Mobility Compass

• Sinanmis, Renan
• Woods, Lee

Abstract

Highway pavements deteriorate over time as successive wheel loads cause rutting, cracking, texture loss etc. Design standards and pavement performance models account for some of the known contributory factors, such as levels of traffic and vehicle composition. However, such models are limited in their predictive power, and highway authorities must conduct regular pavement condition surveys rather than relying on the standard deterioration models alone. The ways in which multiple factors affect pavement deterioration, including rutting, are complex and are believed to include feedback loops where rutting then influences driving position, exacerbating the rutting levels. Standard regression models are not well suited to representing such complex causal mechanisms. This paper compares two alternative modelling approaches, Structural Equations Models and Auto-Machine Learning and evaluates the predictive ability and practicalities of each. The findings indicate that Auto-Machine Learning (AutoML) may be superior in its predictive ability. However, their “black-box” nature makes the results potentially less useful to practitioners. A process of using machine learning to help inform a structural equations model is proposed.

Topics

• driving
• machine learning
• machinery
• learning
• equation
• modeling
• survey
• regression analysis
• sidewalk
• pavement
• supervisor
• cracking
• texture
• wheel
• highway
• deterioration
• beltway
• design standard
• wheel load
• wheel load
• pavement performance
• rutting
• condition survey
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