Millions of passenger journeys across the New York City metro region every day present operators with a dataset that, when harnessed, has the potential to offer deep and broad insight to help in every aspect of managing its transit systems.
Sally Librera, Americas Transit Market Leader at AECOM, asked panelists how they were using data that for “a long time existed in transportation systems but hasn’t always been harnessed in a way to really give clear information and to be a useful tool.”
Rich Davey, President of NYC Transit Authority, agreed the public transportation industry could benefit from an increased deployment of data to help planning, maintenance, and performance. “I don’t think the [public] industry has done a really great job of leveraging that kind of data to make investments… But our maintenance team is not only doing that, they’re really embracing it, which is great. The data is setting them free,” he said.
He identified other areas where data was already being used to improve performance, such as looking more specifically at on-time performance from station to station versus an entire line, and identifying specific points along the line that were impacting overall performance of a subway line, or where new bus lanes could benefit communities. He also welcomed initiatives such as the Transit Tech Lab , which provides a pathway for early and growth-stage technology companies to work with New York City’s regional transit agencies to solve public transportation challenges.
“I think that’s a way we’ve been able to collectively leverage the private sector to help us use our data more smartly, or otherwise leverage it to make investments.”
AECOM has extensive experience in deploying data analysis to help transit operators improve planning and performance, including in Covid-19 recovery scenarios. One such initiative involved collaboration with Bay Area Transit, “to explore how post-pandemic transit network and investment scenarios could serve Communities of Concern.”
Hear how NYC Transit is leveraging its data to improve customer service here: