Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

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dc.authorid0000-0002-7201-6963
dc.authorid0000-0003-1840-9958
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2024-09-25T20:00:08Z
dc.date.available2024-09-25T20:00:08Z
dc.date.issued2024
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü en_US
dc.description.abstractCurrently, most traffic simulations require residents' travel plans as input data; however, in real scenarios, it is difficult to obtain real residents' travel behavior data for various reasons, such as a large amount of data and the protection of residents' privacy. This study proposes a method combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) for analyzing and compensating spatiotemporal features in residents' travel data. By exploiting the spatial feature extraction capability of CNNs and the advantages of LSTMs in processing time-series data, the aim is to achieve a traffic simulation close to a real scenario using limited data by modeling travel time and space. The experimental results show that the method proposed in this article is closer to the real data in terms of the average traveling distance compared with the use of the modulation method and the statistical estimation method. The new strategy we propose can significantly reduce the deviation of the model from the original data, thereby significantly reducing the basic error rate by about 50%.en_US
dc.description.sponsorshipPrince Sattam bin Abdulaziz University [PSAU/2023/01/26325]en_US
dc.description.sponsorshipThis work was supported by the Prince Sattam bin Abdulaziz University through project number (PSAU/2023/01/26325) . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.doi10.7717/peerj-cs.2035
dc.identifier.issn2376-5992
dc.identifier.pmid38855251en_US
dc.identifier.scopus2-s2.0-85193485749en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.7717/peerj-cs.2035
dc.identifier.urihttps://hdl.handle.net/20.500.12491/14087
dc.identifier.volume10en_US
dc.identifier.wosWOS:001223368200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzYK_20240925en_US
dc.subjectTraffic Data Analysisen_US
dc.subjectCNNen_US
dc.subjectLSTMen_US
dc.subjectData Analysisen_US
dc.subjectClassificationen_US
dc.titleSpatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel dataen_US
dc.typeArticleen_US

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