Comparison of different Bayesian methods for estimating error bars with incident duration prediction

Abstract

Traffic congestion which has been a common problem faced by almost all of the metropolitan cities across the world is caused by non-recurring traffic incidents such as accidents and vehicle breakdowns in many cases. Therefore, accurate prediction of the incident duration has become an essential part of Intelligent Transport Systems (ITS). Although the data-driven deterministic regression models can serve this purpose, the performances of these models are subject to varying test conditions. In this study, we consider two different data-sets, one from Singapore and the other from the Netherlands, and explore stochastic models (Bayesian Support Vector Regression, i.e. BSVR and Gaussian Process, i.e. GP) that not only perform prediction but also provide error bars as confidence measures along with the predicted values. Moreover, we also verify whether these methods can anticipate when the predictions are unreliable. To this end, we analyze the specificity and sensitivity of the two methods for detecting prediction errors with different tolerance levels. We observe that for 70% specificity, the sensitivity of BSVR and GP is 67% and 78% respectively, for the incidents in Singapore. On the other hand, for the incidents in the Netherlands, the two methods have 55% and 60% sensitivity respectively, for the same level of specificity. Therefore, our proposed method of Bayesian models with error bars leads to more reliable predictions of the traffic incident duration, as well as forecasts to some extent when the predictions are unreliable.