Transportation model: defining the generator
Well done; you have defined your model inputs and outputs and the model processes you characterized using Python methods. Now, it's time to put a generator together that will sequence all your model processes.
Questo esercizio fa parte del corso
Discrete Event Simulation in Python
Istruzioni dell'esercizio
- Call the function
road_travel()created before, which calculates the road travel time, and clock in the time it takes to complete it. - Call the function
wait_traffic_light()created before, which calculates the waiting time at traffic lights, and clock in the time it takes to complete it.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
def all_processes(env, inputs):
road_stretch, distance_total, traffic_light = 0, 0, 0
while True:
road_stretch += 1
# Call function calculates road travel time
distance, distance_total = ____(inputs, distance_total)
yield env.____(distance/inputs['Speed_limit_ms'])
print(f"> Road Stretch #{road_stretch} \nLength = {distance} m , Cumulative distance travelled = {distance_total} m , Total time elapsed = {env.now} sec")
traffic_light += 1
# Call function that calculates waiting time at a traffic light
waitTime_traffic_light_sec = ____(inputs, distance_total)
yield env.____(waitTime_traffic_light_sec)
print(f"> Traffic Light #{traffic_light} \nWait time = {waitTime_traffic_light_sec} sec, Time lapsed = {env.now} sec")