Michalaka, D., Ho, C.-H., Ren, K., Chen, Y., Hao, X., Brown K., Huynh, N., and David, W., J., “Assessing Bike Suitability of Transportation Infrastructure Segments” TRBAM-25-04657, acceptance for presentation at 2025 Annual Meeting of Transportation Research Board, Washington DC, January 6-11, 2025.
Ren, K. and Ho, C.-H. “Enhancing Anomaly Detection in Cycling Paths Using a Hybrid LSTM-VQ-VAE Deep Learning Model.” Proceedings in the 2024 7th Artificial Intelligence and Cloud Computing Conference, December 14-16, Tokyo, Japan.
Zhang, D., & Ho, C. H. (2024). “Diagnostics of Road Conditions Using Acceleration Sensor: Machine Learning-LSTM autoencoder and Gaussian Mixture Model”. In 2024 International Conference on Advanced Robotics and Intelligent Systems (ARIS) (pp. 1-5). IEEE, doi: 10.1109/ARIS62416.2024.10680001
Ho, C. -H. and Ren, K., (2024), "Vibration Data Mining and Machine Learning for Anomaly Detection of Cycling Trails Using Instrumented Bike," 2024 9th International Conference on Big Data Analytics (ICBDA), Tokyo, Japan, 2024, pp. 123-127, doi: 10.1109/ICBDA61153.2024.10607239.
Zhang, D., & Ho, C. H. (2024). Distribution fitting and ANOVA test to analyze pavement sensing patterns for condition assessments. Built Environment Project and Asset Management. https://doi.org/10.1108/BEPAM-10-2023-0185
Ho, C. H., Qiu, P., Zhang, Y., & Ren, K. (2024). A Generic Deep Learning–Based Computing Algorithm in Support of the Development of Instrumented Bikes. ASCE OPEN: Multidisciplinary Journal of Civil Engineering, 2(1), 04024003, https://ascelibrary.org/doi/10.1061/AOMJAH.AOENG-0025
Zhang, D., Ho, C.H., and Zhang, F. (2023). Evaluating the impact of factors in vehicle based pavement sensing implementation: sensor placement, pavement temperature, speed, and threshold, Journal of Infrastructure Preservation and Resilience, 4, 1 (2023), https://doi.org/10.1186/s43065-022-00065-2