Forecasting Short-term Container Vessel Traffic Volume Using Hybrid ARIMA-NN Model

Sadeghi Gargari, Negar; Akbari, Hassan; Panahi, Roozbeh

doi:10.29252/ijcoe.3.3.47

Forecasting Short-term Container Vessel Traffic Volume Using Hybrid ARIMA-NN Model

Authors

Tarbiat Modares University

10.29252/ijcoe.3.3.47

Abstract

A combination of linear and non-linear models results in a more accurate prediction in comparison with using linear or non-linear models individually to forecast time series data. This paper utilizes the linear autoregressive integrated moving average (ARIMA) model and non-linear artificial neural network (ANN) model to develop a new hybrid ARIMA-ANN model for prediction of container vessel traffic volume. The suggested hybrid method consists of an optimized feed-forward, back-propagation model with a hybrid training algorithm. The database of monthly traffic of Rajaee Port for thirteen years from 2005-2018 is taken into account. The performance of the developed model in forecasting short-term traffic volume is evaluated using various performance criteria such as correlation coefficient (R), mean absolute deviation (MAD), mean squared error (MSE) and mean absolute percentage error (MAPE). The developed model provides useful insights into container traffic behavior. Comparing the results with the real data-sets demonstrates the superior performance of the hybrid models than using models individually in forecasting traffic data.

Keywords

20.1001.1.25382667.2019.4.3.6.8

References

[1] Bichou K. Port operations, planning, and logistics. CRC Press; 2014 Apr 16. [2] Haiyan W, Youzhen W. Vessel traffic flow forecasting with the combined model based on support vector machine. In2015 International Conference on Transportation Information and Safety (ICTIS) 2015 Jun 25 (pp. 695-698). IEEE. https://doi.org/10.1109/ICTIS.2015.7232151 [3] Zissis D, Xidias EK, Lekkas D. Real-time vessel behavior prediction. Evolving Systems. 2016 Mar 1;7(1):29-40. https://doi.org/10.1007/s12530-015-9133-5 [4] Mostafa MM. Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management. 2004 Apr 1;31(2):139-56. https://doi.org/10.1080/0308883032000174463 [5] Nicolau V, Aiordachioaie D, Popa R. Neural network prediction of the wave influence on the yaw motion of a ship. In2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541) 2004 Jul 25 (Vol. 4, pp. 2801-2806). IEEE. https://doi.org/10.1109/IJCNN.2004.1381100 [6] Lagerweij R, de Vries G, van Someren M. Learning a model of ship movements. University of Amsterdam. 2009 Dec 24. [7] Zhou Y, Daamen W, Vellinga T, Hoogendoorn SP. Ship classification based on ship behavior clustering from AIS data. Ocean Engineering. 2019 Mar 1;175:176-87. https://doi.org/10.1016/j.oceaneng.2019.02.005 [8] Perera LP, Oliveira P, Soares CG. Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems. 2012 Mar 6;13(3):1188-200. https://doi.org/10.1109/TITS.2012.2187282 [9] Ebada A, Maksoud MA. Prediction of Ship Turning Manoeuvre Using Artificial Neural Networks. University Duisburg, Essen. 2005. [10] Simsir U, Ertugrul S. Prediction of manually controlled vessels’ position and course navigating in narrow waterways using Artificial Neural Networks. Applied Soft Computing. 2009 Sep 1;9(4):1217-24. https://doi.org/10.1016/j.asoc.2009.03.002 [11] Hajbi A. Traffic forecasting in Moroccan ports. InSupply Chain Forum: An International Journal 2011 Jan 1 (Vol. 12, No. 4, pp. 26-35). Taylor & Francis. https://doi.org/10.1080/16258312.2011.11517278 [12] Anderson DR, Sweeney DJ, Williams TA, Camm JD, Cochran JJ. Statistics for business & economics. Nelson Education; 2016 Jan 29. [13] Zhang W, Zhao S. Forecasting Research on the Total Volume of Import and Export Trade of Ningbo Port by Gray Forecasting Model. JSW. 2013 Feb 1;8(2):466-71. https://doi.org/10.4304/jsw.8.2.466-471 [14] Chou CC, Chu CW, Liang GS. A modified regression model for forecasting the volumes of Taiwan’s import containers. Mathematical and Computer Modelling. 2008 May 1;47(9-10):797-807. https://doi.org/10.1016/j.mcm.2007.05.005 [15] Lam WH, Ng PL, Seabrooke W, Hui EC. Forecasts and reliability analysis of port cargo throughput in Hong Kong. Journal of urban Planning and Development. 2004 Sep;130(3):133-44. https://doi.org/10.1061/(ASCE)0733-9488(2004)130:3(133) [16] Du S, Li T, Gong X, Yang Y, Horng SJ. Traffic flow forecasting based on hybrid deep learning framework. In2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) 2017 Nov 24 (pp. 1-6). IEEE. https://doi.org/10.1109/ISKE.2017.8258813 [17] Eslami P, Jung K, Lee D, Tjolleng A. Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm. Maritime Economics & Logistics. 2017 Aug 1;19(3):538-50. https://doi.org/10.1057/mel.2016.1 [18] Wang L, Zou H, Su J, Li L, Chaudhry S. An ARIMA‐ANN hybrid model for time series forecasting. Systems Research and Behavioral Science. 2013 May;30(3):244-59. https://doi.org/10.1002/sres.2179 [19] Zhang Y, Ye Z. Short-term traffic flow forecasting using fuzzy logic system methods. Journal of Intelligent Transportation Systems. 2008 Aug 15;12(3):102-12. https://doi.org/10.1080/15472450802262281 [20] Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003 Jan 1;50:159-75. https://doi.org/10.1016/S0925-2312(01)00702-0 [21] Box GE, Jenkins GM. Time Series Analysis: Forecasting and Control.1994 [22] Jugović A, Hess S, Poletan Jugović T. Traffic demand forecasting for port services. Promet-Traffic&Transportation. 2011 Jan 1;23(1):59-69. https://doi.org/10.7307/ptt.v23i1.149 [23] Beale MH, Hagan MT, Demuth HB. Neural network toolbox™ user’s guide. The MathWorks. 2010 Sep. [24] Agami N, Atiya A, Saleh M, El-Shishiny H. A neural network based dynamic forecasting model for Trend Impact Analysis. Technological Forecasting and Social Change. 2009 Sep 1;76(7):952-62. https://doi.org/10.1016/j.techfore.2008.12.004 [25] Skorpil V, Stastny J. Neural networks and back propagation algorithm. Electron Bulg Sozopol. 2006 Sep:20-2. [26] Nunes da Silva I, Hernane Spatti D, Andrade Flauzino R, Bartocci Liboni LH, Franco dos Reis Alves S. Artificial Neural Networks. A Practical Course. 2017. [27] Bergstra J, Bengio Y. Random search for hyper-parameter optimization. Journal of machine learning research. 2012;13(Feb):281-305. [28] Gökkuş Ü, Yıldırım MS, Aydin MM. Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society. 2017;2017. https://doi.org/10.1155/2017/2984853 [29] Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks: The state of the art. International journal of forecasting. 1998 Mar 1;14(1):35-62. https://doi.org/10.1016/S0169-2070(97)00044-7 [30] McKenzie S. Social sustainability: Towards some definitions. Systems Research and Behavioral Science. 2004;259(27):1–31. [31] Wang S, Wang S, Gao S, Yang W. Daily Ship Traffic Volume Statistics and Prediction Based on Automatic Identification System Data. In2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) 2017 Aug 26 (Vol. 2, pp. 149-154). IEEE. https://doi.org/10.1109/IHMSC.2017.149
1- Bichou K. (2014 Apr 16) Port operations, planning, and logistics. CRC Press. [DOI:10.4324/9781315850443]
1- Bichou K. (2014 Apr 16) Port operations, planning, and logistics. CRC Press. [DOI:10.4324/9781315850443]
Haiyan W, Youzhen W. (2015) Vessel traffic flow forecasting with the combined model based on support vector machine, International Conference on Transportation Information and Safety (ICTIS) (pp. 695-698). [DOI:10.1109/ICTIS.2015.7232151]
Haiyan W, Youzhen W. (2015) Vessel traffic flow forecasting with the combined model based on support vector machine, International Conference on Transportation Information and Safety (ICTIS) (pp. 695-698). [DOI:10.1109/ICTIS.2015.7232151]
Zissis D, Xidias EK, Lekkas D. (2016), Real-time vessel behavior prediction, Evolving Systems, 7(1):29-40. [DOI:10.1007/s12530-015-9133-5]
Zissis D, Xidias EK, Lekkas D. (2016), Real-time vessel behavior prediction, Evolving Systems, 7(1):29-40. [DOI:10.1007/s12530-015-9133-5]
Mostafa MM. (2004), Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management.;31(2):139-56. [DOI:10.1080/0308883032000174463]
Mostafa MM. (2004), Forecasting the Suez Canal traffic: a neural network analysis. Maritime Policy & Management.;31(2):139-56. [DOI:10.1080/0308883032000174463]
Nicolau V, Aiordachioaie D, Popa R. (2004), Neural network prediction of the wave influence on the yaw motion of a ship. IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), (Vol. 4, pp. 2801-2806).
Nicolau V, Aiordachioaie D, Popa R. (2004), Neural network prediction of the wave influence on the yaw motion of a ship. IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), (Vol. 4, pp. 2801-2806).
Lagerweij R, de Vries G, van Someren M. (2009), Learning a model of ship movements. University of Amsterdam.
Lagerweij R, de Vries G, van Someren M. (2009), Learning a model of ship movements. University of Amsterdam.
Zhou Y, Daamen W, Vellinga T, Hoogendoorn SP., (2019), Ship classification based on ship behavior clustering from AIS data. Ocean Engineering. 2019, 175:176-87. [DOI:10.1016/j.oceaneng.2019.02.005]
Zhou Y, Daamen W, Vellinga T, Hoogendoorn SP., (2019), Ship classification based on ship behavior clustering from AIS data. Ocean Engineering. 2019, 175:176-87. [DOI:10.1016/j.oceaneng.2019.02.005]
Perera LP, Oliveira P, Soares CG., (2012), Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems.;13(3):1188-200. [DOI:10.1109/TITS.2012.2187282]
Perera LP, Oliveira P, Soares CG., (2012), Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction. IEEE Transactions on Intelligent Transportation Systems.;13(3):1188-200. [DOI:10.1109/TITS.2012.2187282]
Ebada A, Maksoud MA., (2005), Prediction of Ship Turning Manoeuvre Using Artificial Neural Networks. University Duisburg, Essen.
Ebada A, Maksoud MA., (2005), Prediction of Ship Turning Manoeuvre Using Artificial Neural Networks. University Duisburg, Essen.
Simsir U, Ertugrul S., (2009), Prediction of manually controlled vessels' position and course navigating in narrow waterways using Artificial Neural Networks. Applied Soft Computing; 9(4):1217-24. [DOI:10.1016/j.asoc.2009.03.002]
Simsir U, Ertugrul S., (2009), Prediction of manually controlled vessels' position and course navigating in narrow waterways using Artificial Neural Networks. Applied Soft Computing; 9(4):1217-24. [DOI:10.1016/j.asoc.2009.03.002]
Hajbi A., (2011), Traffic forecasting in Moroccan ports; Supply Chain Forum: An International Journal (Vol. 12, No. 4, pp. 26-35). [DOI:10.1080/16258312.2011.11517278]
Hajbi A., (2011), Traffic forecasting in Moroccan ports; Supply Chain Forum: An International Journal (Vol. 12, No. 4, pp. 26-35). [DOI:10.1080/16258312.2011.11517278]
Anderson DR, Sweeney DJ, Williams TA, Camm JD, Cochran JJ., (2016), Statistics for business & economics. Nelson Education.
Anderson DR, Sweeney DJ, Williams TA, Camm JD, Cochran JJ., (2016), Statistics for business & economics. Nelson Education.
Zhang W, Zhao S., (2013), Forecasting Research on the Total Volume of Import and Export Trade of Ningbo Port by Gray Forecasting Model. JSW;8(2):466-71. [DOI:10.4304/jsw.8.2.466-471]
Zhang W, Zhao S., (2013), Forecasting Research on the Total Volume of Import and Export Trade of Ningbo Port by Gray Forecasting Model. JSW;8(2):466-71. [DOI:10.4304/jsw.8.2.466-471]
Chou CC, Chu CW, Liang GS., (2008), A modified regression model for forecasting the volumes of Taiwan's import containers. Mathematical and Computer Modelling;47(9-10):797-807. [DOI:10.1016/j.mcm.2007.05.005]
Chou CC, Chu CW, Liang GS., (2008), A modified regression model for forecasting the volumes of Taiwan's import containers. Mathematical and Computer Modelling;47(9-10):797-807. [DOI:10.1016/j.mcm.2007.05.005]
Lam WH, Ng PL, Seabrooke W, Hui EC., (2004), Forecasts and reliability analysis of port cargo throughput in Hong Kong. Journal of urban Planning and Developmen;130(3):133-44. [DOI:10.1061/(ASCE)0733-9488(2004)130:3(133)]
Lam WH, Ng PL, Seabrooke W, Hui EC., (2004), Forecasts and reliability analysis of port cargo throughput in Hong Kong. Journal of urban Planning and Developmen;130(3):133-44. [DOI:10.1061/(ASCE)0733-9488(2004)130:3(133)]
Du S, Li T, Gong X, Yang Y, Horng SJ., (2017), Traffic flow forecasting based on hybrid deep learning framework; 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE(و (pp. 1-6). [DOI:10.1109/ISKE.2017.8258813]
Du S, Li T, Gong X, Yang Y, Horng SJ., (2017), Traffic flow forecasting based on hybrid deep learning framework; 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE(و (pp. 1-6). [DOI:10.1109/ISKE.2017.8258813]
Eslami P, Jung K, Lee D, Tjolleng A., (2017), Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm. Maritime Economics & Logistics;19(3):538-50. 1 [DOI:10.1057/mel.2016.1]
Eslami P, Jung K, Lee D, Tjolleng A., (2017), Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm. Maritime Economics & Logistics;19(3):538-50. 1 [DOI:10.1057/mel.2016.1]
Wang L, Zou H, Su J, Li L, Chaudhry S., (2013), An ARIMA‐ANN hybrid model for time series forecasting. Systems Research and Behavioral Science.;30(3):244-59. [DOI:10.1002/sres.2179]
Wang L, Zou H, Su J, Li L, Chaudhry S., (2013), An ARIMA‐ANN hybrid model for time series forecasting. Systems Research and Behavioral Science.;30(3):244-59. [DOI:10.1002/sres.2179]
Zhang Y, Ye Z., (2008), Short-term traffic flow forecasting using fuzzy logic system methods. Journal of Intelligent Transportation Systems;12(3):102-12. [DOI:10.1080/15472450802262281]
Zhang Y, Ye Z., (2008), Short-term traffic flow forecasting using fuzzy logic system methods. Journal of Intelligent Transportation Systems;12(3):102-12. [DOI:10.1080/15472450802262281]
Zhang GP., (2003), Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing; 50:159-75. [DOI:10.1016/S0925-2312(01)00702-0]
Zhang GP., (2003), Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing; 50:159-75. [DOI:10.1016/S0925-2312(01)00702-0]
Box GE, Jenkins GM., (1994), Time Series Analysis: Forecasting and Control.
Box GE, Jenkins GM., (1994), Time Series Analysis: Forecasting and Control.
Jugović A, Hess S, Poletan Jugović T., (2011), Traffic demand forecasting for port services. Promet-Traffic & Transportatio;23(1):59-69. [DOI:10.7307/ptt.v23i1.149]
Jugović A, Hess S, Poletan Jugović T., (2011), Traffic demand forecasting for port services. Promet-Traffic & Transportatio;23(1):59-69. [DOI:10.7307/ptt.v23i1.149]
Beale MH, Hagan MT, Demuth HB., (2010), Neural network toolbox™ user's guide. The MathWorks.
Beale MH, Hagan MT, Demuth HB., (2010), Neural network toolbox™ user's guide. The MathWorks.
Agami N, Atiya A, Saleh M, El-Shishiny H., (2009), A neural network based dynamic forecasting model for Trend Impact Analysis. Technological Forecasting and Social Change;76(7):952-62. [DOI:10.1016/j.techfore.2008.12.004]
Agami N, Atiya A, Saleh M, El-Shishiny H., (2009), A neural network based dynamic forecasting model for Trend Impact Analysis. Technological Forecasting and Social Change;76(7):952-62. [DOI:10.1016/j.techfore.2008.12.004]
Skorpil V, Stastny J., (2006), Neural networks and back propagation algorithm. Electron Bulg Sozopol;20-2.
Skorpil V, Stastny J., (2006), Neural networks and back propagation algorithm. Electron Bulg Sozopol;20-2.
Nunes da Silva I, Hernane Spatti D, Andrade Flauzino R, Bartocci Liboni LH, Franco dos Reis Alves S., (2017), Artificial Neural Networks. A Practical Course. [DOI:10.1007/978-3-319-43162-8]
Nunes da Silva I, Hernane Spatti D, Andrade Flauzino R, Bartocci Liboni LH, Franco dos Reis Alves S., (2017), Artificial Neural Networks. A Practical Course. [DOI:10.1007/978-3-319-43162-8]
Bergstra J, Bengio Y., (2012), Random search for hyper-parameter optimization. Journal of machine learning research;13(Feb):281-305.
Bergstra J, Bengio Y., (2012), Random search for hyper-parameter optimization. Journal of machine learning research;13(Feb):281-305.
Gökkuş Ü, Yıldırım MS, Aydin MM., (2017), Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society. [DOI:10.1155/2017/2984853]
Gökkuş Ü, Yıldırım MS, Aydin MM., (2017), Estimation of container traffic at seaports by using several soft computing methods: a case of Turkish Seaports. Discrete Dynamics in Nature and Society. [DOI:10.1155/2017/2984853]
Zhang G, Patuwo BE, Hu MY., (1998), Forecasting with artificial neural networks: The state of the art. International journal of forecasting;14(1):35-62. [DOI:10.1016/S0169-2070(97)00044-7]
Zhang G, Patuwo BE, Hu MY., (1998), Forecasting with artificial neural networks: The state of the art. International journal of forecasting;14(1):35-62. [DOI:10.1016/S0169-2070(97)00044-7]
McKenzie S., (2004), Social sustainability: Towards some definitions. Systems Research and Behavioral Science;259(27):1-31.
McKenzie S., (2004), Social sustainability: Towards some definitions. Systems Research and Behavioral Science;259(27):1-31.
Wang S, Wang S, Gao S, Yang W., (2017), Daily Ship Traffic Volume Statistics and Prediction Based on Automatic Identification System Data, 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ,(Vol. 2, pp. 149-154). [DOI:10.1109/IHMSC.2017.149]
Wang S, Wang S, Gao S, Yang W., (2017), Daily Ship Traffic Volume Statistics and Prediction Based on Automatic Identification System Data, 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ,(Vol. 2, pp. 149-154). [DOI:10.1109/IHMSC.2017.149]