Lebedeva O. A. Poltavskaya Yu. O., Gozbenko V. E. Selecting a transportation route in the metropolitan system. Modern technologies. System analysis. Modeling, 2018, Vol. 59, No. 3, pp. 76–82. DOI: 10.26731/1813-9108.2018.3(59).76-82.
Determining the transportation route in the metropolitan system is one of the ways to improve the efficiency of the railway, associated with determining the optimal number of rolling stock on the line and taking into account the intervals, which can eventually lead to a decrease in the total travel time. Metropolitan systems play an important role in meeting the demand for urban transport in major cities. The task of restoring the correspondence matrix in railway transport is important for optimal control of the transport process, which includes increasing the capacity, optimizing the timetable, and the ability to simulate in real time. The introduction of new payment systems provides an opportunity to obtain information about the time of entry / exit to the station. But the choice of the train and / or the route of transportation remains unknown. The article presents a solution to the problem, which does not require the cost of additional equipment, except for the fare accounting system, which has already been universally implemented in the railway. The analysis of existing models that determine the main factors affecting the size of the capacity of railway stations and the degree of their significance are analyzed. The analysis helped to identify the main inconsistencies in the railway network, errors that may arise as a result of data collection, and ways of their calibration. As a result, it is proposed to use a probabilistic model that can be used to carry out an experimental analysis of the restoration of the correspondence matrix.
1. Kripak M.N., Kolesnik A.I. Problemy i perspektivy razvitiya transportnoi infrastruktury v sovremennykh gorodakh [Problems and prospects for the development of transport infrastructure in modern cities]. Sbornik nauchnykh trudov Angarskogo gosudarstvennogo tekhnicheskogo universiteta [Proceedings of the Angarsk State Technical University], 2014, Vol. 1, pp. 194–198.
2. Kripak M.N., Lebedeva O.A. Otsenka sostoyaniya ulichno-dorozhnoi seti krupnogo goroda [Assessment of the state of the road network of a large city]. Sovremennye tekhnologii. Sistemnyi analiz. Modelirovanie [Modern technologies. System analysis. Modeling], 2016, No. 3 (51), pp. 171–174.
3. Gozbenko V.E., Kripak M.N., Pashkova A.S., Ivankov A.N. Metody prognozirovaniya i optimizatsii transportnoi seti s uchetom moshchnosti passazhiro i gruzopotokov [Methods of forecasting and optimizing the transport network taking into account passenger and cargo traffic]. IrGUPS Publ., Irkutsk, 2008, p. 76. Dep. in VINITI. 15.04.2008, No. 330-V2008.
4. Mikhailov A.Yu., Kopylova T.A. Sistema kriteriev otsenki kachestva funktsionirovaniya intermodal'nykh uzlov passa-zhirskogo transporta [The system of criteria for assessing the quality of functioning of intermodal passenger transport hubs]. Izvestiya vuzov. Investitsii. Stroitel'stvo. Nedvizhimost' [Proceedings of Universities. Investment. Construction. Real estate], 2014, No. 6 (11), pp. 73–80.
5. Matveeva M.A., Kovaleva T.S., Sharov M.I. Povyshenie kachestva funktsionirovaniya intermodal'nykh uzlov prigorodnogo zheleznodorozhnogo passazhirskogo transporta Irkutskoi aglomeratsii [Improving the quality of functioning of the intermodal nodes of the suburban railway passenger transport of the Irkutsk agglomeration]. Izvestiya vuzov. Investitsii. Stroitel'stvo. Nedvizhimost' [Proceedings of Universities. Investment. Construction. Real estate], 2014, No. 6 (11), pp. 115–122.
6. Kripak M.N., Gozbenko V.E., Kolesnik A.I. Optimizatsiya struktury transporta kak mera povysheniya effektivnosti funktsionirovaniya sistemy gorodskogo passazhirskogo transporta [Optimization of the transport structure as a measure to improve the efficiency of the urban passenger transport system]. Materialy 10-oi ezhegodnoi region. nauchno-prakt. konf. [Materials of the 10th annual region. scientific and practical conf.]. Angarsk: AGTA Publ., 2013.
7. Mikhailov A.Yu., Kopylova T.A. Razrabotka otsenochnoi shkaly prodolzhitel'nosti peresadok v intermodal'nykh uzlakh go-rodskogo passazhirskogo transporta [Development of the estimated scale of the duration of transfers in the intermodal nodes of urban passenger transport]. Vestnik IrGTU [Proceedings of Irkutsk State Technical University], 2015, No. 12 (107), pp. 258–263.
8. Lebedeva O.A. Povedencheskaya model' vybora marshruta v metropolitene [Behavioral model of the choice of route in the metro]. Sovremennye tekhnologii i nauchno-tekhnicheskii progress [Modern technologies and scientific and technical progress], 2018, Vol. 1, pp. 110–111.
9. Lebedeva O.A. Metodika primeneniya sistemy oplaty smart-kartami v obshchestvennom transporte [Methods of using the smart card payment system in public transport]. Sovremennye tekhnologii i nauchno-tekhnicheskii progress [Modern technologies and scientific and technical progress], 2018, Vol. 1, pp. 106–107.
10. Poltavskaya Yu.O. Dannye smart-kart kak istochnik informatsii o transportnykh peredvizheniyakh na obshchestvennom transporte [Smart-card data as a source of information about transport movements on public transport]. Sovremennye tekhnologii i nauchno-tekhnicheskii progress. Tezisy dokladov ezhegodnoi mezhdu-narodnoi nauchno-tekhnicheskoi konferentsii imeni V.Ya. Badenikova [Modern technologies and scientific and technical progress. Abstracts of reports of the Badenikov annual international scientific and technical conference]. Angarsk, AnGTU Publ., 2018, pp. 119–120.
11. Karelin N.I., Sharov M.I. K voprosu povysheniya effektivnosti raboty intermodal'nykh uzlov prigorodnogo zhelezno-dorozhnogo passazhirskogo transporta Irkutskoi aglomeratsii [On the issue of improving the efficiency of intermodal nodes of the suburban railway passenger transport of the Irkutsk agglomeration]. V sbornike: REZUL''TATY NAUChNYKh ISSLEDOVANII Sbornik statei Mezhdunarodnoi nauchno-prakticheskoi konferentsii [In the collection: RESULTS OF SCIENTIFIC RESEARCH Collection of articles of the International Scientific and Practical Conference]. In: Sukiasyan A.A. (ed.-in-chief), 2016, pp. 46–52.
12. Olentsevich V.A., Gozbenko V.E. Analiz prichin narusheniya bezopasnosti raboty zheleznodorozhnoi transportnoi sistemy [Analysis of the causes of violation of the safety of the railway transport system]. Sovremennye tekhnologii. Sistemnyi analiz. Modelirovanie [Modern technologies. System analysis. Modeling], 2013, No. 1 (37), pp. 180–183.
13. Kusakabe T., Iryo T., Asakura Y. Estimation method for railway passengers’ train choice behavior with smart card transaction data. Transportation, vol. 37, no. 5, pp. 731–749, 2010.
14. Sheffi Y. Urban transportation networks: Equilibirum analysis with mathematical programming methods. 1985, Englewood Cliffs, NJ: Prentice-Hall, c 1985.
15. Nakayama S., Kitamura R. Route choice model with inductive learning, pp. 63–70, 2000.
16. Bagchi M. White P. The potential of public transport smart card data. Transport Policy, vol. 12, no. 5, pp. 464–474, 2005.
17. Agard B., Morency C. Tr´epanier M. Mining public transport user behaviour from smart card data. In 12th IFAC Symposium on Information Control Problems in Manufacturing-INCOM, 2006, pp. 17–19.
18. Pelletier M.-P., Trepanier M., Morency C. Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies, vol. 19, no. 4, pp. 557–568, 2011.
19. Jin J.G., Tang L.C., Sun L., Lee D.-H. Enhancing metro network resilience via localized integration with bus services. Trans-portation Research Part E: Logistics and Transportation Review, vol. 63, pp. 17– 30, 2014.
20. Borthakur D. Hdfs architecture guide. HADOOP APACHE PROJECT http://hadoop. apache. org/common/docs/current/hdfs de-sign. pdf, 2008.