Skip to main content

Advertisement

SpringerLink
Log in

Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.

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

Similar content being viewed by others

Abbreviations

NAP:

Normalized Traffic Accident Propensity

EEG:

Electroencephalogram

R-CNN:

Region Based Convolutional Neural Network

ANN:

Artificial Neural Network

MEC:

Mobile Edge Computing

MLP:

Multiple layer Perceptron Neural Networks

SVM:

Support Vector Machine

GDP:

Gross Domestic Product

HDFS:

Hadoop Distributed File System

EGS:

Expanded Graphites

YOLO:

You only look once

Fuzzy ARTMAP:

Fuzzy Adaptive Resonance Theory

RF:

Random Forest

KM-MBFO:

K-means Modified Bacterial Foraging

References

  1. Twala B (2014) Extracting grey relational systems from incomplete road traffic accidents data: the case of Gauteng Province in South Africa. Expert Syst 31(3):117

    Article  Google Scholar 

  2. Zhao Y-F, Zhang S-R, Ma Z-L (2018) Analysis of traffic accident severity on highway tunnels using the partial proportion odds model. China Journal of Highway and Transport 31(9):159–166

  3. Lei Y, Yuan L (2014) Traffic incident duration prediction based on adaptive neural-fuzzy inference system. Comput Eng 40(2):189–192,198

    Google Scholar 

  4. Yousefzadeh-Chabok S, Hosseinpour M, Kouchakinejad-Eramsadati L et al (2016) Comparison of revised trauma score, injury severity score and trauma and injury severity score for mortality prediction in elderly trauma patients. Ulusal travma ve acil cerrahi dergisi = Turkish Journal of Trauma & Emergency Surgery: TJTES 22(6):536–540

    Google Scholar 

  5. Gokulakrishnan P, Ganeshkumar P (2015) Road accident prevention with instant emergency warning message dissemination in vehicular ad-hoc network. PLoS One 10(12):e0143383

    Article  Google Scholar 

  6. Taamneh M, Taamneh S, Alkheder S (2016) Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks. Int J Inj Control Saf Promot 24(3):1–8

    Google Scholar 

  7. Lee SL (2015) Predicting traffic accident severity using classification techniques. Adv Sci Lett 21(10):3128–3131

    Article  Google Scholar 

  8. En ZM (2015) A prediction model for traffic accident proneness caused by drunk driving via numerical characteristics of drivers’ EEGs. Journal of Shanghai Jiaotong University(Science) 49(2):287–292

  9. Gour D, Kanskar A (2019) Automated AI based road traffic accident alert system: YOLO Algorithm. IJSTR, August 2019, ISSN 2277–8616

  10. Priya V, Priya C (2019) A cognitive contemplation of road accident prediction through deep learning. IJITEE, December 2019, ISSN: 2278–3075

  11. Wenqi L, Dongyu L, Menghua Y (2017) A model of traffic accident prediction based on convolutional neural network[C]//2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE): 198–202

  12. Liao C, Shou G, Liu Y, Hu Y, Guo Z (2017) Intelligent traffic accident detection system based on mobile edge computing[C]//2017 3rd IEEE InternationalConference on Computer and Communications (ICCC): 2110–2115

  13. De Ona J, Mujalli RO, Calvo FJ (2011) Analysis of traffic accident injury severity on Spanish rural highways using Bayesian Network. Accidents Analysis and Prevention 43(2011):402–411

    Article  Google Scholar 

  14. Chong MM, Abraham A, Paprzycki M (2014) Traffic accidents analysis using decision trees and neural networks. IJITCS 6(2):22–28

    Article  Google Scholar 

  15. Lee J-Y, Chung J-H, Son B (1955–1963) Analysis of traffic accident size for Korean highway using structural equation models. Accid Anal Prev 40(2008):2008

    Google Scholar 

  16. Abdelwahab HT, Abdel-Aty MA (2017) Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections. Transportation research record 1746 paper no. 01–2234

  17. Dogru N, Subasi A (2018) Traffic accident detection using random forest classifier.15th L&T Conference, 2018

  18. Nair SC, Elayidom MS, Gopalan S (2019) KM-MBFO: a hybrid hadoop map reduce access for clustering big data by adopting modified bacterial foraging optimization algorithm. IJRTE, ISN:2277–3878, September 2019

  19. Olutayo VA, Eludire AA (2014) Traffic accident analysis using decision trees and neural networks. International Journal of Information Technology and Computer Science 2:22–28

  20. Bhatti F, Shah MA, Maple C, Islam SU (2019) A novel internet of things-enabled accident detection and reporting system for smart city environments. Sensors 19(9):2071

  21. Al-Balushi IA, Yousif JH, Al-Shezawi MO (2017) Car accident notification based on Mobile cloud computing. Social Science Electronic Publishing 2(2):46–50

    Google Scholar 

  22. Bera S, Misra S, Rodrigues JJPC (2015) Cloud computing applications for smart grid: a survey. Parallel & Distributed Systems IEEE Transactions on 26(5):1477–1494

    Article  Google Scholar 

  23. Toosi AN (2014) Interconnected cloud computing environments: challenges, taxonomy, and survey. ACM Comput Surv 47(1):1–47

    Article  Google Scholar 

  24. Oguchi M, Hara R (2016) A speculative control mechanism of cloud computing systems based on emergency disaster information using SDN. Procedia Comput Sci 98:515–521

    Article  Google Scholar 

  25. Tamura Y, Yamada S (2016) Reliability computing and management considering the network traffic for a cloud computing. Ann Oper Res 244(1):163–176

    Article  MathSciNet  Google Scholar 

  26. Bowen D, Huang R, Xie Z (2018) KID model-driven things-edge-cloud computing paradigm for traffic data as a service. IEEE Netw 32(1):34–41

    Article  Google Scholar 

  27. Abbass H, Tang J, Amin R, Ellejmi M, Kirby S (2014) The computational air traffic control brain: computational red teaming and big data for real-time seamless brain-traffic integration. J Air Traffic Control 56(2):10–17

  28. Huang Z, Cao F, Jin C, Yu Z, & Huang R (2017) Carbon emission flow from self-driving tours and its spatial relationship with scenic spots–A traffic-related big data method. J Clean Prod 142:946–955

  29. Balaji Ganesh R (2015) An intelligent video surveillance framework with big data management for Indian road traffic system. Int J Comput Appl 123(10):12–19

    Google Scholar 

  30. Bortyakov DE, Mescheryakov SV, Shchemelinin DA (2014) Integrated management of big data traffic systems in distributed production environments. Spbspu Journal Computer Science Telecommunication & Control Systems, pp 105–113

  31. Weihua C, Jun Z, Peng W (2017) Network traffic detection and analysis based on big data flow. J Nanjing Univ Sci Technol 41(3):294–300

    Google Scholar 

  32. Wang L-L, Ngan HYT, Yung NHC (2015) Automatic incident classification for big traffic data by adaptive boosting SVM. Inf Sci:467

Download references

Acknowledgments

This work was supported by Scientific Research Project of Education Department of Shaanxi Provincial Government (Project No. 18JK0450), and Natural Science Basic Research Plan of Shaanxi Province (Project No. 2020JQ-682).

Funding

Scientific Research Project of Education Department of Shaanxi Provincial Government (Project No. 18JK0450) ; Natural Science Basic Research Plan of Shaanxi Province (Project No. 2020JQ-682).

Author information

Authors and Affiliations

  1. College of Transportation Engineering, Chang’an University, Xi’an, 710064, Shaanxi, China

    Zhun Tian & Shengrui Zhang

  2. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, Shaanxi, China

    Zhun Tian

  3. Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area (Chang’an University), Xi’an, 710064, Shaanxi, China

    Shengrui Zhang

Authors
  1. Zhun Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengrui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhun Tian.

Additional information

Publisher’s note

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

This article is part of the Topical Collection: Special Issue on Network In Box, Architecture, Networking and Applications Guest Editor: Ching-Hsien Hsu

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, Z., Zhang, S. Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast. Peer-to-Peer Netw. Appl. 14, 2511–2523 (2021). https://doi.org/10.1007/s12083-020-00994-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-020-00994-3

Keywords

Search

Navigation