Skip to main content

Veovo to ease subway crowding in New York

Veovo is working with the Metropolitan Transportation Authority (MTA) to help ease crowded subways in New York as part of a one-year pilot. It follows an agreement made last year between the MTA and Partnership for New York City to launch the Transit Tech Lab to vet technologies designed to modernise the city’s public transit system. Natalia Quintero, director of the Transit Tech Lab, says: “With Veovo's sensors and analytics, the MTA has more reliable data to inform service changes and improve safe
August 7, 2019 Read time: 2 mins

Veovo is working with the 1267 Metropolitan Transportation Authority (MTA) to help ease crowded subways in New York as part of a one-year pilot.

It follows an agreement made last year between the MTA and Partnership for New York City to launch the Transit Tech Lab to vet technologies designed to modernise the city’s public transit system.

Natalia Quintero, director of the Transit Tech Lab, says: “With Veovo's sensors and analytics, the MTA has more reliable data to inform service changes and improve safety on platforms.”

Veovo’s Passenger Predictability solution is expected to provide pre-emptive alerts of potential overcrowding at stations, allowing the MTA to take preventive measures.

Veovo says its platform uses a combination of various sensor technologies along with advanced deep learning algorithms to provide a real-time overview of passenger volumes, how they move within and between stations, their average wait time and occupancy on trains.

Data is used to detect and predict irregularities such as repairs and delays. This enables the MTA to pinpoint the impact on occupancy and dwell times to better anticipate future passenger volumes and movement, the company adds.

Additionally, sharing the data could enable transit users to make more informed travel decisions, by taking into account factors like time of departure or choice of station.

During the pilot, the solution will be rolled out on the L-train line, coinciding with the Canarsie tunnel reconstruction, which was damaged by Hurricane Sandy in 2012.

For more information on companies in this article

Related Content

  • Manchester seeks smart but not selective transport solutions
    January 25, 2018
    Smarter transport relies on better communications both with travellers and between transport providers. Andrew Williams reports. Inrix’s prediction that the cost of traffic congestion will rise by 63% to £21bn per year by 2030 clearly illustrates that, in addition to the ongoing inconvenience and inefficiency, ongoing gridlock is a significant drain on the economy. It is against this backdrop that a Cisco-led consortium has launched CitySpire, a smart transport programme that uses location-based services a
  • Auckland reduces airport journey times
    April 16, 2018
    Getting from the centre of Auckland to the city’s airport used to be fraught with unwanted stress for passengers – but a new system combining radar, Bluetooth and Wi-Fi is smoothing things over. Andrew Stone investigates. Struggling to cope with steady growth in passenger numbers and the costly traffic congestion which that can entail, New Zealand’s Auckland International Airport has deployed an innovative system that is smoothing traffic and passenger flows. The same system is also offering new, data-led
  • Congestion charging in New York edges a wheel-length closer
    May 16, 2023
    'This is about more than reducing traffic' says city mayor, pledging transit investment
  • Cubic: predictive analytics is putting fortune tellers out of business
    November 23, 2018
    The rise of machine learning and artificial intelligence means that fortune tellers will soon be out of business. Ed Chavis takes a behind the scenes look at the world of predictive analytics ver since organisations started taking advantage of insights derived from Big Data, data scientists concentrated their efforts on the ability to make correct assumptions about the future. A few years later, with the help of automation, developments in machine learning (ML) and advancements in the application of a