Public Transportation Prediction with Convolutional Neural Networks

Dancho Panovski; Titus Zaharia

doi:10.1007/978-3-030-38822-5_10

Public Transportation Prediction with Convolutional Neural Networks

Dancho Panovski
, Titus Zaharia

CNRS UMR 5157 SAMOVAR

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019
rédacteurs en chef	Ana Lúcia Martins, Joao Carlos Ferreira, Alexander Kocian
Editeur	Springer
Pages	150-161
Nombre de pages	12
ISBN (imprimé)	9783030388218
Les DOIs	https://doi.org/10.1007/978-3-030-38822-5_10
état	Publié - 1 janv. 2020
Evénement	3rd EAI International Conference on Intelligent Transport Systems, INTSYS 2019 - Braga, Portugal Durée: 4 déc. 2019 → 6 déc. 2019

Série de publications

Nom	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	310 LNICST
ISSN (imprimé)	1867-8211

Une conférence

Une conférence	3rd EAI International Conference on Intelligent Transport Systems, INTSYS 2019
Pays/Territoire	Portugal
La ville	Braga
période	4/12/19 → 6/12/19

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 11 Villes et communautés durables

Accès au document

10.1007/978-3-030-38822-5_10

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Panovski, D., & Zaharia, T. (2020). Public Transportation Prediction with Convolutional Neural Networks. Dans A. L. Martins, J. C. Ferreira, & A. Kocian (eds.), Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019 (p. 150-161). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol 310 LNICST). Springer. https://doi.org/10.1007/978-3-030-38822-5_10

Panovski, Dancho ; Zaharia, Titus. / Public Transportation Prediction with Convolutional Neural Networks. Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019. Editeur / Ana Lúcia Martins ; Joao Carlos Ferreira ; Alexander Kocian. Springer, 2020. p. 150-161 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{b920b65efeaf497990404bdeb4cc9a8f,

title = "Public Transportation Prediction with Convolutional Neural Networks",

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.",

keywords = "Convolutional neural networks, Deep learning, Machine learning, Public transportation, Traffic prediction, Traffic simulation",

author = "Dancho Panovski and Titus Zaharia",

note = "Publisher Copyright: {\textcopyright} 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 3rd EAI International Conference on Intelligent Transport Systems, INTSYS 2019 ; Conference date: 04-12-2019 Through 06-12-2019",

year = "2020",

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doi = "10.1007/978-3-030-38822-5\_10",

language = "English",

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series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

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Panovski, D & Zaharia, T 2020, Public Transportation Prediction with Convolutional Neural Networks. Dans AL Martins, JC Ferreira & A Kocian (eds), Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, VOL. 310 LNICST, Springer, p. 150-161, 3rd EAI International Conference on Intelligent Transport Systems, INTSYS 2019, Braga, Portugal, 4/12/19. https://doi.org/10.1007/978-3-030-38822-5_10

Public Transportation Prediction with Convolutional Neural Networks. / Panovski, Dancho; Zaharia, Titus.
Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019. Ed. / Ana Lúcia Martins; Joao Carlos Ferreira; Alexander Kocian. Springer, 2020. p. 150-161 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol 310 LNICST).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - Public Transportation Prediction with Convolutional Neural Networks

AU - Panovski, Dancho

AU - Zaharia, Titus

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Convolutional neural networks

KW - Deep learning

KW - Machine learning

KW - Public transportation

KW - Traffic prediction

KW - Traffic simulation

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DO - 10.1007/978-3-030-38822-5_10

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T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

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BT - Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019

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PB - Springer

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Panovski D, Zaharia T. Public Transportation Prediction with Convolutional Neural Networks. Dans Martins AL, Ferreira JC, Kocian A, rédacteurs en chef, Intelligent Transport Systems. From Research and Development to the Market Uptake - 3rd EAI International Conference, INTSYS 2019. Springer. 2020. p. 150-161. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-38822-5_10

Public Transportation Prediction with Convolutional Neural Networks

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Autres fichiers et liens

Empreinte digitale

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