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Travel Time Forecasting Based on Fuzzy Patterns

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Języki publikacji
EN
Abstrakty
EN
Estimating travel time is one of the most important processes in logistics as well as in everyday life. In particular, when it comes to transportation services, efficient time management can be a competitive advantage, not to mention customer satisfaction, which can be easily translated into business success. Therefore, in this study we analyze various travel time estimation methods in combination with a well-known Fuzzy C-Means clustering algorithm. The proposed FCM-based solution has significant advantages, allowing for the determination of the optimal travel time. In an extensive numerical experiment, we present the application of the proposed method to estimate the time of a taxi trip around New York. Due to division of the city area into detailed areas and taking into account information about the travel time in the analysis, a model was obtained, that perfectly forecasts speed of taxi travel. In this study we consider various, competitive approaches to build such a model.
Twórcy
  • Department of Computer Science, Lublin University of Technology, ul. Nadbystrzycka 36B, 20-618 Lublin, Poland
  • Faculty od Technology Fundamentals, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland
  • Faculty od Technology Fundamentals, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland
  • Faculty od Technology Fundamentals, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland
Bibliografia
  • 1. Guo G., Zhang T. A residual spatio-temporal architecture for travel demand forecasting. Transp. Res. Part C Emerg. Technol. 2020;115:102-639.
  • 2. Li Y., Lu J., Zhang L. Zhao Y. Taxi booking mobile app order demand prediction based on short-term traffic forecasting. Transp. Res. Rec. 2017;2634(1):57–68.
  • 3. Park D., Rilett L. R., Han G. Spectral basis neural networks for real-time travel time forecasting. J. Transp. Eng. 1999;125(6):515–523.
  • 4. Yang M., Liu Y., You Z. The reliability of travel time forecasting. IEEE Trans. Intell. Transp. Syst.11. 2009;1:162–171.
  • 5. Liao S., Zhou L., Di X., Yuan B., Xiong J. Largescale short-term urban taxi demand forecasting using deep learning, of23rd Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE. 2018;428–433.
  • 6. Luo H., Cai J., Zhang K., Xie R., Zheng L. A multitask deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences. J. Traffic Transp. Eng. (Engl. Ed.). 2020.
  • 7. Rodrigues F., Markou I., Pereira F.C. Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach. Inf. Fusion. 2019;49:120–129.
  • 8. Zhang Z., Wang Y., Chen P., He Z., Yu G. Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns. Transp. Res. Part C Emerg. Technol. 2017;85:476–493.
  • 9. Davis N., Raina G., Jagannathan K. A multi-level clustering approach for forecasting taxi travel demand, IEEE of 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE. 2016;223–228.
  • 10. Chintakayala P.K. & Maitra B. Modeling generalized cost of travel and its application for improvement of taxies in Kolkata, Journal of Urban Planning and Development. 2010;136(1):42–49.
  • 11. Faghih-Imani A., Anowar S., Miller E.J., Eluru N. Hail a cab or ride a bike? A travel time comparison of taxi and bicycle-sharing systems in New York City, Transportation Research Part A: Policy and Practice. 2017;101(C):11–21.
  • 12. Markou I., Rodrigues F., Pereira F.C. Real-time taxi demand prediction using data from the web, 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE. 2018;1664–1671.
  • 13. Tang J., Zhang S., Chen X., Liu F., Zou Y. Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city – China, Physica A. 2018;493(C):430–443.
  • 14. Nie Q., Xia J., Qian Z., An C. Cui Q. Use of multisensor data in reliable short-term travel time forecasting for urban roads: Dempster–Shafer approach, Transp. Res. Rec. 2015;2526(1):61–69.
  • 15. Castro P.S., Zhang D., Li S. Urban traffic modelling and prediction using large scale taxi GPS traces, in: J. Kay, P. Lukowicz, H. Tokuda, P. Olivier, and Kr¨uger A., (eds.) Springer, Pervasive Computing. 2012;7319:57–72.
  • 16. Friedman J.H. Stochastic gradient boosting, Comput. Stat. Data Anal. 2002;38(4):367–378.
  • 17. Kocev D., Vens C., Struyf J. Dˇzeroski S. Tree ensembles for predicting structured outputs, Pattern Recognit. 2013;46(3):817–833.
  • 18. Bezdek J.C., Ehrlich R., Full W. FCM: The fuzzy c-means clustering algorithm, Comput. Geosci. 1984;10(2-3):191–203.
  • 19. Donovan B., Work D. New York City Taxi Trip Data (2010-2013); 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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