Signal Timing Solution that Does It All

The code|GREEN model integrates and simplifies the signal timing workflow. Even an unsophisticated user can create and field-deploy a timing plan in under 2 minutes.

Seamlessly Integrate All Workflow with


One software for a three-step signal timing process


Collect Data


code|GREEN uses a single camera with AI image recognition technology used in autonomous vehicles. It employs Convolutional Neural Networks (CNN) and a deep learning algorithm to process visual data.

The Data Collection

code|GREEN assigns a unique ID to each vehicle and tracks its path through the center of the intersection very precisely.  Essentially, code|GREEN fetches turning movement counts directly from the field to the software to enable the creation of data-driven day-of-the-week, time-of-the-day timing plans.

Why Use Artificial Intelligence?

With deep machine learning we can improve predictability. Metropolitan mobility managers can then make faster and more informed decisions on signal timing, suggested routing to system users, and capacity allocation.”
William Chernicoff, Head Of Research And Innovation| Toyota Mobility Foundation
CNNs not only give the best performance compared to other detection algorithms, they even outperform humans in cases such as classifying objects into fine-grained categories(…)”
Hijazi, S., Kumar, R., Rowen, C. (2017).
Using Convolutional Neural Networks for Image Recognition. Cadence Embedded Neural Network Summit.

Create Timing Plans


Existing timing plan generation software operates in isolation — one has to physically input data into the software and then print out the output for further application in the field.

code|GREEN solves this problem by directly importing the field-collected data into a single software that uses a mathematical algorithm to generate and deploy a timing plan. Its biggest advantage is the ability to create a wide variety of data-driven, time-specific timing plans, including ones for each day of the week, for special events, off-peak plans with lower cycle lengths, and seasonal plans.


The intelligent algorithm operates by deploying two optimization levels: a Local Optimizer and a Global Optimizer. The Local Optimizer computes optimal cycle lengths and green splits for each movement. The Global Optimization routine creates time tunnels through the signals, enabling vehicle progression.


To create a new timing plan, all one needs to do is to set up an arterial and import collected TMC’s from the centrally stored database. A phase-sequence diagram is then generated suggesting the optimal order and duration of coordinated phases. The software then computes a time-space diagram that illustrates the movement along the arterial and offsets. Manual tweaks allow further refinement if needed. The generated plan can then be reviewed, scheduled, saved, and deployed, all within the software and in a matter of minutes.

Digitize Operations
Having a digital architecture, In|Sync can invoke any state of parallel green phases as needed.

Instead of relying on pre-set cycle lengths with tedious transition times, like most traffic signal synchronization methods, In|Sync uses a finite-state machine to digitize operations.
Local Optimization
In|Sync uses a rule-based Artificial Intelligence (AI) algorithm, The Greedy Algorithm, to synchronize vehicle demand with green durations at the intersection in real-time.

In|Sync allocates a token for every unique car that joins the queue and an additional token for every 5 seconds a car waits.

The Greedy Algorithm changes the traffic signal light status to minimize the number of tokens issued, producing unparalleled results in the field.
Global Optimization
In|Sync coordinates between traffic signals without increasing side street delay using a concept called “Time Tunnels.”

Time tunnels are created throughout the corridor in real time, with the slope of the tunnel indicating the speed of travel between traffic signals.

The green durations for various phases are based on the Greedy Algorithm, and the time-between-tunnels can vary as well.

Control Intersections


code|GREEN is compatible with all types of controllers, cabinets, detector systems, and ATMS software.  It is able to seamlessly integrate with and control any type of controller.

The Controller Communication

After a timing plan is generated and approved, code|GREEN can download and operate the timing plan in the field. 

The AI processor inputs detector calls for desired phases into the controller that is running in free mode. This does not interfere with vehicle pre-emption or pedestrian operations.