Sustainability & Recovery Challenge Finalists Present Proof-of-Concept Results

At the start of 2022 the Transit Tech Lab launched the Sustainability and Recovery Challenges, soliciting innovative solutions to help the New York metro-region meet environmental sustainability objectives and restore confidence in public transit. The program received nearly 150 applications from companies around the world and worked with subject matter experts to select ten compelling technologies to kick off proofs-of-concept in May.

Companies worked closely with transportation agencies in New York and New Jersey over the course of eight weeks, offering solutions that range from artificial intelligence to hardware that can flag unexpected flooding conditions before they impact commuters.

Each company presents an opportunity to improve the public transportation experience for millions of people in the metro area.

Here’s what we learned:

Environmental Sustainability Challenge

How can we build a more climate-resilient transportation system and increase the energy efficiency of fleets and facilities?

Runwise

Runwise uses wireless technology and web-based software to operate heating and water systems more efficiently and lower utility costs.

The company installed 98 sensors across two Metro-North and two Port Authority buildings. The sensors monitored temperatures and provided seven actionable opportunities to reduce energy consumption and optimize heating in the buildings. Runwise estimates a 15% reduction of carbon emissions in each building if operational during the winter.

StormSensor

StormSensor provides cost-effective stormwater/climate data and analytics to prevent flooding, simplify monitoring and streamline maintenance and storm response.

The company installed a total of six sensors at Port Authority’s Newark Airport, Metro-North’s Mott Haven station and NJ Transit’s Oradell Station Garage. Over four weeks, the company captured one critical flooding event, 314 hours of active water flow and five storms. The dashboard created from the data provided key metrics, including system performance reports, precipitation and real-time water depth. These indicators can help agencies deploy emergency resources faster and reduce asset damage caused by extreme weather events.

The Mobility House

The Mobility House enables intelligent integration of electric vehicles with the grid while minimizing charging costs and optimizing vehicle duty cycles.

The company provided a software simulation to study how New York City Transit (NYCT) could maximize electric bus availability at the Charleston Bus Depot in Staten Island. Based on bus efficiency, the simulation found that 64% of scheduled trips on local routes from the Charleston depot could be operated with the selected electric bus model (assuming a sufficient total number of buses). When using ChargePilot, a CEM solution, the charging infrastructure planned for installation at the Charleston depot is sufficient to operate 96% of the viable trips while generating up to $35,000 in operational savings per month, compared to an unmanaged charging scenario.

Gridmatrix

Gridmatrix’s software processes live feeds from cameras using a cloud-based machine vision algorithm. This processed data is translated into structured metrics on traffic congestion, signal performance, vehicular emissions and roadway safety.

The company analyzed 525 hours of video footage from four critical access points along the George Washington Bridge — the world’s busiest bridge — including the Henry Hudson Parkway, Westside Highway and intersections in Fort Lee, New Jersey. GridMatrix provided key sustainability metrics, including the number of cars, idle time, total gas consumption and CO2 emissions. Collectively, this data can help agencies prioritize traffic projects that reduce carbon emissions. For example, GridMatrix identified a signalized intersection producing 150% more CO2 emissions than the average New York City intersection.

Microgrid Labs

Microgrid Labs offers software that untangles the complexity of fleet electrification, planning and management. The company works with operators to optimize their vehicle battery sizing, charger sizing and energy infrastructure.

The company provided fleet electrification planning for the NJ Transit Greenville Bus Garage using its EVOPT operational management software. Analyzing the operations of 85 buses along six routes, Microgrid Labs identified the energy, battery sizing and charger sizing requirements to help plan for the facility’s electric bus transition. Using their Charge Management solution, the garage could reduce their peak energy consumption from 1.8 MW to approximately 600 kW.

COVID Recovery Challenge

How can we support the recovery of public transit and deliver service that brings riders back?

Blyncsy

Blyncsy catalogs roadway infrastructure and conditions — including pavement markings, obstacles and roadway deterioration — to facilitate maintenance.

The company analyzed 75 NJ Transit bus stops and 55 miles of Port Authority pavement markings and road conditions using crowdsourced images from dash cameras. Blyncsy’s dashboard shows roadway conditions in real time, allowing agencies to streamline surveying processes for bus stop signage, mitigate pavement deterioration and enhance asset management systems.

Clarifai

Clarifai uses machine learning and computer vision software to identify illicit activity from camera feeds.

The company analyzed 90 minutes of video recordings within the NYCT subway system to train proprietary software that can detect unsafe behavior, identify track intrusions and monitor passenger flows. Clarifai’s software demonstrated how existing CCTV video feeds can be used to provide real-time alerts with a level of accuracy that improves over time.

Invision AI

Invision AI uses existing cameras and AI vision systems to create real-time 3D digital twins of transit stations.

The company worked with NJ Transit to ingest, process and analyze video footage from eight existing camera streams at Newark-Penn Station, one of the New York metropolitan area’s major transportation hubs. The product created a digital twin of the station concourse, providing a single cohesive view of 9,000+ square feet. Invision analyzed two weeks of video data and detected 1.6 million unique travel patterns with 93% accuracy. The company demonstrated how its privacy-preserving software generates passenger flow analytics and detects anomalous behavior to help agency staff better understand travel patterns and commuter behavior.

Quanergy

Quanergy uses LiDAR hardware paired with seamlessly integrated perception software to provide situational awareness for any space. The end to end solution can report unsafe behavior, offer classified object detection, passenger flow insights and serve a variety of use cases in the security, flow management and industrial automation verticals.

The company tested their 3D LiDAR technology at three NYCT subway stations (Bowling Green, West 4th St, Sutphin Blvd — JFK) and two Metro-North tracks in Grand Central Terminal. The company was able to detect platform crowding, track intrusions and fare evasion with 98% accuracy.

Zensors

Zensors uses artificial intelligence to capture actionable data from existing hardware and cameras to provide operations visibility.

The company worked with NYCT and Metro-North to ingest and process existing CCTV video footage. The software successfully detected fare evasion at Atlantic Ave Station with 93% accuracy, tracked passenger flows in Grand Central Terminal with 91% accuracy and counted railcar utilization with 91% accuracy.