Tutorial — Policy Comparison (Webster vs. Green Wave)#

This tutorial compares two classical traffic-signal control strategies on a Seoul corridor: Webster cycle adaptation (cycle-length optimization based on saturation flow) and Green-Wave offset coordination (offset optimization so that platoons travel at signal speed without stopping).

Step 1 — Generate a baseline with default signals#

Simulate traffic in Gangnam Station, Seoul within a 1 km radius for one hour
under medium traffic conditions. Save it as "signal_default".

The simulation uses SUMO’s default signal timings, generated when the network was created.

Step 2 — Apply Webster cycle adaptation#

On the same network and routes, apply Webster cycle adaptation to optimize
the traffic light cycle lengths. Save as "signal_webster".

The agent calls tls_adaptation_tool (which wraps SUMO’s tlsCycleAdaptation.py). The tool requires both the network and the route file, since cycle length is optimized for the actual demand. The output is a supplementary XML configuration file that overrides default cycle lengths. The simulation then re-runs with that additional file.

Step 3 — Apply Green-Wave offset coordination#

On the same network and routes, apply Green-Wave offset coordination. Save
as "signal_greenwave".

The agent calls tls_offset_tool (wrapping SUMO’s tlsCoordinator.py), which computes optimal offsets so that signal groups along a corridor turn green in a coordinated wave.

Step 4 — Compare#

Compare signal_default, signal_webster, and signal_greenwave in terms of:
1. Average trip duration
2. Average waiting time
3. Average speed
4. Total CO2 emissions
Tell me which strategy performs best on each metric and overall.

The agent issues a single SQL query against trips for the three scenarios and returns a comparison table. It typically observes that Green-Wave shines on average speed and trip duration (platoons pass through corridors without stops) while Webster reduces total waiting time when the corridor is saturated.

Step 5 — Generate the cross-scenario HTML report#

Generate an HTML report comparing all three scenarios.

The report includes the cross-scenario comparison table with percentage changes for every KPI, trip-duration distribution histograms, the top congested roads in each scenario, and an inline SVG of the study area for geographic context.

What you’ve practiced#

  • Signal timing optimization with two different SUMO utilities through AgentSUMO’s MCP wrappers (tls_offset_tool, tls_adaptation_tool).

  • Reusing a single network + route pair across multiple signal strategies (signal control is a supplementary configuration, not a network mutation).

  • Three-scenario quantitative comparison through structured SQL.

  • Automated cross-scenario HTML report generation.