Research Article
Public Transportation Prediction with Convolutional Neural Networks
@INPROCEEDINGS{10.1007/978-3-030-38822-5_10, author={Dancho Panovski and Titus Zaharia}, title={Public Transportation Prediction with Convolutional Neural Networks}, proceedings={Intelligent Transport Systems. From Research and Development to the Market Uptake. Third EAI International Conference, INTSYS 2019, Braga, Portugal, December 4--6, 2019}, proceedings_a={INTSYS}, year={2020}, month={1}, keywords={Machine learning Deep learning Convolutional neural networks Public transportation Traffic prediction Traffic simulation}, doi={10.1007/978-3-030-38822-5_10} }- Dancho Panovski
Titus Zaharia
Year: 2020
Public Transportation Prediction with Convolutional Neural Networks
INTSYS
Springer
DOI: 10.1007/978-3-030-38822-5_10
Abstract
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.
