A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting

Xia, Dawen, Wang, Binfeng, Li, Huaqing, Li, Yantao and Zhang, Zili 2016, A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting, Neurocomputing, vol. 179, pp. 246-226, doi: 10.1016/j.neucom.2015.12.013.

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Title A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting
Author(s) Xia, Dawen
Wang, Binfeng
Li, Huaqing
Li, Yantao
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name Neurocomputing
Volume number 179
Start page 246
End page 226
Total pages 18
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-02-29
ISSN 0925-2312
Summary Accurate and timely traffic flow prediction is crucial to proactive traffic management and control in data-driven intelligent transportation systems (D2ITS), which has attracted great research interest in the last few years. In this paper, we propose a Spatial-Temporal Weighted K-Nearest Neighbor model, named STW-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting. More specifically, STW-KNN considers the spatial-temporal correlation and weight of traffic flow with trend adjustment features, to optimize the search mechanisms containing state vector, proximity measure, prediction function, and K selection. urthermore, STW-KNN is implemented on a widely adopted Hadoop distributed computing platform with the MapReduce parallel processing paradigm, for parallel prediction of traffic flow in real time. inally, with extensive experiments on real-world big taxi trajectory data, STW-KNN is compared with the state-of-the-art prediction models including conventional K-Nearest Neighbor (KNN), Artificial Neural Networks (ANNs), Naïve Bayes (NB), Random orest (R), and C4.. The results demonstrate that the proposed model is superior to existing models on accuracy by decreasing the mean absolute percentage error (MAPE) value more than 11.9% only in time domain and even achieves 89.71% accuracy improvement with the MAPEs of between 4% and 6.% in both space and time domains, and also significantly improves the efficiency and scalability of short-term traffic flow forecasting over existing approaches.
Language eng
DOI 10.1016/j.neucom.2015.12.013
Field of Research 010204 Dynamical Systems in Applications
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082156

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