def analyze_average_commute(date_val):
# Display a contiguous commute time from a source station to a target station
# Get DB data
session = manage.get_session()
stop_names = manage.get_stop_names()
station_id_dict = station_id2name('../data/stops_ids_and_names.txt')
# Create a graph representation of the train stops
G = create_train_graph(session, stop_names, station_id_dict, date_val)
target = 4600 # station_id for Tel-Aviv Hashalom
time_len = 60*24
hour_vec = np.array(range(24*60))/60
minute_vec = np.array(range(24*60)) % 60
time_indx_real = map(lambda x: '%04d'%x, hour_vec*100 + minute_vec)
# Calculation of the DOT graph algorithms
dist_vec, path_vec = dynamic_all_to_one(G, target, time_len)
display_graph(G, draw_edge_label=False)
station_id = 5410
xindx = range(len(time_indx_real))
plt.xticks(xindx[240::240], time_indx_real[240::240]), plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5), plt.grid(), plt.ylim(0, 150)
plt.xlabel('time of day'), plt.ylabel('time of commute in minutes'), plt.title(('Commute Time to %s') % (station_id_dict[4600]))
station_id = 3500
plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5)
station_id=8700
plt.plot(xindx[240:], dist_vec[station_id][240:], label=station_id_dict[station_id], linewidth=2.5)
plt.legend()
plt.show()
def evaluate_station_coverage(date_val1,
date_val2,
target_station,
day_part='monrning',
max_trips=10**6):
# Scores each station by its averge wait+commute to the given target function
# INPUT:
# dateval1 - start date of trips
# dateval2 - end date of trips
# target_station_id - relative to this station all the shortest paths will be calculated
# day_part- slice the statistics as 'all', 'morning', 'noon' or 'evening'
# max_trips - limits the number of trips analyzed
trips = get_samples_by_trip(date_val1, date_val2, max_trips)
print("Got %d trip.." % (len(trips)))
G = create_train_graph(trips)
time_len = 60 * 24
hour_vec = np.array(range(24 * 60)) / 60
minuete_vec = np.array(range(24 * 60)) % 60
time_indx_real = map(lambda x: '%04d' % x, hour_vec * 100 + minuete_vec)
wait_vec, path_vec = dynamic_all_to_one(G, target_station, time_len)
station_scores = OrderedDict()
morning_indx = [7 * 60, 12 * 60]
noon_indx = [12 * 60, 17 * 60]
evening_indx = [17 * 60, 22 * 60]
max_wait_val = 120.0
for station in wait_vec:
wait_vals = wait_vec[station]
if day_part == 'all':
wait_vals = filter(lambda x: not np.isinf(x), wait_vals)
elif day_part == 'morning':
wait_vals = filter(lambda x: not np.isinf(x),
wait_vals[morning_indx[0]:morning_indx[1]])
elif day_part == 'noon':
wait_vals = filter(lambda x: not np.isinf(x),
wait_vals[noon_indx[0]:noon_indx[1]])
elif day_part == 'evening':
wait_vals = filter(lambda x: not np.isinf(x),
wait_vals[evening_indx[0]:evening_indx[1]])
wait_vals = np.array(list(wait_vals))
if len(wait_vals) == 0:
station_scores[station] = 0
else:
station_scores[station] = np.maximum(
1 - np.median(wait_vals) / max_wait_val, 0)
return station_scores
def evaluate_station_coverage(date_val1, date_val2, target_station, day_part='monrning', max_trips=10**6):
# Scores each station by its averge wait+commute to the given target function
# INPUT:
# dateval1 - start date of trips
# dateval2 - end date of trips
# target_station_id - relative to this station all the shortest paths will be calculated
# day_part- slice the statistics as 'all', 'morning', 'noon' or 'evening'
# max_trips - limits the number of trips analyzed
trips = get_samples_by_trip(date_val1, date_val2, max_trips)
print("Got %d trip.." % (len(trips)))
G = create_train_graph(trips)
time_len = 60 * 24
hour_vec = np.array(range(24 * 60)) / 60
minuete_vec = np.array(range(24 * 60)) % 60
time_indx_real = map(lambda x: '%04d' % x, hour_vec * 100 + minuete_vec)
wait_vec, path_vec = dynamic_all_to_one(G, target_station, time_len)
station_scores = OrderedDict()
morning_indx = [7 * 60, 12 * 60]
noon_indx = [12 * 60, 17 * 60]
evening_indx = [17 * 60, 22 * 60]
max_wait_val = 120.0
for station in wait_vec:
wait_vals = wait_vec[station]
if day_part == 'all':
wait_vals = filter(lambda x: not np.isinf(x), wait_vals)
elif day_part == 'morning':
wait_vals = filter(lambda x: not np.isinf(x), wait_vals[morning_indx[0]:morning_indx[1]])
elif day_part == 'noon':
wait_vals = filter(lambda x: not np.isinf(x), wait_vals[noon_indx[0]:noon_indx[1]])
elif day_part == 'evening':
wait_vals = filter(lambda x: not np.isinf(x), wait_vals[evening_indx[0]:evening_indx[1]])
wait_vals = np.array(list(wait_vals))
if len(wait_vals) == 0:
station_scores[station] = 0
else:
station_scores[station] = np.maximum(1 - np.median(wait_vals)/max_wait_val, 0)
return station_scores
def analyze_average_commute(date_val):
# Display a contiguous commute time from a source station to a target station
# Get DB data
session = manage.get_session()
stop_names = manage.get_stop_names()
station_id_dict = station_id2name('../data/stops_ids_and_names.txt')
# Create a graph representation of the train stops
G = create_train_graph(session, stop_names, station_id_dict, date_val)
target = 4600 # station_id for Tel-Aviv Hashalom
time_len = 60 * 24
hour_vec = np.array(range(24 * 60)) / 60
minute_vec = np.array(range(24 * 60)) % 60
time_indx_real = map(lambda x: '%04d' % x, hour_vec * 100 + minute_vec)
# Calculation of the DOT graph algorithms
dist_vec, path_vec = dynamic_all_to_one(G, target, time_len)
display_graph(G, draw_edge_label=False)
station_id = 5410
xindx = range(len(time_indx_real))
plt.xticks(xindx[240::240], time_indx_real[240::240]), plt.plot(
xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5), plt.grid(), plt.ylim(0, 150)
plt.xlabel('time of day'), plt.ylabel(
'time of commute in minutes'), plt.title(
('Commute Time to %s') % (station_id_dict[4600]))
station_id = 3500
plt.plot(xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5)
station_id = 8700
plt.plot(xindx[240:],
dist_vec[station_id][240:],
label=station_id_dict[station_id],
linewidth=2.5)
plt.legend()
plt.show()
