Short-term Traffic Flow Forecasting Using Dynamic Linear Models

نویسندگان

  • Tiep Mai
  • Bidisha Ghosh
چکیده

Intelligent Transportation Systems (ITS) is an emerging concept which has been utilised to improve efficiency and sustainability of existing transportation systems. Short term traffic flow forecasting, the process of predicting future traffic conditions based on historical and realtime observations is an essential aspect of ITS. The existing well-known algorithms used for short-term traffic forecasting include time-series analysis based models. Among the timeseries models, the Seasonal Autoregressive Integrated Moving Average (SARIMA) is one of the most precise statistical models in this field. In the existing literature SARIMA models are mostly used in its multiplicative form and the parameters of the model are mostly estimated using a frequentist approach. Estimation of the large scale multiplicative SARIMA model for traffic flow forecasting often proves to be complex and computationally expensive for researchers and end-users. In this paper, an additive SARIMA model has been employed to predict traffic flow in shortterm or near-term future. The Dynamic Linear Model (DLM) representation of the additive SARIMA model has been used here to reduce the number of latent variables. Traditionally in a frequentist approach, point estimations of the SARIMA model parameters are obtained by maximizing the likelihood, but in this paper the marginal posterior density of each of the parameters has been explored by applying a Bayesian inference framework. Markov Chain Monte Carlo (MCMC) sampling method has been used to develop the Bayesian inference framework. For such sampling method for SARIMA, a problem of serial correlation has proved to be quite serious; however in the additive form, with the help of a carefully designed Metropolis-Hastings algorithm (a type of MCMC algorithm) this problem has been mitigated. The efficiency of the proposed prediction algorithm has been evaluated by modelling realtime traffic flow observations available from a certain junction in the city-centre of Dublin.

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تاریخ انتشار 2011