Quickstart

This page runs a small network end to end: build a topology, add demand, compile to a simulation, run it, and read the results. It assumes mesoltm is installed.

A corridor

The simplest network is a corridor — a chain of links. corridor_network builds one from a list of link lengths; the first node is the origin and the last is the destination.

from mesoltm import Vehicle, corridor_network

# Three links of 300 m each: n0 -> n1 -> n2 -> n3.
net = corridor_network([300.0, 300.0, 300.0])

# Release 40 vehicles from the first node, one every 2 s, all bound for the last.
net.set_origin("n0", vehicles=[
    Vehicle(vehicle_id=k, start=2.0 * k, origin="n0", destination="n3")
    for k in range(40)
])
net.set_destination("n3")

sim = net.compile(time_step=1.0, total_time=300.0)
sim.run()

arrived = sum(len(n.get_arrived_trips()) for n in sim.nodes)
print(f"{arrived} vehicles arrived")

With a single path, each vehicle's route is filled in automatically. On networks with choices, you either give each vehicle an explicit route or supply a routing policy.

A grid with shortest-path routing

grid_network builds a rows×cols grid. With all_nodes_od=True, every node can be an origin or a destination, and ShortestPathPolicy plans each vehicle's path over the live graph.

from mesoltm import Vehicle, grid_network, ShortestPathPolicy

net = grid_network(4, 4, link_length=200.0, all_nodes_od=True)
net.set_origin((0, 0), vehicles=[
    Vehicle(vehicle_id=k, start=float(k), origin=(0, 0), destination=(3, 3))
    for k in range(50)
])

sim = net.compile(
    time_step=1.0,
    total_time=400.0,
    routing_policy=ShortestPathPolicy(dynamic=True),
)
sim.run()
print(sum(len(n.get_arrived_trips()) for n in sim.nodes), "vehicles arrived")

Grid nodes are addressed by (row, col) tuples. dynamic=True re-plans on the live graph so the policy can react to a cost that changes with congestion.

Reading results

After run(), inspect the network and derive per-vehicle metrics:

from mesoltm import collect_trips, summarize_trips

trips = collect_trips(sim)          # one record per vehicle
summary = summarize_trips(trips)    # network-level aggregates
print(summary["n_completed"], "completed;",
      round(summary["mean_travel_time"], 1), "s mean travel time")

See Metrics & trip analysis for the full record schema, and Visualizations to plot flows and travel times.

Next steps

• Prefer a config file over Python? See Running a scenario.
• Want to understand what the model computes? Read The Model.
• Ready to build real networks, reroute vehicles, or animate a run? Head to the User Guide.