Public Transit Economics: Ridership, Farebox Recovery & Service Planning

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Turn a weekday of route data into boardings per revenue hour and per revenue km, pool the network correctly and count the routes below the productivity standard.
• Compare each route's peak-hour load with the seats and the total capacity it offers, flag the crowded routes and work out how many extra trips bring them back to the standard.
• Calculate each route's cycle time, the vehicles needed in the peak and off-peak with ROUNDUP, the bus fleet including spares, and the best headway a limited number of buses can run.
• Build a three-variable cost model for the bus division: unit rates per revenue hour, per revenue km and per peak vehicle, the allocated cost of each route and its cost per boarding.
• Measure six years of farebox recovery, the public subsidy in total and per boarding, the fare revenue gap to a target, and chart the recovery trend.
• Model five fare changes with an illustrative elasticity: new fare, boardings, fare revenue, the change against today and the farebox recovery, then compare the linear rule with the constant-elasticity formula.
• Turn scheduled and actual departure times into deviations in minutes, classify every trip as early, on time or late, and measure on-time performance and the average lateness of late trips.
• Rank the eight routes on productivity, cost, farebox recovery and reliability, combine the ranks into a weighted score and chart the productivity ranking.