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Estimation of Scaling Factors for Traffic Counts Based on Stationary and Mobile Sources of Data

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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.

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Acknowledgments

This work was supported by a Research Postgraduate Studentship from the University of Hong Kong, and grants from the University Research Committee of the University of Hong Kong (201511159015), and Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 717512, 17208614). We gratefully acknowledge the Transport Department of HKSAR for providing the ATC and TCS traffic information data and Motion Power Media Limited and Concord Pacific Satellite Technologies Limited for offering the GPS data.

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Authors and Affiliations

  1. The University of Hong Kong, Pofulam Road, Hong Kong Island, Hong Kong

    Fanyu Meng, S. C. Wong, W. Wong & Y. C. Li

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  1. Fanyu Meng
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  2. S. C. Wong
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  3. W. Wong
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  4. Y. C. Li
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Corresponding author

Correspondence to Fanyu Meng.

Appendix 1

Appendix 1

Table 7 Trip Destination Categories in TCS 2011
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Meng, F., Wong, S.C., Wong, W. et al. Estimation of Scaling Factors for Traffic Counts Based on Stationary and Mobile Sources of Data. Int. J. ITS Res. 15, 180–191 (2017). https://doi.org/10.1007/s13177-016-0131-1

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  • DOI: https://doi.org/10.1007/s13177-016-0131-1

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