Loading...
Travel-time prediction with deep learning
Author(s)
Date Issued
February 8, 2017
Type
Conference Paper
Abstract
Travel time prediction is a challenging problem in Intelligent Transportation Systems (ITS). Accurate travel time information helps motorists plan their routes more wisely. This, in turn, alleviates traffic congestion and improves operation efficiency. A number of travel time prediction techniques exist; however, most of them are based on shallow learning architectures. In contrast to deep learning architectures, shallow learning architectures are lack of features-learning capability. In this paper, we propose an effective travel time prediction technique based on a concept of Deep Belief Networks (DBN). In our method, a stack of Restricted Boltzmann Machines (RBM) is used to automatically learn generic traffic features in an unsupervised fashion, and then a sigmoid regression is used to predict travel time in a supervised fashion. The experimental results, based on real traffic data, show that the proposed method can achieve great performance in terms of prediction accuracy.
Citation
IEEE Region 10 Annual International Conference Proceedings TENCON, 1859-1862, 2017