Article Sidebar

Download
PDF
Statistic
Read Counter : 416 Download : 232

Downloads

Download data is not yet available.

Main Article Content

Abstract

The article presents a mathematical model for distributed taxi fleet operations. A technological approach based on mathematical models of transport systems using the Hungarian algorithm was used to model the arrangement of repair and maintenance in the absence of centralized management. The literature review on taxi fleet robotics has shown that central management is the cause of increasing transport service costs up to 30%. The results of approximating the cases of repairing and maintaining the taxi fleet in the absence of centralization to the lognormal and Gaussian distributions are provided based on 2019-2020 data. A blockchain scheme for work organization and maintenance of the taxi fleet within the decentralization framework is developed. The statistical analysis of repair and emergency maintenance cases in the distributed taxi fleets calculated per 1000 cars was 3.6 to 15%. Pearson's criterion c2 was from 0.001 to 0.17. Statistical significance values of the results were p≤0.005. A multivariate cluster analysis of the accident or technical failure occurrence among the distributed taxi fleet vehicles was conducted in months, taking statistical data for the last two years. An algorithm that allows performing optimal assignments for a distributed taxi fleet is developed in this work. A sample calculation of the optimal allocation for taxi fleet vehicles distributed in the state of minimum vehicle repair cost based on the Hungarian algorithm was provided. The application of this algorithm also makes it possible to determine the optimal destinations for vehicles in the taxi fleet.

Keywords

Blockchain Taxi fleet Distributed register Product-service system

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. S. Rajendran and S. Srinivas, “Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities,” Transportation Research Part E: Logistics and Transportation Review, vol. 143, p. 102090, 2020.
  2. D. O. Rodrigues, A. Boukerche, T. H. Silva, A. A. Loureiro, and L. A. Villas, “Combining taxi and social media data to explore urban mobility issues,” Computer Communications, vol. 132, pp. 111–125, 2018.
  3. S. Jiang, L. Chen, A. Mislove, and C. Wilson, “On ridesharing competition and accessibility: Evidence from Uber, Lyft, and taxi,” in Proceedings of the 2018 World Wide Web Conference, pp. 863–872, 2018.
  4. J. Dobroszek, M. Biernacki, and M. Macuda, “Management accounting in logistics and supply chain management: Evidence from Poland,” Zeszyty Teoretyczne Rachunkowości, vol. 106, no. 162, pp. 153–175, 2020.
  5. S. Ma, Y. Zheng, and O. Wolfson, “T-share: A large-scale dynamic taxi ridesharing service”, in 2013 IEEE 29th International Conference on Data Engineering (ICDE), IEEE, pp. 410–421, 2013.
  6. Y. Ge, C. Liu, H. Xiong, and J. Chen, “A taxi business intelligence system,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 735–738, 2011.
  7. J. Cramer and A. B. Krueger, “Disruptive change in the taxi business: The case of Uber,” American Economic Review, vol. 106, no. 5, pp. 177–182, 2016.
  8. L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas, “Predicting taxi-passenger demand using streaming data,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 3, pp. 1393–1402, 2013.
  9. H. Billhardt, A. Fernández, S. Ossowski, J. Palanca, and J. Bajo, “Taxi dispatching strategies with compensations,” Expert Systems with Applications, vol. 122, pp. 173–182, 2019.
  10. H. R. Sayarshad and V. Mahmoodian, “An intelligent method for dynamic distribution of electric taxi batteries between charging and swapping stations,” Sustainable Cities and Society, p. 102605, 2020.
  11. S. Silwal, M. O. Gani, and V. Raychoudhury, “A survey of taxi ride sharing system architectures,” in 2019 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, pp. 144–149, 2019.
  12. Y. Zhang, H. Guo, C. Li, W. Wang, X. Jiang, and Y. Liu, “Which one is more attractive to traveler, taxi or tailored taxi? An empirical study in China,” Procedia Engineering, vol. 137, pp. 867–875, 2016.
  13. Y. M. Nie, “How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China,” Transportation Research Part C: Emerging Technologies, vol. 79, pp. 242–256, 2017.
  14. O. Zeira and S. Lee, U.S. Patent Application No. 16/279,899, 2019.
  15. S. Zhang, H. Wang, Y. F. Zhang, and Y. Z. Li, “A novel two-stage location model of charging station considering dynamic distribution of electric taxis,” Sustainable Cities and Society, vol. 51, p. 101752, 2019.
  16. R. Qi, C. Feng, Z. Liu, and N. Mrad, “Blockchain-powered internet of things, e-governance and e-democracy,” in E-democracy for smart cities, Springer, Singapore, pp. 509–520, 2017.
  17. T. Alladi, V. Chamola, R. M. Parizi, and K. K. R. Choo, “Blockchain applications for industry 4.0 and industrial IoT: A review,” IEEE Access, vol. 7, pp. 176935–176951, 2019.
  18. E. B. Belhadji, G. Dionne, and F. Tarkhani, “A model for the detection of insurance fraud,” The Geneva Papers on Risk and Insurance-Issues and Practice, vol. 25, no. 4, pp. 517–538, 2000.
  19. E. E. Ilina, T. E. Ilina, and B. P. Viktorovich, “Analysis of the application of turbulence models in the calculation of supersonic gas jet,” American Journal of Applied Sciences, vol. 11, no. 11, pp. 1914–1920, 2014.
  20. K. P. O'Keeffe, A. Anjomshoaa, S. H. Strogatz, P. Santi, and C. Ratti, “Quantifying the sensing power of vehicle fleets,” Proceedings of the National Academy of Sciences, vol. 116, n. 26, pp. 12752–12757, 2019.
  21. V. Sabadash, J. Gumnitsky, O. Lyuta, and I. Pochapska, “Thermodynamics of (NH4+) cation adsorption under static conditions,” Chemistry Chemical Technology, vol. 12, no. 2, pp. 143–146, 2018.
  22. F. Y. Wang, Y. Yuan, C. Rong, and J. J. Zhang, “Parallel blockchain: An architecture for CPSS-based smart societies,” IEEE Transactions on Computational Social Systems, vol. 5, no. 2, pp. 303–310, 2018.
  23. J. Sun, J. Yan, and K. Z. Zhang, “Blockchain-based sharing services: What blockchain technology can contribute to smart cities,” Financial Innovation, vol. 2, no. 1, pp. 1–9, 2016.
  24. S. Kim, J. J. E. Chang, H. H. Park, S. U. Song, C. B. Cha, J. W. Kim, and N. Kang, “Autonomous taxi service design and user experience,” International Journal of Human-Computer Interaction, vol. 36, no. 5, pp. 429–448, 2020.
  25. E. Nowińska, “Use and application of the internet of things in the digital transformation of enterprises,” Acta Universitatis Nicolai Copernici. Zarządzanie, vol. 47, no. 2, pp. 21–35, 2020.
  26. D. H. Kaplan, “Growing sustainable transportation in an autocentric community: Current trends and applications,” in Urban and regional planning and development, Springer, Cham, pp. 503–514, 2020.
  27. M. V. Smuk, “Product-service system based on blockchain technology in the automotive industry,” Financial Markets and Banks, vol. 5, pp. 102–107, 2020.
  28. W. Zhang, T. V. Le, S. V. Ukkusuri, and R. Li, “Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems.” Transportation, vol. 47, no. 2, pp. 971–996, 2020.
  29. V. Sabadash, J. Gumnitsky, and O. Lyuta, “Combined adsorption of the copper and chromium cations by clinoptilolite of the sokyrnytsya deposit,” Journal of Ecological Engineering, vol. 21, no. 5, pp. 42–46, 2020.
  30. W. Tu, Q. Li, Z. Fang, S. L. Shaw, B. Zhou, and X. Chang, “Optimizing the locations of electric taxi charging stations: A spatial-temporal demand coverage approach,” Transportation Research Part C: Emerging Technologies, vol. 65, pp. 172–189, 2016.
  31. C. Sullivan and C. O'Fallon, “Vehicle occupancy in New Zealand's three largest urban areas,” in Working Paper presented at the 26th Australian Transport Research Forum (ATRF),' Leading Transport Research in the 21st Century', Wellington, New Zealand, pp. 1–3, 2003.
  32. P. V. Bulat, O. N. Zasuhin, and V. N. Uskov, “On classification of flow regimes in a channel with sudden expansion,” Thermophysics and Aeromechanics, vol. 19, no. 2, pp. 233–246, 2012.
  33. S. Rahili, B. Riviere, S. Olivier, and S. J. Chung, “Optimal routing for autonomous taxis using distributed reinforcement learning,” in 2018 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 556–563, 2018.
  34. W. T. Chai, B. Y. Ooi, S. Y. Liew, and S. Shirmohammadi, “Taxi-sharing: A wireless IoT-gateway selection scheme for delay-tolerant data,” in 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, pp. 1–6, 2018.
  35. I. Indrianto, M. N. I. Susanti, R. R. A. Siregar, and Y. Purwanto, “Smart taxi security system design with internet of things (IoT),” Telkomnika, vol. 17, no. 3, 2019.
  36. N. Davis, G. Raina, and K. Jagannathan, “A multi-level clustering approach for forecasting taxi travel demand,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), IEEE, pp. 223–228, 2016.
  37. C. Tsigkanos, M. Garriga, L. Baresi, and C. Ghezzi, “Cloud deployment tradeoffs for the analysis of spatially-distributed systems of internet-of-things,” 2020, arXiv preprint arXiv:2004.11428.