Logistics

SELECT - AI-based ETA Forecasts for Inland Navigation Logistics Chains

As part of a cooperative collaboration between the TU Berlin and various shipping companies and operators and coordinators of inland waterway and seaport terminals, the SELECT project is implementing the potential of digitalisation for inland waterway logistics chains through the use of innovative data technologies.

Project content

Motivation

In order to increase the attractiveness of inland navigation in freight transport, it is necessary to improve the efficiency and reliability of its logistics chains. Currently, there are high inefficiencies due to a lack of possibilities to synchronise the individual processes in terms of time, especially from the shipping companies, ports and adjacent transports. Despite the introduction of the Inland AIS infrastructure for tracking inland vessels a few years ago, there is still a high level of uncertainty in the planning and control of waterside logistics chains. This is due to the many dynamic influencing factors, e.g. water levels and lock utilisation, which have an effect on the transport routes of inland vessels and which do not yet allow reliable forecasts of arrival times at actor interfaces.

 

Objectives

The aim of the project is to develop an IT system for port operators and shipping companies that automatically and dynamically forecasts the transport routes of inland vessels and thus their arrival times (ETA) at inland and seaports, generates situation-specific recommendations for action for waterborne transport and port handling on the basis of this on the system side and enables a digital exchange of this information between the actors. This digital decision assistant is intended to enable the actors to select suitable actions in relation to the expected arrival time, taking into account the entire logistical process flow.

The SELECT project aims to contribute to the long-term improvement of efficiency, reliability, sustainability and IT networking of inland navigation stakeholders. At the same time, the project will create an important basis for the implementation of future data-based projects by assessing potentials and restrictions of inland navigation data.

 

Solution approach

With the help of artificial intelligence (AI) methods from the field of machine learning, an IT system is being developed that is able to dynamically and automatically determine a reliable time of arrival of inland vessels at inland ports and seaports as well as other important reference points (ETA - Estimated Time of Arrival). For this purpose, data from various actors, including transport routes, waterways, vehicles and transhipment processes, are used in the project and transferred into intelligent forecasting models. Based on a comparison of the predicted journey times with additional process and environmental information, the SELECT IT system will permanently monitor the continuation of the further transport and evaluate its effects on the overall logistical process. When inefficiencies and disruptions are detected, situation-specific measures for process planning and control are suggested to the actors, e.g. a ship-related allocation of suitable loading and unloading times as well as optimal travel speeds.

This digital decision assistant is intended in particular to enable the operators of sea and inland terminals as well as shipping companies or shipmasters to select optimal actions in relation to the expected arrival time, taking into account the further course of the logistical process. Furthermore, the technology functions as a previously missing digital interface for the transmission of the ETA, related measures and additional transport-related information between the actors involved.

Results, dates and publications

Data collection completed and first forecast models developed (03/2022)

After the requirements and process analysis carried out at the start of the project, in which relevant areas of application and design requirements of an AI-based ETA forecast were collected with the involvement of various representatives of inland navigation, the first half of the project duration was characterised by the procurement, processing and analysis of comprehensive databases. For the period from 2017 to 2019, movement data on historical vessel movements was obtained in the form of Inland AIS data for two inland navigation corridors. These are the corridors Rhine-Main area - ARA ports (via Main, Rhine) and Berlin - Hamburg (via Mittellandkanal, Elbeseitenkanal, Elbe) with the corresponding intermediate ports, which comprise the pilot relations of the project and allow an investigation of natural and artificial waterways (see figure). In addition to the AIS data, several public data sources such as weather data, river levels and notices-to-skippers (NtS) on infrastructure disruptions and ice conditions were obtained for Germany and the Netherlands. It was also possible to obtain data on lock opening times, geo-data on waterways and port terminals as well as company-specific data, especially from the port and terminal operators involved in the project.

Parallel to the data acquisition, activities were started to develop the ML models for the ETA forecast. For this purpose, the overall forecast problem was initially segmented into sub-problems analogous to the sub-processes of the multi-link process chain. Another central activity was the identification of suitable input variables (features) for the individual subproblems. Within the framework of an agile development approach, forecasting models have so far been implemented for the sub-problems of forecasting transit times and the Estimated Time of Departure (ETD) in seaports based on them, as well as forecasting the travel time between ports, including lock processes, intermediate stops for loading / unloading and travel interruptions. For the latter, several approaches for segmenting the process chain were tested. Several ML methods were also tested during the development with regard to the highest quality of results, including Artificial Neural Networks, Gradient Boosting, Random Forest and Support Vector Machines. In addition to the development of a model for the lead time in inland ports, the existing models are currently being improved incrementally, e.g. by taking into account additional data or features.

In addition to the development of the forecast, work was carried out in parallel on the decision assistant based on it. In the first step, ETA-relevant disruption scenarios in inland navigation, e.g. the delay of a ship at the seaport, were identified together with the practice partners, as well as suitable action alternatives for the actors involved. This information is currently being implemented on the IT side in the form of a knowledge-based system. In order to integrate the individual ETA functionalities into an overall system, a demonstrator with a practicable user interface is also currently being developed.

Further information on the SELECT project (procedure, partial results, etc.) can be found in this interim report on forecast development and in the following publications:

 

  • Poschmann, P.; Weinke, M.; Straube, F. (2022): „Predicting Estimated Arrival Times in Logistics using Machine Learning“. In: Wang, J. (Hrsg.): Encyclopedia of Data Science and Machine Learning. Hershey, USA: IGI Global, S. 2683-2701
  • Poschmann, P.; Weinke., M.; Straube, F.; Kliewer, J.; Gerhardt, F. (2022): „Künstliche Intelligenz in der Binnenschifffahrt: Steigerung der Zuverlässigkeit von Binnenschifftransporten durch datenbasierte Ankunftszeitprognosen". In: Internationales Verkehrswesen (74) 2/2022. [Veröffentlichung im Mai 2022]
  • Poschmann, P.; Weinke, M.; Straube, F. (2021): „Predictions of Disruptions in Multi-Modal Transport Chains Using Artificial Intelligence". In: Bundesvereinigung Logistik (BVL) e.V. (Hrsg.): International Scientific Symposium on Logistics - Conference Volume. Bremen, S. 85-90.
  • Teßmann, G. (2021): „Machine Learning: KI-basierte Entscheidungsassistenz für Logistikketten der Binnenschifffahrt". In: Schiffahrt, Hafen, Bahn und Technik (06 / 2021), S. 100-101
  • Pressemitteilungen zum Projekt u. a. hier:

     

     

 

Presentations on the SELECT project are or were given at the following events:

  • Netzwerkveranstaltung Digitale Testfelder Wasserstraße (DTW) des Bundesministeriums für Digitales und Verkehr (BMDV), Berlin, 28.06.2022 [ausstehend]
  • Hamburg International Conference of Logistics (HICL) der Technischen Universität Hamburg (TUHH), 23.09.2021 
  • International Scientific Symposium on Logistics (ISSL) der Bundesvereinigung Logistik (BVL), 15.06.2021 

 

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Project launched in March 2020 (03/2020)

Under the leadership of the Chair of Logistics at TU Berlin, the SELECT project ("Smart decision support for inland navigation logistics chains through ETA forecasts") started on 1.3.2020. It is funded for 3 years as part of the "Innovative Port Technologies" (IHATEC) initiative of the Federal Ministry of Transport and Digital Infrastructure (BMVI): https://www.innovativehafentechnologien.de/projekt-select-gestartet/.

As part of a collaboration with various stakeholders in the German port industry (including inland shipping companies and operators of inland and seaport terminals), the SELECT project will leverage significant potential for inland navigation logistics chains from the growing data stock, including Inland AIS. With the help of artificial intelligence methods (machine learning), a digital decision assistant will be developed in SELECT that will enable inland navigation stakeholders to optimise transport routes by providing time of arrival (ETA) forecasts and related recommendations for action.

By increasing reliability, efficiency and environmental sustainability, the SELECT project contributes to increasing the competitiveness and attractiveness of inland navigation compared to alternative transport modes.

The kick-off for the project took place on 31.3.2020.

Project organization

Project sponsor:

Project executing agency:

  • TÜV Rheinland Consulting GmbH

Collaborative partners:

  • Technische Universität Berlin, Fachgebiet Logistik (Konsortialführer)
  • BEHALA Berliner Hafen- und Lagerhausgesellschaft mbH
  • Deutsche Binnenreederei AG
  • Duisburger Hafen AG
  • HGK Shipping GmbH

Associated partners: 

  • Contargo GmbH & Co. KG
  • HVCC Hamburg Vessel Coordination Center GmbH
  • Rhenus PartnerShip GmbH & Co. KG

Project duration:

01.03.2020 - 28.02.2023

Contact

M.Sc.

Jonas Brands

Research Associate

brands@logistik.tu-berlin.de

+49 30 314-28438

Organization name Logistics
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