Investigation on linearisation of data-driven transport research: two representative case studies
Artikel i vetenskaplig tidskrift, 2020

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.

data-driven transport research

nonlinear bunker-consumption model

mechanical engineering computing

nonlinear regression problem

linear regression method

transportation engineering

transportation

least squares approximations

nonlinearity

nonlinear regression function

regression model

regression analysis

data transformation

Författare

Yun Zou

University of Technology Sydney

Yan Kuang

Griffith University

Yue Zhi

Binzhou Medical University

Xiaobo Qu

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

IET Intelligent Transport Systems

1751-956X (ISSN) 1751-9578 (eISSN)

Vol. 14 7 675-683

Ämneskategorier

Teknisk mekanik

Sannolikhetsteori och statistik

Reglerteknik

DOI

10.1049/iet-its.2019.0551

Mer information

Senast uppdaterat

2020-09-18