Authors: Wen Shen; Zhihua Wei; Chao Yang; Renxian Zhang
Addresses: Department of Computer Science and Technology, Tongji University, Shanghai, China ' Department of Computer Science and Technology, Tongji University, Shanghai, China ' School of Transportation Engineering, Tongji University, Shanghai, 201804, China ' Department of Computer Science and Technology, Tongji University, Shanghai, China
Abstract: How to use historical data of public smart card to predict user behaviour attracts a lot of attention. This paper aims at modelling travel patterns and predicting future travel behaviour of metro system smart card holders. We apply Gaussian mixture model (GMM) on time series to model user behaviour. We propose a new method based on the perplexity for finite GMM and use expectation-maximisation (EM) algorithm to estimate parameters of GMM. In order to predict the future travel behaviour, we introduce the Gaussian process regression (GPR) to define distributions over GMM, which can not only tell the probability of travelling at a certain moment but also tell the reliability of the prediction. Experimental results show that our whole system in the centre of GMM and GPR can effectively mine the hidden knowledge of historical data of smart card, and thus model the travel patterns and predict future travel behaviour.
Keywords: Gaussian mixture model; GMM; perplexity; Gaussian process regression; GPR; travel pattern modelling; behaviour prediction.
International Journal of Simulation and Process Modelling, 2018 Vol.13 No.6, pp.548 - 556
Available online: 18 Oct 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article