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A Novel Fuzzy Comprehensive Evaluation Model for Application Effect of Connected Vehicle System in a Tunnel Scenario

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Abstract

This paper proposes a novel fuzzy comprehensive evaluation model based on the 2-tuple linguistic model and grey target decision method (2TL-GTD) for evaluating the comprehensive application effect of the connected vehicle system. Since the evaluation indicator system of the research problem belongs to multi-source heterogeneous data, the original data is firstly converted into a unified 2-tuple form using linguistic 2-tuple model, which can avoid information distortion or loss. Second, the deviation maximization method is used to objectively determine the weight of each indicator. Then, the grey target decision-making method is used to deal with the uncertain information in the data and select the optimal solution according to the bullseye degree, which allows a comprehensive evaluation of the application effectiveness of the connected vehicle system. Taking the tunnel scenario as an example to verify the effectiveness of 2LT-GTD, it is found that in the tunnel scenario, the connected vehicle system can remind the driver to prepare in advance, effectively reducing traffic accidents and pollutant emissions. Based on the results, it can provide technical improvement directions for relevant technical departments to achieve the coordinated development of safe driving, efficient driving and ecological driving.

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References

  1. State Statistical Bureau: 2019 National economic and social development statistics’ annual report of PRC (2019)

  2. Farah, H., Koutsopoulos, H.N.: Do cooperative systems make drivers’ car-following behavior safer? Transp. Res. Part C Emerg. Technol. 41, 61–72 (2014)

    Article  Google Scholar 

  3. Sharma, A., Zheng, Z., Kim, J., Bhaskar, A., Haque, M.M.: Is an informed driver a better decision maker? A grouped random parameters with heterogeneity-in-means approach to investigate the impact of the connected environment on driving behaviour in safety-critical situations. Anal. Methods Accid. Res. 27, 100127 (2020)

    Google Scholar 

  4. Tibljas, A.D., Giuffre, T., Surdonja, S., Trubia, S.: Introduction of autonomous vehicles: roundabouts design and safety performance evaluation. Sustainability 10(4), 1060 (2018)

    Article  Google Scholar 

  5. Molina, C.B.S.T., Almeida, J.R., Vismari, L.F., González, R.I.R., Naufal, J.K., Camargo, J.B.: Assuring fully autonomous vehicles safety by design: The autonomous vehicle control (AVC) module strategy. In: 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), vol. 16 (2017)

  6. Farah, H., Koutsopoulos, H.N., Saifuzzaman, M., Koelbl, R., Fuchs, S., Bankosegger, D.: Evaluation of the effect of cooperative infrastructure-to-vehicle systems on driver behavior. Transp. Res. Part C Emerg. Technol. 21(1), 42–56 (2012)

    Article  Google Scholar 

  7. Wen, J., Wu, C., Zhang, R., Xiao, X., Nv, N., Shi, Y.: Rear-end collision warning of connected automated vehicles based on a novel stochastic local multivehicle optimal velocity model. Accid. Anal. Prev. 148, 105800 (2020)

    Article  Google Scholar 

  8. Chang, X., Li, H., Rong, J.: Effects of on-board unit on driving behavior in connected vehicle traffic flow. J. Adv. Transp. 2019, 1–12 (2019)

    Google Scholar 

  9. Yu, B., Bao, S., Feng, F., Sayer, J.: Examination and prediction of drivers’ reaction when provided with V2I communication-based intersection maneuver strategies. Transp. Res. Part C Emerg. Technol. 106, 17–28 (2019)

    Article  Google Scholar 

  10. Xiao, Q., Shan, M., Gao, M.: Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction. Appl. Soft Comput. 95, 106538 (2020)

    Article  Google Scholar 

  11. Nachappa, T.G., Piralilou, S.T., Gholamnia, K., Ghorbanzadeh, O., Rahmati, O., Blaschke, T.: Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. J. Hydrol. 590, 125275 (2020)

    Article  Google Scholar 

  12. Danish, F., Sarbast, M., Rana, F.T., Omid, G., Szabolcs, D., Ahsen, M., Thomas, B.: Analyzing the importance of driver behavior criteria related to road safety for different driving cultures. Int. J. Environ. Res. Public Health 17(6), 1893 (2020)

    Article  Google Scholar 

  13. Cheng, M., Lu, Y.: Investment efficiency of urban infrastructure systems: Empirical measurement and implications for China. Habitat Int. 70, 91–102 (2017)

    Article  Google Scholar 

  14. Madhu, P., Dhanalakshmi, C.S., Mathew, M.: Multi-criteria decision-making in the selection of a suitable biomass material for maximum bio-oil yield during pyrolysis. Fuel 277, 118109 (2020)

    Article  Google Scholar 

  15. Liu, S., Yu, W., Chan, F.T.S., Niu, B.: A variable weight-based hybrid approach for multi-attribute group decision making under interval-valued intuitionistic fuzzy sets. Int. J. Intell. Syst. 36(2), 1015–1052 (2020)

    Article  Google Scholar 

  16. Zhang, X., Jin, F., Liu, P.: A grey relational projection method for multi-attribute decision making based on intuitionistic trapezoidal fuzzy number. Appl. Math. Model. 37(5), 3467–3477 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  17. Bu, F., He, J., Li, H., Fu, Q.: Interval-valued intuitionistic fuzzy MADM method based on TOPSIS and grey correlation analysis. Math. Biosci. Eng. (MBE) 17(5), 5584–5603 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  18. Yang, Z., Yang, K., Wang, Y., Su, L., Hu, H.: Multi-objective short-term hydropower generation operation for cascade reservoirs and stochastic decision making under multiple uncertainties. J. Clean. Prod. 276, 122995 (2020)

    Article  Google Scholar 

  19. Liang, Y., Qin, J., Martinez, L.: Consensus-based multicriteria group preference analysis model with multigranular linguistic distribution information. IEEE Trans. Fuzzy Syst. 28(12), 3145–3160 (2020)

    Article  Google Scholar 

  20. Wen, J., Zhen, B., Pu, Z.: An improved method used for evaluating potential environmental pollution risk based on spatial distribution and density of farms. Environ. Sci. Pollut. Res. 28(9), 10564–10575 (2021)

    Article  Google Scholar 

  21. Burchart-Korol, D., Gazda-Grzywacz, M., Zarębska, K.: Research and prospects for the development of alternative fuels in the transport sector in Poland: a review. Energies 13(11), 2988 (2020)

    Article  Google Scholar 

  22. Carsten, O.M.J., Tate, F.N.: Intelligent speed adaptation: accident savings and cost-benefit analysis. Accid. Anal. Prev. 37(3), 407–416 (2005)

    Article  Google Scholar 

  23. Kolosz, B., Grant-Muller, S.: Sustainability assessment approaches for intelligent transport systems: the state of the art. IET Intell. Transp. Syst. 10(5), 287–297 (2016)

    Article  Google Scholar 

  24. Awasthi, A., Chauhan, S.S., Omrani, H.: Application of fuzzy TOPSIS in evaluating sustainable transportation systems. Expert Syst. Appl. 38(10), 12270–12280 (2011)

    Article  Google Scholar 

  25. Herrera, F., Martinez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Article  Google Scholar 

  26. Rao, C., He, Y.W., Wang, X.: Comprehensive evaluation of non-waste cities based on two-tuple mixed correlation degree. Int. J. Fuzzy Syst. 23(2), 369–391 (2021)

    Article  Google Scholar 

  27. Wang, Y., Li, H.: Complex chemical process operation evaluations using a novel analytic hierarchy process model integrating deep residual network with principal component analysis. Chemom. Intell. Lab. Syst. 191, 118–128 (2019)

    Article  Google Scholar 

  28. Lee, J., Park, B., Malakorn, K.: Sustainability assessments of cooperative vehicle intersection control at an urban corridor. Transp. Res. Part C Emerg. Technol. 32, 193–206 (2013)

    Article  Google Scholar 

  29. Rao, C., Goh, M., Zheng, J.: Decision mechanism for supplier selection under sustainability. Int. J. Inf. Technol. Decis. Mak. 16(2), 591–591 (2017)

    Article  Google Scholar 

  30. Xiao, Q., Shan, M., Gao, M.: Evaluation of the coordination between China’s technology and economy using a grey multivariable coupling model. Technol. Econ. Dev. Econ. 27(1), 24–44 (2021)

    Article  Google Scholar 

  31. Mao, S., Kang, Y., Zhang, Y.: Fractional grey model based on non-singular exponential kernel and its application in the prediction of electronic waste precious metal content. ISA Trans. 107, 12–26 (2020)

    Article  Google Scholar 

  32. Rao, C., Yan, B.: Study on the interactive influence between economic growth and environmental pollution. Environ. Sci. Pollut. Res. 27(31), 39442–39465 (2020)

    Article  Google Scholar 

  33. Mao, S., Zhu, M., Wang, X.: Grey–Lotka–Volterra model for the competition and cooperation between third-party online payment systems and online banking in China. Appl. Soft Comput. 95, 106501 (2020)

    Article  Google Scholar 

  34. Herrera, F., Herrera-Viedma, E., Martinez, L.: A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans. Fuzzy Syst. 16(2), 354–370 (2008)

    Article  Google Scholar 

  35. Xiao, X., Duan, H., Wen, J.: A novel car-following inertia gray model and its application in forecasting short-term traffic flow. Appl. Math. Model. 89, 546–570 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  36. Anwar, M.: Potential vs prevalent vs popular vs proven biodiesel feedstocks: a critical 4P selection process. Fuel 298, 120712 (2021)

    Article  Google Scholar 

  37. Yue, Z.: An extended TOPSIS for determining weights of decision makers with interval numbers. Knowl. Based Syst. 24(1), 146–153 (2011)

    Article  Google Scholar 

  38. Dursun, M., Arslan, O.: An integrated decision framework for material selection procedure: a case study in a detergent manufacturer. Symmetry 10(11), 657 (2018)

    Article  MATH  Google Scholar 

  39. Wang, P., Wang, J., Wei, G., Wei, C., Wei, Y.: The multi-attributive border approximation area comparison (MABAC) for multiple attribute group decision making under 2-tuple linguistic neutrosophic environment. Informatica 30(4), 799–818 (2019)

    Article  Google Scholar 

  40. Wang, Y., Luo, Y.: On rank reversal in decision analysis. Math. Comput. Model. 49, 1221–1229 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  41. Rani, P., Mishra, A.R., Mardani, A.: An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: application in pharmacological therapy selection for type 2 diabetes. Appl. Soft Comput. 94, 106441 (2020)

    Article  Google Scholar 

  42. Sun, R., Hu, J., Zhou, J., Chen, X.: A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization. Int. J. Fuzzy Syst. 20, 2144–2160 (2018)

    Article  Google Scholar 

  43. Yang, Z., Yang, K., Wang, Y., Su, L., Hu, H.: Long-term multi-objective power generation operation for cascade reservoirs and risk decision making under stochastic uncertainties. Renew. Energy 164, 313–330 (2021)

    Article  Google Scholar 

  44. Yue, L., Abdel-Aty, M., Wu, Y., Wang, L.: Assessment of the safety benefits of vehicles’ advanced driver assistance, connectivity and low level automation systems. Accid. Anal. Prev. 117, 55–64 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This work was jointly supported by the National Nature Science Foundation of China (Grant numbers 61403288, U1764262, 71871174); the Science Fund for Creative Research Groups of the National Nature Science Foundation of Hubei Province (Grant number 2017CFA008).

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Authors and Affiliations

  1. School of Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Shuai Wang, Jianghui Wen & Congjun Rao

  2. Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Jianghui Wen

  3. Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Haijian Li & Xiaohua Zhao

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  1. Shuai Wang
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  2. Jianghui Wen
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Correspondence to Jianghui Wen or Haijian Li.

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Wang, S., Wen, J., Li, H. et al. A Novel Fuzzy Comprehensive Evaluation Model for Application Effect of Connected Vehicle System in a Tunnel Scenario. Int. J. Fuzzy Syst. 24, 1986–2004 (2022). https://doi.org/10.1007/s40815-022-01254-7

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  • DOI: https://doi.org/10.1007/s40815-022-01254-7

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