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https://hdl.handle.net/123456789/1357| Τύπος: | Άρθρο σε επιστημονικό περιοδικό |
| Τίτλος: | Predicting roundabout lane capacity using artificial neural networks |
| Συγγραφέας: | [EL] Αναγνωστόπουλος, Απόστολος[EN] Anagnostopoulos, Apostolos [EL] Κεχαγιά, Φωτεινή[EN] Kehagia, Foteini [EL] Δαμάσκου, Ευτέρπη[EN] Damaskou, Efterpi [EL] Μουρατίδης, Αναστάσιος[EN] Mouratidis, Anastassios [EL] Αρετούλης, Γεώργιος[EN] Aretoulis, Georgios |
| Ημερομηνία: | 18/11/2021 |
| Περίληψη: | Several roundabout capacity methods and approaches have been proposed until now. They are mainly based either on regression equations of observed capacity and gap acceptance or on stochastic models through simulation techniques. However, all of them rely on different variables and factors and are being adapted to local driving behavior. Hence, it is not clear if existing techniques are appropriate for reliable capacity estimations and optimal design of Greek roundabouts. This paper presents the results of an experimental research that has been conducted as a first step in the optimization of roundabouts capacity estimation, based on a dataset of 11 roundabouts in Greece. The study firstly aims to understand what geometric roundabout features and driving behavior parameters influence capacity. Artificial neural networks (NN) were tested and developed to predict accurate roundabout capacities. Appropriate roundabouts were selected for the analysis and their operational performance was filmed using a UAV and a stabilized camera during peak periods. Video image processing techniques and algorithms allowed the extraction of empirical data (traffic flows, gap acceptance parameters) and accurate geometric characteristics. The results demonstrate that artificial neural networks can predict the capacity of roundabouts accurately. |
| Γλώσσα: | Αγγλικά |
| Σελίδες: | 6 |
| DOI: | 10.25103/jestr.145.24 |
| ISSN: | 1791-2377 |
| Θεματική κατηγορία: | [EL] Πολιτική μηχανική[EN] Civil Engineering |
| Λέξεις-κλειδιά: | Roundabouts; Artificial Neural Networks; Capacity; Driving Behavior; Capacity Models; UAVs |
| Κάτοχος πνευματικών δικαιωμάτων: | © 2021 School of Science, IHU. All rights reserved. |
| Όροι και προϋποθέσεις δικαιωμάτων: | This is an Open Access article distributed under the terms of the Creative Commons Attribution License. |
| Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη: | http://www.jestr.org/downloads/Volume14Issue5/fulltext241452021.pdf |
| Ηλεκτρονική διεύθυνση περιοδικού: | http://www.jestr.org/index.php |
| Τίτλος πηγής δημοσίευσης: | Journal of Engineering Science and Technology Review (JESTR) |
| Τεύχος: | 5 |
| Τόμος: | 14 |
| Σελίδες τεκμηρίου (στην πηγή): | 210-215 |
| Σημειώσεις: | This work was partly supported by the Greek Ministry of Education and Religious Affairs in the scope of the project entitled “Study on the improvement of road safety and capacity of Greek roundabouts, by using emerging technologies and artificial neural networks models” at the Aristotle University of Thessaloniki, project No. 99030. The project was co-funded via the European Social Fund (ESF). |
| Εμφανίζεται στις συλλογές: | Ερευνητικές ομάδες |
Αρχεία σε αυτό το τεκμήριο:
| Αρχείο | Περιγραφή | Σελίδες | Μέγεθος | Μορφότυπος | Έκδοση | Άδεια | |
|---|---|---|---|---|---|---|---|
| Predicting Roundabout Lane Capacity using Artificial Neural Networks.pdf | Predicting Roundabout Lane Capacity using Artificial Neural Networks | 2.16 MB | Adobe PDF | - |  | Δείτε/ανοίξτε |