Traffic flow analysis based on the real data using neural networks

Teresa Pamuła

doi:10.1007/978-3-642-34050-5_41

Traffic flow analysis based on the real data using neural networks

Teresa Pamuła

Department of Transport Systems, Traffic Engineering and Logistics (RT/RT4)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.

Original language	English
Title of host publication	Telematics in the Transport Environment - 12th International Conference on Transport Systems Telematics, TST 2012, Selected Papers
Pages	364-371
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-34050-5_41
Publication status	Published - 2012
Event	12th International Conference on Transport Systems Telematics, TST 2012 - Katowice-Ustron, Poland Duration: 10 Oct 2012 → 13 Oct 2012

Publication series

Name	Communications in Computer and Information Science
Volume	329 CCIS
ISSN (Print)	1865-0929

Conference

Conference	12th International Conference on Transport Systems Telematics, TST 2012
Country/Territory	Poland
City	Katowice-Ustron
Period	10/10/12 → 13/10/12

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

neural network
prediction
time series
traffic flow analysis
traffic flow classification

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-642-34050-5_41

Cite this

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title = "Traffic flow analysis based on the real data using neural networks",

abstract = "The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.",

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

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

year = "2012",

doi = "10.1007/978-3-642-34050-5\_41",

language = "English",

isbn = "9783642340499",

series = "Communications in Computer and Information Science",

pages = "364--371",

booktitle = "Telematics in the Transport Environment - 12th International Conference on Transport Systems Telematics, TST 2012, Selected Papers",

note = "12th International Conference on Transport Systems Telematics, TST 2012 ; Conference date: 10-10-2012 Through 13-10-2012",

}

Pamuła, T 2012, Traffic flow analysis based on the real data using neural networks. in Telematics in the Transport Environment - 12th International Conference on Transport Systems Telematics, TST 2012, Selected Papers. Communications in Computer and Information Science, vol. 329 CCIS, pp. 364-371, 12th International Conference on Transport Systems Telematics, TST 2012, Katowice-Ustron, Poland, 10/10/12. https://doi.org/10.1007/978-3-642-34050-5_41

Traffic flow analysis based on the real data using neural networks. / Pamuła, Teresa.
Telematics in the Transport Environment - 12th International Conference on Transport Systems Telematics, TST 2012, Selected Papers. 2012. p. 364-371 (Communications in Computer and Information Science; Vol. 329 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Traffic flow analysis based on the real data using neural networks

AU - Pamuła, Teresa

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N2 - The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.

AB - The paper presents the analysis of traffic data for determining classes of time series of traffic flow intensity for use in traffic forecasting employing neural networks. Data from traffic detectors on the main access road to the city of Gliwice in the period of past year is the basis for statistical analysis. Four classes of time series are proposed as representative of the traffic flow. The time series map temporarily smoothed detector counts. Different smoothing periods are used to retain the dynamic characteristics of the flows. A neural network is developed to classify incoming traffic data into the proposed time series classes. The specific time series implies a traffic control or management strategy, which indicates the capability of the NN to work out decisions for use in Intelligent Transportation Systems (ITS) applications.

KW - neural network

KW - prediction

KW - time series

KW - traffic flow analysis

KW - traffic flow classification

UR - https://www.scopus.com/pages/publications/84868244484

U2 - 10.1007/978-3-642-34050-5_41

DO - 10.1007/978-3-642-34050-5_41

M3 - Conference contribution

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SN - 9783642340499

T3 - Communications in Computer and Information Science

SP - 364

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BT - Telematics in the Transport Environment - 12th International Conference on Transport Systems Telematics, TST 2012, Selected Papers

T2 - 12th International Conference on Transport Systems Telematics, TST 2012

Y2 - 10 October 2012 through 13 October 2012

ER -

Traffic flow analysis based on the real data using neural networks

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this