Skip to main content

An environment-agnostic framework for implementing and comparing intersection control algorithms

Project description

IntersectionControl

:warning: This repository is under active development for my BEng thesis and there is a high chance many APIs and components will change in the near future

test Documentation Status

An environment-agnostic framework for implementing and comparing intersection control algorithms

Algorithm Environment Interaction

Getting Started

Installation

$ pip install intersection-control

Refer to the documentation for all installation options

Usage

For a more detailed description of various use-cases, please refer to the documentation.

To run a simple experiment using the QBIM algorithm and SumoEnvironment:

Import the desired algorithm/environment:

from intersection_control.environments.sumo import SumoEnvironment, RandomDemandGenerator
from intersection_control.algorithms.qb_im import QBIMIntersectionManager, QBIMVehicle

Instantiate the environment:

The RandomDemandGenerator here is used to programmatically add vehicles to specifically to the Sumo environment. 
Alternatively, Sumo based [demand generation](https://sumo.dlr.de/docs/Demand/Introduction_to_demand_modelling_in_SUMO.html)
could be used
demand_generator = RandomDemandGenerator({
    "NE": 2, "NS": 2, "NW": 2, "EN": 2, "ES": 2, "EW": 2,
    "SN": 2, "SE": 2, "SW": 2, "WN": 2, "WE": 2, "WS": 2
}, 0.05)
env = SumoEnvironment("path/to/intersection.sumocfg",
                      demand_generator=demand_generator, time_step=0.05, gui=True)

Instantiate the vehicles and intersection managers:

intersection_managers = {QBIMIntersectionManager(intersection_id, env, 10, 0.05) for intersection_id in
                         env.intersections.get_ids()}  # In this Sumo network there is only one intersection
vehicles = {QBIMVehicle(vehicle_id, env, communication_range=75) for vehicle_id in env.vehicles.get_ids()}

Run the main loop:

STEP_COUNT = 360000  # 1 hour
for _ in range(STEP_COUNT):
    env.step()
    removed_vehicles = {v for v in vehicles if v.get_id() in env.get_removed_vehicles()}
    for v in removed_vehicles:
        v.destroy()
    new_vehicles = {QBIMVehicle(vehicle_id, env, communication_range=75)
                    for vehicle_id in env.get_added_vehicles()}
    vehicles = (vehicles - removed_vehicles).union(new_vehicles)
    for vehicle in vehicles:
        vehicle.step()
    for intersection_manager in intersection_managers:
        intersection_manager.step()

This simple example is available in misc/main.py:

QBIM Sumo Experiment

Exploring the code

For a full description of the code's structure please refer to the documentation

The directory structure is as follows:

IntersectionControl
├── docs  # Documentation images and files
├── intersection_control  # The main source code package
│   ├── core  # Defines all interfaces and defines the component structure
│   │   ├── environment  # Provides an interface for any environment to implement
│   │   │   ├── environment.py  # Defines the base Environment class
│   │   │   ├── intersectiont_handler.py  # Defines the base IntersectionHandler class 
│   │   │   └── vehicle_handler.py  # Defines the base VehicleHandler class
│   │   ├── algorithm
│   │   │   ├── vehicle.py  # Defines the base Vehicle class
│   │   │   └── intersection_manager.py  # Defines the base IntersectionManager class
│   │   ├── communication.py  # Provides an interface for communication - V2V or V2I is possible. Specifically, defines the base MessagingUnit class
│   │   └── performance_indication.py  # Defines the base PerformanceIndicator class (Not yet implemented)
│   ├── algorithms  # A collection of intersection control algorithm implementations (for now only QBIM). These are implementations of core.Vehicle and core.IntersectionManager
│   ├── environments  # A collection of environment implementations (for now only SUMO). These are implementations of core.Environment
│   └── communication  # A collection of communication implementations (for now only DistanceBasedUnit). These are implementations of core.MessagingUnit
├── test  # unit tests for various components
└── misc  # Miscellaneous stand-alone scripts and experiments

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

intersection_control-0.2.0.tar.gz (34.4 kB view details)

Uploaded Source

Built Distribution

intersection_control-0.2.0-py3-none-any.whl (43.1 kB view details)

Uploaded Python 3

File details

Details for the file intersection_control-0.2.0.tar.gz.

File metadata

  • Download URL: intersection_control-0.2.0.tar.gz
  • Upload date:
  • Size: 34.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for intersection_control-0.2.0.tar.gz
Algorithm Hash digest
SHA256 009698f174f1f9fd72b1c6c5f984793f226ce5214d345c6b9604dc376ecd36b8
MD5 881d7410cffa090b2d943679a9f511d9
BLAKE2b-256 a4d298c6903457ce9068b7bea199f7a1a817d144d86bd9a86b368ff0a61fb67c

See more details on using hashes here.

File details

Details for the file intersection_control-0.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for intersection_control-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 88ddf988a4310e2376354fa8aa3ab03c067e95ed9a0897a48269d449be37f213
MD5 f40fc6436cbf99c8342b1472734f1a00
BLAKE2b-256 8e8bfc56ad261bf33e7cd917337ce6064872c600c658281cdc455a326cad339e

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page