Individual Commute Time Recognition Based on the Hierarchical Semantic Model

Individual commute time recognition is essential for traffic demand management. However, this problem has yet to be studied. In this study, we propose a hierarchical semantic model (HSM) to recognize individual commute time. To the best of our knowledge, this work is the first to integrates large scale travellers commute time prediction at an individual level. HSM consists of a low and a high semantic layer. The low semantic layer models spatial, temporal and environmental information, whereas the high semantic layer recognises commute time using the hidden Markov model on the basis of the low semantic layer outputs. Experimental results demonstrate the effectiveness of our proposed model for individual commute time recognition.