Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network

Teresa Pamuła

doi:10.1007/978-3-642-41647-7_19

Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network

Teresa Pamuła

Katedra Systemów Transportowych, Inżynierii Ruchu i Logistyki (RT/RT4)

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

7 Cytowania z bazy Scopus

Abstrakt

The paper presents the development of a short-term forecasting method for determining traffic flow values. The study is based on the data from two video detectors located at the ends of a transit road in the city of Gliwice. The data were recorded 24 h/day for a period of one year. Neural networks (NN) were used in the prediction models. The effects, of the size of a time window and the length of selected data registration, on the learning rate of the nets and on the quality of prediction were studied. Tests were performed using three classes of time series corresponding to: working days, Saturdays and Sundays. The aim of the study was to elaborate an accurate short-term predicting method, which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS).

Język oryginału	angielski
Tytuł publikacji goszczącej	Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers
Wydawca	Springer Verlag
Strony	147-154
Liczba stron	8
ISBN (drukowany)	9783642416460
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-41647-7_19
Status publikacji	Opublikowano - 2013
Wydarzenie	13th International Conference on Transport Systems Telematics, TST 2013 - Katowice-Ustron, Polska Czas trwania: 23 paź 2013 → 26 paź 2013

Seria publikacji

Nazwa	Communications in Computer and Information Science
Tom	395 CCIS
ISSN (drukowany)	1865-0929

Konferencja

Konferencja	13th International Conference on Transport Systems Telematics, TST 2013
Kraj/Terytorium	Polska
Miejscowość	Katowice-Ustron
Okres	23/10/13 → 26/10/13

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 11 Zrównoważone miasta i społeczności

Obszary tematyczne ASJC Scopus

Informatyka ogólna
Matematyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-642-41647-7_19

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Pamuła, T. (2013). Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network. W Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers (str. 147-154). (Communications in Computer and Information Science; Tom 395 CCIS). Springer Verlag. https://doi.org/10.1007/978-3-642-41647-7_19

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title = "Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network",

abstract = "The paper presents the development of a short-term forecasting method for determining traffic flow values. The study is based on the data from two video detectors located at the ends of a transit road in the city of Gliwice. The data were recorded 24 h/day for a period of one year. Neural networks (NN) were used in the prediction models. The effects, of the size of a time window and the length of selected data registration, on the learning rate of the nets and on the quality of prediction were studied. Tests were performed using three classes of time series corresponding to: working days, Saturdays and Sundays. The aim of the study was to elaborate an accurate short-term predicting method, which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS).",

keywords = "neural network, time series, traffic flow prediction",

author = "Teresa Pamu{\l}a",

year = "2013",

doi = "10.1007/978-3-642-41647-7\_19",

language = "English",

isbn = "9783642416460",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "147--154",

booktitle = "Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers",

address = "Germany",

note = "13th International Conference on Transport Systems Telematics, TST 2013 ; Conference date: 23-10-2013 Through 26-10-2013",

}

Pamuła, T 2013, Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network. w Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers. Communications in Computer and Information Science, tom 395 CCIS, Springer Verlag, str. 147-154, 13th International Conference on Transport Systems Telematics, TST 2013, Katowice-Ustron, Polska, 23/10/13. https://doi.org/10.1007/978-3-642-41647-7_19

Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network. / Pamuła, Teresa.
Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers. Springer Verlag, 2013. str. 147-154 (Communications in Computer and Information Science; Tom 395 CCIS).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network

AU - Pamuła, Teresa

PY - 2013

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N2 - The paper presents the development of a short-term forecasting method for determining traffic flow values. The study is based on the data from two video detectors located at the ends of a transit road in the city of Gliwice. The data were recorded 24 h/day for a period of one year. Neural networks (NN) were used in the prediction models. The effects, of the size of a time window and the length of selected data registration, on the learning rate of the nets and on the quality of prediction were studied. Tests were performed using three classes of time series corresponding to: working days, Saturdays and Sundays. The aim of the study was to elaborate an accurate short-term predicting method, which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS).

AB - The paper presents the development of a short-term forecasting method for determining traffic flow values. The study is based on the data from two video detectors located at the ends of a transit road in the city of Gliwice. The data were recorded 24 h/day for a period of one year. Neural networks (NN) were used in the prediction models. The effects, of the size of a time window and the length of selected data registration, on the learning rate of the nets and on the quality of prediction were studied. Tests were performed using three classes of time series corresponding to: working days, Saturdays and Sundays. The aim of the study was to elaborate an accurate short-term predicting method, which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS).

KW - neural network

KW - time series

KW - traffic flow prediction

UR - https://www.scopus.com/pages/publications/84904691338

U2 - 10.1007/978-3-642-41647-7_19

DO - 10.1007/978-3-642-41647-7_19

M3 - Conference contribution

AN - SCOPUS:84904691338

SN - 9783642416460

T3 - Communications in Computer and Information Science

SP - 147

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BT - Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers

PB - Springer Verlag

T2 - 13th International Conference on Transport Systems Telematics, TST 2013

Y2 - 23 October 2013 through 26 October 2013

ER -

Pamuła T. Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network. W Activities of Transport Telematics - 13th International Conference on Transport Systems Telematics, TST 2013, Selected Papers. Springer Verlag. 2013. str. 147-154. (Communications in Computer and Information Science). doi: 10.1007/978-3-642-41647-7_19

Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

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Inne pliki i linki

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