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Forecasting ridership for a metropolitan transit authority

机译：预测大都会运输当局的乘客量

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摘要

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of rider-ship 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.
机译：近期汽油价格的波动和经济下滑使公共交通系统的管理特别具有挑战性。对于过境服务的规划和运营，准确预测乘船人数是必要的。在本文中，对塔尔萨市大都会运输局每月的乘车人数进行了分析，以确定影响过境使用的相关因素。还基于这些因素开发和评估了替代的预测模型-使用回归分析（具有自回归误差校正），神经网络和ARIMA模型-来预测过境乘车人数。发现这些预测方法的简单组合比单独的模型产生的预测准确性更高。最后，进行了情景分析，以评估过境政策对长期乘车的影响。

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