Abstract
Despite ever-increasing public and financial support for public transportation in recent decades, transit ridership has dropped substantially in the 2010s across the United States. While many theories that explain the reasons behind the decline in ridership have been proposed, no consensus on the primary causes has been reached. By employing gradient boosting decision tree and counterfactual simulations, we examine the complex relationships between transit ridership and key internal and external factors from 2002 to 2017 in 85 of the largest urbanized areas in the US. Among several contributing factors, the declining cost of driving, measured by the decreasing share of carless households combined with lower gasoline prices, was the salient, most influential factor behind the recent decline. Neighborhood change in high-density neighborhoods also led to a moderate net loss in ridership. The only factor that has been mitigating further decline was an increase in transit services. Had this increase not occurred, loss of ridership would have been more than double the actual loss during the study period.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 126-143 |
| Number of pages | 18 |
| Journal | Transportation Research Part A: Policy and Practice |
| Volume | 157 |
| DOIs | |
| State | Published - Mar 2022 |
Keywords
- Car ownership
- Gradient boosting decision tree
- Machine learning
- Neighborhood change
- Peak Millennials
- Public transit ridership
- Uber
ASJC Scopus subject areas
- Aerospace Engineering
- Business, Management and Accounting (miscellaneous)
- Transportation
- Civil and Structural Engineering
- Management Science and Operations Research
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