Frings, K.; Stroh, M.-F.; Boersma, S.; Stich, V.; Wehling, F.: Development Of A Data Concept For An Algorithm To Enable Relay Traffic For Trucks. In: Herberger, D.; Hübner, M.; Stich, V. (Eds.): Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1. Hannover : publish-Ing., 2023, S. 332-341. DOI: https://doi.org/10.15488/13452
Abstract: | |
In road haulage, transports are interrupted by truck drivers to comply with driving and rest times. On long-distance routes, these interruptions lead to a considerable increase in transport time. Transport interruption can be avoided by so-called relay traffic: a vehicle (e. g. semi-trailer) is handed over to a rested driver at the end of the driving time. This type of transport requires a certain company size. In Germany, however, transport companies have 11 employees on average. Intra-company relay traffic is therefore not economically viable for most transport companies. To organize an intermodal transport across forwarding companies, long-distance routes need to be split into partial routes to divide them between freight forwarders and carriers. This paper presents a data concept for an algorithm to find the best possible route sections along a previously defined start and endpoint. The developed data concept includes order-specific data, forwarder-specific data, real-time traffic data, geographical data as well as data from freight forwarding software and telematics to be the basis for the route sectioning algorithm. In this paper, different data sources, external services and logistic systems are analyzed and evaluated. It is shown which data is needed and what the best ways are to select and derive this data from the different data sources. | |
License of this version: | CC BY 3.0 DE |
Document Type: | BookPart |
Publishing status: | publishedVersion |
Issue Date: | 2023 |
Appears in Collections: | Proceedings CPSL 2023 - 1 Proceedings CPSL 2023 - 1 |
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Germany | 24 | 48.00% |
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United States | 10 | 20.00% |
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China | 3 | 6.00% |
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Japan | 2 | 4.00% |
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India | 2 | 4.00% |
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United Kingdom | 2 | 4.00% |
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No geo information available | 1 | 2.00% |
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Hong Kong | 1 | 2.00% |
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France | 1 | 2.00% |
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Canada | 1 | 2.00% |
other countries | 3 | 6.00% |
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