MC.run¶

MC.run(file_out, weeks, weeks_equi, capacity={}, trials=100, node_p=0.1, p_norm='', max_dist=500)¶

Run Monte Carlo code. Hereby the number of drivers for each hour represent the number of MC steps. During the equilibration run, the trajectory is not edited until sttarting the production run. If a user or POI choice fails, it is repeated for the given number of trials. POI probabilities are added to the nodes to set the probability for choosing certain nodes as a destination. The probability matrix for the graph nodes can be normalized using the options

week - Normalize all node probabilities with the maximum value from the week
day - Normalize all node probabilities each day with the maximum value from the day
hour - Normalize all node probabilities each hour with the maximum value from the hour
empty string - Do not normalize

Once finished, the trajectories are saved in form of a dictionary for

nodes - Node trajectory
cs - Charging station trajectory
dist - Charging station accumulated walking distance

Parameters:

Parameters:	file_out : string file link for output object file weeks : integer Number of weeks to simulate weeks_equi : integer Number of weeks for equilibration capacity : dictionary Dictionary containing charing station nodes and capacities trials : integer, optional Number of trials for faild user and node selections per driver node_p : dictionary, float, optional Probability of all nodes not covered by pois each hour each weekday, either a float for the same probability each hour, dictionary of hours for same hour distribution for all days, a dictionary of days for the same hour probability for different days, or a dictionary of days each with a dictionary of hours p_norm: string, optional Normalize POI dicts with maximum value based on given type \(\rightarrow\) largest value is equal to one max_dist : float, optional Maximal allowed walking distance from charging station to node in m, for nodes not covered by given POI objects
Returns:	traj : dictionary Dictionary containing trajectories inputs and distance accumulation

file_out : string: file link for output object file
weeks : integer: Number of weeks to simulate
weeks_equi : integer: Number of weeks for equilibration
capacity : dictionary: Dictionary containing charing station nodes and capacities
trials : integer, optional: Number of trials for faild user and node selections per driver
node_p : dictionary, float, optional: Probability of all nodes not covered by pois each hour each weekday, either a float for the same probability each hour, dictionary of hours for same hour distribution for all days, a dictionary of days for the same hour probability for different days, or a dictionary of days each with a dictionary of hours
p_norm: string, optional: Normalize POI dicts with maximum value based on given type \(\rightarrow\) largest value is equal to one
max_dist : float, optional: Maximal allowed walking distance from charging station to node in m, for nodes not covered by given POI objects

Returns:

traj : dictionary: Dictionary containing trajectories inputs and distance accumulation