Wavelet-Bayesian hierarchical stochastic model for short-term traffic flow at noncritical junctions

Abstract

In ITS (Intelligent Transportation System) equipped urban transportation systems noncritical junctions are often ignored in short-term traffic condition prediction algorithms as the traffic data collection systems in these junctions are not adequate. The paper proposes a shortterm traffic volume model based on a combination of discrete wavelet transform (DWT) and Bayesian hierarchical methodology (BHM) applicable to non-critical junctions lacking continuous data collection systems. Unlike typical short-term traffic condition forecasting algorithms, large traffic flow datasets including information on current traffic scenarios are not required for the proposed model. In this model, a non-functional representation of the daily `trend? of urban traffic flow observations is achieved using DWT while the fluctuations in the traffic flow in addition to the variations represented by the `trend? are modeled as a stochastic process using BHM. The time-varying variance (within day) of these fluctuations over the `trend? in urban traffic flow observations at a signalized intersection has been estimated in the model. The effectiveness and the accuracy of the model have been compared with a conventional short-term traffic flow forecasting time-series model based on Holt-Winters Exponential Smoothing (HWES) technique. Both the models are applied at two signalized intersections at the city-centre of Dublin and their performances have been discussed.