Author:
Hightower Ashley,Ziedan Abubakr,Guo Jing,Zhu Xiaojuan,Brakewood Candace
Reference44 articles.
1. Ayman, A., J. Martinez, P. Pugliese, A. Dubey, and A. Laszka, 2022. Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes. Presented at 2022 IEEE International Conference on Smart Computing (SMARTCOMP), Helsinki, Finland. Accessed December 15, 2023. 10.1109/SMARTCOMP55677.2022.00023.
2. Prediction of “L” Train’s daily ridership in downtown Chicago during the COVID-19 Pandemic;Azimian;Findings,2021
3. Time series forecasting of quarterly railroad grain carloadings;Babcock;Transp. Res. Part E: Logist. Transp. Rev.,1999
4. TCRP Synthesis 66: Fixed-Route Transit Ridership Forecasting and Service Planning Methods;Boyle,2006
5. Caicedo, J.D., M.C. Gonzàlez, and J.L. Walker, 2023. Public Transit Demand Prediction During Highly Dynamic Conditions: A Meta-Analysis of State-of-the-ART Models and Open-Source Benchmarking Infrastructure. Accessed December 15, 2023.https://doi.org/10.48550/arXiv.2306.06194.