Advancing Sustainable Mobility Infrastructure and Environment Through Automated Low Threshold Analyzation of Road and Cycling Path Surfaces

Authors

  • Professor Oliver Tamine Institute for Mobility and Infrastructure in Rural Areas, Hochschule Furtwangen University, Robert-Gerwig-Platz 1 78120, Furtwangen
  • Professor Jochen Baier Institute for Mobility and Infrastructure in Rural Areas, Hochschule Furtwangen University, Robert-Gerwig-Platz 1 78120, Furtwangen

Abstract

This paper presents a novel application of digital ecosystems in the realm of sustainable mobility - the Automatic Bike Path Analysis (ABPA) system. ABPA utilizes machine learning techniques and smartphone-generated video data to automatically assess the condition of bike paths, aiming to enhance the safety and comfort of cyclists. By leveraging the concept of digital ecosystems, ABPA seamlessly integrates data collection, analysis, and visualization, offering a holistic approach towards real time infrastructure maintenance. This research highlights the potential of digital ecosystems to drive sustainable transformations and enhance the well-being of cyclists and sustainable mobility. The findings contribute to the growing body of
knowledge in the intersection of digital technologies making a significant impact on fostering greener and more resilient mobility.

Keywords: AI, Machine Learning, Image recognition, CNN, Automatic detection, Conditions analysis, Bicycle paths, Surface
condition

Cite As

O. Tamine, J. Baier,  "Advancing Sustainable Mobility Infrastructure and Environment Through
Automated Low Threshold Analyzation of Road and Cycling Path Surfaces", Engineering Intelligent
Systems, vol. 32 no. 1, pp. 9-17, 2024.



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Published

2024-01-01

Issue

Vol. 32 No. 1 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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