Date: October 13, 2021
Author(s): Edgar Mejia
Abstract
If the health of a public transportation agency can be measured, in part, by its ridership, then the Los Angeles County Metropolitan Transportation Agency could use a trip to the doctor. A recent report from the UCLA Institute of Transportation Studies shows that Metro experienced a steady ridership decline between 2007 and 2015. Despite an overall decline in the system’s ridership numbers, however, the year 2020 also revealed the high degree of reliance on Metro’s service, in general, and the bus system in particular.
In early 2020, the COVID-19 pandemic emerged, destabilizing U.S. economic, business, entertainment, and public transportation sectors. According to data from the National Transit Database, monthly transit ridership hit a low of 156.6 million rides in April 2020, 81.3% lower than the 835.2 million rides taken in April 2019.
In the midst of these challenges, Metro has allocated significant resources (time, money, political) to rework its bus network through the NextGen initiative, the first sweeping overhaul of the bus system in 25 years. Metro must carefully consider the travel patterns of current and future transit riders.
About the Project
Travel patterns have been significantly altered due to COVID-19. However, LA Metro experienced the smallest percent drop in public transit ridership during this time. Though no public confirmation is currently available, anecdotally, we are witnessing sustained ridership in non-traditional peak hour traffic areas such as in South Los Angeles, and low-income neighborhoods in the South Bay. I will analyze travel patterns from NextGen research to better understand public transportation travel patterns. The project topic would involve identifying multiple low-cost opportunities to adjust LA Metro bus services to improve customer experience by better matching LA Metro service to major travel patterns as identified from Metro’s LBS cell phone database as well as Census “On the Map” data and Metro ridership data. The analysis would include reviewing changes to travel patterns and volumes (as seen in cell phone data patterns) resulting from the impact of COVID-19.