What the Onramp Microsimulation on traffic-simulation.de Shows

The onramp microsimulation on traffic-simulation.de is an interactive model of how vehicles merge from an onramp into a main traffic stream. It shows that congestion at onramps is not simply caused by "too many cars," but by how inflow, merging pressure, and driver behavior interact. You can adjust general inflow, onramp flow, truck percentage, and individual driving parameters, then watch whether traffic stays free-flowing or breaks down into a jam. It is best used as a conceptual demonstration of traffic flow dynamics, not as a calibrated prediction of a specific real-world highway.

What the simulation actually models

The page presents a microscopic traffic model, meaning each vehicle is simulated individually rather than as a smooth fluid. Two behavioral layers drive the results:

• Car-following behavior — how a driver adjusts speed based on the vehicle ahead. Key parameters include desired maximum speed (v0), time gap (T), and maximum acceleration (a).
• Lane-changing behavior — how and when drivers change lanes, including politeness, a lane-change threshold, and separate right-bias settings for cars and trucks.

The onramp itself adds a merging stream. Vehicles entering from the ramp must find a gap in the main flow, which couples the two behaviors: a driver may need to change lanes or adjust speed to merge, and surrounding vehicles may need to react.

Why onramps become bottlenecks

An onramp concentrates several effects in one place:

1. Added demand — the onramp flow increases the total number of vehicles the downstream road must carry.
2. Forced lane changes — merging vehicles must enter the main stream, and through-traffic may shift left to make room.
3. Local speed reduction — a merging vehicle often travels slower than the main flow, so following vehicles brake.
4. Instability — small disturbances (a single brake tap) can amplify into a standing wave or full breakdown when density is already high.

This is why an onramp can become congested even when the main road upstream looks fine: the bottleneck is created by the merging interaction, not by the road geometry alone.

Reading the controls and what to test

The interface exposes several inputs. Their practical meaning:

A useful experiment sequence:

1. Start with low general inflow and low onramp flow. Confirm traffic stays free-flowing.
2. Raise onramp flow gradually. Note the point where merging vehicles begin to force main-flow braking.
3. Raise general inflow as well. Observe whether breakdown happens suddenly or gradually.
4. Increase truck percentage. Compare how much earlier congestion appears.
5. Reduce the time gap or increase politeness. See whether merging becomes smoother or more disruptive.

The Ramp-Metering Game on the page turns this into a control task: you regulate onramp flow to keep the main road flowing. It demonstrates the core trade-off of ramp metering — holding back ramp vehicles to protect mainline throughput.

What the results mean, and what they don't

The simulation illustrates general mechanisms:

• Traffic breakdown at onramps is a dynamic instability, not just a capacity limit.
• Ramp metering can preserve mainline flow, but at the cost of queuing on the ramp.
• Driver parameters (time gap, politeness, acceleration) materially change when and whether congestion appears.

It does not give you engineering-grade forecasts. The model is a simplified, generic representation; real onramps depend on geometry, signage, speed limits, vehicle mix, and local driving culture. Treat outputs as qualitative illustrations of cause and effect.

Where to go next

The page links to the underlying sources on GitHub (MovSim) and to the book Traffic Flow Dynamics / Verkehrsdynamik by Martin Treiber, for readers who want the equations behind the model. If you want to understand merging and bottleneck formation conceptually, running a few controlled experiments with the controls above will teach more than reading a static description.