Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms

Nikolas Julio, Ricardo Giesen and Pedro Lizana

Research in Transportation Economics, 2016, vol. 59, issue C, 250-257

Abstract: Most transit agencies are trying to increase their ridership. To achieve this goal, they are looking to maintain or even improve their level of service. This is very hard, since traffic congestion is normally increasing. As a result, bus travel times are higher and less reliable, which makes harder to predict travel times and avoid bunching. Being able to accurately predict bus travel speeds and update this prediction with real-time information could improve the quality and reliability of the information given to users, and increase the effectiveness of control schemes.

Keywords: Transit; Public transport; Transantiago; Speed prediction; Machine learning; Statistical learning; Real-time information; Artificial neural networks; Support vector machines; Support vector regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.retrec.2016.07.019

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