Traffic flow forecasting is an important application of computational intelligence and an active research topic in Intelligent Transportation Systems (ITS). However, traditional methods called single-link traffic flow forecasting usually predict one link's unidirectional traffic flow at a time, which do not take the relevance of adjacent links into account and make the ITS have a low efficiency. In this paper, we propose a new approach named multi-link traffic flow forecasting using neural networks (NNs), which can predict traffic flows on all the road links of one junction simultaneously. Experimental results show that it can not only increase the efficiency of ITS but also improve the performance of prediction. Furthermore, we combine multi-task learning with the multi-link traffic flow forecasting and obtain a better performance of prediction. All these experiments indicate that the multi-link traffic flow forecasting is a much more effective approach for traffic flow forecasting.
['Ya Gao', 'Shiliang Sun']
Multi-link traffic flow forecasting using neural networks