Neighbouring Link Travel Time Inference Method Using Artificial Neural Network




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Peer reviewed



This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods.


The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.


travel time estimate, sparse historic data, artificial neural network, traffic model


Luong H. V. et al. (2018) Neighbouring link travel time inference method using artificial neural network. Computational Intelligence (SSCI), 2017 IEEE Symposium Series on,


Research Institute

Institute of Artificial Intelligence (IAI)