
CitySwift passes New York’s public transport strategy
New York Metropolitan Transportation Authority (MTA) will be working with Galway-based CitySwift on a new data-driven public transport strategy.
The Transit Tech Lab – a public-private initiative created by the MTA and the Partnership for New York City to bring private sector innovation to improve public transit – announced CitySwift as one of three winners of its Covid-19 Response Challenge, which will implement high-tech solutions across the MTA and the Port Authority for New York and New Jersey.
“CitySwift is incredibly excited to be helping the MTA embrace data in smart, innovative ways to improve bus services and support Covid-19 recovery in New York City,” said Brian O’Rourke, co-founder and CEO, CitySwift. “As new mobility patterns emerge, there is a lot to learn from big data about changing trends and how best to plan and shape bus services for the future.
“Data has the power to transform the economics of the bus sector and help attract a new generation of passengers. It’s the key to delivering frequent, fast, reliable services that meet the needs of the modern bus user and offer an attractive, affordable, and sustainable alternative to the private car. It will enable the MTA to offer faster journey times and optimise frequency by intelligently matching vehicle supply with passenger demand.”
The Challenge was launched in response to the pandemic and fielded over 200 global applicants. The companies selected demonstrated promising results after a 12-week proof of concept, during which CitySwift fully integrated with and augmented the MTA’s existing transport data sources. They will now scale their solutions across their respective partner agencies over a one-year pilot.
CitySwift will work closely with the MTA and the Partnership for New York City on aligned goals to improve passenger experience and reduce carbon emissions through public transport network optimisation and passenger capacity predictions.
CitySwift’s data-driven technology platform will be used to predict bus capacities and run times; optimise timetables; and track, monitor and automatically report network performance across Brooklyn, Manhattan, and Queens.
In Brooklyn, CitySwift’s AI-based capacity prediction technology will be used to share accurate stop-by-stop capacity levels with bus users for every vehicle, for any specific date and time up to two weeks in advance. This will enable passengers to make an informed, safe and confident return to public transport, and help ensure a sustainable and carbon-neutral future for New York City.
“These pilots demonstrate the private sector’s ongoing support of public agencies amid the COVID-19 pandemic,” said Natalia Quintero, senior vice president of innovation at the partnership for New York City. “The Lab looks forward to working with pilot winners to evaluate how each tool can bring customers back to public transit safely and efficiently. We want to especially thank our partners at the MTA and Port Authority for their commitment to integrating innovative tools that mobilise customers returning to mass transit.”
“We are pleased to take part in Transit Tech Lab’s Covid-19 Response Challenge,” said Robert Galvin, chief technology officer at the Port Authority of New York and New Jersey. “This challenge has inspired a great call to action among the tech sector to bring forth creative technology and solutions to address the ongoing challenges associated with the pandemic.”
Founded in 2016, CitySwift is a homegrown Galway company with an international roadmap. A UK market leader working with National Express, Go-Ahead Group and Transport for Wales, the business is expanding into Europe and North America.
CitySwift’s specialist bus data engine enables the collection of large amounts of information across transport networks – statistics like passenger numbers, peak locations, usage, and delays. It then organises, analyses, and combines this with external big data sources to deliver two major benefits: increased efficiency and improved passenger experience.
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