Short-term prediction of level of service in highways based on bluetooth identification

Wilby, Mark Richard and Rodríguez González, Ana Belén and Fernández Pozo, Rubén and Vinagre Díaz, Juan José (2020). Short-term prediction of level of service in highways based on bluetooth identification. "IEEE Transactions on Intelligent Transportation Systems" ; pp. 1-10. ISSN 1524-9050. https://doi.org/10.1109/TITS.2020.3008408.

Description

Title: Short-term prediction of level of service in highways based on bluetooth identification
Author/s:
  • Wilby, Mark Richard
  • Rodríguez González, Ana Belén
  • Fernández Pozo, Rubén
  • Vinagre Díaz, Juan José
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Intelligent Transportation Systems
Date: 2020
ISSN: 1524-9050
Subjects:
Freetext Keywords: Level of service; traffic prediction; Bluetooth identification; classifier; travel time
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Matemática Aplicada
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

A precise knowledge about future traffic will eventu- ally open a new era in traffic management. Research has focused on the still unresolved problem of predicting travel time (TT). However, practitioners favor the level of service (LOS) as a meaningful metric that avoids the continuous fluctuations and link-specificity of TT. Evolving from TT to LOS opens a new research line in the field, moving the underlying mathematical problem from regression to classification. This study proposes a short-term LOS classifier to fulfill this requirement. Given that traffic conditions are mostly free-flow throughout the day, LOS classes are unbalanced. Therefore, we based our predictor on a Random Undersampling Boost algorithm (RUSBoost), especially suited to overcome this issue. We trained and validated this LOS predictor with 12 months of arrival travel time data, captured by a Bluetooth network with 6 links, in real operation on the SE-30 highway (Seville, Spain). This classifier achieved an average recall of 82.8 % for prediction horizons up to 15 minutes, reaching 92.5 % predicting congestion. We reached this performance by exploiting two facts that we empirically demonstrated: (i) information from every link (even those in the opposite direction) contributes to increase the accuracy of the prediction; and (ii) traffic presents different behavior depending on the day of the week, which we used to segment the data and construct specific classifiers. These promising results show the potential of the proposed LOS predictor, providing a new perspective into traffic forecast and the subsequent traffic management that yields with what practitioners demand.

More information

Item ID: 65838
DC Identifier: http://oa.upm.es/65838/
OAI Identifier: oai:oa.upm.es:65838
DOI: 10.1109/TITS.2020.3008408
Official URL: https://ieeexplore.ieee.org/document/9146382
Deposited by: Memoria Investigacion
Deposited on: 12 Apr 2021 13:08
Last Modified: 22 Jul 2021 22:30
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