Simulation-2023

Taran Singh 1789254

Instructions to run the simulation:

(Note only the files in folder 3-Touring-Machines-and-Electric-elections are related to the actual simulation, the rest are assignments unrelated to the simulation )

1. Install the packages listed in 'requirements.txt'
2. Change the simulation parameters in the jupyter notebook called traffic_vis.ipynb to your preference. (See below for an explanation of the parameters)
3. Run the jupyter notebook called traffic_vis.ipynb (note that at the end there's some code which can take a bit to finish, if doesn't sound like something you want to sit through, that's okay there are some csv-files included which already contain the data that it would output)
4. Profit?

Simulation Parameters

The traffic simulation is controlled by the following parameters:

1. road_length: The length of the one-lane road in the simulation. A larger value represents a longer road, allowing more space for vehicles to move, while a smaller value represents a shorter road, which might result in more congestion and reduced traffic flow.
   Example: road_length = 2000
2. vehicle_count: The total number of vehicles in the simulation. A higher value leads to more vehicles on the road, potentially increasing the chances of congestion and slower traffic, while a smaller value means fewer vehicles, which could result in faster traffic flow.
   Example: vehicle_count = 20
3. max_speed: The maximum speed that a vehicle can achieve in the simulation. A higher value allows vehicles to move faster, potentially improving traffic flow, while a smaller value limits the speed of the vehicles, possibly leading to slower traffic flow.
   Example: max_speed = 60
4. slowdown_probability: The probability of a vehicle randomly slowing down by 1 unit in each time step. A higher value increases the chances of vehicles slowing down, causing fluctuations in traffic flow and possibly resulting in congestion, while a smaller value decreases the chances of vehicles slowing down, leading to more consistent traffic flow.
   Example: slowdown_probability = 0.5
5. slow_vehicle_count: This parameter determines the number of slow-moving vehicles in the simulation. Increasing this value would introduce more slow-moving vehicles, which could potentially cause more disruptions in the traffic flow and increase congestion.
   Example: vehicle_count = 20
6. slow_vehicle_max_speed: This parameter sets the maximum speed that slow-moving vehicles can travel at. A lower value for this parameter would cause these slow-moving vehicles to travel at a slower pace, potentially causing more disruptions and congestion in the traffic flow.
   Example: vehicle_count = 30
7. model_steps: The number of time steps the simulation will run. Each step allows the vehicles to move according to the Nagel-Schreckenberg model. A higher value provides more opportunities for the vehicles to interact and evolve their states, while a smaller value results in a shorter simulation with fewer interactions.
   Example: steps = 50
8. occurrence_step: This parameter sets the initial step at which a specific event occurs in the simulation. Changing this value would affect when the event first happens, potentially influencing the simulation's dynamics.
   Example: occurrence_step = 2
9. repeat_occurrence_steps: This parameter determines the frequency of a specific event occurring in the simulation after the initial occurrence. Changing this value would affect how often the event repeats, potentially influencing the overall dynamics of the simulation.
   Example: repeat_occurrence_steps = 20