Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco
place - north america, place - urban, mode - bus, mode - rail, ridership - forecasting, ridership - modelling
Transit direct ridership models (DRMs), forecast
Transit direct ridership models (DRMs) are commonly used both for descriptive analysis and for forecasting, but are rarely evaluated for their ability to predict beyond the estimation data set. This research does so, using two DRMs estimated for rail and bus ridership in San Francisco. The models are estimated from 2009 data, applied to predict 2016 conditions, and compared to actual 2016 ridership. Over this period in San Francisco, observed rail ridership increased by 9% whereas observed bus ridership decreased by 13%. The results show that the models predict 2016 ridership about as well as that for 2009. The rail model correctly predicts the direction of change, but underestimates the magnitude of change. The bus model predicts the direction of change incorrectly, with a predicted 2% increase. A series of sensitivity tests are conducted to better understand the factors driving the ridership changes. These tests produce reasonable rail sensitivities, but reveal that the bus model is too sensitive to frequency, potentially because of the difficulty of estimating the coefficient from cross-sectional data when high-frequency transit also occurs in high-density locations. As the travel forecasting community increases its focus on empirically evaluating forecasts beyond a base year, DRMs must be a part of that.
Permission to publish the abstract has been given by SAGE, copyright remains with them.
Mucci, R.A., & Erhardt, G.D. (2018). Evaluating the Ability of Transit Direct Ridership Models to Forecast Medium-Term Ridership Changes: Evidence from San Francisco. Transportation Research Record. https://doi.org/10.1177/0361198118758632