To alleviate congestion and improve mobility in Midtown Manhattan, we developed an innovative approach that helped NYC leverage real-time data and algorithms in managing traffic flow. After it launched, Midtown in Motion was heralded as “the nation’s most sophisticated traffic management system." We continue to work with our partner, TransCore, and NYC's transportation agency to expand and improve the system.
By the late 2000s, congestion in Midtown Manhattan was getting worse and New York City’s transportation agency (NYCDOT) was looking for ways to better manage traffic. Having recently invested in upgrading their central traffic control system with state-of-the art intersection traffic controllers and a city-wide wireless backbone with sensors to gather metrics on traffic flow and vehicle occupancy, they were exploring using toll tag data to collect per-trip “travel time” data anonymously, which would generate nearly 1 million records daily.
KLD had just helped NYCDOT deploy the city’s first adaptive traffic signal control system in Staten Island, so when the agency approached us about working again with Transcore to take these new datasets and develop an active traffic management system (ATMS) for Midtown, we jumped at the opportunity.
One of our biggest challenges was to build an ATMS on top of NYCDOT’s existing central traffic control system, leveraging emerging datasets to provide real-time signal change recommendations to operators while allowing operators to stay in the driver’s seat and evaluate each recommended change before implementation.
Being able to understand travel time data and how to use it in conjunction with flow and occupancy for traffic control was a big step for the success of this system, which had never been implemented at this large of a scale in an urban area.
We designed the ATMS as a hierarchical system with a “control zone” (initially spanning from 2nd to 6th Avenue and 42nd to 57th Street) and two levels of system control: an avenue-specific level that implements different sets of plans to modify traffic approaching the zone, and an intersection-specific level that changes signal splits in real time within the zone. For the intersection control level, we developing a dynamic queue balancing strategy at critical intersections to prevent propagation of spillovers with stabilized or diminished queues.
Rather than building a fully-autonomous traffic control system, the city wanted Midtown in Motion to be akin to a “decision support system”. In other words, they wanted an ATMS to run autonomously in the background, monitor traffic, and alert the traffic operator when conditions change with a recommended response plan. The operator would then get a chance to review it and if they thought it was valid, they would apply the change.
Our innovation was in bringing all these data sets together and building a real-time traffic control system from scratch with the operator in the loop. TransCore developed the central control system and the needed interfaces to ACDSS, while KLD developed the real time algorithms, ACDSS components, and the user interface for the operator.
Following the initial deployment, extensive analysis of large-scale data sets indicated an improvement in travel time of around 10% within the congested study area. Midtown in Motion has since become a marquee project for NYCDOT and TMC staff continue to use this system extensively.
After the first few years of running and getting comfortable with the system, the Traffic Management Center switched the default setting to “accept” the system’s recommendations.
Since initial deployment in 2011, the control area has been expanded incrementally, now extending from 1st Avenue to 9th Avenue and 57th Street to 34th Street. Things have also changed significantly in Midtown, but Midtown in Motion has become a dynamic tool in NYCDOT’s toolbox to manage traffic, and the data generated by the system has been routinely used by NYCDOT to analyze traffic operations in the area.
As a consistent partner to NYCDOT, we have developed customized system monitoring and evaluation tools and continue to provide system enhancements. We recently designed an updated interface for the TMC operators with additional activity-logging features, and we are in the process of integrating NYCDOT’s extensive Wi-Fi-based travel time data to provide a more robust dataset to support project decision-making.
