Study of narrow waterways congestion based on automatic identification system (AIS) data: A case study of Houston Ship Channel

Abstract

Using automatic identification system (AIS) data, this article first has extended the definition of three widely used roadway congestion indices to maritime transportation systems (MTS), traffic speed index (TSI), traffic rate index (TRI), and dwell time index (DTI). Next, a methodology is developed to measure the indices based on AIS data, considering various factors, including path geometry, time of day, and the type and size of vessels, and finally the method has been applied to the AIS data of the Houston Ship Channel (HSC) to evaluate the applicability in real cases. The results show that although average TSI and TRI cannot represent waterway congestion, the real-time values (rather than the average) at the micro level can help finding location, time, and severity of traffic congestion. Besides, while TSI and TRI have shortcomings, both average and real-time dwell time index (DTI) can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels. When congestion happens at some narrow waterways, vessels need to wait at sea buoy or docks, thus dwell time index (DTI) can quantify traffic congestion better than in-transit indices such as travel speed, TSI. According to HSC DTI, most tankers experience long waiting times at the sea buoy and Galveston Bay, while cargo vessels experience delays at Bayport and Barbour's Cut terminals. This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare congestion for national competing projects at different waterways.

Keywords

• Maritime transport system
• Waterway congestion
• Quantifying congestion
• AIS data
• Houston Ship Channel