Bayesian inference of channelized section spillover via Markov Chain Monte Carlo sampling

Hongsheng Qi, Xianbiao Hu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)478-498
Number of pages21
JournalTransportation Research Part C: Emerging Technologies
Volume97
DOIs
StatePublished - Dec 2018

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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