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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. [ABSTRACT FROM AUTHOR]

Various qualities of transit services, including travel time, frequency, and the presence of alternative paths, affect accessibility—how easily passengers can reach their destinations by transit. Traditional accessibility metrics, such as gravity-type and cumulative-opportunity indices, rely exclusively on travel time as a proxy for travel impedance, failing to consider other qualities of transit services. This study develops a comprehensive transit-based job accessibility index that accounts for the number of feasible alternative routes and their frequency in addition to in-vehicle and out-of-vehicle travel times. It uses the General Transit Feed Specification (GTFS) data and Dijkstra's shortest path algorithm to compute these components of tract-to-tract transit trips in the Chicago Metropolitan Statistical Area (MSA). We demonstrate that conventional accessibility indices considering only gross travel time overestimate job accessibility in the job rich downtown area and suburban train hubs, while they tend to underestimate job accessibility in places mainly served by a suburban bus system. Logistic regression analysis shows that our refined job accessibility measure explains the likelihood of transit commute considerably better than conventional measures. [ABSTRACT FROM AUTHOR]