The Story of a Trip
Response to TRB Innovations in Transit Performance Measurement Challenge
Transit agencies across the US are working hard to be more competitive against driving, bicycling, and ride hailing. Many agencies are adding additional service to core routes or rethinking the way their system is laid out. LYNX, the transit agency serving the Orlando, Florida metropolitan region, is one of these agencies.
When a new transit route is proposed, a common step is to run a model to project the ridership on the new route. The problem is that these models are primarily concerned with things that matter only to the transit agency. Things like farebox revenue, cost, buses in peak service, bus revenue miles, etc. Current models don’t capture the things that are important to transit users—like how much they’ll have to wait, how often they’ll need to transfer, or how long they will spend riding the bus.
As part of a recent study on SR 436—one of its busiest corridors—LYNX considered a range of alternatives to increase the frequency and quality of transit service. Through the use of open-source data formats and software, the LYNX team was able to simulate the impact of its proposed alternatives on a trip-by-trip basis.
What we did
We built a trip routing engine using OpenTripPlanner, an open source trip planning software. OpenTripPlanner requires an OpenStreetMap file and a GTFS transit schedule dataset.
Two trip routing “passes” were performed for all trips interacting with SR 436. The first pass used a baseline (existing) GTFS file. The second pass used a GTFS file that reflected the addition of the proposed alternative. The detailed trip routing outputs were used to measure the impact of the proposed route on riders’ experiences.
The visualization below shows how the total travel time and travel distance of each trip changed between the baseline and alternative scenarios. The visualization only shows trips that would ride the proposed alternative. Our analysis shows that the median travel time for these trips would decrease by about 15 minutes.
- Our narrative describes the problem faced, data sources used, analysis approach, and suggestions for scaling up the workflow
- Our Observable notebook includes additional visualizations, as well as the code used to analyze and visualize the data