| Abstrakt: |
Quantifying waterway traffic characteristics based on Automatic Identification System (AIS) data is beneficial to understand and improve traffic conditions. In this paper, a nested loop algorithm is first presented to segment a waterway and to separate vessel trips, where trip directions are categorized to inbound, outbound, and stop status. Next, to increase computational efficiency, the corresponding vectorized algorithm, which rebuilds the AIS data as an array-based structure, is developed, and traffic features such as travel speed, traffic density, traffic flow, trip attraction, trip generation, and origin-destination (O-D) matrices are extracted. Finally, the methodology is applied to the Houston Ship Channel (HSC) as an implementation instance with one month of AIS data, and the traffic features are quantified for different types of vessels with different widths and drafts at different segments. The vectorized algorithm, along with the trip separation, has considerably decreased processing time compared to the loop-based methods. The results of such analysis can be used for short-term operational planning, such as resource allocation and scheduling, or for long-term waterway projects, such as expansions. [ABSTRACT FROM AUTHOR] |
