Real-Twin/tutorial_realtwin.py at main · ORNL-Real-Sim/Real-Twin · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
##############################################################################
# Copyright (c) 2024-, Oak Ridge National Laboratory                          #
# All rights reserved.                                                       #
#                                                                            #
# This file is part of RealTwin and is distributed under a GPL               #
# license. For the licensing terms see the LICENSE file in the top-level     #
# directory.                                                                 #
#                                                                            #
# Contributors: ORNL Real-Twin Team                                          #
# Contact: realtwin@ornl.gov                                                 #
##############################################################################

""" Sample script to demonstrate the usage of RealTwin for general traffic simulation."""


import realtwin as rt

# Please refer to the official documentation for more details on RealTwin preparation before running the simulation
# # https://real-twin.readthedocs.io/en/latest/index.html

if __name__ == '__main__':

    # Step 1: Prepare your configuration file (in YAML format)
    CONFIG_FILE = "./realtwin_config.yaml"

    # Step 2: initialize the realtwin object
    twin = rt.RealTwin(input_config_file=CONFIG_FILE, verbose=True)

    # Step 3: check simulator env: if SUMO, VISSIM, Aimsun, etc... are installed
    twin.env_setup(sel_sim=["SUMO", "VISSIM"])

    # Step 4: Create Matchup Table from SUMO network
    # twin.generate_inputs()

    updated_sumo_net = r"./datasets/example2/updated.net.xml"
    twin.generate_inputs(incl_sumo_net=updated_sumo_net)

    # BEFORE step 5, there are three steps to be performed:
    # 1. Prepare Traffic Demand and save it to Traffic Folder in input directory
    # 2. Prepare Control Data (Signal) and save it to Control Folder in input directory
    # 3. Manually fill in the Matchup Table in the input directory

    # Step 5: generate abstract scenario
    twin.generate_abstract_scenario()

    # AFTER step 5, Double-check the Matchup Table in the input directory to ensure it is correct.

    # Step 6: generate scenarios
    twin.generate_concrete_scenario()

    # Step 7: simulate the scenario
    twin.prepare_simulation()

    # Step 8: perform calibration, Available algorithms: GA: Genetic Algorithm, SA: Simulated Annealing, TS: Tabu Search
    twin.calibrate(sel_algo={"turn_inflow": "GA", "behavior": "GA"})

    # Step 9 (ongoing): post-process the simulation results
    twin.post_process()  # keyword arguments can be passed to specify the post-processing options

    # Step 10 (ongoing): visualize the simulation results
    twin.visualize()  # keyword arguments can be passed to specify the visualization options