Enhancing Predictions in Signalized Arterials with Information on Short-Term Traffic Flow Dynamics

摘要

Short-term traffic flow predictions are an essential part of intelligent transportation systems. Previous research underlines the difficulty in systematically assessing the predictability of traffic flow near capacity or during congested conditions. In this article a neural network prediction scheme is proposed that is consistent with the pattern-based evolution of traffic flow and has the capability of exploiting past information to acquire knowledge on the traffic dynamics in order to enhance predictability. Findings indicate that pattern-based predictions are more accurate-in the traffic flow regimes considered-when compared to other local and global prediction techniques that operate under the time-series consideration. The pattern-based prediction scheme was also found to outperform the other methods tested in the knowledge of the anticipated traffic flow state in all traffic flow conditions considered.