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  • Investigation on linearisation of data-driven transport research: two representative case studies

    Author(s)
    Zou, Yun
    Kuang, Yan
    Zhi, Yue
    Qu, Xiaobo
    Griffith University Author(s)
    Kuang, Yan
    Year published
    2020
    Metadata
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    Abstract
    Transportation engineering, as a practical engineering discipline, relies heavily on the accurate calibration of importation parameters from field data. In the real world, most transport relations possess inherent non‐linearity. Two prevailing methods for handling non‐linear regression are the non‐linear least‐squares method (LSM) with an iterative solution, and linearisation for the non‐linear regression function. The second method applies a linear regression method to solve the non‐linear regression problem but requires a data transformation of the observations from variant coordinates, and the objective function is suspected ...
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    Transportation engineering, as a practical engineering discipline, relies heavily on the accurate calibration of importation parameters from field data. In the real world, most transport relations possess inherent non‐linearity. Two prevailing methods for handling non‐linear regression are the non‐linear least‐squares method (LSM) with an iterative solution, and linearisation for the non‐linear regression function. The second method applies a linear regression method to solve the non‐linear regression problem but requires a data transformation of the observations from variant coordinates, and the objective function is suspected to be changed accordingly. This work describes the authors’ investigation into the problem of non‐linear regression through two illustrative examples, the calibration of three non‐linear (either exponential or logarithmic) single‐regime models for fundamental diagram and the regression of non‐linear (power) bunker‐consumption model, by applying the weighted LSM (WLSM) and the ordinary LSM to calibrate. It is found that linearising the regression model leads to deviations, and the data transformation can create even more concern with the WLSM because the weights can be redistributed after the data transformation. A further investigation into the linear regression and the non‐linear regression gives more suggestions on the choice of regression method.
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    Conference Title
    IET Intelligent Transport Systems
    Volume
    14
    Issue
    7
    DOI
    https://doi.org/10.1049/iet-its.2019.0551
    Subject
    Transport engineering
    Electrical engineering
    Electronics, sensors and digital hardware
    Science & Technology
    Transportation Science & Technology
    Transportation
    Publication URI
    http://hdl.handle.net/10072/401560
    Collection
    • Conference outputs

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