Traffic Signal Simulation Software: A Full Comparison

Traffic congestion affects how people and goods move through cities. Before changing road layouts or signal timings, transportation teams need a way to assess the likely effects. Traffic signal simulation software models these scenarios and shows how traffic and signals behave under the selected conditions.

However, choosing the right software means understanding what each tool is built to do. Some focus on timing plans, while others support control algorithms or connections to specific field controllers. License terms also determine which functions you can test.

In this article, we compare six traffic signal simulation software programs using their documented features and licensing terms. We explain their controller support and the results available for review, then show what to check before field deployment.

What does traffic signal simulation software do?

A traffic signal simulator models vehicle movements and signal behavior under defined conditions. You use the model to examine what happens when you change the road layout or the way signals operate.

The evidence you need depends on the task:

• Timing analysis helps you develop or assess a signal plan, such as how green time is allocated between movements.
• Traffic simulation models interactions between vehicles and intersections to study outcomes such as queues.
• Controller testing checks how a particular control implementation responds to defined inputs.

For controller testing, check which control software the model runs or connects to. A script that selects a green phase represents one control choice. To test a field controller, connect the model to the control code you need to assess.

Traffic signal simulation software comparison at a glance

A traffic simulation software comparison starts with the work each tool supports.

An application programming interface (API) lets software exchange instructions or data. To use it with a controller, you still need a compatible connection.

Here are some of the options to consider:

Compare traffic signal simulation tools in detail

PTV Vissim: External controller studies

PTV Vissim combines traffic simulation with external signal-control support and a separate COM interface for automation. This makes it useful for studies that need to examine controller behavior while running a series of traffic scenarios.

COM prepares data and automates scenarios, reducing repeated manual setup. It reads all network object attributes, although write access depends on the attribute. External signal control provides a separate path for running supported controller procedures.

The controller/detector record captures selected control values and detector data, helping you trace the events behind the traffic animation. For a controller study, select the recorded fields before the run, then use the saved output to examine how the controller responded to detector inputs.

The practical limit is compatibility and licensing. Confirm support for your controller software and version, and ensure the trial includes both the control link and any COM automation you need. That gives you a way to evaluate the complete workflow before choosing a license.

Aimsun Next: Named control-system integrations

Aimsun Next offers documented integrations with named control systems, making it useful when a project specifies the controller to be tested. For adaptive traffic control systems, its manual identifies supported links and the components needed to connect them, helping you establish whether the intended setup is feasible.

Scenarios and experiments organize study alternatives, while replications represent individual simulation runs. Keeping each replication’s seed and settings with its results lets you trace an outcome back to the inputs that produced it.

For example, the M60/SEPAC integration uses a gateway and adapter to connect an M60 emulator. This provides a path for testing how that control implementation responds within the traffic model. During setup, trace detector inputs into the controller and follow its responses back to the simulation.

Under the 26.0.0 manual’s rules, adaptive-control interfaces are included in annual editions. External control software still requires its own license or agreement and setup files. Older dongle-based licenses have different terms.

Signal-optimization interfaces have separate requirements: the same manual specifies an Advanced or Expert annual edition. A study that combines adaptive control with a separate timing-optimization tool therefore needs the appropriate access for both.

Use a demo to test the named control system and configuration your project requires. Include the adapter and external control software licenses in that setup so you can assess the connection as well as the simulation results.

Synchro Studio: Signal timing and microsimulation

Synchro assesses signal timing through deterministic analysis, while SimTraffic models the movements of individual vehicles. The pairing is useful when you need to assess a timing plan and then examine how traffic responds to it.

Scenario management keeps alternatives in one project file, making it easier to organize baseline and proposed timing plans within the same study.

For a corridor project, use Synchro to assess a proposed timing plan, then use SimTraffic to examine vehicle interactions across adjacent intersections. This connects the timing analysis to its modeled traffic effects and helps you see how a change at one junction affects the surrounding network.

The 3D Viewer and Warrants modules are optional and require additional licenses. Include them in the quote only where their functions support the study, and check whether a demo relies on either module.

Synchro Studio’s documented strength here is the link between timing analysis and microsimulation. The product overview does not establish a general custom-controller API or support for particular controller firmware. If your project extends into controller-code testing, ask Cubic to demonstrate the required connection and the records available for inspection.

TransModeler: Signal operations and edition choices

TransModeler supports signal-operation and network-analysis studies, with edition choices that affect both model size and customization. The full product adds interfaces for teams that need custom traffic control or automated study workflows.

Caliper’s edition comparison lists the traffic-control API and GISDK automation for the full product; neither is marked as included in SE. These interfaces support work that goes beyond configuring a standard model. SE is limited to 20 intersections and 100 links, so its suitability depends on interface needs as well as network size.

Version 8 adds signal overlaps and validation displays that compare simulated volumes with observed counts. The displays help you identify differences between the traffic model and measured conditions before using the model to assess a proposed change.

For a small-network study, first compare the required features with SE’s limits. Even a small model needs the full product if the workflow depends on its traffic-control API. For custom control, use a demonstration to assess the interface and inspect the resulting control records.

Volume validation and controller validation answer different questions. The validation displays help assess traffic volumes, while a controller’s event sequence needs a separate check against the expected behavior. Plan both checks when your study covers traffic outcomes and control logic.

Caliper offers a 30-day SE evaluation; evaluating the full product starts with a demonstration request. Specify the edition and license term when arranging access so the evaluation includes the functions your project needs.

SUMO: Open-source signal-control experiments

SUMO is an open-source option for developing signal-control experiments, with built-in signal programs and interfaces for external code. It suits teams that want to inspect or modify their control logic and connect it directly to a traffic model.

Through TraCI, your code reads detector information and changes signal behavior as the simulation advances. The Python interface connects an algorithm’s observations and decisions to the model, giving researchers a practical way to test their own control strategies.

For a custom algorithm, define which detector readings it receives and how each decision changes the signal state. A connection to separate controller software also needs a mapping that preserves the inputs and responses under test. Your team owns the implementation and maintenance of that connection.

SUMO provides signal-state and switching records for examining light behavior, alongside trip results for studying traffic outcomes. Waiting time, time loss, and trip duration have separate definitions. Selecting the appropriate measure helps you answer the study question without treating every reported value as delay.

Saved model configurations and random seeds support repeatable experiments and make results easier to review. Some options and implementation issues affect repeatability, so retain the settings with each run and plan repeated runs where the study uses randomness.

Open-source access gives your team room to adapt the experiment to its own requirements. The trade-off is engineering time: building and maintaining the model and control code remains part of the project budget.

CityFlow: Traffic-signal algorithm research

CityFlow is an open-source traffic simulator designed for reinforcement-learning research and released under the Apache 2.0 license. Its focus suits projects where the main goal is to develop and evaluate a signal-control algorithm.

Python functions set signal phases and advance the simulation, while data-access functions supply observations to the algorithm. Replay helps you inspect the resulting traffic behavior. Together, these functions support an experiment that connects what an algorithm observes with the actions it takes.

To build that experiment, define the observations and permitted actions, then choose output measures that answer the research question. CityFlow’s public code and documentation provide a starting point; assess maintenance activity and Python compatibility before committing to a long-term workflow.

A research interface is only part of a field-controller test. Python phase control does not establish that CityFlow runs a particular controller’s firmware. If that is a project requirement, verify the connection and exactly which control implementation it tests.

How to choose traffic signal simulation software for your project

Start with the decision your project needs to make. A signal timing study, a controller test, and an algorithm experiment each call for different capabilities. The guidance below explains what to prioritize for each task, then shows how licensing and integration work affect your final choice.

Choose tools for a signal timing study

Start with the timing decision and the study boundary. A corridor study needs to account for interactions between signals, while an isolated-intersection study has a narrower scope. Ask which analysis method your project requires and which software component supplies its results.

Traffic signal timing software and microsimulation serve related purposes. If you need both, establish what each step contributes before choosing the package.

Keep the timing plan and traffic assumptions with the results. When you change the plan, record what else changed in the model to help explain the result.

Match software to the controller you need to test

Name the traffic signal controller software and version you need to test. Ask how the simulator connects to that code and whether the supplier supports the link. If your team must build it, include that work in the project scope.

Request a demo with the configuration you plan to use. Trace the inputs into the controller and follow its decisions back to the model.

For mission-critical systems, identify which requirements need physical hardware tests. Tests of a device interface or cabinet component need to address the hardware itself.

Choose an environment for algorithm research

Define what the algorithm observes and which actions it controls. Choose a measure that answers your research question. For example, use trip results to study how a strategy changes travel time under a stated demand pattern.

Your code controls signals through the interfaces in SUMO and CityFlow. Check that each candidate exposes the data your algorithm needs and supports the test conditions.

Keep the model and run settings so another engineer has what they need to repeat the work. Before comparing results from different tools, check that the reported measures mean the same thing.

Check the license and integration work

Request a quote that names the edition and modules you need. If the controller software needs its own license, include it in the scope. Check that the trial license permits the tasks you intend to test.

Assign an owner for software systems integration. The owner maintains the configuration mappings and checks the connection after software updates.

This also applies to open-source tools. Include the time needed to build and maintain the model alongside software access costs.

How to evaluate a traffic simulation setup before deployment

Define what a test needs to show before running it. Keep the inputs and results so someone else can review how you reached your conclusion.

Start with a baseline network and a scenario that reflects the task. Write down the expected behavior using the project’s requirements before you inspect the run.

For a traffic study, compare the model with measured road conditions. Check whether simulated counts and travel patterns match the field observations used for the study. Then check the controller’s response against the project rules. A model that matches traffic volumes still needs a separate check of its control logic.

Record each of these tests:

• Software setup: Note the software versions and enabled modules to identify the tested setup.
• Test inputs: Save the network and demand inputs with any controller configuration used in the run.
• Output measures: Define each measure and keep the output used to calculate it.
• Run settings: Record the settings and plan for repeated runs where the study uses randomness.
• Result checks: Compare expected and observed events, then document any differences you have yet to resolve.

After deployment, traffic signal analytics helps you monitor live intersection performance. Use those observations to revisit the model when road conditions change.

When traffic simulation requires custom integration

Custom software helps when evaluation reveals a gap between your tools and the workflow you need. For our virtual traffic light controller project, a German client needed to load and monitor controller configurations.

That task goes beyond choosing a traffic model. Check how your tools handle controller configuration files and where the tools still need to connect.

If you need to test a controller through a traffic model, our ITS engineering teams build virtual M60 setups linked to Aimsun Next. Start with the controller version you need to test, then check which interfaces and setup files it requires.

Final words

Traffic signal simulation software helps you explore how changes to signals and road layouts affect traffic flow. Choosing the right tool means matching its features to your project and checking whether its results support the decision you need to make. A realistic model is a starting point; reliable conclusions also depend on the inputs and tests behind it.

For projects that need custom controller integration, contact Eastgate’s ITS team to discuss your setup. Bring the controller version and your test goals so we can scope the connection to your simulation environment.