Skip to main content
SpringerLink
Log in

Game Theoretic Systematic Approach for Transportation Quality Assessment

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The public transportation system of every nation is the most important aspect of its overall infrastructure. Transport management officials are under enormous pressure to meet the needs of the general public in light of the increasing traffic, especially in densely populated regions. Conspicuously, evaluating the quality of transportation services offered by government officials has become essential. In this research, the transportation quality of services is examined. An Internet of Things (IoT) architecture is used to collect real-time ambient information in smart public vehicles. The probabilistic Bayesian Belief Model is used to categorize data using a quantitative measure of the Probability of Transportation Grade (PoTG) for service quality. In addition, the temporal data abstraction is used to compute the Transportation Quality Index (TQI), a numerical measure for the temporally cumulative quality assessment. Moreover, 2-player game-theoretic decision-making is then used to evaluate quality in a time-sensitive way. A simulated environment with 236,436 data segments is used to test the proposed architecture. Enhanced in terms of Sensitivity(94.43%), Accuracy(94.46%), Specificity(94.44%), F-measure(94.78%), Temporal Delay(26.79 seconds), and Reliability (92.69%) are registered comparative to state-of-the-art methodologies for estimating the performance enhancement of the proposed framework.

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
Fig. 8

Similar content being viewed by others

Notes

  1. Source: https://www.iot-analytics

  2. Source: http://transferproject.org/wp-content/uploads/2014/05/A.-High-Quality-Public-Transport.pdf

  3. Source: https://www.chroniclelive.co.uk/news/north-east-news/poor-public-transport-costing-newcastle-23473632

  4. Source:https://theconversation.com/why-is-the-u-s-unwilling-to-pay-for-good-public-transportation-56788

  5. Source:https://www.unep.org/explore-topics/energy/what-we-do/transport

  6. Source:https://parking.uci.edu/AT/modes/OCTA.cfm

References

  1. Jeong HH, Shen YC, Jeong JP, Oh TT (2021) A comprehensive survey on vehicular networking for safe and efficient driving in smart transportation: A focus on systems, protocols, and applications. Veh Communications 31:100349

    Google Scholar 

  2. WuY, Dai H-N, Wang H, Xiong Z, Guo S (2022) A survey of intelligent network slicing management for industrial iot: Integrated approaches for smart transportation, smart energy, and smart factory. IEEE Communications Surveys & Tutorials

  3. Bellini P, Nesi P, Pantaleo G (2022) Iot-enabled smart cities: A review of concepts, frameworks and key technologies. Appl Sci 12(3):1607

    Article  Google Scholar 

  4. Chui KT (2022) Driver stress recognition for smart transportation: Applying multiobjective genetic algorithm for improving fuzzy c-means clustering with reduced time and model complexity. Sustainable Comput: Inform Syst 100668

  5. Sahal R, Alsamhi SH, Brown KN, O’Shea D, McCarthy C, Guizani M (2021) Blockchain-empowered digital twins collaboration: smart transportation use case. Machines 9(9):193

    Article  Google Scholar 

  6. Yinglei H, Dexin Q, Shengyuan Z (2022) Smart transportation travel model based on multiple data sources fusion for defense systems. Soft Comput 1–13

  7. Mohapatra H, Rath AK, Panda N (2022) Iot infrastructure for the accident avoidance: an approach of smart transportation. Int J Inf Technol 1–8

  8. Darwish SM, Abdel-Samee BE (2019) Game theory based solver for dynamic vehicle routing problem. In International Conference on Advanced Machine Learning Technologies and Applications. Springer, 2019, pp 133–142

  9. Zhang Q, Wang W, Peng Y, Zhang J, Guo Z (2018) A game-theoretical model of port competition on intermodal network and pricing strategy. Trans Res Part E: Logistics Trans Review 114:19–39

    Article  Google Scholar 

  10. Sohail M, Khan S, Ahmad R, Singh D, Lloret J (2019) Game theoretic solution for power management in iot-based wireless sensor networks. Sensors 19(18):3835

    Article  Google Scholar 

  11. Hernández R, Cárdenas C, Muñoz D (2018) Game theory applied to transportation systems in smart cities: analysis of evolutionary stable strategies in a generic car pooling system. Int J Interactive Design Manufact (IJIDeM) 12(1):179–185

    Article  Google Scholar 

  12. Cieśla M, Sobota A, Jacyna M (2020) Multi-criteria decision making process in metropolitan transport means selection based on the sharing mobility idea. Sustainability 12(17):7231

    Article  Google Scholar 

  13. Ali Y, Zheng Z, Haque MM, Wang M (2019) A game theory-based approach for modelling mandatory lane-changing behaviour in a connected environment. Transp Res part C: Emerging Technol 106:220–242

    Article  Google Scholar 

  14. Chattaraj D, Bera B, Das AK, Saha S, Lorenz P, Park Y (2021) Block-clap: Blockchain-assisted certificateless key agreement protocol for internet of vehicles in smart transportation. IEEE Trans Veh Technol 70(8):8092–8107

    Article  Google Scholar 

  15. Chopra L, Chakraborty S, Mondal A, Chakraborty S (2021) Parima: Viewport adaptive 360-degree video streaming. In Proceedings of the Web Conference 2021:2379–2391

    Google Scholar 

  16. Terenzi A, Ortolani N, Nolasco I, Benetos E, Cecchi S (2021) Comparison of feature extraction methods for sound-based classification of honey bee activity. IEEE/ACM Trans Audio Speech Language Process 30:112–122

    Article  Google Scholar 

  17. Martinez-Soto CE, Cucić S, Lin JT, Kirst S, Mahmoud ES, Khursigara CM, Anany H et al (2021) Phida: A high throughput turbidimetric data analytic tool to compare host range profiles of bacteriophages isolated using different enrichment methods. Viruses 13(11):2120

    Article  Google Scholar 

  18. Ruiz-Garcia L, Lunadei L, Barreiro P, Robla I (2009) A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors 9(6):4728–4750

    Article  Google Scholar 

  19. Chowdhury A, Shankaran R, Kavakli M, Haque MM (2018) Sensor applications and physiological features in drivers’ drowsiness detection: A review. IEEE Sensors J 18(8):3055–3067

    Article  Google Scholar 

  20. López-Barneo J, Nurse C, Nilsson G, Buck L, Gassmann M, Bogdanova AY (2010) First aid kit for hypoxic survival: sensors and strategies. Physiological Biochemical Zoology 83(5):753–763

    Article  Google Scholar 

  21. Chatterjee SG, Chatterjee S, Ray AK, Chakraborty AK (2015) Graphene-metal oxide nanohybrids for toxic gas sensor: a review. Sensors and Actuators B: Chemical 221:1170–1181

    Article  Google Scholar 

  22. Nagase T, Araki T, Kitamura S, Araki M, Ono H (2005) An intelligent sensor for etc ad hoc networks. In 2005 IEEE 61st Vehicular Technology Conference, vol 5 IEEE pp 2915–2918

  23. Maisonneuve N, Stevens M, Niessen ME, Steels L (2009) Noisetube: Measuring and mapping noise pollution with mobile phones. In Information technologies in environmental engineering. Springer 2009:215–228

    Google Scholar 

  24. Yadav N, Mishra A, Narang J (2019) Electrochemical sensor method for food quality evaluation. In Evaluation Technologies for Food Quality. Elsevier 2019:793–815

    Google Scholar 

  25. Yorulmaz O, Pearson TC, Çetin AE (2011) Cepstrum based feature extraction method for fungus detection. In Sensing for Agriculture and Food Quality and Safety III, vol 8027 International Society for Optics and Photonics, 2011 p 80270E

  26. Spencer BF, Jo H, Mechitov KA, Li J, Sim S-H, Kim RE, Cho S, Linderman LE, Moinzadeh P, Giles RK et al (2016) Recent advances in wireless smart sensors for multi-scale monitoring and control of civil infrastructure. J Civil Structural Health Monitor 6(1):17–41

    Article  Google Scholar 

  27. Sharma V, Thind T (2013) Techniques for detection of rusting of metals using image processing: A survey. Int J Emerging Sci Eng 1:60–62

    Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the project number (IF2/PSAU/2022/01/22929)

Funding

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through the project number (IF2/PSAU/2022/01/22929)

Author information

Authors and Affiliations

  1. College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia

    Abdullah Alqahtani, Shtwai Alsubai & Mohemmed Sha

  2. Department of Computer Applications, National Institute of Technology Kurukshetra, Kurukshetra, India

    Munish Bhatia

Authors
  1. Abdullah Alqahtani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shtwai Alsubai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohemmed Sha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Munish Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shtwai Alsubai.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alqahtani, A., Alsubai, S., Sha, M. et al. Game Theoretic Systematic Approach for Transportation Quality Assessment. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02233-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02233-4

Keywords

Search

Navigation