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Prethodno priopćenje
https://doi.org/10.7225/toms.v05.n02.002

Forecasting Transport Mode Use with Support Vector Machines Based Approach

Ivana Semanjski   ORCID icon orcid.org/0000-0003-2795-8094 ; Ghent University, Department of Telecommunications and Information Processing, Ghent, Belgium
Angel Lopez   ORCID icon orcid.org/0000-0002-9080-4859 ; Facultad de Ingeniería en Electricidad y Computación, Politécnica del Litoral, Guayaquil, Ecuador
Sidharta Gautama ; Ghent University, Department of Telecommunications and Information Processing, Ghent, Belgium

Puni tekst: engleski, pdf (3 MB) str. 111-120 preuzimanja: 284* citiraj
APA 6th Edition
Semanjski, I., Lopez, A. i Gautama, S. (2016). Forecasting Transport Mode Use with Support Vector Machines Based Approach. Transactions on Maritime Science, 05 (02), 111-120. https://doi.org/10.7225/toms.v05.n02.002
MLA 8th Edition
Semanjski, Ivana, et al. "Forecasting Transport Mode Use with Support Vector Machines Based Approach." Transactions on Maritime Science, vol. 05, br. 02, 2016, str. 111-120. https://doi.org/10.7225/toms.v05.n02.002. Citirano 21.01.2020.
Chicago 17th Edition
Semanjski, Ivana, Angel Lopez i Sidharta Gautama. "Forecasting Transport Mode Use with Support Vector Machines Based Approach." Transactions on Maritime Science 05, br. 02 (2016): 111-120. https://doi.org/10.7225/toms.v05.n02.002
Harvard
Semanjski, I., Lopez, A., i Gautama, S. (2016). 'Forecasting Transport Mode Use with Support Vector Machines Based Approach', Transactions on Maritime Science, 05(02), str. 111-120. https://doi.org/10.7225/toms.v05.n02.002
Vancouver
Semanjski I, Lopez A, Gautama S. Forecasting Transport Mode Use with Support Vector Machines Based Approach. Transactions on Maritime Science [Internet]. 2016 [pristupljeno 21.01.2020.];05(02):111-120. https://doi.org/10.7225/toms.v05.n02.002
IEEE
I. Semanjski, A. Lopez i S. Gautama, "Forecasting Transport Mode Use with Support Vector Machines Based Approach", Transactions on Maritime Science, vol.05, br. 02, str. 111-120, 2016. [Online]. https://doi.org/10.7225/toms.v05.n02.002

Sažetak
Since information and communication technologies have become an integral part of our everyday lives, it only seems logical that the smart city concept should attempt to explore the role of an integrated information and communication approach to city asset management and raising the quality of life of its citizens. Raising the quality of life relies not only on improving the management of a city’s systems (e.g. transportation system) but also on the provision of timely and relevant information to its citizens to allow them to make better informed decisions. This requires the use of forecasting models. In this paper, a support vector machine-based model is developed to predict future mobility behavior from crowdsourced data. Crowdsourced data are collected through a dedicated smartphone app tracking mobility behavior. The use of a forecasting model of this type can facilitate the management of a smart city’s mobility system while simultaneously ensuring the timely provision of relevant pretravel information to its citizens.

Ključne riječi
Component; Travel behavior; Smart city; Crowdsourceing; GNSS; Smartphones; Transport mode; Forecasting; Support vector machines; Pre-travel information service

Hrčak ID: 167822

URI
https://hrcak.srce.hr/167822

Posjeta: 450 *