Transit Ridership Estimator

The density factor uses a logarithmic curve rather than a straight line, which reflects a well-documented real-world pattern: ridership gains from added density taper off at high densities rather than growing without limit. Going from a low-density suburban area to a moderately dense one tends to add riders quickly, while going from an already-dense urban area to an even denser one adds riders more slowly in percentage terms, since walking, cycling, and existing transit capacity start to compete for the same trips.

Est. Daily Boardings estimates the raw number of riders using this specific stop or route, while Est. Transit Mode Share estimates the percentage of ALL trips in the area that would be taken by transit rather than driving, walking, or another mode -- it's capped at 40% in this model, reflecting that even the strongest transit corridors rarely see transit capture anywhere close to a majority of trips.

The Cost Competitiveness factor compares your round-trip transit fare against an estimated daily driving cost built from parking plus a rough gas-and-wear allowance -- when parking is expensive, driving becomes relatively less attractive compared to transit, which the model reflects as higher ridership. This mirrors real behavior: parking pricing and availability are among the most direct levers cities have over commute mode choice, often more so than transit fare itself.

This is a simplified, illustrative model built from generalized relationships between density, frequency, access, and cost -- it is not calibrated against your specific corridor's actual travel patterns the way a real ridership forecast (using tools like the FTA's Simplified Trips-on-Project Software or a full regional travel demand model) would be. Use it to compare scenarios directionally against each other early in planning, and commission a proper ridership study before any capital investment decision.