An efficient traffic state estimation model based on fuzzy c-mean clustering and mdl using fcd | [Journal of Theoretical and Applied Information Technology • 2020]

Author(s):

1. FATEMEH AHANIN: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti, Putra Malaysia, Jalan Universiti 1 Serdang, 43400 Seri Kembangan, Selangor,Malaysia

2. NORWATI MUSTAPHA: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti, Putra Malaysia, Jalan Universiti 1 Serdang, 43400 Seri Kembangan, Selangor,Malaysia

3. NASIR BIN SULAIMAN: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti, Putra Malaysia, Jalan Universiti 1 Serdang, 43400 Seri Kembangan, Selangor,Malaysia

4. MASLINA ZOLKEPLI: Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti, Putra Malaysia, Jalan Universiti 1 Serdang, 43400 Seri Kembangan, Selangor,Malaysia

Abstract:

Monitoring and estimating of large-scale traffic have major role in traffic congestion reduction. Floating Car Data (FCD) is one of the best methods for collecting traffic data due to its versatility and cost efficiency. However, FCD suffers from data sparseness and many researches have been done to improve traffic estimation accuracy with respect to data sparsity. In this paper, a new model based on Fuzzy C-Mean (FCM) clustering and Minimum Description Length (MDL) is proposed to estimate the missing traffic state using FCD. First the Fuzzy clustering is implemented to cluster the road segments based on similarity of their speed at each time slot. Then the MDL principle is applied to estimate the missing traffic state. The experimentation results show that the proposed model can estimate the missing data more accurately than the HMM-based model using the same dataset.

Page(s): 2787-2799

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 14, Year: 2020

Keywords:

pattern mining , Fuzzy cmean Clustering , Traffic State Estimation , Minimum Description Length

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