Forecasting ridership for a metropolitan transit authority

Forecasting ridership for a metropolitan transit authority

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Article ID: iaor20115886
Volume: 45
Issue: 7
Start Page Number: 696
End Page Number: 705
Publication Date: Aug 2011
Journal: Transportation Research Part A
Authors: , ,
Keywords: statistics: regression, planning, forecasting: applications
Abstract:

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.

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