Real-Time Headway State Identification and Saturation Flow Rate Estimation: A Hidden Markov Chain Model
Abstract
Saturation flow rate (SFR) denotes the maximum sustainable flow rate during the green signal. Calibration of SRF is not a problem that can be solved once and for all. Due to various reasons such as degrading infrastructure or changes in the surrounding environment, a well-calibrated SFR could become outdated and it is expensive to recalibrate following traditional methods. This manuscript proposes a model to calculate saturation flow rate in a real-time fashion from loop detector-data that is readily available. The problem is formulated as a Markov Chain model with the goal of identifying traffic headway states. A total of five states and the transitional behavior are defined. The distribution of headway given the underlying state is also presented. The SFR estimation is converted to the identification of stable headway. The proposed model is tested and validated, which shows the proposed model is able to generate satisfactory results.
Recommended Citation
H. Qi and X. Hu, "Real-Time Headway State Identification and Saturation Flow Rate Estimation: A Hidden Markov Chain Model," Transportmetrica A: Transport Science, vol. 16, no. 3, pp. 840 - 864, Taylor & Francis, Jan 2020.
The definitive version is available at https://doi.org/10.1080/23249935.2020.1722285
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Hidden Markov Model; Intersection Capacity; Saturation Flow Rate
International Standard Serial Number (ISSN)
2324-9935; 2324-9943
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Taylor & Francis, All rights reserved.
Publication Date
01 Jan 2020
Comments
This research is supported by Zhejiang Province Public Welfare Scientific Research Project [grant number LGF18E080003].