In ITS (Intelligent Transportation System) equipped urban transportation systems non-critical 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 short-term 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-center of Dublin and their performances have been discussed.
Abstract