在SUMO场景中划分既有路网为等尺寸二维网格的方法咨询

Hey there! As someone who’s worked with SUMO for traffic analysis, I totally get what you’re aiming for—gridding your road network and pulling per-grid metrics like density and average speed is such a useful way to get spatial traffic insights. Let me walk you through the best tools, libraries, and approaches to make this happen:

This is your bread and butter for this task. TraCI (Traffic Control Interface) lets you interact with a running SUMO simulation in real time, and it supports Python, C++, and other languages—Python is probably the easiest for beginners.

Here’s the basic workflow you can follow:

• First, grab the boundary of your road network using traci.simulation.getNetBoundary()—this gives you the min/max x and y coordinates, so you can calculate how to split the area into equal-sized grids (e.g., 50x50 meter squares).
• For every timestep in your simulation, pull each vehicle’s position (traci.vehicle.getPosition(vehID)) and speed (traci.vehicle.getSpeed(vehID)).
• Map each vehicle to its corresponding grid by rounding its coordinates to the nearest grid cell.
• Aggregate the data per grid: count the number of vehicles (to calculate density as vehicles per grid area) and average the speeds of all vehicles in the cell.

Here’s a quick snippet to illustrate the logic (Python):

import traci

# Initialize connection to SUMO
traci.start(["sumo", "-c", "your_network.sumocfg"])

# Get network boundary to define grid
x_min, y_min, x_max, y_max = traci.simulation.getNetBoundary()
grid_size = 50  # Adjust this to your desired grid cell size (meters)
grid_area = grid_size ** 2

while traci.simulation.getMinExpectedNumber() > 0:
    traci.simulationStep()
    grid_data = {}
    
    # Iterate over all active vehicles
    for veh_id in traci.vehicle.getIDList():
        x, y = traci.vehicle.getPosition(veh_id)
        speed = traci.vehicle.getSpeed(veh_id)
        
        # Calculate which grid cell the vehicle is in
        grid_x = int((x - x_min) // grid_size)
        grid_y = int((y - y_min) // grid_size)
        grid_key = (grid_x, grid_y)
        
        # Update grid data
        if grid_key not in grid_data:
            grid_data[grid_key] = {"vehicle_count": 0, "total_speed": 0.0}
        grid_data[grid_key]["vehicle_count"] += 1
        grid_data[grid_key]["total_speed"] += speed
    
    # Compute final metrics for each grid
    grid_metrics = {}
    for key, data in grid_data.items():
        density = data["vehicle_count"] / grid_area
        avg_speed = data["total_speed"] / data["vehicle_count"] if data["vehicle_count"] > 0 else 0.0
        grid_metrics[key] = {"density": density, "average_speed": avg_speed}
    
    # Do something with grid_metrics (e.g., save to CSV, visualize)

traci.close()

Once you’ve got the raw grid metrics, these libraries will make your life easier:

• Pandas: Convert your grid metrics into a DataFrame to organize, filter, and export data (e.g., save as a CSV file for later analysis).
• Matplotlib/Seaborn: Create heatmaps or contour plots to visualize density and speed across your grid—this is great for spotting congestion hotspots at a glance.

Before diving into code, you can use the SUMO GUI to visualize a grid overlay on your network:

• Open your simulation in sumo-gui, go to View > Grid, and adjust the grid size to match what you plan to use in your code. This helps you confirm that your grid aligns with the road network correctly.

• TraCI Python Tutorial: Start with the official TraCI Python guide—it walks you through connecting to SUMO, pulling vehicle data, and basic simulation control. This is the foundation you’ll build your grid logic on.
• SUMO Spatial Analysis Examples: Check out the official SUMO example repository for scripts that handle regional traffic stats—you can adapt these to work with grid cells instead of arbitrary regions.

A quick tip: Start with a small, simple test network (like SUMO’s default grid or circle network) to debug your grid logic before scaling up to your own full-sized road network. It’ll save you a ton of time!

内容的提问来源于stack exchange，提问作者Rehab11