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Algorithms 2017, 10(2), 37; doi:10.3390/a10020037

A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects

1
Key Laboratory of Spatial Information Precessing and Application System Technology, Institude of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editor: Toly Chen
Received: 20 February 2017 / Revised: 21 March 2017 / Accepted: 22 March 2017 / Published: 24 March 2017

Abstract

Location prediction has attracted much attention due to its important role in many location-based services, such as food delivery, taxi-service, real-time bus system, and advertisement posting. Traditional prediction methods often cluster track points into regions and mine movement patterns within the regions. Such methods lose information of points along the road and cannot meet the demand of specific services. Moreover, traditional methods utilizing classic models may not perform well with long location sequences. In this paper, a spatial-temporal-semantic neural network algorithm (STS-LSTM) has been proposed, which includes two steps. First, the spatial-temporal-semantic feature extraction algorithm (STS) is used to convert the trajectory to location sequences with fixed and discrete points in the road networks. The method can take advantage of points along the road and can transform trajectory into model-friendly sequences. Then, a long short-term memory (LSTM)-based model is constructed to make further predictions, which can better deal with long location sequences. Experimental results on two real-world datasets show that STS-LSTM has stable and higher prediction accuracy over traditional feature extraction and model building methods, and the application scenarios of the algorithm are illustrated. View Full-Text
Keywords: location prediction; trajectory mining; spatial-temporal data; semantic trajectory; feature extraction; neural networks location prediction; trajectory mining; spatial-temporal data; semantic trajectory; feature extraction; neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wu, F.; Fu, K.; Wang, Y.; Xiao, Z.; Fu, X. A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects. Algorithms 2017, 10, 37.

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