Name
Abstract | ||
|---|---|---|
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 two-phase 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2632048.2632050 | UbiComp |
Keywords | Field | DocType |
ais trace,container throughput estimation,data mining,open data | Open data,Simulation,Computer science,Real-time computing,Global Positioning System,Throughput,Multimedia | Conference |
Citations | PageRank | References |
5 | 0.54 | 13 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longbiao Chen | 1 | 123 | 10.60 |
| Daqing Zhang | 2 | 3619 | 217.31 |
| Gang Pan | 3 | 1501 | 123.57 |
| Leye Wang | 4 | 551 | 36.79 |
| Xiaojuan Ma | 5 | 325 | 49.27 |
| Chao Chen | 6 | 2032 | 185.26 |
| Shijian Li | 7 | 1155 | 69.34 |