Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099390" target="_blank" >RIV/61989100:27240/15:86099390 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/SSCI.2015.16" target="_blank" >http://dx.doi.org/10.1109/SSCI.2015.16</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SSCI.2015.16" target="_blank" >10.1109/SSCI.2015.16</a>
Alternative languages
Result language
angličtina
Original language name
Cellular Network Based Real-Time Urban Road Traffic State Estimation Framework Using Neural Network Model Estimation
Original language description
This paper presents real time road traffic state estimation framework together with its evaluation. To evaluate the framework, a three-layer Artificial Neural Network model is proposed and used to estimate complete link traffic state. The inputs to the ANN model include probe vehicle's position, time stamps and speeds. To model the arterial road network the microscopic simulation SUMO is used to generate aggregated speed and FCD export files which are used in the training and evaluation of the ANN model. Besides, real A-GPS data gathered using A-GPS mobile phone on a moving vehicle on the sample roads is used to evaluate the ANN model. The performance of the ANN model is evaluated using the performance indicators RMSE and MPAE and on average the MPAE is less than 1.2%. The trained ANN model is also used to estimate the sample road link speeds and compared with ground truth speed (aggregate edge states) on a 10-minute interval for 1hr. The estimation accuracy using MAE and estimation availability indicated that reliable link speed estimation can be generated and used to indicate real-Time urban road traffic condition. (C) 2015 IEEE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
ISBN
978-1-4799-7560-0
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
38-44
Publisher name
IEEE
Place of publication
New York
Event location
Kapské Město
Event date
Dec 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000380431500006