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Connectivity analysis of V2V communication with discretionary lane changing approach

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Abstract

Vehicular ad hoc network (VANET) has become an exigent domain of Intelligent Transport system (ITS). Providing efficient communication among rapidly moving vehicles is a challenging task in highway environment. This paper analysis the connectivity of High Speed Mobility and Lane Changing (HSMLC) based on Discretionary Lane Changing (DLC) approach for V2V environment. The Recurrent Neural Network System (RNN) which models a driver’s decision to perform a Discretionary Lane Changing (DLC) process on highways. The RNN system model the DLC decision making using Adaptive Cruise Control (ACC) computational model. The ACC mechanism defines and extends traditional cursive control based on the input metrics which are extracted from Highway Traffic Management System database (HTMS). The RNN was trained and tested with HTMS data collected from Tamilnadu highway of India with ACC properties. The result part reviews the proposed DLC trajectories by lane changing phases, connectivity probability during DLC and packet delivery rate. During 65Kmph with 100 vehicles, the DLC takes highly 4.3 to 5.2 s for lane changing process and during this moment the PDR and throughput of the networks are 62 to 75% and 31.7 to 39Kbps, respectively. The simulation work done by two different simulators such as SUMO—mobility simulator and NS2—network simulator.

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References

  1. Talari S, Shafie-khah M, Siano P, Loia V, Joao AT (2017) A review of smart cities based on the internet of things concept. MDPI Energies 10(4):1–21

    Google Scholar 

  2. Ren G, Zhang Y, Liu H, Zhang Ke, Yongli Hu (2019) A new lane-changing model with consideration of driving style. Int J Intell Transp Syst Res 17:181–189

    Google Scholar 

  3. Pan TL, Lam WHK, Sumalee A, Zhong RX (2016) Modeling the impacts of mandatory and discretionary lane-changing maneuvers. Transp Res Part C Emerg Technol 68:403–424

    Article  Google Scholar 

  4. Keyvan-Ekbatani VL, Knoop WD (2016) Categorization of the lane change decision process on freeways. Transp Res Part C Emerg Technol 69:515–526

    Article  Google Scholar 

  5. Paridel K, Mantadelis Y, A. et al (2014) Analyzing the efficiency of context-based grouping on collaboration in VANETs with large-scale simulation. J Ambient Intell Humaniz Comput 5:475–490. https://doi.org/10.1007/s12652-012-0115-1

    Article  Google Scholar 

  6. Sun DJ, Elefteriadou L (2012) Lane-changing behavior on urban streets: an ‘“in-vehicle”’ field experiment-based study. Comput Aided Civ Infrastruct Eng 27:525–542

    Article  Google Scholar 

  7. Romo A, Hernandez S, Cheu RL (2014) Identifying pre-crash factors between cars and trucks on interstate highways: mixed logit model approach. J Transp Eng. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000621

    Article  Google Scholar 

  8. Verma S, Paulus R, Agrawal A (2018) A survey on vehicular mobility models and mobility management. J Emerg Technol Innov Res 5(12):196–204

    Google Scholar 

  9. Akhtar N, Ergen SC, Ozkasap O (2015) Vehicle mobility and communication channel models for realistic and efficient highway VANET simulation. IEEE Trans Veh Technol 64(1):248–262

    Article  Google Scholar 

  10. Ramakrishnan B, Bhagavath Nishanth R, Milton Joe M, Shaji RS (2015) Comprehensive analysis of highway, Manhattan and free way mobility models for vehicular ad hoc network. Int J Wirel Mob Comput 9(1):78–89

    Article  Google Scholar 

  11. Dou Y, Ni D, Wang Z, Wang J, Yan F (2016) Strategic car-following gap model considering the effect of cut-ins from adjacent lanes. IET Intel Transp Syst 10(10):658–665

    Article  Google Scholar 

  12. Gramaglia M, Trullols-Cruces O, Naboulsi D, Fiore M, Calderonm M (2016) Mobility and connectivity in highway vehicular networks: a case study in Madrid. Comput Commun 78(5):28–44

    Article  Google Scholar 

  13. Das S, Bowles BA, Houghland CR, Hunn SJ, Zhang YL (1999) Knowledge based model of traffic behavior in freeways. In: International Conference, ISBN: 1581130864, pp 14–18

  14. Wu M, Brackstone MM (2000) Fuzzy sets and systems for a motorway microscopic simulation model. Fuzzy Sets Syst 116:65–76

    Article  Google Scholar 

  15. Vechione M, Balal E, Cheu RL (2018) Comparisons of mandatory and discretionary lane changing behavior on freeways. Int J Transp Sci Technol 7(2):124–136

    Article  Google Scholar 

  16. Balal C, Sarkodie-Gyan M (2014) Analysis of discretionary lane changing parameters on freeways. Int J Transp Sci Technol 3(3):277–296

    Article  Google Scholar 

  17. Naseri H, Nahvi A, Karan FSN (2017) A real-time lane changing and line changing algorithm for driving simulators based on virtual driver behavior. J Simul 11:357–368

    Article  Google Scholar 

  18. Naskath J, Paramasivan B et al (2020) A study on modeling vehicles mobility with MLC for enhancing vehicle-to-vehicle connectivity in VANET. J Ambient Intell Humaniz Comput, ISSN: 1868-5137. https://doi.org/10.1007/s12652-020-02559-x

  19. Cao X, Young W, Sarv M (2013) Exploring duration of lane change execution. Aust Transp Res Forum Proc. https://doi.org/10.1132/2013/6745223,pp.2-4

    Article  Google Scholar 

  20. Balal E, Cheu RL, Gyan TS (2016) A binary decision model for discretionary lane changing move based on fuzzy inference system. Transp Res Part C Emerg Technol 67:47–61

    Article  Google Scholar 

  21. Talebpour A, Mahmassani HS, Hamdar SH (2015) Modeling lane-changing behavior in a connected environment: a game theory approach. In: 21st International Symposium on Transportation and Traffic Theory, Transportation Research Procedia, pp 420–440. https://doi.org/10.1016/j.trpro.2015.06.022

  22. Cao X, Kim I, Young W (2016) A study of mandatory lane-changing execution behavior model considering conflicts. In: Australasian Transport Research Forum (ATRF), 38th, Melbourne, Victoria, Australia, Article ID. 113943509

  23. Khan Z, Fan P, Fang S (2017) On the connectivity of vehicular ad hoc network under various mobility scenarios. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2761551

    Article  Google Scholar 

  24. Chen R, Sheng Z, Zhong Z, Ni M, Leung V, Michelson DG, Hu M (2014) Connectivity analysis for cooperative vehicular ad hoc networks under Nakagami fading channel. IEEE Commun Lett 18(10):1787–1790

    Article  Google Scholar 

  25. Vivitha V, Naskath J, Paramasivan B (2018) High speed realistic mobility model for TN-multi lane highway environment. Int J Eng Technol 7(4.5):151–154

    Article  Google Scholar 

  26. Tamilnadu Highway Department (2020) Highway traffic information. Available from: https://tnhighways.gov.in/index.php/en

  27. Gunter G, Stern R, Work DB (2019) Modeling adaptive cruise control vehicles from experimental data: model comparison. In: International Conference: 2019 IEEE Intelligent Transportation Systems Conference—ITSC. https://doi.org/10.1109/ITSC.2019.8917347

  28. Moripour S, Sarvi M, Rose G, Mazloumi E (2012) Lane-changing decision model for heavy vehicle drivers. J Intell Transp Syst 16(1):24–35

    Article  Google Scholar 

  29. Torti E, Musci M, Guareschi F, Leporati F, Piastra M (2019) Deep recurrent neural networks for edge monitoring of personal risk and warning situations. Hindawi J 2019:1–10

    Google Scholar 

  30. Ali Y, Zheng Z, Haque M (2018) Connectivity’s impact on mandatory lane-changing behaviour: evidences from a driving simulator study. Transp Res Part C 93:292–309. https://doi.org/10.1016/j.trc.2018.06.008

    Article  Google Scholar 

  31. Guo M, Wu Z, Zhu H (2018) Empirical study of lane-changing behavior on three Chinese freeways. PLoS ONE 13(1):1–22. https://doi.org/10.1371/journal.pone.0191466

    Article  Google Scholar 

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Naskath, J., Paramasivan, B., Mustafa, Z. et al. Connectivity analysis of V2V communication with discretionary lane changing approach. J Supercomput 78, 5526–5546 (2022). https://doi.org/10.1007/s11227-021-04086-8

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