Metro correspondence matrix analysis

Mariia Nekraplonna; Dmitry Namiot

Metro correspondence matrix analysis

Mariia Nekraplonna, Dmitry Namiot

Abstract

This article is devoted to the analysis of traffic flows based on correspondence matrices. Such matrices describe the number of displacements between two points for a certain time interval. From a practical point of view, data relating to the Moscow Metro are considered. Accordingly, the correspondence matrix describes the movement between stations. Theoretically, such data describes all the characteristics of passenger traffic. In practice, it depends, of course, on the selected data processing model. Often, such matrices are used only for simple statistics, such as the number of passengers transported over time. At the same time, the space-time information interesting for digital urbanism is lost. For example, how were the trips distributed over time, how stable are these distributions, etc. The paper provides a detailed review of existing approaches to the analysis of data in correspondence matrices. As a practical task, a short-term forecast of passenger traffic is considered. It is noted that the short-term traffic forecast is a challenge that has been the subject of many research papers in the past few decades. Most of the work has been historically devoted to the analysis of traffic flows exclusively by road transport. The study of railways and, in particular, underground transport with its specificity has long been ignored. Relevant studies have been conducted only recently.

Full Text:

PDF (Russian)

References

Arem, B. v., H. R. Kirby, M. J. M. v. d. Vlist, and J. C. Whittaker. Recent Advances and Applications in the Field of Short-Term Traffic Forecasting. International Journal of Forecasting, Vol. 13, 1997.

Huisken, G., and M. v. Maarseveen. Congestion Prediction on Motorways: A Comparative Analysis. Proc., 7th World Congress on Intelligent Transport Systems, 2000.

Van Hinsbergen, C. P. I., J. W. C. Van Lint, and F. M. Sanders. Short-Term Traffic Prediction Models. Proc., 14th World Congress on Intelligent Transport Systems: ITS for a Better Life, Beijing: Research Institute of Highway, Chinese Ministry of Communications, 2007.

Wang Y., M. Papageorgiou, and A. Messmer. A Real Time Freeway Network Traffic Surveillance Tool. IEEE Transactions on Control Systems Technology, Vol. 14, No. 1, 2006.

Antoniou, C., M. E. Ben-Akiva, and H. N. Koutsopoulos. Online Calibration of Traffic Prediction Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1934, Transportation Research Board of the National Academies, Washington, D.C., 2005.

Calvert, S. C., J. W. C. Van Lint, and S. P. Hoogendoorn. A Hybrid Travel Time Prediction Framework for Planned Motorway Roadworks. Proc., 13th International IEEE Conference on Intelligent Transportation Systems, 2010.

Wu, C.-H., C.-C. Wei, D.-C. Su, M.-H. Chan, and J.-M. Ho. Travel Time Prediction with Support Vector Regression. Proc., 2003 IEEE Conference on Intelligent Transportation Systems, Shanghai, China, 2003.

Sun, H., H. X. Liu, H. Xiao, R. R. He, and B. Ran. Use of Local Linear Regression Model for ShortTerm Traffic Forecasting. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, Transportation Research Board of the National Academies, Washington, D.C., 2003.

Williams, B. M., and L. A. Hoel. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: A Theoretical Basis and Empirical Results. Journal of Transportation Engineering, Vol. 129, No. 6, 2003.

Al-Deek, H. M., M. P. D. Angelo, and M. C. Wang. Travel Time Prediction with Nonlinear Time Series. Proc., Fifth International Conference on Applications of Advanced Technologies in Transportation, Reston, Va., 1998.

Stathopoulos, A., and M. G. Karlaftis. A Multivariate State Space Approach for Urban Traffic Flow Modeling and Prediction. Transportation Research Part C: Emerging Technologies, Vol. 11, No. 2, 2003.

Park, D., L. Rilett, and G. Han. Spectral Basis Neural Networks for Real-Time Travel Time Forecasting. Journal of Transportation Engineering, Vol. 125, No. 6, 1999.

Rilett, L. R., and D. Park. Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks. In Transportation Research Record: Journal of the Transportation Research Board, No. 1752, Transportation Research Board of the National Academies, Washington, D.C., 2001.

Nikovski, D., N. Nishiuma, Y. Goto, and H. Kumazawa. Univariate Short-Term Prediction of Road Travel Times. Proc., 8th International IEEE Conference on Intelligent Transportation Systems, 2005.

Hobeika, A. G., and C. K. Kim. Traffic-Flow-Prediction Systems Based on Upstream Traffic. Proc., 1994 Vehicle Navigation and Information Systems Conference, IEEE, 1994.

Wild, D. Short-Term Forecasting Based on a Transformation and Classification of Traffic Volume Time Series. International Journal of Forecasting, Vol. 13, 1997.

Voort, M. v. d., M. Dougherty, and S. Watson. Combining Kohonen Maps with ARIMA Time Series Models to Forecast Traffic Flow. Transportation Research, Vol. 4, No. 5, 1996.

Aboudolas, K., M. Papageorgiou, A. Kouvelas, and E. Kosmatopoulos. A Rolling-Horizon QuadraticProgramming Approach to the Signal Control Problem in Large-Scale Congested Urban Road Networks. Transportation Research Part C: Emerging Technologies, Vol. 18, No. 5, 2010.

Mahmassani, H. S. DynaSMART-X Home. 2004.

Ben-Akiva, M. E., N. Bierlaire, D. Burton, H. N. Koutsopoulos, and R. G. Mishalani. Network State Estimation and Prediction for Real-Time Transportation Management Applications. Presented at 81st Annual Meeting of the Transportation Research Board, Washington, D.C., 2002.

Nagel, K., and M. Schreckenberg. A Cellular Automaton Model for Freeway Traffic. Journal de Physique I, Vol. 2, 1992.

Chopard, B., A. Dupuis, and P. O. Luthi. A Cellular Automata Model for Urban Traffic Andits Application to the City of Geneva. Proceedings of Traffic and Granular Flow, 1997.

Wahle, J., and M. Schreckenberg. A Multi-Agent System of On-Line Simulations Based on RealWorld Traffic Data. Proc., Hawaii International Conference on System Science, IEEE, 2001.

Miska, M. P. Real-Time Traffic Management by Microscopic Online Simulation. Delft University of Technology, 2007.

Kerner, B. S. The Physics of Traffic. Springer-Verlag, 2004.

Box, G. E. P., and G. Jenkins. Time Series Analysis: Forecasting and Control. Holden-Day, 1976.

Nihan, N. L. Use of the Box and Jenkins Time Series Technique in Traffic Forecasting. Transportation, Vol. 9, 1980.

Lee, S., and D. B. Fambro. Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. In Transportation Research Record: Journal of the Transportation Research Board, No. 1678, TRB, National Research Council, Washington, D.C., 1999.

Chrobok, R., O. Kaumann, J. Wahle, and M. Schreckenberg. Different Methods of Traffic Forecast Based on Real Data. European Jorunal of Operational Research, Vol. 155, 2004.

Williams, B. M. Multivariate Vehicular Traffic Flow Prediction. In Transportation Research Record: Journal of the Transportation Research Board, No. 1776, TRB, National Research Council, Washington, D.C., 1999.

Kamarianakis, Y., and P. Prastacos. Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches. In Transportation Research Record: Journal of the Transportation Research Board, No. 1857, Transportation Research Board of the National Academies, Washington, D.C., 2003.

Yang, J.-S. Travel Time Prediction Using the GPS Test Vehicle and Kalman Filtering Techniques. Proc., 2005 American Control Conference, 2005.

Kirby, H. R., S. M. Watson, and M. S. Dougherty. Should We Use Neural Networks or Statistical Models for Short-Term Motorway Traffic Forecasting? International Journal of Forecasting, Vol. 13, 1997.

Al-Deek, H., S. Ishak, and M. Wang. A New Short-Term Traffic Prediction and Incident Detection System on I-4. Florida Department of Transportation, University of Central Florida, 2001.

Lingras, P., S. Sharma, and M. Zhong. Prediction of Recreational Travel Using Genetically Designed Regression and Time-Delay Neural Network Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1805, Transportation Research Board of the National Academies, Washington, D.C., 2002.

Alecsandru, C.-D. A Hyrbid Model-Based and Memory-Based Short-Term Traffic Prediction System. Louisiana State University and Agricultural and Mechanical College, 2003.

Park, B., C. J. Messer, and T. Urbanik II. Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Networks. In Transportation Research Record 1651, TRB, National Research Council, Washington, D.C., 1998.

Xie, Y., and Y. Zhang. A Wavelet Network Model for Short-Term Traffic Volume Forecasting. Journal of Intelligent Transportation Systems, Vol. 10, No. 3, 2006.

van Lint, J. W. C. Online Learning Solutions for Freeway Travel Time Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 9, No. 1, 2008.

Smith, B. L., B. M. Williams, and R. K. Oswald. Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting. Transportation Research Part C, Vol. 10, 2002.

Coufal, D., and E. Turunen. Short Term Prediction of Highway Travel Time Using Data Mining and Neuro-Fuzzy Methods. Neural Network World, Vol. 3–4, 2004.

Huisken, G. Soft-Computing Techniques Applied to Short-Term Traffic Flow Forecasting. Systems Analysis Modelling Simulation, Vol. 43, No. 2, 2003.

INRIX. Available at http://www.inrix.com. Accessed Jan. 3, 2007.

Wu, C.-H., J.-M. Ho, and D. T. Lee. Travel-Time Prediction with Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems, Vol. 5, No. 4, 2004.

Vvedenie v matematicheskoe modelirovanie transportnyh potokov: Uchebnoe posobie / Izdanie 2-e, ispr. i dop. A. V. Gasnikov i dr. Pod red. A. V. Gasnikova. — M.: MCNMO, 2013. — ISBN 978-5-4439-0040-7

Habarov V. I. Markovskaja model' transportnyh korrespondencij / V. I. Habarov, D. O. Molodcov, S. G. Homjakov // Doklady TUSUR. – 2012. – # 1(25). – Ch. 1. – S. 113–117.

Yang Li, Xudong Wang, Shuo Sun, Xiaolei Ma, Guangquan Lu, Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transportation Research Part C: Emerging Technologies, Vol. 77, 2017.

Yuxing Sun a, Biao Leng , Wei Guan, A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, Vol. 166, 2015.

Xiaoqing Dai, Lijun Sun, Yanyan Xu. Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach. Journal of Advanced Transportation Vol. 2018.

Chao Yang, Fenfan Yan, Xiangdong Xu. Daily metro origin-destination pattern recognition using dimensionality reduction and clustering methods. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Chao Yang, Fenfan Yan, Xiangdong Xu. Clustering Daily Metro Origin-Destination Matrix in Shenzhen China. Applied Mechanics and Materials, Vol. 743, 2015.

Namiot, Dmitry, Oleg Pokusaev, and Vasily Kupriyanovsky. "On railway stations statistics in Smart Cities." International Journal of Open Information Technologies 7.4 (2019): 19-24.

Namiot, Dmitry, Oleg Pokusaev, and Varvara Lazutkina. "On passenger flow data models for urban railways." International Journal of Open Information Technologies 6.3 (2018): 9-14.

Refbacks

There are currently no refbacks.

Abava Кибербезопасность IT Congress 2024

ISSN: 2307-8162