Systems Modelling & Simulation: The Sharing of Traffic Knowledge
usage: main.py [-h] [-n N] [-r R] [-thr THRESH] [-tmax MAX_TIME]
[-atis ATIS_P] [-p TPEAK_MEAN TPEAK_STD] [-o SAVE_PATH] [-ap]
[-ar] [-aa] [-v]
Systems Modelling and Simulation
optional arguments:
-h, --help show this help message and exit
-n N, --num_actors N number of vehicles/actors to generate per simulation
run
-r R, --runs R number of times to run the simulation for
-thr THRESH, --congestion_threshold THRESH
threshold when to consider a link congested,
volume/capacity
-tmax MAX_TIME, --max_run_time MAX_TIME
max time of each simulation run (in hours)
-atis ATIS_P, --atis_percentage ATIS_P
percentage of vehicles using the ATIS system
-p TPEAK_MEAN TPEAK_STD, --peak TPEAK_MEAN TPEAK_STD
mean and standard deviation of a normal distribution
that represents a peak in traffic
-o SAVE_PATH, --out_file SAVE_PATH
place to save the result of running the simulations
-ap, --atis-prevision
ATIS will make use of predictions to estimate the
fastest route
-ar, --atis-real ATIS will make use of real times to estimate the
fastest route
-aa, --atis-adherence
ATIS will make use of other atis users' data to
estimate the fastest route
-v, --verbose allow helpful prints to be displayed
-pl, --plots display plots at the end of the simulation regarding
the network occupation
Advanced traveller information systems (ATIS) have seen a recent surge in popularity among urban users. These systems have the ability to considerably increase traffic flow, across a city's streets but are limited by their penetration ratio among the city's population.
In this work we simulate a road network where different levels of information percolation are tested. The analogy with real life is to assess the extend to which an ATIS can improve total travel time and road utilization, and quantify this usefulness by means of improvements of traffic flow on several metrics.
We out here trying to revolutionize traffic y'all.
In the paper, three scenarios are analysed: a normal scenario, a accident scenario and a saturated scenario, using the Adherence ATIS. In the matrix below, when can observe the command used to run the correspondent experiment and its results.
| Scenario | Command | Global Actors distribution | Actors by Edge distribution | Note |
|---|---|---|---|---|
| Normal Scenario | python src/main.py -r 100 -atis 0.3 -aa |
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- |
| Accident Scenario | python src/main.py -r 100 -atis 0.3 -aa |
 |
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Create an AccidentEvent at 10:00 in edge (3, 6) with factor 0.2 in simulator.py, function create_accident_events() |
| Saturated Scenairo | python src/main.py -r 100 -atis 0.3 -aa -n 900 |
 |
 |
- |
Additionally, to be able to evaluate the tool performance when varying certain parameters, such as the atis percentage, a tool wrapper was developed in the file plotter.py.
This wrapper can be used as in:
usage: plotter.py [-h] [-i INPUT] [-o OUTPUT]
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
File where the run stats will be saved
-o OUTPUT, --output OUTPUT
Output directory for the plots
The obtained graphs when running it are:
| Real ATIS | Prevision ATIS | Adherence ATIS | |
|---|---|---|---|
| Varying Atis Percentage |  |
 |
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| Varying Total Actors in Simulation |  |
 |
 |
- NetworkX: Used for modelling the road network.
- Numpy: Used for data handling.
- Pandas: Used for data handling.
- Seaborn: Used data visualization.
- Matplotlib: Used for data visualization.
To install the dependencies, one must run the following commands in a terminal containing python3:
- In Mac/ Linux:
python3 -m venv venv
. venv/bin/activate
pip install -r requirements.txt
python3 generator.py- Windows:
py -3 -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
py -3 generator.pyIn the end you can deactivate the virtual environment by running:
deactivate