New data-driven approach to generate typologies of road segments

Abstract

The main objective of this study is to put forward a typology to describe better associations between road segments and driving patterns as reflected by driving speed. As the first step, a regression model is developed to examine the association between road segments and driving speed. Then, various unsupervised machine learning techniques, including k-means, AHC, and k-proto, are used to develop typologies of road segments. Speed data from a fleet of taxis operating in Montreal, Quebec, are used to validate the discrimination power of the various typologies. Results demonstrate that combining k-means and Gower distance produces the most accurate road typology. Various statistical tests, including ANOVA, Leven, and post hoc analyses, confirmed that the speed values of the various road types are significantly different. Finally, R² of regression models developed for various road types demonstrated that the generated road types better elucidate the variability of driving speed.

KEYWORDS:

• Road type
• driving speed
• road features
• data-driven model

Disclosure statement

No potential conflict of interest was reported by the author(s).