Near-Time Predictions of Future Truck Locations
Ladda ner
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2018
Författare
Srinivasan, Abhishek
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Transport , Grundläggande vetenskaper , Hållbar utveckling , Innovation och entreprenörskap (nyttiggörande) , Annan teknik , Transport , Basic Sciences , Sustainable Development , Innovation & Entrepreneurship , Other Engineering and Technologies