DocumentCode :
3717236
Title :
Traffic forecasting in complex urban networks: Leveraging big data and machine learning
Author :
Florin Schimbinschi;Xuan Vinh Nguyen;James Bailey;Chris Leckie;Hai Vu;Rao Kotagiri
Author_Institution :
Department of Computing and Information Systems, The University of Melbourne
fYear :
2015
Firstpage :
1019
Lastpage :
1024
Abstract :
Accurate network-wide real time traffic forecasting is essential for next generation smart cities. In this context, we study a novel and complex traffic data set and explore the potential to apply big data and machine learning analysis. We evaluate several hypotheses and find that the availability of big data is able to facilitate more accurate predictions. Furthermore, we find that spatial aspects have more influence than temporal ones and that careful choice of thresholding parameters is crucial for high performance classification.
Keywords :
"Forecasting","Big data","Prediction algorithms","Roads","Neural networks","Spatiotemporal phenomena","Traffic control"
Publisher :
ieee
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BigData.2015.7363854
Filename :
7363854
Link To Document :
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