Citation

Meng F, Wong SC, Wong W, Li YC. Int. J. Intell. Transp. Syst. Res. 2017; 15(3): 180-191.

Copyright

(Copyright © 2017, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s13177-016-0131-1

PMID

unavailable

Abstract

To combine mobile sources and stationary sources, a modeling approach to quantify the variability of the linear projection function using a non-linear regression method is established in this study. Weights that vary spatial-temporally are assigned to neighboring scaling factors. Together with a normalized weighted average function, the subject scaling factor is determined. The framework is applied to a case study in Hong Kong combining Global Positioning System data and the annual traffic counts from 85 fixed stations in Annual Traffic Census database. The performance of the models is assessed based on relative root mean square error and Akaike information criterion.

Language: en