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In the digital era, major shipping lines are developing instant quote and online booking platforms. In one of the first attempts to investigate the post-event effects of this trend, we evaluate how a shipping line's online quote platform impacts its shipper portfolio and the ordered container volume. In order to control for unobserved, time-varying effects that could be correlated with the platform's implementation, we apply the regression discontinuity design (RDD) method to the import trade data from 2016 to 2019 of a top shipping line that released its platform in August 2018. We also adopt global polynomial regression and local liner regression so as to control for the effects of different polynomial time trends. We find the container volume assigned to the shipping line declined slightly after the online platform was launched. The container orders of small shippers increase by 3.97 TEU on average after online platform adoption. The volume of assigned containers from other shippersdeclines. These findings hold fruitful implications for shipping lines. A significant increase in the number of small shippers and their container volumes demonstrate administrative cost saving and risk mitigation for a shipping line. However, it also leads to possible loss of large customers. [ABSTRACT FROM AUTHOR]

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]