Bus Travel Time Prediction Model Based on Profile Similarity

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 13; p. 2869
• Main Authors: Cristóbal, Teresa, Padrón, Gabino, Quesada-Arencibia, Alexis, Alayón, Francisco, de Blasio, Gabriel, García, Carmelo R
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28-06-2019; MDPI
• Subjects: automatic vehicle location; Buses; Cluster analysis; Clustering; Estimates; Global positioning systems; GPS; intelligent transport systems; International conferences; Mass transit; Model testing; Neural networks; Prediction models; Public transportation; road-based mass transit systems; Short term; Timetables; Transport vehicles; Transportation planning; Transportation systems; Travel; Travel time; travel time prediction; Vehicles; United States > US; road-based mass transit systems; automatic vehicle location; clustering; travel time prediction; intelligent transport systems
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19132869

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
-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.