This project conducted an analysis of Miami-Dade Transit’s on-time performance using GTFS static data as well as data acquired from the Swiftly API from October 2022 to March 2023. Then predict the bus delay time based on advanced machine learning models.
miami-dade transit on-time performance analysis.pdf: this provides a detailed report of Miami-Dade Transit on-time performance, which was posted on Transit Alliance Miami.
Visualization.ipynb: We applied two measurements for analyzing the on-time performance of transit vehicles: arrival time differencee and headway difference.
service time & bus number passing by a stop.ipynb: We computed the daily service time for each route and the daily number of transit vehicles serving each transit stop to understand the transit service supply.
Project_Report.pdf: This study predicted the bus on-time performance based on several machine learning models, including decision tree, random forest, support vector machine, and XGBoost.
TRB.pdf: We developed a time-fixed effects model to examine the association of service reliability with transit ridership. This paper is submitted to the 2024 TRB Conference.
