Intelligent Pavement Condition Rating System for Cycle Routes and Greenways

Syed M. Haider Shah, Syed M. Haider Shah, Waqar Shahid Qureshi, Gerard O’ Dea, Ihsan Ullah, Ihsan Ullah

2025

Abstract

This study introduces an intelligent framework for assessing cycling infrastructure, addressing the limitations of traditional pavement evaluation methods. At the core of the system is the CRSI, a 1-to-5 rating scale specifically designed to evaluate cycle routes based on critical factors like surface quality, vegetation encroachment, and drainage. A dataset of over 40,000 frames, extracted from videos captured using handlebar-mounted GoPro cameras and annotated by experts, forms the foundation of the system. Four deep learning (DL) models LeNet, AlexNet, EfficientNet-B2, and Swin Transformer-Tiny were trained and evaluated for Cycle Route Surface Index (CRSI) classification. Among all models, Swin Transformer-Tiny performed the best, achieving an impressive accuracy of 99.90%. To further test its robustness, we evaluated the system on four new videos, from which four separate frame sets were generated. Among these, Swin Transformer-Tiny again delivered the highest accuracy, reaching 86.67%, confirming its reliability across different datasets. This CRSI-based framework provides a scalable, automated solution for evaluating cycling infrastructure, empowering transportation agencies to improve maintenance and ensure safer, more accessible cycling networks.

Paper Citation

in Harvard Style

Shah S., Qureshi W., Dea G. and Ullah I. (2025). Intelligent Pavement Condition Rating System for Cycle Routes and Greenways. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 668-675. DOI: 10.5220/0013505000003941

in Bibtex Style

@conference{vehits25,
author={Syed Shah and Waqar Qureshi and Gerard Dea and Ihsan Ullah},
title={Intelligent Pavement Condition Rating System for Cycle Routes and Greenways},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={668-675},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013505000003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Intelligent Pavement Condition Rating System for Cycle Routes and Greenways
SN - 978-989-758-745-0
AU - Shah S.
AU - Qureshi W.
AU - Dea G.
AU - Ullah I.
PY - 2025
SP - 668
EP - 675
DO - 10.5220/0013505000003941
PB - SciTePress