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https://hdl.handle.net/123456789/1420| Τύπος: | Άρθρο σε επιστημονικό περιοδικό |
| Τίτλος: | A data-driven model for pedestrian behavior classification and trajectory prediction |
| Συγγραφέας: | [EL] Παπαθανασοπούλου, Βασιλεία[EN] Papathanasopoulou, Vasileia [EL] Σπυροπούλου, Ιωάννα[EN] Spyropoulou, Ioanna [EL] Περάκης, Χαράλαμπος[EN] Perakis, Charalampos [EL] Γκίκας, Βασίλης[EN] Gikas, Vassilis [EL] Ανδρικοπούλου, Ελένη[EN] Andrikopoulou, Eleni |
| Ημερομηνία: | 18/10/2021 |
| Περίληψη: | Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian’s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian’s movement is achieved, yielding satisfactory results. |
| Γλώσσα: | Αγγλικά |
| Σελίδες: | 12 |
| DOI: | 10.1109/OJITS.2022.3169700 |
| EISSN: | 2687-7813 |
| Θεματική κατηγορία: | [EL] Μηχανική, διεπιστημονική προσέγγιση[EN] Engineering, interdisciplinary |
| Λέξεις-κλειδιά: | Behavior classification; distraction; pedestrian speed prediction; pedestrian trajectory prediction; random forests; GNSS,; position fix |
| Κάτοχος πνευματικών δικαιωμάτων: | © The Author(s) 2021. |
| Όροι και προϋποθέσεις δικαιωμάτων: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη: | https://ieeexplore.ieee.org/document/9762760 |
| Ηλεκτρονική διεύθυνση περιοδικού: | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8784355 |
| Τίτλος πηγής δημοσίευσης: | IEEE Open Journal of Intelligent Transportation Systems |
| Τόμος: | 3 |
| Σελίδες τεκμηρίου (στην πηγή): | 328-339 |
| Σημειώσεις: | This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme Human Resources Development, Education and Lifelong Learning 2014–2020 in the context of the project “Advanced WiFi-RTT Based Localization Techniques for the Development and Testing of Pedestrian behavior Classification” (MIS 5049177). |
| Εμφανίζεται στις συλλογές: | Ερευνητικές ομάδες |
Αρχεία σε αυτό το τεκμήριο:
| Αρχείο | Περιγραφή | Σελίδες | Μέγεθος | Μορφότυπος | Έκδοση | Άδεια | |
|---|---|---|---|---|---|---|---|
| OJITS3169700.pdf | 3.79 MB | Adobe PDF | - |  | Δείτε/ανοίξτε |