TY - GEN
T1 - A stacked generalization framework for city traffic related geospatial data analysis
AU - Liu, Xiliang
AU - Yu, Li
AU - Peng, Peng
AU - Lu, Feng
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Analyzing traffic related geospatial data often lacks in priori knowledge and encounters parameter setting problems due to the dynamic characteristics of city traffic. In this paper, we propose a pervasive, scalable framework for city traffic related geospatial data analysis based on a stacked generalization. Firstly we analyze the optimal linear combination based on stepwise iteration, and also prove its theoretical validity via error-ambiguity decomposition. Secondly we integrate six classical approaches into this framework, including linear least squares regression, autoregressive moving average, historical mean, artificial neural network, radical basis function neural network, support vector machine, and conduct experiments with a real city traffic detecting dataset. We further compare the proposed framework with other four linear combination models. It suggests that the proposed framework behaves more robust than other models both in variance and bias, showing a promising direction for city traffic related geospatial data analysis.
AB - Analyzing traffic related geospatial data often lacks in priori knowledge and encounters parameter setting problems due to the dynamic characteristics of city traffic. In this paper, we propose a pervasive, scalable framework for city traffic related geospatial data analysis based on a stacked generalization. Firstly we analyze the optimal linear combination based on stepwise iteration, and also prove its theoretical validity via error-ambiguity decomposition. Secondly we integrate six classical approaches into this framework, including linear least squares regression, autoregressive moving average, historical mean, artificial neural network, radical basis function neural network, support vector machine, and conduct experiments with a real city traffic detecting dataset. We further compare the proposed framework with other four linear combination models. It suggests that the proposed framework behaves more robust than other models both in variance and bias, showing a promising direction for city traffic related geospatial data analysis.
KW - City traffic
KW - Ensemble learning
KW - Geospatial data
KW - Robustness
KW - Stacked generalization
UR - http://www.scopus.com/inward/record.url?scp=84992741058&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45835-9_23
DO - 10.1007/978-3-319-45835-9_23
M3 - Conference contribution
AN - SCOPUS:84992741058
SN - 9783319458342
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 276
BT - Web Technologies and Applications - APWeb 2016 Workshops, WDMA, GAP, and SDMA, Proceedings
A2 - Zhu, Jia
A2 - Zhang, Rong
A2 - Chang, Lijun
A2 - Zhang, Wenjie
A2 - Liu, Kuien
A2 - Morishima, Atsuyuki
A2 - Fu, Tom Z.J.
A2 - Yang, Xiaoyan
A2 - Zhang, Zhiwei
PB - Springer Verlag
T2 - 18th International Conference on Web Technologies and Applications, APWeb 2016 and Workshop on 2nd International Workshop on Web Data Mining and Applications, WDMA 2016 and 1st International Workshop on Graph Analytics and Query Processing, GAP 2016 and 1st International Workshop on Spatial-temporal Data Management and Analytics, SDMA 2016
Y2 - 23 September 2016 through 25 September 2016
ER -