Public Transit Demand Prediction During Highly Dynamic Conditions: A
  Meta-Analysis of State-of-the-Art Models and Open-Source Benchmarking
  Infrastructure
Juan D. Caicedo Marta C. González Joan L. Walker
Real-time demand prediction is a critical input for dynamic bus routing.
While many researchers have developed numerous complex methods to predict
short-term transit demand, the applications have been limited to short, stable
time frames and a few stations. How these methods perform in highly dynamic
environments has not been studied, nor has their performance been
systematically compared. We built an open-source infrastructure with five
common methodologies, including econometric and deep learning approaches, and
assessed their performance under stable and highly dynamic conditions. We used
a time series from smartcard data to predict demand for the following day for
the BRT system in Bogota, Colombia. The dynamic conditions in the time series
include a month-long protest and the COVID-19 pandemic. Both conditions
triggered drastic shifts in demand. The results reveal that most tested models
perform similarly in stable conditions, with MAAPE varying from 0.08 to 0.12.
The benchmark demonstrated that all models performed significantly worse in
both dynamic conditions compared to the stable conditions. In the month-long
protest, the increased MAAPE ranged from 0.14 to 0.24. Similarly, during the
COVID-19 pandemic, the increased MAAPE ranged from 0.12 to 0.82. Notably, in
the COVID-19 pandemic condition, an LSTM model with adaptive training and a
multi-output design outperformed other models, adapting faster to disruptions.
The prediction error stabilized within approximately 1.5 months, whereas other
models continued to exhibit higher error rates even a year after the start of
the pandemic. The aim of this open-source codebase infrastructure is to lower
the barrier for other researchers to replicate and reproduce models, facilitate
a collective effort within the research community to improve the benchmarking
process and accelerate the advancement of short-term ridership prediction
models.