Latest revision as of 23:16, 28 January 2021

Abstract

International audience; Good, efficient and reliable public transportation systems are of crucial importance for all major cities today. In this paper, we propose a concrete solution to a particular problem: improve the prediction of the bus arrival time at each bus stop station on a given itinerary, by taking to account global and local traffic contexts. The main principle consists of modeling the traffic data as an image structure, adapted for applying CNN deep neural networks. The results obtained shows that the proposed approach outperforms traditional machine learning techniques, such as OLS (Ordinary Least Squares) or SVR (Support Vector Regression) with different kernels (RBF or Polynomial), with more than 18% better accuracy prediction, while being computationally faster.

Original document

The different versions of the original document can be found in:

• https://hal.archives-ouvertes.fr/hal-02468759

• http://link.springer.com/content/pdf/10.1007/978-3-030-38822-5_10,

http://dx.doi.org/10.1007/978-3-030-38822-5_10 under the license http://www.springer.com/tdm

• https://rd.springer.com/chapter/10.1007/978-3-030-38822-5_10,

https://link.springer.com/chapter/10.1007/978-3-030-38822-5_10,

https://academic.microsoft.com/#/detail/2999268026