TY - GEN
T1 - Container throughput estimation leveraging ship GPS traces and open data
AU - Chen, Longbiao
AU - Zhang, Daqing
AU - Pan, Gang
AU - Wang, Leye
AU - Ma, Xiaojuan
AU - Chen, Chao
AU - Li, Shijian
N1 - Publisher Copyright:
Copyright © 2014 by the Association for Computing Machinery, Inc. (ACM).
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Traditionally, the port container throughput, a crucial measurement of regional economic development, was manually collected by port authorities. This requires a large amount of human effort and often delays publication of this important figure. In this paper, by leveraging ubiquitous positioning techniques and open data, we propose a twophase approach to estimation of port container throughput in real-time. First, we obtain the number of container ships arriving at berth by analyzing the ships' GPS traces. Then we estimate the throughput of each ship, in terms of number of containers transshipped, by considering the ship's berthing time, capacity, length, breadth, and crane operation performance, as extracted from different data sources. Evaluation results using real-world datasets from Hong Kong and Singapore show that the proposed approach not only estimates the container throughput quite accurately, but also outperforms the baseline method significantly.
AB - Traditionally, the port container throughput, a crucial measurement of regional economic development, was manually collected by port authorities. This requires a large amount of human effort and often delays publication of this important figure. In this paper, by leveraging ubiquitous positioning techniques and open data, we propose a twophase approach to estimation of port container throughput in real-time. First, we obtain the number of container ships arriving at berth by analyzing the ships' GPS traces. Then we estimate the throughput of each ship, in terms of number of containers transshipped, by considering the ship's berthing time, capacity, length, breadth, and crane operation performance, as extracted from different data sources. Evaluation results using real-world datasets from Hong Kong and Singapore show that the proposed approach not only estimates the container throughput quite accurately, but also outperforms the baseline method significantly.
KW - Ais trace
KW - Container throughput estimation
KW - Open data
U2 - 10.1145/2632048.2632050
DO - 10.1145/2632048.2632050
M3 - Conference contribution
AN - SCOPUS:84908566566
T3 - UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
SP - 847
EP - 851
BT - UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
PB - Association for Computing Machinery, Inc
T2 - 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2014
Y2 - 13 September 2014 through 17 September 2014
ER -