An Innovative Approach for the Short-term Traffic Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people's daily life. In the last twenty years, many traffic flow prediction approaches have been proposed. However, some of these approaches use the regression based mechanisms, which cannot achieve accurate short-term traffic flow predication. While, other approaches use the neural network based mechanisms, which cannot work well with limited amount of training data. To this end, a light weight tensor-based traffic flow prediction approach is proposed, which can achieve efficient and accurate short-term traffic flow prediction with continuous traffic flow data in a limited period of time. In the proposed approach, first, a tensor-based traffic flow model is proposed to establish the multi-dimensional relationships for traffic flow values in continuous time intervals. Then, a CANDECOMP/PARAFAC decomposition based algorithm is employed to complete the missing values in the constructed tensor. Finally, the completed tensor can be directly used to achieve efficient and accurate traffic flow prediction. The experiments on the real dataset indicate that the proposed approach outperforms many current approaches on traffic flow prediction with limited amount of traffic flow data.