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BISE 62(5) - October 2020

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  • Zeitschriftenartikel
    Virtual Reality
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Wohlgenannt, Isabell; Simons, Alexander; Stieglitz, Stefan
  • Zeitschriftenartikel
    The Negative Effects of Institutional Logic Multiplicity on Service Platforms in Intermodal Mobility Ecosystems
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Schulz, Thomas; Böhm, Markus; Gewald, Heiko; Celik, Zehra; Krcmar, Helmut
    Digitalization is changing the mobility sector. Companies have developed entirely new mobility services, and mobility services with pre-digital roots, such as ride-sharing and public transport, have leveraged digitalization to become more convenient to use. Nevertheless, private car use remains the dominant mode of transport in most developed countries, leading to problems such as delays due to traffic congestion, insufficient parking spaces, as well as noise and air pollution. Emerging intermodal mobility ecosystems take advantage of digital advances in mobility services by providing individual, dynamic and context-aware combinations of different mobility services to simplify door-to-door mobility and contribute to the reduction of private car use. However, the service platforms are limited in terms of functional range, for example they may lack integrated ticketing and rely on static data, which makes intermodal mobility inconvenient. This article adopts the service-dominant logic perspective to analyze service ecosystems for intermodal mobility and their service provision. Drawing on traditional institutional literature, the authors question the assumption that service logic is dominant for all actors of a service ecosystem. By applying activity theory, the article illustrates how an institutional logic multiplicity among actors can negatively affect the functional range of service platforms. The results of a qualitative study in Germany show that, in particular, the state logic of some actors, which is characterized by the obligation to provide mobility, impairs the quality of service platforms in supporting citizens in intermodal mobility.
  • Zeitschriftenartikel
    Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Filipiak, Dominik; Węcel, Krzysztof; Stróżyna, Milena; Michalak, Michał; Abramowicz, Witold
    The presented method reconstructs a network (a graph) from AIS data, which reflects vessel traffic and can be used for route planning. The approach consists of three main steps: maneuvering points detection, waypoints discovery, and edge construction. The maneuvering points detection uses the CUSUM method and reduces the amount of data for further processing. The genetic algorithm with spatial partitioning is used for waypoints discovery. Finally, edges connecting these waypoints form the final maritime traffic network. The approach aims at advancing the practice of maritime voyage planning, which is typically done manually by a ship’s navigation officer. The authors demonstrate the results of the implementation using Apache Spark, a popular distributed and parallel computing framework. The method is evaluated by comparing the results with an on-line voyage planning application. The evaluation shows that the approach has the capacity to generate a graph which resembles the real-world maritime traffic network.
  • Zeitschriftenartikel
    An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Balster, Andreas; Hansen, Ole; Friedrich, Hanno; Ludwig, André
    Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
  • Zeitschriftenartikel
    Information Systems in Intermodal Transportation and Traffic Management
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Abramowicz, Witold; Hahn, Axel; Ludwig, André; Stróżyna, Milena
  • Zeitschriftenartikel
    Interview with Martin Gnass and Martin Kolbe on “Challenges and Hot Topics in the Intermodal Logistics Industry”
    (Business & Information Systems Engineering: Vol. 62, No. 5, 2020) Ludwig, André; Stróżyna, Milena