Bayesian Inference of Channelized Section Spillover Via Markov Chain Monte Carlo Sampling
Channelized section spillover (CSS) is usually referred to the phenomenon of a traffic flow being blocked upstream and not being able to enter the downstream channelized section. CSS leads to extra delays, longer queues, and a biased detection of the flow rate. An estimation of CSS, including its occurrence and duration, is helpful for analysis of the state of traffic flow, as a basis for traffic evaluation and management. This has not been studied or reported in prior literature. A Bayesian model is developed through this research to estimate CSS, with its occurrence and duration formulated as a posterior distribution of given travel time and flow rate data. Basic properties of CSS are discussed initially, followed by a macroscopic model that explicitly models the CSS and encapsulates first-in-first-out (FIFO) behavior at an upstream section, with a goal of generating the prior distribution of CSS duration. Posterior distribution is then constructed using the detected flow rate and travel time vehicles samples. The Markov Chain Monte Carlo (MCMC) sampling method is used to solve this Bayesian model. The proposed model is implemented and tested in a channelized intersection and its modeling results are compared with Vissim simulation outputs, which demonstrated satisfactory results.
H. Qi and X. Hu, "Bayesian Inference of Channelized Section Spillover Via Markov Chain Monte Carlo Sampling," Transportation Research Part C: Emerging Technologies, vol. 97, pp. 478-498, Elsevier Ltd, Dec 2018.
The definitive version is available at https://doi.org/10.1016/j.trc.2018.11.004
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Bayesian model; Channelized section spillover; Monte Carlo Markov Chain
International Standard Serial Number (ISSN)
Article - Journal
© 2018 Elsevier Ltd, All rights reserved.
01 Dec 2018