As city governments start to look into the future of transit — autonomous vehicles, alternative transit and more — it can be difficult to visualize all possible outcomes. Micro-level analysis is often needed to look at each individual city and determine new travel models as you introduce new services and regulations.
All of this city-level data can be exceedingly complex to go through.
While Josie Kressner was pursuing her Ph.D. in transportation engineering at Georgia Tech in the early 2000s, she saw how inefficiencies within the state’s department of transportation were affecting the selection of which roads and transit services to improve or focus on.
“We were learning the old way of doing it, which dates back to the 1950s. At the same time, I had this idea about how we could combine aggregated location data with consumer marketing data and create detailed micro-data about how people travel,” says Kressner. Kressner’s concept involved using what she calls “synthetic people”, to avoid compromising real individuals’ privacy.
“This way, the public sector can ask detailed, interesting questions about how people actually travel.”
Kressner received a National Science Foundation grant to test her idea in 2014. She grew the concept over the next four years, eventually launching urban planning software startup Transport Foundry.
Transport Foundry’s first product, CityCast, helps transportation planners ask “what if” questions about the future using a data-driven simulation. For example, they can tackle problems like where to add households or new bus routes by simulating how people are traveling or where they cluster for work.
“We’ve spent a lot of time thinking about the user experience, since there’s a lot of data coming back and forth between the users and us,” says Kressner.
Once a city planner enters their scenario and city, the cloud-based platform runs a simulation and comes back with different data visualizations to see every possible outcome of the change. The user does not need submit any data prior to using CityCast, since the platform draws from a mix of data sources that the user can combine in order to build their simulation.
There are six major data types within the platform: road networks, households, businesses, transit system, flow of trips between locations in the city, and trip patterns. “You can tell CityCast to run a simulation for a city of this size with these data sets and run 20 different scenarios,” says Kressner.
A main customer of Transport Foundry are the transit consulting firms that help agencies re-plan their services and routes to better serve the community. The platform helps them develop and improve their recommendations for the transit agency.
Right now, Atlanta’s Gateway85 community improvement district, a group of businesses in Gwinnett County, is using CityCast to better route freight traffic through their area and hopefully create a better visitor experience. They’ve explored scenarios such as designating a freight-only highway on-ramp and exit ramp. After going over the CityCast scenarios, they will come up with a list of projects that will best improve traffic conditions and the area’s economy.
“On a more long-term basis, commercial developers can look at the reports and see where the public sector projects business is going to happen, and advertisers can see what kind of people are driving by their billboard,” says Kressner.
Looking to the future, Kressner is exploring how agencies and planners could use CityCast as autonomous vehicles get introduced into the market. “You can see the impact on traffic if certain regulations are passed or if only high-income residents buy [autonomous vehicles]. You can also see the impact of deadhead trips, where there’s no one in the vehicle during the trip,” says Kressner.
Transport Foundry employs a SaaS revenue model in order to provide full-time support to customers. They have customers in Atlanta, Washington D.C. and Norfolk, Virginia right now. Kressner envisions them eventually moving to operate more like AWS, in a pay-as-you-go model to avoid the larger upfront cost.
The startup has been fully self-funded up to this point with the help of two grants. Due to high demand for their product, they will be raising capital in the next few months to scale their capabilities and grow faster to onboard more customers.