Getting Started

Getting Started#

Quick Start#

  1. Prepare the input files:

    • Ensure you have the following files in the inputs directory:

      • infra.json: Infrastructure data

      • slow_zones.json: Slow zone definitions

      • demand/odx_demand.csv: Passenger demand data

      • schedules/empirical_schedule.json: Train schedule data

  2. Configure the simulation:

    • Open the load-balance/config.yaml file

    • Adjust the following parameters as needed:

      • simulation.number_of_replications

      • simulation.start_time_of_day

      • simulation.end_time_of_day

      • demand_level

      • station

      • short_turning

      • logger.should_log_trajectories

      • logger.log_interval

  3. Run the simulation:

    python -m transit_lab_simmetro.simulation_runner.runner

  4. Check the output:

    • Look for the generated log files in the specified log folder (default: log_folder_path)

    • You’ll find CSV and JSON files with various simulation data

Understanding the Inputs and Config#

  1. Infrastructure (infra.json):

    • Defines blocks, stations, and path information for Northbound and Southbound directions

  2. Slow Zones (slow_zones.json):

    • Specifies areas with reduced speed limits

  3. Demand Data (odx_demand.csv):

    • Contains passenger arrival rates at 15-minute intervals for weekdays and non-weekdays for each OD pair

  4. Schedule Data (empirical_schedule.json):

    • Defines train dispatch times and routes

  5. Configuration (config.yaml):

    • simulation: Set replication count, start and end times

    • demand_level: Adjust overall passenger demand. Used as a multiplier to uniformly scale demand.

    • station: Specify holding station (e.g., “O-Hare” or “Clark/Lake”)

    • short_turning: Set short turning location (“UIC” or “Western”)

    • logger: Configure logging options

By adjusting these inputs and configurations, you can simulate various scenarios and analyze the rail system’s performance under different conditions.

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