A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation
Saved in:
| Title: | A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation |
|---|---|
| Authors: | Liu, S. Y. (author), Lin, S. (author), Wang, Y. B. (author), De Schutter, B.H.K. (author), Lam, W. H.K. (author) |
| Source: | IFAC-PapersOnLine. 53:15029-15034 |
| Publisher Information: | Elsevier BV, 2020. |
| Publication Year: | 2020 |
| Subject Terms: | Markov traffic model, 11. Sustainability, 0502 economics and business, 05 social sciences, Urban traffic network, 0211 other engineering and technologies, 02 engineering and technology, Traffic signals, Bayesian |
| Description: | In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2020.12.2003 |
| Access URL: | https://www.sciencedirect.com/science/article/pii/S2405896320326355 http://resolver.tudelft.nl/uuid:51d90eb9-c6ee-46dc-80c2-c4bc70f7743a |
| Rights: | Elsevier TDM |
| Accession Number: | edsair.doi.dedup.....2e3a3af80c9a45a01ed2e67583e2e75c |
| Database: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks. |
|---|---|
| ISSN: | 24058963 |
| DOI: | 10.1016/j.ifacol.2020.12.2003 |