A novel dynamic path planning method td learning supported modified spatiotemporal gnn-lstm model on large urban networks
| dc.contributor.author | Karaağaç, Abdullah | |
| dc.date.accessioned | 2025-04-22T10:03:14Z | |
| dc.date.available | 2025-04-22T10:03:14Z | |
| dc.date.issued | 2025 | |
| dc.department | KMÜ, Teknik Bilimler Meslek Yüksekokulu, Mimarlık ve Şehir Planlama Bölümü | |
| dc.description.abstract | In this study, a new approach will be discussed in which routing is done by predicting future traffic and the learning algorithm is optimized during navigation. Traffic has a complex structure that is constantly changing. Especially for long-term travel, it is not an optimum approach to suggest a route only by considering the traffic situation at the time the navigation request is made. For this reason, the proposed algorithm recommends a route by taking into account future saturation conditions on the vehicle’s route. Singapore was chosen as the study area. The tests were carried out in a simulation environment. The four selected algorithms were tested spatially and temporally. Especially in long-term travels, the superior success of the proposed method compared to other selected methods has been demonstrated. © The Author(s) 2025. | |
| dc.identifier.citation | Karaağaç, A. (2025). A novel dynamic path planning method TD learning supported modified spatiotemporal GNN-LSTM model on large urban networks. Transportation : Planning - Policy - Research - Practice, 1–34. https://doi.org/10.1007/s11116-025-10600-1 | |
| dc.identifier.doi | 10.1007/s11116-025-10600-1 | |
| dc.identifier.issn | 00494488 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://doi.org/10.1007/s11116-025-10600-1 | |
| dc.identifier.wos | WOS:001456189900001 | |
| dc.identifier.wosquality | Q1 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Karaağaç, Mustafa | |
| dc.institutionauthorid | Karaağaç, Abdullah/0000-0001-5737-5880 | |
| dc.language.iso | en | |
| dc.publisher | Springer Netherlands | |
| dc.relation.publicationcategory | Kitap Bölümü - Ulusal | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Graph convolution networks | |
| dc.subject | Long-short term memory | |
| dc.subject | Modified Bellman–Ford algorithm | |
| dc.subject | Navigation | |
| dc.subject | Path planning | |
| dc.subject | Spatiotemporal graph neural networks | |
| dc.subject | Temporal difference learning | |
| dc.title | A novel dynamic path planning method td learning supported modified spatiotemporal gnn-lstm model on large urban networks |
