# 08 ML-põhised meetodid – APA viited

*Zotero jaoks: lisa DOI kaudu, täismetaandmed tulevad automaatselt. Koostatud 2026-08-11 (Crossrefi andmetel).*

## Põhiviited (meetodiülevaade)

Baffoe-Twum, E., Asa, E., & Awuku, B. (2022). Estimation of annual average daily traffic (AADT) data for low-volume roads: A systematic literature review and meta-analysis. *Emerald Open Research, 1*(5). https://doi.org/10.1108/eor-05-2023-0010

Das, S., & Tsapakis, I. (2020). Interpretable machine learning approach in estimating traffic volume on low-volume roadways. *International Journal of Transportation Science and Technology, 9*(1), 76–88. https://doi.org/10.1016/j.ijtst.2019.09.004

Klinkhardt, C., Woerle, T., Briem, L., Heilig, M., Kagerbauer, M., & Vortisch, P. (2021). Using OpenStreetMap as a data source for attractiveness in travel demand models. *Transportation Research Record, 2675*(8), 294–303. https://doi.org/10.1177/0361198121997415

Mahdavian, A., Shojaei, A., Salem, M., Laman, H., Yuan, J.-S., & Oloufa, A. (2021). Automated machine learning pipeline for traffic count prediction. *Modelling, 2*(4), 482–513. https://doi.org/10.3390/modelling2040026

Mathew, S., Pulugurtha, S. S., Bhure, C., & Duvvuri, S. (2023). One-dimensional convolutional neural network model for local road annual average daily traffic estimation. *IEEE Access, 11*, 127229–127241. https://doi.org/10.1109/access.2023.3332125

Pun, L., Zhao, P., & Liu, X. (2019). A multiple regression approach for traffic flow estimation. *IEEE Access, 7*, 35998–36009. https://doi.org/10.1109/access.2019.2904645

Sekuła, P., Vander Laan, Z., Farokhi Sadabadi, K., Kania, K., & Zahedian, S. (2021). Transferability of a machine learning-based model of hourly traffic volume estimation—Florida and New Hampshire case study. *Journal of Advanced Transportation, 2021*, 9944918. https://doi.org/10.1155/2021/9944918

Sfyridis, A., & Agnolucci, P. (2020). Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling. *Journal of Transport Geography, 83*, 102658. https://doi.org/10.1016/j.jtrangeo.2020.102658

## Täiendav kirjandus (GNN, süvaõpe)

Han, D. C. (2024). Prediction of traffic volume based on deep learning model for AADT correction. *Applied Sciences, 14*(20), 9436. https://doi.org/10.3390/app14209436

Jiang, W., Luo, J., He, M., & Gu, W. (2023). Graph neural network for traffic forecasting: The research progress. *ISPRS International Journal of Geo-Information, 12*(3), 100. https://doi.org/10.3390/ijgi12030100

Rahmani, S., Baghbani, A., Bouguila, N., & Patterson, Z. (2023). Graph neural networks for intelligent transportation systems: A survey. *IEEE Transactions on Intelligent Transportation Systems, 24*(8), 8846–8885. https://doi.org/10.1109/tits.2023.3257759

## Lõputöö (USA kontekst, NFAS teed)

Sun, Q. (2020). *Nationwide annual average daily traffic (AADT) estimation on non-federal aid system (NFAS) roads by machine learning with data mining of built-in environment* [Doctoral dissertation, University of Maryland]. https://doi.org/10.13016/yrbr-jwgm