NDLI: Multi-link traffic flow forecasting using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Ya Gao Shiliang Sun
Copyright Year	2010
Description	Author affiliation: Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China (Ya Gao; Shiliang Sun)
Abstract	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.
Starting Page	398
Ending Page	401
File Size	449511
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582914
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	computational intelligence Barium multi-task Roads Time series analysis Artificial neural networks Predictive models multi-link traffic flow forecasting Forecasting Junctions neural network
Content Type	Text
Resource Type	Article

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