Near-Time Predictions of Future Truck Locations

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/255599
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Type: Examensarbete för masterexamen
Master Thesis
Title: Near-Time Predictions of Future Truck Locations
Authors: Srinivasan, Abhishek
Abstract: Location-based services are becoming an increasingly valuable service in the transportation sector. These location-based services rely on current location, but a better service can be provided by predicting the future location. So this thesis aims to investigate near-future location prediction of a specific vehicle using Recurrent Neural Networks (RNNs) and its different variants (a time-series approach). In addition, we discuss the required data pre-processing steps, the architecture of RNNs and the experiments to compare the different variants of RNNs. These experiments show that Long Short-Term Memory Networks (LSTM) have better predictions when compared to simple RNNs and Bi-directional Recurrent Neural Networks (BRNN). The results from this work will be helpful in developing a better short-term location prediction model and will provide better services to SCANIAs customers.
Keywords: Transport;Grundläggande vetenskaper;Hållbar utveckling;Innovation och entreprenörskap (nyttiggörande);Annan teknik;Transport;Basic Sciences;Sustainable Development;Innovation & Entrepreneurship;Other Engineering and Technologies
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för fysik (Chalmers)
Chalmers University of Technology / Department of Physics (Chalmers)
URI: https://hdl.handle.net/20.500.12380/255599
Collection:Examensarbeten för masterexamen // Master Theses



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