TDT

Transit buses can carry 16X more passengers per hour while reducing 6X carbon emissions. However, transit planning is a very hard problem to solve. We propose a digital twin to make planning easy.

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Inspiration

Transportation Planning is one of the most challenging jobs due to a complex fusion of traffic and infrastructure data. Particularly, transit buses route planning is harder as there are multiple routes passing through common stops bi-directionally. To make transit planning easier and more efficient, we propose a digital twin.

What it does

Transit Digital Twin (TDT) will provide a seamless front-end for transit planners to view the real-time transit bus movement in a city or county or even at a given intersection. TDT will have interfaces to fuse other traffic data sources such as crash reports, real-time sensor data from traffic cameras and traffic signal controllers, work-zone information etc. TDT will also support all the vehicle-to-everything (V2X) messaging formats so transit planners and navigate through transportation network efficiently.

How we built it

We used General Transit Data Specification (GTFS) (Google, 2006) from Cincinnati Go Metro public data. We created a database using PostgreSQL to consume the large route/ trips dataset and built the relationship between all the files available in GTFS data feed.

In the back end, we used FastAPI + SQL Alchemy to consume the data and API end points to expose the data. We used React to create a front end application with deck.gl as the graphic library.

Challenges we ran into

Understanding complexity involved in GTFS data and to further clean the data for this project has been the most challenging issue we have solved in this project. Creating the link between database and backend has also been a significant challenge that kept us working through the night.

Accomplishments that we're proud of

Our major accomplishment in this project is being able to use deck.gl graphic library and create a front-end to display transit bus movements in Cincinnati Metropolitan area.

What we learned

Great learning and also one of the best wins in this project is when we realized that we were stuck because PostGIS extension should be manually enabled for the database before we input the GTFS data.

What's next for TDT

We would like to scale the existing TDT digital twin to add plug ins for real-time transit data using protocol buffer, AI powered data insights, create standardized messages for V2X and IoT sensors, signal controllers, and crash/crime databases. This will create a smart digital twin that traffic agencies can use to help improve the safety of our transportation and also plan sustainable transit.

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