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Short-Term Traffic Prediction Using a Binary Neural Network.

This paper presents a binary neural network algorithm for short-term traffic flow prediction. The algorithm can process both univariate and multi variate data from a single traffic sensor using time series prediction (temporal lags) and can combine information from multiple traffic sensors with time series prediction ( spatial-temporal lags). The algorithm provides Intelligent Decision Support (IDS) for road network managers to proactively manage problems on the network as the predictions generated may be used to determine if traffic control interventions need to be applied. The algorithm can operate in near-real-time and dynamically; using data from UTC or UTMC systems. It is based on the Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour prediction algorithm, which is designed for scalability and fast performance. The AURA k-NN predictor outperforms other machine learning techniques with respect to prediction accuracy and is able to train and predict rapidly. The basic AURA k-NN time series prediction algorithm was extended by incorporating average daily profiles and variable weighting into the prediction in this paper. The average daily profile of a variable is calculated as the average reading of the variable for a particular time of day and day of the week after removing outliers. When data vectors are matched in the AURA k-NN, the daily profile adds an extra dimension to the match. This process was further enhanced by weighting the profile using variable weighting to vary the profile’s significance. It is shown that incorporating these two additional aspects improves the accuracy of the prediction compared to the standard AURA k-NN, resulting in a very fast and accurate traffic prediction tool