The conditional probability of travel speed and its application to short-term prediction

Over the past a few decades, a large number of researchers have worked on embedding mathematical methods into the prediction of traffic conditions. However, a lot of previous work only considered the temporal and spatial covariates as static explanatory variables. Different from previous studies, this paper investigates the conditional probability of freeway travel speed based on its lag values. Such conditional probability can be regarded as an attribute for each road segment. Based on this attribute, this paper proposes a conditional cumulative distribution function (CCDF)-based travel speed prediction approach. A real-world case study is conducted to predict short-term freeway speeds. The proposed CCDF-based model is compared with the Neural Networks (NN) model and the Support Vector Regression (SVR) model. The proposed model is faster in model training. Moreover, it can significantly improve the prediction accuracy, with 7.11% accuracy increase, compared to traditional NN and SVR prediction approaches.