Driving heterogeneity identification using machine learning: A review and framework for analysis

Driving heterogeneity identification using machine learning: A review and framework for analysis

Driving heterogeneity significantly influences traffic performance, contributing to traffic disturbances, increased crash risks, and inefficient fuel use and emissions. With the growing availability of driving behaviour data, Machine Learning (ML) techniques have become widely used for analysing dri...

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Bibliographic Details
Main Authors: Xue Yao, Simeon C. Calvert, Serge P. Hoogendoorn
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Transportation Research Interdisciplinary Perspectives
Subjects:
Driving heterogeneity
Identification
Machine Learning (ML)
Traffic flow
Traffic data analysis
Online Access:http://www.sciencedirect.com/science/article/pii/S2590198225001903
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