8. Ridership Forecasting

TL;DR

This content discusses the importance of ridership forecasting for transit agencies and various methods used to predict ridership, including regression models, simultaneous equation models, and network-based models.

Transcript

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Key Insights

• 🪡 Transit agencies need to forecast ridership in order to make important decisions and understand the impact of changes on ridership and revenue.
• 💱 Ridership models can be used to predict revenues or fare changes, plan for future ridership, and predict ridership changes due to service changes.

Questions & Answers

Q: Why do agencies need ridership models?

Ridership models help agencies predict revenues, estimate the impact of fare changes, plan for future ridership, and predict ridership changes due to service changes.

Q: What factors affect transit ridership?

Factors such as auto ownership and availability, fuel prices, demographics, and activity system (e.g. job centers, population density) can influence transit ridership.

Q: What are the limitations of cross-sectional models?

Cross-sectional models do not capture the relationship between supply and demand, or the effects of competing and complementary routes. They also assume fixed total demand.

Q: What are the advantages of simultaneous equation models?

Simultaneous equation models can capture the relationship between supply and demand, as well as the effects of competing and complementary routes. They provide more detailed and accurate predictions of ridership.

Summary & Key Takeaways

• Ridership forecasting is important for transit agencies as it helps them understand the impact of changes to their network on ridership and revenue.

• There are different roles for ridership models, including predicting revenues or fare changes, general agency planning, and predicting ridership as a result of service changes.

• Exogenous factors (e.g. auto ownership, fuel prices) and endogenous factors (e.g. fare, headway, route structure) affect transit ridership, and models need to account for these factors.