Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60255
|Title:||Bus travel time prediction model based on profile similarity||Authors:||Cristobal, Teresa
de Blasio, Gabriel
Garcia, Carmelo R.
|UNESCO Clasification:||3327 Tecnología de los sistemas de transporte
120304 Inteligencia artificial
|Keywords:||Road-based mass transit systems; intelligent transport systems; travel time prediction; clustering; automatic vehicle location
Intelligent transport systems
Travel time prediction
Automatic vehicle location
|Issue Date:||2019||Journal:||Sensors||Abstract:||In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.||URI:||http://hdl.handle.net/10553/60255||ISSN:||1424-8220||DOI:||10.3390/s19132869||Source:||Sensors [ISSN 1424-8220], v. 19 (13)|
|Appears in Collections:||Artículos|
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