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This study proposes high-frequency container port congestion measures based on Automatic Identification System (AIS) data. Vessel movement information of 3,957 container ships from March 2017 to April 2017 is included. The world top 20 container ports' berth and anchorage areas are identified through Density Based Spatial Clustering of Applications with Noise (DBSCAN) and convex hull methods, and their hourly port congestion statuses are depicted in terms of the traffic volume and turnaround time. The constructed congestion measures overcome the disadvantages of the traditionally used port or industry data, which is heterogenous, behind the time and not easy to obtain publicly. The higher frequency (hourly) of the proposed measures can effectively monitor any slight change in port performance. A Long Short-Term Memory (LSTM) neural network model is then proposed for congestion prediction using constructed congestion measures. Point prediction and sequence prediction are both performed. We innovatively introduce congestion propagation effects into the prediction model as input features. Using Shanghai, Singapore and Ningbo ports as case studies, results show that the inclusion of congestion propagation effect can improve the prediction performance especially for sequence prediction. This study provides significant implications and decision support for container shipping market participants. [ABSTRACT FROM AUTHOR]

This study aims to model the complex relationship among the traffic characteristics (i.e. traffic flow, traffic speed, traffic density), environmental conditions (i.e. darkness condition, weather condition and traffic lane width) and frequency of ship collisions in the Yangtze River estuary areas. Taking into account the effects of ship approach angle, ship type, relative speed and darkness condition on the capability of ship collision avoidance, a dynamic ship domain model has been presented to measure ship collision frequency. Considering the big variation associated with the collision frequency, the measured ship collision frequency is further modeled as a loglogistic-distributed function of traffic characteristics and environmental conditions. Results show that the collision frequency is greater for (i) the bigger traffic flow; (ii) the bigger traffic density; (iii) the narrower traffic lane width; and (iv) the adverse weather condition. The darkness condition is the influencing factor that produces the biggest effect on the ship collision frequency. [ABSTRACT FROM AUTHOR]