@inproceedings{4bcda26e532141929588b18339aff9d8,
title = "Prediction of Transportation Orders in Logistics Based on LSTM: Cargo Taxi",
abstract = "This work presents the application of LSTM neural network in prediction of transportation orders. In case of logistic transport, the empty return routs can be minimized by matching new orders in the vicinity of drop off of actual order. This is a similar approach to taxi, thus the approach is named cargo-taxi. To find the new orders in the vicinity of carrier, an LSTM network is used to predict the next towns that the carrier would visit basing on his actual route and archival routs that the network was trained on. This research focus on proof of concept, the way of constructing the training data, and the research of parameters influence on time of training and prediction.",
keywords = "LSTM, cargo-taxi, matching in logistics, planing",
author = "Tomasz Grzejszczak and Adam Ga{\l}uszka and Jaros{\l}aw {\'S}mieja and Marek Harasny and Maciej Zalwert",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Work-Conference on Artificial Neural Networks, IWANN 2023 ; Conference date: 19-06-2023 Through 21-06-2023",
year = "2023",
doi = "10.1007/978-3-031-43078-7\_33",
language = "English",
isbn = "9783031430770",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "403--410",
editor = "Ignacio Rojas and Gonzalo Joya and Andreu Catala",
booktitle = "Advances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings",
address = "Germany",
}