Title

Bayesian Inference of Channelized Section Spillover Via Markov Chain Monte Carlo Sampling

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.

Department(s)

Civil, Architectural and Environmental Engineering

Comments

Research is supported by the National Natural Science Foundation of China (Grant No. 51408538 ); Zhejiang province public welfare scientific research project (Grant No. LGF18E080003 ); Shanghai Rising-Star Program ( 17QB1400600 ) and the Fundamental Research Funds for the Central Universities.

Keywords and Phrases

Bayesian model; Channelized section spillover; Monte Carlo Markov Chain

International Standard Serial Number (ISSN)

0968-090X

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Elsevier Ltd, All rights reserved.

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