Research article Special Issues

Data analytics in transport: Does Simpson's paradox exist in rule of ship selection for port state control?


  • Received: 13 September 2022 Revised: 17 October 2022 Accepted: 19 October 2022 Published: 28 October 2022
  • Although previous studies have applied artificial intelligence techniques to improve the accuracy and efficiency of ship selection in port state control (PSC) inspections, the new inspection regime (NIR) is still in effect and widely adopted by PSC authorities in the Tokyo Memorandum of Understanding to select ships for inspection. It considers seven features, and each candidate value of a certain feature is assigned a fixed weighting point. The sum of the weighting points of these seven features determines the risk level of a ship. The assumption behind the NIR is that ships with values attached with higher weighting points should have more deficiencies. However, this paper finds that Simpson's paradox may exist for this assumption; that is, the average number of deficiencies of ships with values attached with higher weighting points is lower than that of ships with values attached with lower weighting points. Therefore, this paper examines the plausibility of the NIR's weighted-sum method and further explores which feature flips the effect. Finally, we arrive at the conclusion that the features selected by NIR are coupled with each other, so we should not use a simple weighted-sum method to determine the risk level of a candidate ship. Based on the results, we further provide suggestions for PSC authorities with respect to the improvement of the ship selection scheme of NIR.

    Citation: Simon Tian, Xinyi Zhu. Data analytics in transport: Does Simpson's paradox exist in rule of ship selection for port state control?[J]. Electronic Research Archive, 2023, 31(1): 251-272. doi: 10.3934/era.2023013

    Related Papers:

  • Although previous studies have applied artificial intelligence techniques to improve the accuracy and efficiency of ship selection in port state control (PSC) inspections, the new inspection regime (NIR) is still in effect and widely adopted by PSC authorities in the Tokyo Memorandum of Understanding to select ships for inspection. It considers seven features, and each candidate value of a certain feature is assigned a fixed weighting point. The sum of the weighting points of these seven features determines the risk level of a ship. The assumption behind the NIR is that ships with values attached with higher weighting points should have more deficiencies. However, this paper finds that Simpson's paradox may exist for this assumption; that is, the average number of deficiencies of ships with values attached with higher weighting points is lower than that of ships with values attached with lower weighting points. Therefore, this paper examines the plausibility of the NIR's weighted-sum method and further explores which feature flips the effect. Finally, we arrive at the conclusion that the features selected by NIR are coupled with each other, so we should not use a simple weighted-sum method to determine the risk level of a candidate ship. Based on the results, we further provide suggestions for PSC authorities with respect to the improvement of the ship selection scheme of NIR.



    加载中


    [1] O. F. Abioye, M. A. Dulebenets, M. Kavoosi, J. Pasha, O. Theophilus, Vessel schedule recovery in liner shipping: Modeling alternative recovery options, IEEE Trans. Intell. Transp. Syst., 22 (2021), 6420–6434. https://doi.org/10.1109/TITS.2020.2992120 doi: 10.1109/TITS.2020.2992120
    [2] S. Baştuğ, H. Haralambides, S. Esmer, E. Eminoğlu, Port competitiveness: Do container terminal operators and liner shipping companies see eye to eye?, Mar. Policy., 135 (2022), 104866. https://doi.org/10.1016/j.marpol.2021.104866 doi: 10.1016/j.marpol.2021.104866
    [3] M. A. Dulebenets, Multi-objective collaborative agreements amongst shipping lines and marine terminal operators for sustainable and environmental-friendly ship schedule design, J. Clean. Prod., 342 (2022), 130897. https://doi.org/10.1016/j.jclepro.2022.130897 doi: 10.1016/j.jclepro.2022.130897
    [4] Z. Elmi, P. Singh, V. K. Meriga, K. Goniewicz, M. Borowska-Stefańska, S. Wiśniewski, M. A. Dulebenets, Uncertainties in liner shipping and ship schedule recovery: A state-of-the-art review, J. Mar. Sci. Eng., 10 (2022), 563. https://doi.org/10.3390/jmse10050563 doi: 10.3390/jmse10050563
    [5] K. Wang, S. Wang, L. Zhen, X. Qu, Cruise service planning considering berth availability and decreasing marginal profit, Transp. Res. Part B Methodol., 95 (2017), 1–18. https://doi.org/10.1016/j.trb.2016.10.020 doi: 10.1016/j.trb.2016.10.020
    [6] L. Zhen, Y. Hu, S. Wang, G. Laporte, Y. Wu, Fleet deployment and demand fulfillment for container shipping liners, Transp. Res. Part B Methodol., 120 (2019), 15–32. https://doi.org/10.1016/j.trb.2018.11.011 doi: 10.1016/j.trb.2018.11.011
    [7] L. Zhen, Q. Sun, W. Zhang, K. Wang, W. Yi, Column generation for low carbon berth allocation under uncertainty, J. Oper. Res. Soc., 72 (2021), 2225–2240. https://doi.org/10.1080/01605682.2020.1776168 doi: 10.1080/01605682.2020.1776168
    [8] L. Wu, Y. Adulyasak, J. F. Cordeau, S. Wang, Vessel service planning in seaports, Oper. Res., 70 (2022), 2032–2053. https://doi.org/10.1287/opre.2021.2228 doi: 10.1287/opre.2021.2228
    [9] S. Wang, L. Zhen, D. Zhuge, Dynamic programming algorithms for selection of waste disposal ports in cruise shipping, Transp. Res. Part B Methodol., 108 (2018), 235–248. https://doi.org/10.1016/j.trb.2017.12.016 doi: 10.1016/j.trb.2017.12.016
    [10] L. Zhen, Y. Wu, S. Wang, G. Laporte, Green technology adoption for fleet deployment in a shipping network, Transp. Res. Part B Methodol., 139 (2020), 388–410. https://doi.org/10.1016/j.trb.2020.06.004 doi: 10.1016/j.trb.2020.06.004
    [11] W. Yi, L. Zhen, Y. Jin, Stackelberg game analysis of government subsidy on sustainable off-site construction and low-carbon logistics, Clean. Logist. Supply Chain., 2 (2021), 100013. https://doi.org/10.1016/j.clscn.2021.100013 doi: 10.1016/j.clscn.2021.100013
    [12] W. Yi, S. Wu, L. Zhen, G. Chawynski, Bi-level programming subsidy design for promoting sustainable prefabricated product logistics, Clean. Logist. Supply Chain., 1 (2021), 100005. https://doi.org/10.1016/j.clscn.2021.100005 doi: 10.1016/j.clscn.2021.100005
    [13] S. Wang, D. Zhuge, L. Zhen, C. Y. Lee, Liner shipping service planning under sulfur emission Regulations, Transp. Sci., 55 (2021), 491–509. https://doi.org/10.1287/trsc.2020.1010 doi: 10.1287/trsc.2020.1010
    [14] Paris MoU, Organization of Paris MoU, 2019. Available form: https://www.parismou.org/about-us/organisation
    [15] Tokyo MoU, Information Sheet of the New Inspection Regime (NIR), 2014. Available from: http://www.tokyo-mou.org/doc/NIR-information%20sheet-r.pdf
    [16] European Commission, Ex-post evaluation of Directive 2009/16/EC on Port State Control: Final Report, 2018. Available from: https://data.europa.eu/doi/10.2832/154686
    [17] R. Yan, S. Wang, Ship inspection by port state control—review of current research, Smart Transp. Syst., (2019), 233–241. https://doi.org/10.1007/978-981-13-8683-1_24 doi: 10.1007/978-981-13-8683-1_24
    [18] P. Cariou, M. Q. Mejia, F. C. Wolff, An econometric analysis of deficiencies noted in port state control inspections, Marit. Policy Manag., 34 (2007), 243–258. https://doi.org/10.1080/03088830701343047 doi: 10.1080/03088830701343047
    [19] P. Cariou, M. Q. Mejia, F. C. Wolff, Evidence on target factors used for port state control inspections, Mar. Policy., 33 (2009), 847–859. https://doi.org/10.1016/j.marpol.2009.03.004 doi: 10.1016/j.marpol.2009.03.004
    [20] M. C. Tsou, Big data analysis of port state control ship detention database, J. Mar. Eng. Technol., 18 (2019), 113–121. https://doi.org/10.1080/20464177.2018.1505029 doi: 10.1080/20464177.2018.1505029
    [21] S. Knapp, P. H. Franses, A global view on port state control: Econometric analysis of the differences across port state control regimes, Marit. Policy Manag., 34 (2007), 453–482. https://doi.org/10.1080/03088830701585217 doi: 10.1080/03088830701585217
    [22] F. J. Ravira, F. Piniella, Evaluating the impact of PSC inspectors' professional profile: A case study of the Spanish Maritime Administration, WMU J. Marit. Aff., 15 (2016), 221–236. https://doi.org/10.1007/s13437-015-0096-y doi: 10.1007/s13437-015-0096-y
    [23] A. Graziano, P. Cariou, F. C. Wolff, M. Q. Mejia, J. U. Schröder-Hinrichs, Port state control inspections in the European Union: Do inspector's number and background matter?, Mar. Policy., 88 (2018), 230–241. https://doi.org/10.1016/j.marpol.2017.11.031 doi: 10.1016/j.marpol.2017.11.031
    [24] R. F. Xu, Q. Lu, W. J. Li, K. X. Li, H. S. Zheng, A risk assessment system for improving port state control inspection, in: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, (2007), 818–823. https://doi.org/10.1109/ICMLC.2007.4370255
    [25] Z. Yang, Z. Yang, J. Yin, Z. Qu, A risk-based game model for rational inspections in port state control, Transp. Res. Part E Logist. Transp. Rev., 118 (2018), 477–495. https://doi.org/10.1016/j.tre.2018.08.001 doi: 10.1016/j.tre.2018.08.001
    [26] S. Wang, R. Yan, X. Qu, Development of a non-parametric classifier: Effective identification, algorithm, and applications in port state control for maritime transportation, Transp. Res. Part B Methodol., 128 (2019), 129–157. https://doi.org/10.1016/j.trb.2019.07.017 doi: 10.1016/j.trb.2019.07.017
    [27] D. Dinis, A. P. Teixeira, C. Guedes Soares, Probabilistic approach for characterising the static risk of ships using Bayesian networks, Reliab. Eng. Syst. Saf., 203 (2020), 107073. https://doi.org/10.1016/j.ress.2020.107073 doi: 10.1016/j.ress.2020.107073
    [28] R. Yan, S. Wang, C. Peng, An artificial intelligence model considering data imbalance for ship selection in port state control based on detention probabilities, J. Comput. Sci., 48 (2021), 101257. https://doi.org/10.1016/j.jocs.2020.101257 doi: 10.1016/j.jocs.2020.101257
    [29] R. Yan, S. Wang, Ship detention prediction using anomaly detection in port state control: model and explanation, Electron. Res. Arch., 30 (2022), 3679–3691. https://doi.org/10.3934/era.2022188 doi: 10.3934/era.2022188
    [30] E. H. Simpson, The interpretation of interaction in contingency tables, J. R. Stat. Soc. Ser. B Methodol., 13 (1951), 238–241. https://doi.org/10.1111/j.2517-6161.1951.tb00088.x doi: 10.1111/j.2517-6161.1951.tb00088.x
    [31] C. R. Blyth, On Simpson's paradox and the sure-thing principle, J. Am. Stat. Assoc., 67 (1972), 364–366. https://doi.org/10.1080/01621459.1972.10482387 doi: 10.1080/01621459.1972.10482387
    [32] J. Zidek, Maximal Simpson-disaggregations of 2 × 2 tables, Biometrika., 71 (1984), 187–190. https://doi.org/10.2307/2336411 doi: 10.2307/2336411
    [33] Y. Bishop, S. Fienberg, P. Holland, R. Light, F. Mosteller, Discrete multivariate analysis: Theory and practice, Appl. Psychol. Meas., 1 (1977). https://doi.org/10.1177/014662167700100218 doi: 10.1177/014662167700100218
    [34] M. G. Pavlides, M. D. Perlman, How likely is Simpson's paradox?, Am. Stat., 63 (2009), 226–233. https://www.jstor.org/stable/25652271
    [35] S. Sunder, Simpson's reversal paradox and cost allocation, J. Account. Res., 21 (1983), 222–233. https://doi.org/10.2307/2490944 doi: 10.2307/2490944
    [36] A. Mehrez, J. R. Brown, M. Khouja, Aggregate efficiency measures and Simpson's Paradox, Contemp. Account. Res., 9 (1992), 329–342. https://doi.org/10.1111/j.1911-3846.1992.tb00884.x doi: 10.1111/j.1911-3846.1992.tb00884.x
    [37] S. P. Curley, G. J. Browne, Normative and descriptive analyses of Simpson's paradox in decision making, Organ. Behav. Hum. Decis. Process., 84 (2001), 308–333. https://doi.org/10.1006/obhd.2000.2928 doi: 10.1006/obhd.2000.2928
    [38] N. D. Melumad, A. Ziv, Reduced quality and an unlevel playing field could make consumers happier, Manag. Sci., 50 (2004), 1646–1659. https://doi.org/10.1287/mnsc.1040.0277 doi: 10.1287/mnsc.1040.0277
    [39] W. Zhu, J. Wu, T. Fu, J. Wang, J. Zhang, Q. Shangguan, Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP, J. Intell. Connect. Veh., 4 (2021), 80–91. https://doi.org/10.1108/JICV-03-2021-0004 doi: 10.1108/JICV-03-2021-0004
    [40] N. Lyu, Y. Wang, C. Wu, L. Peng, A. F. Thomas, Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions, J. Intell. Connect. Veh., 5 (2022), 17–35. https://doi.org/10.1108/JICV-07-2021-0008 doi: 10.1108/JICV-07-2021-0008
    [41] H. Zhao, C. Zhang, An online-learning-based evolutionary many-objective algorithm, Inf. Sci., 509 (2020), 1–21. https://doi.org/10.1016/j.ins.2019.08.069 doi: 10.1016/j.ins.2019.08.069
    [42] S. Wang, R. Yan, A global method from predictive to prescriptive analytics considering prediction error for "Predict, then optimize" with an example of low-carbon logistics, Clean. Logist. Supply Chain., 4 (2022), 100062. https://doi.org/10.1016/j.clscn.2022.100062 doi: 10.1016/j.clscn.2022.100062
    [43] R. Yan, S. Wang, Integrating prediction with optimization: Models and applications in transportation management, Multimodal Transp., 1 (2022), 100018. https://doi.org/10.1016/j.multra.2022.100018 doi: 10.1016/j.multra.2022.100018
    [44] S. Wang, X. Tian, R. Yan, Y. Liu, A deficiency of prescriptive analytics—No perfect predicted value or predicted distribution exists, Electron. Res. Arch., 30 (2022), 3586–3594. https://doi.org/10.3934/era.2022183 doi: 10.3934/era.2022183
    [45] M. A. Dulebenets, R. Moses, E. E. Ozguven, A. Vanli, Minimizing carbon dioxide emissions due to container handling at marine container terminals via hybrid evolutionary algorithms, IEEE Access., 5 (2017), 8131–8147. https://doi.org/10.1109/ACCESS.2017.2693030 doi: 10.1109/ACCESS.2017.2693030
    [46] M. Dulebenets, A diploid evolutionary algorithm for sustainable truck scheduling at a cross-docking facility, Sustainability., 10 (2018), 1333. https://doi.org/10.3390/su10051333 doi: 10.3390/su10051333
    [47] J. Pasha, A. L. Nwodu, A. M. Fathollahi-Fard, G. Tian, Z. Li, H. Wang, et al., Exact and metaheuristic algorithms for the vehicle routing problem with a factory-in-a-box in multi-objective settings, Adv. Eng. Inform., 52 (2022), 101623. https://doi.org/10.1016/j.aei.2022.101623 doi: 10.1016/j.aei.2022.101623
    [48] M. Kavoosi, M. A. Dulebenets, O. F. Abioye, J. Pasha, H. Wang, H. Chi, An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem, Adv. Eng. Inf., 42 (2019), 100972. https://doi.org/10.1016/j.aei.2019.100972 doi: 10.1016/j.aei.2019.100972
    [49] M. Rabbani, N. Oladzad-Abbasabady, N. Akbarian-Saravi, Ambulance routing in disaster response considering variable patient condition: NSGA-Ⅱ and MOPSO algorithms, J. Ind. Manag. Optim., 18 (2022), 1035. https://doi.org/10.3934/jimo.2021007 doi: 10.3934/jimo.2021007
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1234) PDF downloads(81) Cited by(2)

Article outline

Figures and Tables

Figures(7)  /  Tables(12)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog