The airfreight industry of shipping goods with special handling needs, also known as special cargo, suffers from nontransparent shipping processes, resulting in inefficiency. The LARA project (Lane Analysis and Route Advisor) aims at addressing these limitations and bringing innovation in special cargo route planning so as to improve operational deficiencies and customer services. In this chapter, we discuss the special cargo domain knowledge elicitation and modeling into an ontology. We also present research into cargo incidents, namely, automatic classification of incidents in free-text reports and experiments in detecting significant features associated with specific cargo incident types. Our work mainly addresses two of the main technical priority areas defined by the European Big Data Value (BDV) Strategic Research and Innovation Agenda, namely, the application of data analytics to improve data understanding and providing optimized architectures for analytics of data-at-rest and data-in-motion, the overall goal is to develop technologies contributing to the data value chain in the logistics sector. It addresses the horizontal concerns Data Analytics, Data Processing Architectures, and Data Management of the BDV Reference Model. It also addresses the vertical dimension Big Data Types and Semantics.

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Lane Analysis & Route Advisor
Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands

Reshadat, V., Kolkman, T., Zervanou, K., Zhang, Y. (Yingqian), Akçay, A., Snijder, C., … de Jong, E. (2022). Knowledge modeling and incident analysis for special cargo. In Technologies and Applications for Big Data Value (pp. 519–544). doi:10.1007/978-3-030-78307-5_23