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Open AccessArticle

Bus Travel Time Prediction Model Based on Profile Similarity

Institute for Cybernetics, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017 Las Palmas, Spain
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Sensors 2019, 19(13), 2869; https://doi.org/10.3390/s19132869
Received: 7 May 2019 / Revised: 15 June 2019 / Accepted: 26 June 2019 / Published: 28 June 2019
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. View Full-Text
Keywords: road-based mass transit systems; intelligent transport systems; travel time prediction; clustering; automatic vehicle location road-based mass transit systems; intelligent transport systems; travel time prediction; clustering; automatic vehicle location
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MDPI and ACS Style

Cristóbal, T.; Padrón, G.; Quesada-Arencibia, A.; Alayón, F.; de Blasio, G.; García, C.R. Bus Travel Time Prediction Model Based on Profile Similarity. Sensors 2019, 19, 2869.

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