Real-time public transportation prediction with machine learning algorithms

Dancho Panovski; Titus Zaharia

doi:10.1109/ICCE46568.2020.9043077

Real-time public transportation prediction with machine learning algorithms

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

As part of Intelligent Transportation Systems (ITS) public transportation plays a critical and essential role for the mobility in every modern city. In this paper, we introduce a novel method for the real-time prediction of buss arrival times in the various bus stops over a given itinerary. The proposed approach exploits machine and deep learning algorithms, including optimal least square (OLS) linear regression, support vector regression (SVR) and fully-connected neural networks (FNN). The experimental results obtained show that the FNN approach outperforms, in terms of mean absolute prediction error, both SVR (by 7, 62 %) and OLS (for 15, 74 %).

langue originale	Anglais
titre	2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9781728151861
Les DOIs	https://doi.org/10.1109/ICCE46568.2020.9043077
état	Publié - 1 janv. 2020
Evénement	2020 IEEE International Conference on Consumer Electronics, ICCE 2020 - Las Vegas, États-Unis Durée: 4 janv. 2020 → 6 janv. 2020

Série de publications

Nom	Digest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume	2020-January
ISSN (imprimé)	0747-668X

Une conférence

Une conférence	2020 IEEE International Conference on Consumer Electronics, ICCE 2020
Pays/Territoire	États-Unis
La ville	Las Vegas
période	4/01/20 → 6/01/20

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 11 Villes et communautés durables

Accès au document

10.1109/ICCE46568.2020.9043077

Contient cette citation

Panovski, D., & Zaharia, T. (2020). Real-time public transportation prediction with machine learning algorithms. Dans 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 Article 9043077 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol 2020-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE46568.2020.9043077

@inproceedings{88ae65a36f084f3dad405f6602babb3d,

title = "Real-time public transportation prediction with machine learning algorithms",

abstract = "As part of Intelligent Transportation Systems (ITS) public transportation plays a critical and essential role for the mobility in every modern city. In this paper, we introduce a novel method for the real-time prediction of buss arrival times in the various bus stops over a given itinerary. The proposed approach exploits machine and deep learning algorithms, including optimal least square (OLS) linear regression, support vector regression (SVR) and fully-connected neural networks (FNN). The experimental results obtained show that the FNN approach outperforms, in terms of mean absolute prediction error, both SVR (by 7, 62 \%) and OLS (for 15, 74 \%).",

author = "Dancho Panovski and Titus Zaharia",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Consumer Electronics, ICCE 2020 ; Conference date: 04-01-2020 Through 06-01-2020",

year = "2020",

month = jan,

day = "1",

doi = "10.1109/ICCE46568.2020.9043077",

language = "English",

series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Conference on Consumer Electronics, ICCE 2020",

}

Panovski, D & Zaharia, T 2020, Real-time public transportation prediction with machine learning algorithms. Dans 2020 IEEE International Conference on Consumer Electronics, ICCE 2020., 9043077, Digest of Technical Papers - IEEE International Conference on Consumer Electronics, VOL. 2020-January, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Conference on Consumer Electronics, ICCE 2020, Las Vegas, États-Unis, 4/01/20. https://doi.org/10.1109/ICCE46568.2020.9043077

Real-time public transportation prediction with machine learning algorithms. / Panovski, Dancho; Zaharia, Titus.
2020 IEEE International Conference on Consumer Electronics, ICCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9043077 (Digest of Technical Papers - IEEE International Conference on Consumer Electronics; Vol 2020-January).

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 - Real-time public transportation prediction with machine learning algorithms

AU - Panovski, Dancho

AU - Zaharia, Titus

PY - 2020/1/1

Y1 - 2020/1/1

N2 - As part of Intelligent Transportation Systems (ITS) public transportation plays a critical and essential role for the mobility in every modern city. In this paper, we introduce a novel method for the real-time prediction of buss arrival times in the various bus stops over a given itinerary. The proposed approach exploits machine and deep learning algorithms, including optimal least square (OLS) linear regression, support vector regression (SVR) and fully-connected neural networks (FNN). The experimental results obtained show that the FNN approach outperforms, in terms of mean absolute prediction error, both SVR (by 7, 62 %) and OLS (for 15, 74 %).

AB - As part of Intelligent Transportation Systems (ITS) public transportation plays a critical and essential role for the mobility in every modern city. In this paper, we introduce a novel method for the real-time prediction of buss arrival times in the various bus stops over a given itinerary. The proposed approach exploits machine and deep learning algorithms, including optimal least square (OLS) linear regression, support vector regression (SVR) and fully-connected neural networks (FNN). The experimental results obtained show that the FNN approach outperforms, in terms of mean absolute prediction error, both SVR (by 7, 62 %) and OLS (for 15, 74 %).

U2 - 10.1109/ICCE46568.2020.9043077

DO - 10.1109/ICCE46568.2020.9043077

M3 - Conference contribution

AN - SCOPUS:85082607139

T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics

BT - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Consumer Electronics, ICCE 2020

Y2 - 4 January 2020 through 6 January 2020

ER -

Real-time public transportation prediction with machine learning algorithms

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation