Graduation Year

2026

Date of Submission

12-2025

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Reader 1

Professor Mark Huber

Abstract

Technological advancements in machine learning have contributed to the recent rise of predictive modelling for vehicular infrastructure maintenance. Many cities around the world are shifting from reactive maintenance to proactive planning. The efficacy of machine learning algorithms has reduced the need of inefficient processes like periodic inspections. This thesis applies supervised learning techniques to create a predictive maintenance model for Copenhagen’s bicycle pavements. Cycling is an integral element within the cultural fabric of Copenhagen, and the city has four times as many bikes as cars. Due to this incredibly high ridership, even small improvements in rider experience can lead to tremendous cumulative gains. The findings suggest that supervised learning models can be effectively applied within the city’s context to augment the existing engineering judgement and optimize maintenance schedules. The thesis concludes that by focusing on collecting more granular data, the city of Copenhagen can shift to an entirely proactive schedule, creating several layers of improvements in the rider experience.

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This thesis is restricted to the Claremont Colleges current faculty, students, and staff.

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