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Applying improved K-means algorithm into official service vehicle networking environment and research

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Abstract

In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions and official service vehicles. In order to improve the real-time and forward-looking route control, an official service vehicle flow forecasting method is introduced to construct a virtual official service vehicle queue. A multi-commodity flow (official service vehicle flow) backpressure route method is proposed, and an official service vehicle control strategy is designed to improve the self-adaptive route of K-means algorithm. In addition, the weight of backpressure strategy is improved according to traffic pressure conditions, and the adaptability of backpressure route algorithm is improved by using optimized parameters. Finally, the simulation results show that the proposed method can effectively control traffic vehicles and improve traffic smoothness.

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References

  • Arzoo MK, Prof A, Rathod K (2017) K-means algorithm with different distance metrics in spatial data mining with uses of NetBeans IDE 8.2. Int Res J Eng Technol 4(4):2363–2368

    Google Scholar 

  • Chen L, Ye F, Ruan Y et al (2018) An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J Image Video Process 2018(1):109

    Article  Google Scholar 

  • Cohen-Addad V, Klein PN, Mathieu C (2019) Local search yields approximation schemes for k-means and k-median in euclidean and minor-free metrics. SIAM J Comput 48(2):644–667

    Article  MathSciNet  MATH  Google Scholar 

  • Deng J, Tyson G, Cuadrado F et al (2019) Keddah: network evaluation powered by simulating distributed application traffic. ACM Trans Model Comput Simul 29(3):16

    Article  Google Scholar 

  • Ferrandez SM, Harbison T, Weber T et al (2016) Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm. JIEM 9(2):374–388

    Article  Google Scholar 

  • Friggstad Z, Rezapour M, Salavatipour MR (2019) Local search yields a PTAS for k-means in doubling metrics. SIAM J Comput 48(2):452–480

    Article  MathSciNet  MATH  Google Scholar 

  • Han G, Yang X, Liu L et al (2017) A joint energy replenishment and data collection algorithm in wireless rechargeable sensor networks. IEEE Internet Things J 5(4):2596–2604

    Article  Google Scholar 

  • Hussain I, Bingcai C (2017) Cluster formation and cluster head selection approach for vehicle ad-hoc network (VANETs) using K-means and floyd-Warshall technique. Int J Adv Comput Sci Appl 8(12):11–15

    Google Scholar 

  • Inam M, Li Z, Ali A et al (2019) A novel protocol for vehicle cluster formation and vehicle head selection in vehicular ad-hoc networks. Int J Electron Inf Eng 10(2):103–119

    Google Scholar 

  • Karagiannis D, Argyriou A (2018) Jamming attack detection in a pair of RF communicating vehicles using unsupervised machine learning. Veh Commun 13:56–63

    Google Scholar 

  • Liang TY, Li YJ (2017) A location-aware service deployment algorithm based on K-means for cloudlets. Mob Inf Syst 2017:1–10

    Google Scholar 

  • Lin L, Chen J, Qu DY et al (2019) Study on traffic state discrimination method based on K-means clustering algorithm. J Qingdao Univ Technol 40(4):109–114

    Google Scholar 

  • Luan WB, Wang L, Zhang N et al (2018) Daily classification of urban rail transit operation characteristics based on data clustering technology. Urban Rail Transit Res 4:14–17

    Google Scholar 

  • Maciejewski M, Bischoff J, Kai N (2016) An assignment-based approach to efficient real-time city-scale taxi dispatching. IEEE Intell Syst 31(1):68–77

    Article  Google Scholar 

  • Mohanty A, Mahapatra S, Bhanja U (2019) Traffic congestion detection in a city using clustering techniques in VANETs. Indones J Electr Eng Comput Sci 13(2):884–891

    Article  Google Scholar 

  • Nawrin S, Rahman MR, Akhter S (2017) Exploreing k-means with internal validity indexes for data clustering in traffic management system. Int J Adv Comput Sci Appl 8(3):264–268

    Google Scholar 

  • Peng Y, Liu X, Shen C et al (2019) An improved optical flow algorithm based on mask-R-CNN and K-means for velocity calculation. Appl Sci 9(14):2808

    Article  Google Scholar 

  • Pholsena K, Pan L (2018) Traffic status evaluation based on possibilistic fuzzy C-means clustering algorithm. In: 2018 IEEE third international conference on data science in cyberspace (DSC). IEEE

  • Qi H, Li J, Di X et al (2019) Improved K-means clustering algorithm and its applications. Recent Patents Eng 13(4):403–409

    Article  Google Scholar 

  • Ramalingam M, Thangarajan R (2020) Mutated k-means algorithm for dynamic clustering to perform effective and intelligent broadcasting in medical surveillance using selective reliable broadcast protocol in VANET. Comput Commun 150:563–568

    Article  Google Scholar 

  • Sharma DK, Dhurandher SK, Agarwal D et al (2019) kROp: k-Means clustering based routing protocol for opportunistic networks. J Ambient Intell Hum Comput 10(4):1289–1306

    Article  Google Scholar 

  • Taherkhani N, Pierre S (2016) Centralized and localized data congestion control strategy for vehicular ad hoc networks using a machine learning clustering algorithm. IEEE Trans Intell Transp Syst 17(11):3275–3285

    Article  Google Scholar 

  • Velea R, Ciobanu C, Margarit L et al (2017) Network traffic anomaly detection using shallow packet inspection and parallel k-means data clustering. Stud Inform Control 26(4):387–396

    Article  Google Scholar 

  • Venkatesh G, Arunesh K (2019) Map reduce for big data processing based on traffic aware partition and aggregation. Clust Comput 22(5):12909–12915

    Article  Google Scholar 

  • Wang X, Li J, Shang X (2019) The state subdivision of public traffic vehicles based on K-means algorithm. Int J Manuf Technol Manag 33(3–4):133–149

    Google Scholar 

Download references

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Correspondence to Jianghua Lv.

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Communicated by Mu-Yen Chen.

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Meng, X., Lv, J. & Ma, S. Applying improved K-means algorithm into official service vehicle networking environment and research. Soft Comput 24, 8355–8363 (2020). https://doi.org/10.1007/s00500-020-04893-w

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  • DOI: https://doi.org/10.1007/s00500-020-04893-w

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