Ship arrival prediction and its value on daily container terminal operation

Ocean Engineering - Tập 157 - Trang 73-86 - 2018
Jingjing Yu1, Guolei Tang1, Xiangqun Song1, Xuhui Yu1, Yue Qi2, Da Li1, Yong Zhang1
1State Key Laboratory of Coastal and Offshore Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116023, Liaoning, China
2Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China

Tài liệu tham khảo

Al-Allaf, 2010, Improving the performance of backpropagation neural network algorithm for image compression/decompression system, J. Comput. Sci., 6, 1347, 10.3844/jcssp.2010.1347.1354 Balabin, 2011, Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data, Anal. Chim. Acta, 692, 63, 10.1016/j.aca.2011.03.006 Breiman, 2001, Random forest, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Breiman, 1984 Brinkmann, 2011, Operations system of container terminals: a compendious overviews, 25 Charles, 2014, Automatic and efficient human pose estimation for sign language videos, Int. J. Comput. Vis., 110, 70, 10.1007/s11263-013-0672-6 Cohen, 1960, A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37, 10.1177/001316446002000104 Cutler, 2007, Random forests for classification in ecology, Ecology, 88, 2783, 10.1890/07-0539.1 Daganzo, 1990, Crane productivity and ship delay in ports, Transport. Res. Rec., 1 De'Ath, 2000, Classification and regression trees: a powerful yet simple technique for ecological dataanalysis, Ecology, 81, 3178, 10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2 Demirci, 2003, Simulation modelling and analysis of a port investment, Simulation, 79, 94, 10.1177/0037549703254523 Du, 2013, Study on the ship arrival pattern of container terminals, Appl. Mech. Mater., 409–410, 1197, 10.4028/www.scientific.net/AMM.409-410.1197 Eng, 2014, Predicting host tropism of influenza a virus proteins using random forest, BMC Med. Genet., 73 Fancello, 2011, Prediction of arrival times and human resources allocation for container terminal, Marit. Econ. Logist., 13, 142, 10.1057/mel.2011.3 Felicisimo, 2013, Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study, Landslides, 10, 175, 10.1007/s10346-012-0320-1 Gandhiraj, 2007, Auditory-based wavelet packet filterbank for speech recognition using neural work, 666 Gupta, 2014, An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins, Nucleic Acids Res., 42, 4800, 10.1093/nar/gku132 Hartmann, 2011, Simulation of container ship arrivals and quay occupation', 135 Heung, 2014, Predictive soil parent material mapping at a regional-scale: a Random Forest approach, Geoderma, 214–215, 141, 10.1016/j.geoderma.2013.09.016 Idris, 2013, Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification, Appl. Intell., 39, 659, 10.1007/s10489-013-0440-x Kermani, 2005, Performance of the Levenberg-Marquardt neural network training method in electronic nose applications, Sensor. Actuator. B Chem., 110, 13, 10.1016/j.snb.2005.01.008 Kia, 2002, Investigation of port capacity under a new approach by computer simulation, Comput. Ind. Eng., 42, 533, 10.1016/S0360-8352(02)00051-7 Kozan, 1997, Comparison of analytical and simulation planning models of seaport container terminals, Transport. Plann. Technol., 20, 235, 10.1080/03081069708717591 Landis, 1977, Measurement of observer agreement for categorical data, Biometrics, 33, 159, 10.2307/2529310 Lee, 2010, Integrated discrete berth allocation and quay crane scheduling in port container terminals, Eng. Optim., 42, 747, 10.1080/03052150903406571 Liou, 2008, Detecting hospital fraud and claim abuse through diabetic outpatient services, Health Care Manag. Sci., 11, 353, 10.1007/s10729-008-9054-y Lustgarten, 2008, Improving classification performance with discretization on biomedical datasets, 445 Malekipirbazari, 2015, Risk assessment in social lending via random forests, Expert Syst. Appl., 42, 4621, 10.1016/j.eswa.2015.02.001 Malinowska, 2014, Classification and regression tree theory application for assessment of building damage caused by surface deformation, Nat. Hazards, 73, 317, 10.1007/s11069-014-1070-2 Ministry of Transport of the People's Republic of China (MTPRC), 2014 Moorthy, 2006, Berth management in container terminal: the template design problem, OR Spectrum, 28, 495, 10.1007/s00291-006-0036-5 NBZC: NBZC. http://www.portnbzs.com.cn/Index/index/(Accessed from August 2017) Özgüven, 2013, Quay length optimization using a stochastic knapsack model. J. Waterway, port, coastal, Ocean Eng., 139, 424 Pallotta, 2013, Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction, Entropy, 15, 2218, 10.3390/e15062218 Pandey, 2012, Detecting phishing e-mails using text and data mining, 249 Pani, 2013, Delay prediction in container terminals: a comparison of machine learning methods Pani, 2014, A data mining approach to forecast late arrivals in a transhipment container terminal, Transport, 29, 175, 10.3846/16484142.2014.930714 Pani, 2015, Prediction of late/early arrivals in container terminals - a qualitative approach, Eur. J. Transport Infrastruct. Res., 15, 536 Parolas, 2016, Prediction of vessel's estimated time of arrival (ETA) using machine learning-a port of Rotterdam case study PIANC MarCom Working Group 121, 2014 Prasad, 2006, Newer classification and regression tree techniques: bagging and random forests for ecological prediction, Ecosystems, 9, 181, 10.1007/s10021-005-0054-1 Quy, 2008, Risk- and simulation-based optimization of channel depths: entrance channel of cam pha coal port, Simulation, 84, 41, 10.1177/0037549708088958 Ravi, 2008, Soft computing system for bank performance prediction, Appl. Soft Comput., 8, 305, 10.1016/j.asoc.2007.02.001 Rodriguez-Galiano, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, Isprs J. Photogramm, 67, 93, 10.1016/j.isprsjprs.2011.11.002 Rodrigues-Molins, 2014, A genetic algorithm for robust berth allocation and quay crane assignment, Prog. Artif. Intell, 2, 177, 10.1007/s13748-014-0056-3 Rojas, 1996 Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0 Rutkowski, 2013, 'Decision trees for mining data streams based on the McDiarmid's bound', IEEE Trans. Knowl. Data Eng., 25, 1272, 10.1109/TKDE.2012.66 Shabayek, 2002, A simulation model for the Kwai Chung container terminals in Hong Kong, Eur. J. Oper. Res., 140, 1, 10.1016/S0377-2217(01)00216-8 Tang, 2014, Simulation-based optimization for generating the dimensions of a dredged coastal entrance channel, Simulation, 90, 1059, 10.1177/0037549714540954 Tang, 2016, Effect of entrance channel dimensions on berth occupancy of container terminals, Ocean Eng., 117, 174, 10.1016/j.oceaneng.2016.03.047 Tang, 2010, Value of medium-range precipitation forecasts in inflow prediction and hydropower optimization, Water Resour. Manag., 24, 2721, 10.1007/s11269-010-9576-1 Tran, 2009, Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems, Expert Syst. Appl., 36, 9378, 10.1016/j.eswa.2009.01.007 WEKA: WEKA. http://www.cs.waikato.ac.nz/ml/weka/(Accessed from August 2017) Wang, 2012, Automatic authentication and distinction of Epimedium koreanum and Epimedium wushanense with HPLC fingerprint analysis assisted by pattern recognition techniques, Biochem. Systemat. Ecol., 40, 138, 10.1016/j.bse.2011.10.014 Wu, 2015, Arrival discipline of late flight based on container liner operation and its application, J. Dalian Marit. Univ., 41, 77 Wu, 2001, A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, IEEE Trans. Fuzzy Syst., 9, 578, 10.1109/91.940970 Xu, 2012, Robust berth scheduling with uncertain vessel delay and handling time, Ann. Oper. Res., 192, 123, 10.1007/s10479-010-0820-0 Yu, 2007, Modern container terminal management. China communication, beijing.Al-allaf, O.N.A., (2010). 'Improving the performance of backpropagation neural network algorithm for image compression/decompression system, J. Comput. Sci., 6, 1347