We launched the Transit Tech Lab to help the MTA evaluate innovative solutions to critical public transportation challenges. After receiving nearly 100 applications, judges chose six companies to take part in an 8-week intensive accelerator with the MTA that wrapped up at the end of April.
Selected companies responded to either a subway or a bus challenge. Here’s an inside look at the results from the collaboration.
Subway Challenge: How can we better predict, prevent and lessen the burden of subway delays?
During the accelerator, Axon Vibe developed a smartphone app that enables customers to plan bus and subway journeys and receive personalized notifications using real-time New York City Transit data combined with anonymized location information and historical travel patterns. To support operations and customer service, Axon Vibe also explored opportunities for the MTA to message customers who regularly take certain subway and bus lines during service interruptions.
Veovo and New York City Transit employees installed sensors at the Court Street subway station during the accelerator to count the number of passengers using the station, provide data on crowding conditions and measure how long it takes customers to pass from turnstile to platform — calculations typically done manually. The data can be used to predict future passenger volumes, inform service changes and create an instantaneous warning system to mitigate overcrowding.
Bus Challenge: How can we help buses move faster and more efficiently?
Labs used computer vision and machine learning to analyze 1,800 hours of bus-mounted video footage and telematics data and provide insights to the MTA. It found that the MTA’s Select Bus Service B46 buses encounter a bus-lane obstacle every 1:20 minutes, and 39% are personal vehicle. The data can be used to modify route design, make service changes and implement traffic signal priority at critical intersections.
With the aim of enforcing and deterring bus lane obstructions, PIPS Technology studied the viability of identifying license plates in bus lanes using existing bus hardware and cameras. Analyzing video captured by previously installed MobileView cameras on New York City Transit buses, PIPS detected license plates of 8% of stopped cars with 28% accuracy. With PIPS-produced Automatic License Plate Recognition cameras, they detected 96% of cars with 93% accuracy. PIPS attributed the results to camera differences in frame rate, focal point, video output and lens type.
During the Transit Tech Lab accelerator, Preteckt analyzed 30 days of historical bus telematics information from 314 buses’ Aftertreatment Systems and was able to accurately predict bus system failures two days in advance approximately 60% of the time. Preteckt’s insights can be used to increase the Mean Distance Between Failures — a critical bus metric — and reduce costs associated with fleet maintenance.
Remix worked closely with bus operations planners on the Bronx Bus Network Redesign, an important part of New York City Transit’s Fast Forward NYC Plan. Remix trained 50 planners who created 186 maps in a fraction of the typical timeframe, increasing productivity and enabling them to achieve project milestones. Using Remix’s software, planners were able to streamline internal collaboration, integrate demographic and environmental data to project the plan impacts and create mapped visualizations for public engagement.
What We Learned
The use of data to improve operations and service quickly emerged as the unifying theme of the 2019 Transit Tech Lab. Companies analyzed existing data and captured new data, integrating them to produce actionable insights that can drive improvements in performance, customer engagement, planning and maintenance. The format of the Transit Tech Lab brought together colleagues from different departments and highlighted that each technology had useful applications for a range of functions.
In the coming months, selected companies will have an opportunity to pursue a year-long pilot to operationalize their technology at the MTA. Thank you to the participating companies and MTA team leads for an outstanding job and collaboration. We look forward to a productive year ahead.