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Authors: Traianos-Ioannis Theodorou 1 ; Athanasios Salamanis 2 ; Dionysios D. Kehagias 2 ; Dimitrios Tzovaras 2 and Christos Tjortjis 3

Affiliations: 1 Centre for Research & Technology Hellas and International Hellenic University, Greece ; 2 Centre for Research & Technology Hellas, Greece ; 3 International Hellenic University, Greece

ISBN: 978-989-758-242-4

Keyword(s): Short-Term Traffic Prediction, Atypical Conditions, Automatic Incident Detection, Support Vector Machines, K-Nearest Neighbour, Autoregressive Integrated Moving Average.

Abstract: One of the most challenging goals of the modern Intelligent Transportation Systems comprises the accurate and real-time short-term traffic prediction. The achievement of this goal becomes even more critical when the presence of atypical traffic conditions is concerned. In this paper, we propose a novel hybrid technique for short-term traffic prediction under both typical and atypical conditions. An Automatic Incident Detection (AID) algorithm, based on Support Vector Machines (SVM), is utilized to check for the presence of an atypical event (e.g. traffic accident). If such an event occurs, the k-Nearest Neighbors (k-NN) non-parametric regression model is used for traffic prediction. Otherwise, the Autoregressive Integrated Moving Average (ARIMA) parametric model is activated for the same purpose. In order to evaluate the performance of the proposed model, we use open real world traffic data from Caltrans Performance Measurement System (PeMS). We compare the proposed model with the uni tary k-NN and ARIMA models, which represent the most commonly used non-parametric and parametric traffic prediction models. Preliminary results show that the proposed model achieves larger accuracy under both typical and atypical traffic conditions. (More)

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Paper citation in several formats:
Theodorou, T.; Theodorou, T.; Salamanis, A.; Kehagias, D.; Kehagias, D.; Tzovaras, D. and Tjortjis, C. (2017). Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model.In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-242-4, pages 79-89. DOI: 10.5220/0006293400790089

@conference{vehits17,
author={Traianos{-}Ioannis Theodorou. and Traianos{-}Ioannis Theodorou. and Athanasios Salamanis. and Dionysios D. Kehagias. and Dionysios D. Kehagias. and Dimitrios Tzovaras. and Christos Tjortjis.},
title={Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model},
booktitle={Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2017},
pages={79-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006293400790089},
isbn={978-989-758-242-4},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Short-Term Traffic Prediction under Both Typical and Atypical Traffic Conditions using a Pattern Transition Model
SN - 978-989-758-242-4
AU - Theodorou, T.
AU - Theodorou, T.
AU - Salamanis, A.
AU - Kehagias, D.
AU - Kehagias, D.
AU - Tzovaras, D.
AU - Tjortjis, C.
PY - 2017
SP - 79
EP - 89
DO - 10.5220/0006293400790089

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