Estimation of vehicular journey time variability by Bayesian data fusion with general mixture model
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Pages (from-to) | 13640-13652 |
Journal / Publication | IEEE Transactions on Intelligent Transportation Systems |
Volume | 25 |
Issue number | 10 |
Online published | 27 May 2024 |
Publication status | Published - Oct 2024 |
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Abstract
This paper presents a Bayesian data fusion framework for estimating journey time variability that uses a mixture
distribution model to classify feeding data into different traffic
states. Different from most studies, the proposed framework
offers a generalized statistical foundation for making full use of
multiple traffic data sources to estimate the vehicular journey
time variability. Feeding data collected from multiple data
sources are classified based on the associated traffic conditions,
and the corresponding estimation biases of the individual data
sources are determined by arbitrary distributions. The proposed
framework is implemented and tested on a Hong Kong corridor
with actual data collected from the field. Different statistical
distributions of prior and likelihood knowledge are applied
and compared. The findings of the case study show significant
improvement in the journey time estimations of the proposed
method compared with the individual measurements. The results
also highlight the benefit of incorporating a traffic state classifier
and prior knowledge in the fusion framework. This study
contributes to the development of reliability-based intelligent
transportation systems based on advanced traffic data analytics.
© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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Citation Format(s)
Estimation of vehicular journey time variability by Bayesian data fusion with general mixture model. / Wu, Xinyue; Chow, Andy H. F. ; Zhuang, Li et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 10, 10.2024, p. 13640-13652.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 25, No. 10, 10.2024, p. 13640-13652.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review