholar]
  21. DongC. ShaoC. LiJ. XiongZ. An improved deep learning model for traffic crash prediction.J. Adv. Transp.2018201811310.1155/2018/3869106
     [Google Scholar]
  22. ShinstineD.S. WulffS.S. KsaibatiK. Factors associated with crash severity on rural roadways in Wyoming.J. Traffic Transp. Eng.20163430832310.1016/j.jtte.2015.12.002
     [Google Scholar]
  23. NazneenS. RezapourM. KsaibatiK. Determining causal factors of severe crashes on the fort peck Indian reservation”, Journal of Advanced TransportationMontana201818
     [Google Scholar]
  24. ChenC. ZhangG. QianZ. TarefderR.A. TianZ. Investigating driver injury severity patterns in rollover crashes using support vector machine models.Accid. Anal. Prev.20169012813910.1016/j.aap.2016.02.01126938584
     [Google Scholar]
  25. AlomariA. H. TaamnehM. M. Front-seat Seatbelt Compliance in Jordan: An observational study.Adv. transportation Stud202052101116
     [Google Scholar]
  26. Al-OmariA. ShatnawiN. KhedaywiT. MiqdadyT. Prediction of traffic accidents hot spots using fuzzy logic and GIS.Applied Geomatics202012214916110.1007/s12518‑019‑00290‑7
     [Google Scholar]
  27. AchuA.L. AjuC.D. SureshV. ManoharanT.P. ReghunathR. Spatio-temporal analysis of road accident incidents and delineation of hotspots using geospatial tools in thrissur district, Kerala, India.KN – J. Cartograp. Geogr. Info.201969425526510.1007/s42489‑019‑00031‑1
     [Google Scholar]
  28. RadzuanN.Q. HassanM.H.A. Abdul MajeedA.P.P. MusaR.M. Mohd RazmanM.A. Abu KassimK.A. Predicting serious injuries due to road traffic accidents in Malaysia by means of artificial neural network.Symposium on Intelligent Manufacturing and MechatronicsSingaporeSpringer20197580
     [Google Scholar]
  29. AlizadehM.N. TaleieS.M. Vehicle sensor data acquisition and distribution.U.S. Patent 20200336541A12020
     [Google Scholar]
  30. CordellJ.P. WellandR.V. MckelvieS.J. LudwigJ.H. Method and apparatus for providing goal oriented navigational directions.U.S. Patent 20200110951A12020
     [Google Scholar]
  31. DohertyJ.A. BerklondJ.S. CheraseK.W. ChavezS.M. KesslerR.E. MarhonP.D. MerlingerD.W. ShaominM.J. Method and system for managing interactions between vehicles of different levels of autonomy.C.N. Patent 113286733A2021
     [Google Scholar]
/content/journals/eng/10.2174/0118722121248202231003064459
Loading
Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest
Recent Pat Eng 19, E041223224189 (2025); https://doi.org/10.2174/0118722121248202231003064459
/content/journals/eng/10.2174/0118722121248202231003064459
/content/journals/eng/10.2174/0118722121248202231003064459
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): economic inequality; machine learning; predictive models; random forest; Road accidents; road safety

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test