Vessel estimated time of arrival prediction system based on a path-finding algorithm

Maritime Transport Research - Tập 2 - Trang 100012 - 2021
Kikun Park1, Sunghyun Sim1, Hyerim Bae1
1Major in Industrial Data Science & Engineering, Department of Industrial Engineering, Pusan National University, Republic of Korea

Tài liệu tham khảo

UNCTAD, Review of Maritime Transport 2019, 2019, pp. 1–132. 2015 Notteboom, 2006, The time factor in liner shipping services, Maritime Econ. Logistics, 8, 19, 10.1057/palgrave.mel.9100148 Aydin, 2016, Speed optimization and bunkering in liner shipping in the presence of uncertain service times and time windows at ports, Eu. J. Operational Res., 259 ASEANLINES, Poor schedule reliability makes US import pick up scheduling impossible (8 2018). URL https://www.aseanlines.com/Show.aspx?id=4182. Hasheminia, 2017, Strategic trade-off between vessel delay and schedule recovery: an empiri- cal analysis of container liner shipping, Maritime Policy &amp, Management, 44, 458 Vernimmen, 2007, Schedule unreliability in liner shipping: Origins and con- sequences for the hinterland supply chain, Maritime Econ. Logistics, 9, 193, 10.1057/palgrave.mel.9100182 Meijer, 2017 Fransoo, 2013, The critical role of ocean container transport in global supply chain perfor- mance, Product. Operations Manage., 22, 10.1111/j.1937-5956.2011.01310.x Xu, 2012, Robust berth scheduling with uncertain vessel delay and handling time, Annals OR, 192, 123, 10.1007/s10479-010-0820-0 Dushaj, 2018 Parolas, 2016 Alessandrini, 2018, Estimated time of arrival using historical ves- sel tracking data, IEEE Trans. Intell. Transport. Syst. PP, 1 S. Ayhan, P. Costas, H. Samet, Predicting estimated time of arrival for commercial flights, 2018, pp. 33–42. doi:10.1145/3219819.3219874. Wang, 2018, A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area, Transport. Res. Part C-Emerging Technol., 95, 280, 10.1016/j.trc.2018.07.019 Fleischman, 2013, Predicting ambulance time of arrival to the emergency department using global positioning system and google maps, Prehospital Emergency Care, 17, 10.3109/10903127.2013.811562 Achar, 2019, Bus arrival time prediction: a spatial Kalman filter approach, IEEE Trans. Intell. Transp. Syst. Serry, 2017 2019 Yan, 2020, Analysis of global marine oil trade based on automatic identification system (AIS) data, J. Transport Geogr., 83, 10.1016/j.jtrangeo.2020.102637 Adland, 2017, Are AIS-based trade volume estimates reliable? The case of crude oil exports, Maritime Policy Manage., 1 Brancaccio, 2020, Geography, transportation, and endogenous trade costs, Econometrica, 88, 657, 10.3982/ECTA15455 Mou, 2010, Study on collision avoidance in busy waterways by using AIS data, Ocean Eng. Ocean Eng, 37, 483, 10.1016/j.oceaneng.2010.01.012 Zhang, 2017, A systematic approach for collision risk analysis based on AIS data, J. Navig., 70, 1, 10.1017/S0373463317000212 Liu, 2019, A novel framework for regional collision risk identification based on AIS data, Appl. Ocean Res., 89, 261, 10.1016/j.apor.2019.05.020 Wang, 2013, A spatial–temporal forensic analy- sis for inland–water ship collisions using AIS data, Saf. Sci., 57, 187, 10.1016/j.ssci.2013.02.006 van Etten, 2017, R Package gdistance: distances and routes on geographical grids, J. Stat. Soft., 76, 10.18637/jss.v076.i13 M. Noto, H. Sato, A Method for the Shortest Path Search by Extended Dijkstra Algorithm, Vol. 3, 2000. doi:10.1109/ICSMC.2000.886462. Rong, 2019, Ship trajectory uncertainty prediction based on a Gaussian Process model, Ocean Eng., 182, 499, 10.1016/j.oceaneng.2019.04.024 Uney, 2019 II, 2019 B. M. CITY, Port Logistics, Current status and overview of geographical conditions. URL https://english.busan.go.kr/bsport. S. Technology, Port of Busan, Busan port throughput. URL https://www.ship-technology.com/projects/portofbusan/.