MARKOV CHAIN MONTE CARLO BASED INVERSE MODELING OF TRAFFIC FLOWS USING GPS DATA

作者：Tossavainen Olli Pekka^*; Work Daniel B

来源：Networks and Heterogeneous Media, 2013, 8(3): 803-824.

DOI：10.3934/nhm.2013.8.803

• Parameter estimation
• fundamental diagram
• Markov Chain
• Monte Carlo
• traffic flow
• cell transmission model

In large scale deployments of traffic flow models, estimation of the model parameters is a critical but cumbersome task. A poorly calibrated model leads to erroneous estimates in data-poor environments, and limited forecasting ability. In this article we present a method for calibrating flow model parameters for a discretized scalar conservation law using only velocity measurements. The method is based on a Markov Chain Monte Carlo technique, which is used to approximate statistics of the posterior distribution of the model parameters. Numerical experiments highlight the difficulty in estimating jam densities and provide a new approach to improve performance of the sampling through re-parameterization of the model. Supporting source code for the numerical experiments is available for download at https://github.com/dbwork/MCMC-based-inverse-modeling-of-traffic.

• 出版日期2013-9
• 单位Microsoft