Citation

Pandey A, Sharma M, Biswas S. Int. J. Transp. Sci. Technol. 2023; 12(2): 354-372.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.ijtst.2022.02.008

PMID

unavailable

Abstract

Heterogeneity is one of those characteristics which differentiate traffic conditions of a developing country from other developed nations. The heterogeneity which represents the diversity among vehicle categories is suspected to have adverse influences on lane discipline, congestion potential, and road users' safety. However, the influence of heterogeneity on the above-mentioned parameters has been only measured indirectly by considering traffic composition as an indicator of the prevailed heterogeneity-level. No direct relationship between the heterogeneity and other parameters has been established as there is no methodology available yet for quantifying the heterogeneity of mixed traffic. The present study addresses this problem and conceptualizes the 'Heterogeneity Index' (HI) to quantify the heterogeneity present in a mixed traffic stream. HI is conceived as a measure of the dispersion of Passenger Car Units (PCU) for different vehicle categories from its central value. A higher value of HI signifies more diverse vehicle categories present in the traffic stream. PCU of a vehicle category was estimated using the speed-based method and the individual speeds were predicted based on classified volumes using the Gaussian Process Regression model developed in this study. This paper also recommends several categorical levels for easy perception about the intensity of heterogeneity. Further, the sensitivity analysis explored the dynamic aspects of HI.

RESULTS showcased how the HI of a traffic stream may vary subject to the combined or the individual change in traffic volume, traffic composition and classified speeds. The outcomes of the study will be useful to estimate the intensity of heterogeneity that prevailed within a mixed traffic stream with varying traffic conditions.

Language: en

Keywords

Clustering; Gaussian process regression; Heterogeneous traffic; PCU; Urban roads