On feature selection for traffic congestion prediction

Traffic congestion prediction plays an important role in route guidance and traffic management. We formulate it as a binary classification problem. Through extensive experiments with real-world data, we found that a large number of sensors, usually over 100, are relevant to the prediction task at one sensor, which means wide area correlation and high dimensionality of the data. This paper investigates the first time into the feature selection problem for traffic congestion prediction. By applying feature selection, the data dimensionality can be reduced remarkably while the performance remains the same. Besides, a new traffic jam probability scoring method is proposed to solve the high-dimensional computation into many one-dimensional probabilities and its combination.

► We study the missing topic for traffic jam predict, say, feature selection/ranking. ► We score the relevance of each sensor in predicting jams at a given sensor. ► The number of relevant sensors for each prediction is high, over 100 in general. ► The optimal feature number is subject to the targeting sensor. ► Predict with optimally selected features outperforms that using all features.