Application of artificial neural networks for shortterm prediction of container train flows in direction of China – Europe via Kazakhstan

• Autorzy: Abdirassilov Z. , Sładkowski A.

Identyfikatory

DOI

10.20858/tp.2018.13.4.10

• Języki publikacji: EN

Abstrakty

EN

International container transport plays an important role in the exchange of goods between China and Europe, and accordingly, the efficiency of the transportation increases with the organization of special container lines (land and sea). Owing to its geographical location, the territory of Kazakhstan has become one of the main international landlines for passage of container cargo in recent years. Priority is given to solution of such problems as reduction of cargo delivery time, simplification of customs operations, setting attractive and competitive tariffs, ensuring a high degree of cargo safety, development of transport infrastructure, assessment of the transit potential of railway network of the country, and predicting future cargo flows. This article shows the use of artificial neural networks (ANN) for predicting container train flows in the direction of China – Europe. For this purpose, a three-layer perceptron with a learning algorithm, based on the back-propagation of the error signal, was used. A concreto example shows how the ANN training process is conducted and how the adjustable parameters are selected.

Słowa kluczowe

EN

container train; international transport corridor; artificial neural networks

PL

pociąg kontenerowy; międzynarodowy korytarz transportowy; sztuczna sieć neuronowa

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2018
• Tom: T. 13, z. 4
• Strony: 103--113
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Abdirassilov Z.

• Kazakh Academy of Transport and Communications named after M. Tynyshpayev Shevchenko, 97, Almaty, 050012, Kazakhstan

autor

Sładkowski A.

• Silesian University of Technology, Faculty of Transport Krasińskiego, 8, Katowice, 40-019, Poland

Bibliografia

• 1. Главная тема: Соединяя восток и запад. Available at: http://transexpress.kz/ru/magazines.php?id=494. [In Russian: The main theme: Connecting East and West].
• 2. Mrówczyńska, B. & Łachacz, K. & Haniszewski, T. & Sładkowski, A. A comparison of forecasting the results of road transportation needs. Transport. 2012. Vol. 27. No. 1. P. 73-78. ISSN 1648-4142.
• 3. Mikluščák, T. & Gregor, M. & Janota A. Using Neural Networks for Route and Destination Prediction in Intelligent Transport Systems. In: Mikulski, J. (ed.) Telematics in the Transport Environment. TST 2012. Communications in Computer and Information Science. Springer, Berlin, Heidelberg. 2012. Vol. 329. P. 380-387. ISBN 978-3-642-34049-9.
• 4. Amita, J. & Jain, S.S. & Garg, P.K. Prediction of Bus Travel Time Using ANN: A Case Study in Delhi. Transportation Research Procedia. 2016. Vol. 17. P. 263-272.
• 5. Ma, X. & Dai, Z. & He, Z. & Ma, J. & Wang, Y. & Wang, Y. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors. 2017. Vol. 17. No. 4. E818. P. 1-16.
• 6. Сладковски, А. Контейнерные перевозки Запад – Восток, Восток – Запад. In: Миндур, М. (ред.) Транспорт в товарообмене между Европой и Азией. Варшава – Радом: IteE – PIB. 2011. P. 254-283. [In Russian: Sladkowski, A. Container shipments West – East, East – West. In: Mindur, M. (ed.) Transport in the exchange of goods between Europe and Asia. Warsaw –Radom: IteE – PIB].
• 7. Паршина, Р.И. Развитие транзитных и международных контейнерных перевозок. Экспедирование и логистика. 2004. No. 2. P. 14-18. [In Russian: Parshina, R.I. Development of transit and international container transportation. Forwarding and logistics].
• 8. Шелковый путь: успех в решении логистических проблем. Available at:https://kapital.kz/expert/58535/shelkovyj-put-uspeh-v-reshenii-logisticheskih-problem.html [In Russian: Silk Road: success in solving logistics problems].
• 9. Der-Horng, L. & Jian Gang, J. & Jiang Hang, C. Schedule Template Design and Storage Allocation for Cyclically Visiting Feeders in Container Transshipment Hubs. Transportation Research Record. 2012. No. 2273. P. 87-95.
• 10. КТЖ добился лучших показателей по скорости контейнерных поездов на маршруте Китай – Европа – Китай. Available at: http://www.inform.kz/ru/ktzh-dobilsya-luchshihpokazateley-po-skorosti-konteynernyh-poezdov-na-marshrute-kitay-evropa-kitay_a2811543. [In Russian: KTZh achieved the best rates for the speed of container trains on the route China –Europe – China].
• 11. Стратегия развития акционерного общества Национальная компания «Қазақстан темір жолы» до 2025 года. Утверждена решением Совета директоров АО НК «ҚТЖ» от 26 ноября 2015 года, No 11. Available at: https://ktzh-gp.kz/upload/strategiya_razvitiya_ktzh.pdf. [In Russian: The development strategy of the joint-stock company National company “Kazakhstan temir zholy” until 2025. Approved by the decision of the Board of Directors of JSC NC “KTZh” on November 26, 2015].
• 12. Эффективную методику организации контейнерных поездов Казахстана предложили ученые КазАТУ им. М. Тынышпаева. Available at: http://www.ncste.kz/ru/news/effektivnuyumetodikuorganizacii-konteynernyh-poezdov-kazahstana-predlozhili-uchenye [In Russian: The scientists of KazATU named after M. Tynyshpaev proposed effective method of organization of container trains in Kazakhstan].
• 13. Ускоренные контейнерные поезда. Available at: http://swiftrus.ru/uslugi/uskorennye/ [In Russian: Accelerated container trains].
• 14. Развитие транзитного потенциала. Available at: https://railways.kz/ru/node/969 [In Russian: Development of transit potential].
• 15. Address by the President of the Republic of Kazakhstan, Leader of the Nation, N.Nazarbayev “Strategy Kazakhstan-2050”: new political course of the established state”. Available at: http://www.akorda.kz/en/events/astana_kazakhstan/participation_in_events/address-by-thepresident-of-the-republic-of-kazakhstan-leader-of-the-nation-nnazarbayev-strategy-kazakhstan-2050-new-political-course-of-the-established-state-1.
• 16. Haykin, S. Neural Networks and Learning Machines. Third Edition. New York: Prentice Hall. 2009. 937 p. ISBN 0-13-147139-2.
• 17. Mennon, A. & Mehrotra, K. & Mohan, C.K. & Ranka S. Characterization of class of sigmoid functions with application to neural networks. Neural Networks. 1996. Vol. 9. P. 819-835.
• 18. Дьяконов, В.П. MATLAB. Полный самоучитель. Москва: ДМК Пресс. 2012. 768 p. ISBN 978-5-94074-652-2. [In Russian: D’yakonov, V.P. MATLAB. Complete self-study book. Moscow: DMK Press].
• 19. Потемкин, В.Г. & Медведев, В.С. Нейронные сети. MATLAB 6. Москва: Диалог-МИФИ. 2002. 496 p. ISBN 5-86404-135-1. [In Russian: Potemkin, V.G. & Medvedev, V.S. Neural networks. MATLAB 6. Moscow: DIALOG-MIFI].
• 20. Shramenko, N.Y. Methodological aspect of substantiating the feasibility of intermodal technology for delivery of goods in the international traffic. Науковий вісник НГУ. 2017. No. 4. P. 145-150.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).