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Trust in forecasts? Correlates with ridership forecast accuracy for fixed-guideway transit projects

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Abstract

The accuracy of ridership forecasts for fixed-guideway transit projects in the United States has improved in recent decades. A better understanding of the causes for this improvement can help decision makers, project evaluators, and other forecast users identify ridership forecasts that are most likely to be reliable. The analysis in this paper applies a series of linear regression models to evaluate the relationship between ridership forecast accuracy for 67 New Starts projects completed between 1983 and 2012 and four types of project characteristics: time between forecast and observation, local experience with the project mode, physical characteristics, and financial characteristics. The results indicate that local experience and financial characteristics (including the share of a project’s costs funded by federal grants) are not significantly related to forecast accuracy, but there are differences by project mode, where forecasts for commuter rail projects are less accurate than those for other modes. The time until ridership observation does relate to forecast accuracy. However, not all of this elapsed time is important. The length of time required for project planning and development does not have a significant relationship with forecast error, nor does the total time between forecast preparation and ridership observation. However, the length of time required to construct the project is significantly associated with the accuracy of the ridership forecast. These results can help planners, policy makers, and other decision makers make judgments about the degree of trust they should place in transit ridership forecasts.

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Notes

  1. The five acts that authorized capital funding for rapid transit projects during the period of this study were the Urban Mass Transportation Act (1964–1987); the Surface Transportation and Uniform Relocation Assistance Act (STURAA) (1987–1991); the Intermodal Surface Transportation Efficiency Act (ISTEA) (1991–1997); the Transportation Equity Act for the 21st Century (TEA-21) (1997–2005); and the Safe Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) (2005–2012). Since the sample is limited to projects that were completed before 2012, no forecasts completed under Moving Ahead for Progress in the 21st Century (MAP-21) (2012–2015) and the Fixing America’s Surface Transportation Act (FAST-Act) (2015 to present) are included in the analysis.

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  1. Department of Urban Planning and Design, Harvard Graduate School of Design, 48 Quincy Street, Cambridge, MA, 02138, USA

    Carole Turley Voulgaris

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Correspondence to Carole Turley Voulgaris.

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Appendix

Appendix

See Tables 5, 6, 7, 8, 9, 10, 11 and 12.

Table 5 Time variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 6 Sources of time variables for project in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 7 Accuracy variables for projects in study sample (1/3) (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 8 Sources of accuracy variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 9 Funding, scale, and experience variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 10 Sources of funding, scale, and experience variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 11 External control variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Table 12 Sources of external control variables for projects in study sample (a) (1/3), (b) (2/3) and (c) (3/3)
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Voulgaris, C.T. Trust in forecasts? Correlates with ridership forecast accuracy for fixed-guideway transit projects. Transportation 47, 2439–2477 (2020). https://doi.org/10.1007/s11116-019-10024-8

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