Forecasting ridership for a metropolitan transit authority

Document Type

Journal Article

Publication Date


Subject Area

place - north america, ridership - forecasting, ridership - modelling, mode - bus


Public transit ridership, Forecasting models, Scenario analysis


The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors—using regression analysis (with autoregressive error correction), neural networks, and ARIMA models—to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.


Permission to publish the abstract has been given by Elsevier, copyright remains with them.


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