Use of Data from Automatic Identification Systems to Generate Inland Waterway Trip Information

Abstract

Data from archived automatic identification systems in the Paducah, Kentucky, region were analyzed to produce reliable trip data for inland waterway vessels. Because of confidentiality concerns, few options for finding such trip data exist; this lack of data affects the quality of risk calculations. A combination of geographic information systems, relational databases, custom programming, and data visualization tools was applied to extract meaningful vessel traffic information and to detect events occurring within ports and waterways. The geographic configuration of the Paducah port area made the generation of trip data more difficult. However, this problem was overcome by the categorization of all trips into general river movements and the calculation of the total number of towboat trips transiting in the area through river movements or engagement in fleeting, docking, or lockage operations. The data from automatic identification systems were discovered to be of high quality and capable of supporting many analyses. These analyses included waterway and port congestion, hotspot identification, accident reconstruction (and near-miss investigation), and the impact of extreme weather on port and waterway traffic.

GIS-based identification and visualization of multimodal freight transportation catchment areas

“…Onboard navigation devices transmit location and characteristics of vessels in real time to receivers on shore, satellite, buoy, and other vessels (U.S. Coast Guard n.d.). In the U.S., AIS is mandatory along the Ohio River, between Mileposts 593 and 606, and in the Lower Mississippi River, up to Milepost 254.5 (Dobbins and Langsdon 2013). Even though AIS is not required in all U.S. inland waterways, most vessels use the AIS transponder (DiJoseph and Mitchell 2015).…”

Safety management of waterway congestions under dynamic risk conditions—A case study of the Yangtze River

“…The fuzzy decision making matrix R is constructed in terms of n criteria and m RCOs. (12) (3) Establish the normalised fuzzy decision matrix.…”

“…Vessel traffic management systems in Europe inland waterways were assessed to tackle the problems of increasing waiting time at locks and inland ports due to traffic congestion and to explore the optimal use of the available capacity of inland shipping [11] . Valid trip data for inland waterway vessels were extracted from Automatic Identification System (AIS) in the Paducah for supporting analysis on port congestion in combination with other databases [12] . Besides, there was also research focusing on dealing with congestion issues from a management perspective.…”

Strategic Modelling of Passenger Transport in Waterways: The Case of the Magdalena River

“…Very few studies on passenger transport by inland waterways have been reported in the literature. For instance, Dobbins and Langsdon (2013) reported the use of data from automatic identification systems to generate inland waterway trip information, allowing the information to be used to make risk analyses. Recently, environmental concerns and perceptions about comfort and safety have been introduced in the studies using hybrid mode, choicelatent variable modelling (Kim et al 2012;Márquez et al 2014).…”