
# Import 3rd party libraries
import warnings
import os
import json
import requests
import pandas as pd
import numpy as np
import seaborn as sns
import geopandas as gpd
import pickle
from datetime import date
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
#!pip install descartes
#!pip install folium
import folium
from folium import Choropleth
from folium import GeoJson
from folium import Marker
from folium.plugins import MarkerCluster
from folium.plugins import HeatMap
from shapely.geometry import MultiPoint

# Configure Notebook
%matplotlib inline
plt.style.use('fivethirtyeight')
sns.set_context("notebook")
warnings.filterwarnings('ignore')


trips_data = pd.read_pickle('trips_data_final.pkl')
trips_data.head()


# Check for what kind of data is in the DataFrame
trips_data.columns.values.tolist()

['Trip Id',
 'Start Time',
 'End Time',
 'Trip Duration',
 'Start Station Id',
 'Start Station Name',
 'End Station Id',
 'End Station Name',
 'User Type',
 'Subscription Id',
 'Bike Id',
 'Trip Duration (mins)',
 'Start Point',
 'End Point',
 'geometry',
 'merge_time',
 'Longitude (x)',
 'Latitude (y)',
 'Station Name',
 'Climate ID',
 'Year',
 'Month',
 'Day',
 'Time',
 'Temp (°C)',
 'Temp Flag',
 'Dew Point Temp (°C)',
 'Dew Point Temp Flag',
 'Rel Hum (%)',
 'Rel Hum Flag',
 'Wind Dir (10s deg)',
 'Wind Dir Flag',
 'Wind Spd (km/h)',
 'Wind Spd Flag',
 'Visibility (km)',
 'Visibility Flag',
 'Stn Press (kPa)',
 'Stn Press Flag',
 'Hmdx',
 'Hmdx Flag',
 'Wind Chill',
 'Wind Chill Flag',
 'Weather']


trips_time_info = trips_data[['Start Time','End Time','Trip Duration (mins)','Year','Month','Day','Time']].copy()
trips_time_info.head()


trips_time_info['Date'] = pd.to_datetime(trips_time_info[['Year','Month','Day']],format='%Y%m%d')


def plot_daily_trips(year):
    """Plot how many bike share trips occured everyday in the given year"""
    
    # Obtain daily number of trips for the target year
    daily_trips = trips_time_info[trips_time_info['Year']==year].groupby(['Date']).size().reset_index(name='Trips Count')
    
    # Plotting
    fig,ax = plt.subplots(figsize=(14,7))
    sns.lineplot(ax=ax,x='Date',y='Trips Count',data=daily_trips)
    ax.xaxis.set_major_locator(mdates.MonthLocator(bymonthday=1))
    ax.set(ylim=(0,25000))
    ax.set_title('Daily Number of Bike Share Trips in {}'.format(year))
    
    return


plot_daily_trips(2017)


plot_daily_trips(2018)


plot_daily_trips(2019)


plot_daily_trips(2020)


def plot_trip_duration(year):
    """Plot the distribution of trip durations for the given year."""

    fig,ax = plt.subplots(figsize=(9,5))
    ax = sns.histplot(data=trips_time_info[trips_time_info['Year']==year], x='Trip Duration (mins)',kde=True,binwidth=1,stat='count')
    ax.set_title('Distribution of Trip Durations in {}'.format(year))
    ax.set_xlabel('Trip Duration (mins)')
    ax.set_ylabel('Trips Count')
    
    return


plot_trip_duration(2017)


plot_trip_duration(2018)


plot_trip_duration(2019)


plot_trip_duration(2020)


# Obtain the monthly average of trips
count_average = trips_time_info.groupby(['Month']).size().reset_index(name='Trips Count')

# Plotting
fig,ax = plt.subplots(figsize=(11,6))
sns.barplot(ax=ax,x='Month',y='Trips Count',data=count_average)
ax.set_title('Monthly Average of Bike Share Usage between 2017 and 2020')
plt.ticklabel_format(style='plain',axis='y')
plt.show()


# Obtain the monthly average of trips
duration_average = trips_time_info.groupby(['Month'])['Trip Duration (mins)'].mean().reset_index(name='Trip Duration (mins)')

# Plotting
fig,ax = plt.subplots(figsize=(11,6))
sns.barplot(ax=ax,x='Month',y='Trip Duration (mins)',data=duration_average)
ax.set_title('Monthly Average of Bike Share Trip Duration between 2017 and 2020')
plt.ticklabel_format(style='plain',axis='y')
plt.show()


# Make a dictionary of all statutory holidays in Ontario between 2017 and 2020
holidays = { pd.Timestamp('2017-01-01'):"NY",
        pd.Timestamp('2017-02-20'):"FD",
        pd.Timestamp('2017-04-14'):"GF",
        pd.Timestamp('2017-05-22'):"VD",
        pd.Timestamp('2017-07-01'):"CA",
        pd.Timestamp('2017-09-04'):"LD",
        pd.Timestamp('2017-10-09'):"TG",
        pd.Timestamp('2017-12-25'):"CM",
        pd.Timestamp('2017-12-26'):"BD",
        pd.Timestamp('2018-01-01'):'NY',
        pd.Timestamp('2018-02-19'):'FD',
        pd.Timestamp('2018-03-30'):'GF',
        pd.Timestamp('2018-05-21'):'VD',
        pd.Timestamp('2018-07-01'):'CA',
        pd.Timestamp('2018-09-03'):'LD',
        pd.Timestamp('2018-10-08'):'TG',
        pd.Timestamp('2018-12-25'):'CM',
        pd.Timestamp('2018-12-26'):'BD',
        pd.Timestamp('2019-01-01'):'NY',
        pd.Timestamp('2019-02-18'):'FD',
        pd.Timestamp('2019-04-19'):'GF',
        pd.Timestamp('2019-05-20'):'VD',
        pd.Timestamp('2019-07-01'):'CA',
        pd.Timestamp('2019-09-02'):'LD',
        pd.Timestamp('2019-10-14'):'TG',
        pd.Timestamp('2019-12-25'):'CM',
        pd.Timestamp('2019-12-26'):'BD',
        pd.Timestamp('2020-01-01'):'NY',
        pd.Timestamp('2020-02-17'):'FD',
        pd.Timestamp('2020-04-10'):'GF',
        pd.Timestamp('2020-05-18'):'VD',
        pd.Timestamp('2020-07-01'):'CA',
        pd.Timestamp('2020-09-07'):'LD',
        pd.Timestamp('2020-10-12'):'TG' }

# Convert to DataFrame
holidays = pd.Series(holidays).to_frame().reset_index()
holidays.columns = ['Date','Holiday']

# View the DataFrame
holidays.head()


# Combine Holidays with trips_data
trips_time_info = pd.merge(trips_time_info,holidays,how='left',left_on='Date',right_on='Date')

# Fill the blank in "Holiday" column with "NA"
trips_time_info['Holiday'].fillna('NA',inplace=True)

#View the DataFrame
trips_time_info.head()


NY_comparison = trips_time_info[trips_time_info['Month']==1].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=NY_comparison,color='goldenrod',order=['NY','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in January')
plt.show()


FD_comparison = trips_time_info[trips_time_info['Month']==2].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=FD_comparison,color='skyblue',order=['FD','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in February')
plt.show()


GF_comparison = trips_time_info[trips_time_info['Month'].isin([3,4])].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=GF_comparison,color='orchid',order=['GF','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in March and April')
plt.show()


VD_comparison = trips_time_info[trips_time_info['Month']==5].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=VD_comparison,color='yellowgreen',order=['VD','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in May')
plt.show()


CA_comparison = trips_time_info[trips_time_info['Month']==7].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=CA_comparison,color='indianred',order=['CA','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in July')
plt.show()


LD_comparison = trips_time_info[trips_time_info['Month']==9].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=LD_comparison,color='steelblue',order=['LD','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in September')
plt.show()


TG_comparison = trips_time_info[trips_time_info['Month']==10].groupby(['Holiday','Date']).size()\
                .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(5,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=TG_comparison,color='sandybrown',order=['TG','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in October')
plt.show()


CM_BD_comparison = trips_time_info[trips_time_info['Month']==12].groupby(['Holiday','Date']).size()\
                   .groupby(level=0).mean().reset_index(name='Trips Count')

fig,ax = plt.subplots(figsize=(7,4))
sns.barplot(ax=ax,x='Holiday',y='Trips Count',data=CM_BD_comparison,color='turquoise',order=['CM','BD','NA'])
ax.set_title('Bike Share Usage on Holidays and Average Days in December')
plt.show()


trips_time_info = trips_time_info.set_index('Date')
trips_time_info['Day of Week'] = trips_time_info.index.day_name()
trips_time_info.head()


def plot_trips_week(year):
    """Plot the weekly pattern of bike share trip usage for the given year."""
    
    # Get the weekly pattern of bike share usage
    cats = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
    weekly_pattern = trips_time_info[trips_time_info['Year']==year].groupby(['Day of Week','Date']).size().groupby(level=0)\
                     .mean().reindex(cats).reset_index(name='Trips Count')

    # Plotting
    fig,ax = plt.subplots(figsize=(8,5))
    sns.barplot(ax=ax,x='Day of Week',y='Trips Count',data=weekly_pattern)
    ax.set_title('Average Daily Bike Share Trips in a Week in {}'.format(year))
    
    return


plot_trips_week(2017)


plot_trips_week(2018)


plot_trips_week(2019)


plot_trips_week(2020)


user_daily_trips = trips_data[['Start Time','Year','Month','Day','User Type']].copy()
user_daily_trips['Date'] = pd.to_datetime(user_daily_trips[['Year','Month','Day']],format='%Y%m%d')
user_daily_trips.head()


def plot_trips_day(year):
    """Plot the average hourly bike share trips by annual member and casual member for the given year."""
    
    # Obtain the hourly usage of bike share by each type of user
    daily_pattern = user_daily_trips[user_daily_trips['Year']==year].set_index('Start Time')\
                    .groupby(pd.Grouper(level='Start Time',freq='H'))['User Type']\
                    .agg(['count',lambda x:(x=='Annual Member').sum(),lambda x:(x=='Casual Member').sum()])
    daily_pattern.columns = ['rides','annual_members','casual_members']
    daily_pattern = daily_pattern.groupby(daily_pattern.index.hour).mean()
    
    # Plotting
    fig,ax = plt.subplots(figsize=(8,5))
    fig = plt.plot(daily_pattern['annual_members'],label='Annual Members')
    fig = plt.plot(daily_pattern['casual_members'],label='Casual Members')
    plt.xticks(np.arange(0,23,step=2))
    plt.xlabel('Hour of Day')
    plt.ylabel('Trips Count')
    plt.title('Average Hourly Bike Share Trips by Each Type of Users in {}'.format(year))
    plt.legend(loc="upper left")

    return


plot_trips_day(2017)


plot_trips_day(2018)


plot_trips_day(2019)


plot_trips_day(2020)


trips_weather_info = trips_data[['Start Time','Year','Month','Day','Time',
                                 'Weather','Temp (°C)','Rel Hum (%)','Wind Spd (km/h)','Visibility (km)']].copy()
trips_weather_info.head()


weather = trips_weather_info.groupby('Weather').size()
weather

Weather
Clear                              7439574
Fog                                 143329
Freezing Rain                           73
Freezing Rain,Fog                      206
Freezing Rain,Snow                     251
Haze                                 32804
Haze,Blowing Snow                       82
Heavy Rain,Fog                        2111
Heavy Snow                              57
Moderate Rain                         6048
Moderate Rain,Fog                     6802
Moderate Snow                          304
Rain                                233180
Rain,Fog                             57136
Rain,Snow                             2065
Snow                                 84656
Snow,Blowing Snow                      109
Thunderstorms                         7293
Thunderstorms,Fog                     1403
Thunderstorms,Heavy Rain              1237
Thunderstorms,Heavy Rain,Fog          4182
Thunderstorms,Moderate Rain           2444
Thunderstorms,Moderate Rain,Fog        152
Thunderstorms,Rain                   13392
Thunderstorms,Rain,Fog                 476
dtype: int64


# Iteration setup
Others = 0
weather_trip = weather

# Iterate to count the sum of trips under non-listed weather and drop them from the Series
for n in range(len(weather)):
    if weather.keys()[n] not in ['Clear','Fog','Rain','Snow']:
        Others = Others+weather[n]
        weather_trip = weather_trip.drop(labels=weather.keys()[n])

# Combine the count with listed weather
add = {'Others': Others}
weather_trip = weather_trip.append(pd.Series(data=add, index=['Others']))
weather_trip

Clear     7439574
Fog        143329
Rain       233180
Snow        84656
Others     138627
dtype: int64


# Prepare the variables for the chart
name = np.array(weather_trip.keys().tolist())
count = np.array(weather_trip.tolist())
percent = 100.*count/count.sum()

# Plot the pie chart
colors = ['tab:green','tab:orange','tab:blue','tab:red','tab:gray']
pie,texts = plt.pie(count,colors=colors,explode=[0.1]*len(weather_trip),startangle=90,radius=1.1)

# Add the legends
labels = ['{0} - {1:1.1f} %'.format(i,j) for i,j in zip(name,percent)]
plt.legend(pie,labels,loc='best', bbox_to_anchor=(-0.1, 1.),fontsize=10)
plt.title('Bike Share Usage under Different Types of Weather')
plt.show()


# Group trips data by hours
trips_weather_hourly = trips_weather_info.groupby(pd.Grouper(key='Start Time',freq='H')).agg(Trips_Count=('Day','count')
                       ,Weather=('Weather','first'),Temp=('Temp (°C)','max'),Rel_Hum=('Rel Hum (%)','max')
                       ,Wind_Spd=('Wind Spd (km/h)','max'),Visibility=('Visibility (km)','max'))

# View the DataFrame
trips_weather_hourly.head()


# Get weather distribution
trips_weather_hourly = trips_weather_hourly.replace(to_replace=[x for x in trips_weather_hourly['Weather'].unique().tolist() 
                                                                if x not in ('Clear','Fog','Rain','Snow')],value='Others')
weather_dist = trips_weather_hourly.groupby('Weather').size().reindex(index = ['Clear','Fog','Rain','Snow','Others'])
weather_dist

Weather
Clear     28179
Fog        1163
Rain       1648
Snow       1320
Others     1291
dtype: int64


# Prepare the variables for the chart
name = np.array(weather_dist.keys().tolist())
count = np.array(weather_dist.tolist())
percent = 100.*count/count.sum()

# Plot the pie chart
colors = ['tab:green','tab:orange','tab:blue','tab:red','tab:gray']
pie,texts = plt.pie(count,colors=colors,explode=[0.1]*len(weather_dist),startangle=90,radius=1.1)

# Add the legends
labels = ['{0} - {1:1.1f} %'.format(i,j) for i,j in zip(name,percent)]
plt.legend(pie,labels,loc='best', bbox_to_anchor=(-0.1, 1.),fontsize=10)
plt.title('Distribution of Different Weather Types')

plt.show()


fig,ax = plt.subplots(figsize=(9,5))
ax = sns.kdeplot(data=trips_weather_info,x='Temp (°C)',bw_adjust=2)
ax.set_title('Distribution of Bike Share Trips at Different Temperature')
ax.set_xlabel('Temperature (°C)')
ax.set_ylabel('Probability Density')
plt.show()


fig,ax = plt.subplots(figsize=(9,5))
ax = sns.kdeplot(data=trips_weather_info,x='Rel Hum (%)',bw_adjust=2)
ax.set_title('Distribution of Bike Share Trips at Different Relative Humidity Level')
ax.set_xlabel('Relative Humidity (%)')
ax.set_ylabel('Probability Density')
plt.show()


fig,ax = plt.subplots(figsize=(9,5))
ax = sns.kdeplot(data=trips_weather_info,x='Wind Spd (km/h)',bw_adjust=4)
ax.set_title('Distribution of Bike Share Trips at Different Wind Speed')
ax.set_xlabel('Wind Speed (km/h)')
ax.set_ylabel('Probability Density')
plt.show()


fig,ax = plt.subplots(figsize=(9,5))
ax = sns.kdeplot(data=trips_weather_info,x='Visibility (km)',bw_adjust=2)
ax.set_title('Distribution of Bike Share Trips at Different Visibility')
ax.set_xlabel('Visibility (km)')
ax.set_ylabel('Probability Density')
plt.show()


# Import City of Toronto neighbourhood boundaries. 
neighbourhoods = gpd.read_file('toronto_neighbourhoods.shp')

# Drop all columns except the neighbourhood name and geometry. Change the name of the neighbourhood column to name. 
neighbourhoods = neighbourhoods[['FIELD_8', 'geometry']].rename(columns={'FIELD_8': 'name'})

# Remove neighbourhood id from name. 
neighbourhoods['name'] = neighbourhoods['name'].str.replace('\(.*?\)', '').str.strip()

print('There are {} neighbourhoods'.format(len(neighbourhoods)))

neighbourhoods.head()

neighbourhoods.crs

There are 140 neighbourhoods

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich


# Import City of Toronto  subway stations. 
subway_stations = gpd.read_file('subway_stations.shp')

# Drop all columns except the neighbourhood name and geometry. Change the name of the neighbourhood column to name. 
subway_stations = subway_stations[['STATION', 'LINE', 'geometry']]

# Convert to coordinate system EPSG 4326.  
subway_stations = subway_stations.to_crs(epsg=4326)

print('There are {} subway stations'.format(len(subway_stations)))

subway_stations.head()

There are 65 subway stations


# Import bikeshare station data. 
bikeshare_stations = pd.read_csv('bikeshare_stations.csv')

# Add geometry as a point from lat/lon
bikeshare_stations = gpd.GeoDataFrame(bikeshare_stations, geometry=gpd.points_from_xy(bikeshare_stations.lon, bikeshare_stations.lat))

# Add coordinate system EPSG 4326. 
bikeshare_stations.crs = {'init': 'epsg:4326'}

print('There are {} bikeshare stations'.format(len(bikeshare_stations)))

bikeshare_stations.head()

There are 610 bikeshare stations


ax = neighbourhoods.plot(figsize = (15, 8), edgecolor='w', alpha=0.75)
bikeshare_stations.plot(ax = ax, color='green', edgecolor = 'k', label='Bike Stations')
subway_stations.plot(ax = ax, color='red', edgecolor='k', label='Subway Stations')

plt.legend(fontsize=16, loc=2)
plt.xlabel('East, meters', fontsize=18)
plt.ylabel('North, meters', fontsize=18)
plt.show()


# For simplicity in this section, take only columns with location and geometry data from trips_data. 
geo_data = trips_data[['Trip Id', 'Start Time', 'End Time', 'Trip Duration',
       'Start Station Id', 'Start Station Name', 'End Station Id',
       'End Station Name', 'User Type', 'Trip Duration (mins)', 'Start Point', 'End Point',
       'merge_time', 'Station Name', 'Year', 'Month', 'Day', 'Time', 'geometry']]

geo_data.head()


# Function that returns the number of Start and End locations in a given neighbourhood
bikeshare_station_data = bikeshare_stations
 
def stn_departure_counter(station):
    departures = sum(geo_data['Start Station Id'] == station)
    return departures

def stn_arrival_counter(station):
    arrivals = sum(geo_data['End Station Id'] == station)
    return arrivals

# Add a column for number of trips arriving and ending at each bikeshare station
bikeshare_station_data['Departures'] = bikeshare_station_data['Station Id'].apply(stn_departure_counter)
bikeshare_station_data['Arrivals'] = bikeshare_station_data['Station Id'].apply(stn_arrival_counter)

# The three stations with the most number of trips. 
print('The Stations with the greatest number of departing trips are')
bikeshare_station_data.sort_values(by = ['Departures'], ascending = False)[['Station Name', 'Departures']].head()

# View GeoDataFrame
bikeshare_station_data.head()

The Stations with the greatest number of departing trips are


# Function that returns the total number of rides from bicycle stations located within the niehgbourhood. 
neighbourhood_data = neighbourhoods

def departure_counter(neighbourhood):
    departures = sum(bikeshare_station_data.loc[bikeshare_station_data.geometry.apply(lambda x: x.within(neighbourhood)), 'Departures'])
    return departures

def arrival_counter(neighbourhood):
    arrivals = sum(bikeshare_station_data.loc[bikeshare_station_data.geometry.apply(lambda x: x.within(neighbourhood)), 'Arrivals'])
    return arrivals

# Add a column for number of trips arriving and ending in each neighbourhood
neighbourhood_data['Departures'] = neighbourhood_data.geometry.apply(departure_counter)
neighbourhood_data['Arrivals'] = neighbourhood_data.geometry.apply(arrival_counter)
neighbourhood_data.head()


# Summarize the results 
# The neighbourhoods with the greatest and least number of departing trips. 
print('The neighbourhoods with the greatest number of departing trips are {}.'.format(', '.join(
       neighbourhood_data.sort_values(by = ['Departures'], ascending = False)['name'][0:5].tolist())))
print('The neighbourhoods with the least number of departing trips are {}. \n'.format(', '.join(
       neighbourhood_data.sort_values(by = ['Departures'])['name'][0:5].tolist())))

# The neighbourhoods with the greatest and least number of trips arriving. 
print('The neighbourhoods with the greatest number of trips arriving are {}.'.format(', '.join(
       neighbourhood_data.sort_values(by = ['Arrivals'], ascending = False)['name'][0:5].tolist())))
print('The neighbourhoods with the least number of trips arriving are {}. \n'.format(', '.join(
       neighbourhood_data.sort_values(by = ['Arrivals'])['name'][0:5].tolist())))

# The number of neighbourhoods with active bikeshare stations. 
print('There are {} neighbourhoods with active bikeshare stations.'.format(
    sum((neighbourhood_data['Departures'] > 0) | (neighbourhood_data['Arrivals'] > 0))))

The neighbourhoods with the greatest number of departing trips are Waterfront Communities-The Island, Bay Street Corridor, Church-Yonge Corridor, Kensington-Chinatown, Niagara.
The neighbourhoods with the least number of departing trips are Willowdale West, Black Creek, Birchcliffe-Cliffside, Bendale, Beechborough-Greenbrook. 

The neighbourhoods with the greatest number of trips arriving are Waterfront Communities-The Island, Bay Street Corridor, Church-Yonge Corridor, Kensington-Chinatown, Niagara.
The neighbourhoods with the least number of trips arriving are Willowdale West, Black Creek, Birchcliffe-Cliffside, Bendale, Beechborough-Greenbrook. 

There are 71 neighbourhoods with active bikeshare stations.


# Create a GeoDataFrame gemoetry and neighbourhood name as index. 
plot_geography = neighbourhood_data[['name', 'geometry']]
plot_geography = plot_geography.set_index('name')

# Base map
map_1 = folium.Map(location=[43.720, -79.3871], tiles='cartodbpositron', zoom_start=11)

# Add a choropleth map to the base map
Choropleth(geo_data = plot_geography.__geo_interface__, 
           columns = ['name', 'Departures'],
           data = neighbourhood_data, 
           key_on='feature.id', 
           fill_color='YlOrRd', 
           legend_name='Bicycle Trip Departures)'
          ).add_to(map_1)

# Save to a file
map_1.save("Neighbourhood Departures.html")

# Display the map
map_1


# Now plot trip departures on the map using the MarkerCluster Plugin
# Create base map
map_2 = folium.Map(location=[43.7100, -79.3871], tiles='cartodbpositron', zoom_start=11)

# Add points to the map
for idx, row in bikeshare_station_data.to_crs(epsg=4326).iterrows():
    Marker([row.geometry.y, row.geometry.x]).add_to(map_2)

# Cluster points on new map
map_3 = folium.Map(location=[43.7100, -79.3871], tiles='cartodbpositron', zoom_start=11)
mc = MarkerCluster()
for idx, row in bikeshare_station_data.to_crs(epsg=4326).iterrows():
    mc.add_child(Marker([row.geometry.y, row.geometry.x]).add_to(map_2))

map_3.add_child(mc)
    
# Display map
map_3


# Create a 200m buffer around each bikeshare station. 
bikeshare_stations_buffer = bikeshare_station_data.to_crs(epsg=26917).geometry.buffer(200)

# Combine all buffers into one multipolygon for simplicity. 
bike_station_union = bikeshare_stations_buffer.geometry.unary_union

# Convert to GeoDataFrame
bike_station_union = gpd.GeoDataFrame(geometry=[bike_station_union], crs='EPSG:26917')


# Create a column for each subway station that indicates if it has bikeshare station access within 200m. 
subway_stations_data = subway_stations.to_crs(epsg=26917)

subway_stations_data['Bikeshare Access'] = subway_stations_data.geometry.apply(
    lambda x: bike_station_union.contains(x))

subway_stations_data.head()


# Find percent of subway stations with bikeshare access.
print('{} % of subways stations are within 200 meters of a bike station.'.format(
    100*sum(subway_stations_data['Bikeshare Access']) / len(subway_stations_data['Bikeshare Access'])))

67.6923076923077 % of subways stations are within 200 meters of a bike station.


# Create a map showing stations with bikeshare access. 
map_4 = folium.Map(location=[43.6426, -79.3871], 
                   tiles='cartodbpositron', 
                   zoom_start=10)
    
# Plot each polygon on the map
GeoJson(bike_station_union.to_crs(epsg=4326)).add_to(map_4)

# Add points to the map
for idx, row in subway_stations_data.to_crs(epsg=4326).iterrows():
    if row['Bikeshare Access']:
        Marker([row.geometry.y, row.geometry.x], 
               icon=folium.Icon(color='green'),
               popup=row['STATION']).add_to(map_4)
    else:
        Marker([row.geometry.y, row.geometry.x], 
               icon=folium.Icon(color='red'),
               popup=row['STATION']).add_to(map_4)

# Show the map
map_4


# Create a 200m buffer around each subway station. 
subway_stations_buffer = subway_stations_data.to_crs(epsg=26917).geometry.buffer(200)

# Combine all buffers into one multipolygon for simplicity. 
subway_stations_union = subway_stations_buffer.geometry.unary_union

# Convert to GeoDataFrame
subway_stations_union = gpd.GeoDataFrame(geometry=[subway_stations_union], crs='EPSG:26917')


# Create a column for each bikeshare station that indicates if it has subway station access within 200m. 
bikeshare_station_data = bikeshare_station_data.to_crs(epsg=26917)

bikeshare_station_data['Subway Access'] = bikeshare_station_data.geometry.apply(
    lambda x: subway_stations_union.contains(x))

bikeshare_station_data.head()


# Find percent of subway stations with bikeshare access.
print('There are {} bikeshare stations within 200 meters of a subway station.'.format(sum(bikeshare_station_data['Subway Access'])))
print('{} % of bikeshare stations have subway access.'.format(
    100*sum(bikeshare_station_data['Subway Access']) / len(bikeshare_station_data['Subway Access'])))

There are 102 bikeshare stations within 200 meters of a subway station.
16.721311475409838 % of bikeshare stations have subway access.


sns.lmplot(x = 'Departures', y = 'Arrivals', hue = 'Subway Access', data=bikeshare_station_data)

<seaborn.axisgrid.FacetGrid at 0x7fade8c95f10>


#Plot distribution of start times
f, ax = plt.subplots(figsize = (6, 5))

ax = sns.distplot(bikeshare_station_data.loc[bikeshare_station_data['Subway Access'] == True, 'Departures'], label = 'Subway Access')
ax = sns.distplot(bikeshare_station_data.loc[bikeshare_station_data['Subway Access'] == False, 'Departures'], label = 'No Subway Access')

ax.set_title('Distribution of Station Departures by Subway Access', size = 14)
ax.set_xlabel('Total Departures', size = 14)
ax.set_ylabel('Probability Density', size = 14)
ax.set(xlim=(0, 90000))
ax.legend()

<matplotlib.legend.Legend at 0x7fade073fc70>


print('The average number of daily trips departing from bikeshare stations with subway access is {}.'.format(bikeshare_station_data.loc[bikeshare_station_data['Subway Access'] == True, 'Departures'].mean()))

print('The average number of daily trips departing from bikeshare stations without subway access is {}.'.format(bikeshare_station_data.loc[bikeshare_station_data['Subway Access'] == False, 'Departures'].mean()))

The average number of daily trips departing from bikeshare stations with subway access is 15698.960784313726.
The average number of daily trips departing from bikeshare stations without subway access is 12673.370078740158.


# Import bikeshare station data. 
ttc_stations = pd.read_csv('stops.txt')

# Add geometry as a point from lat/lon
ttc_stations = gpd.GeoDataFrame(ttc_stations, geometry = gpd.points_from_xy(ttc_stations.stop_lon, ttc_stations.stop_lat))

# Drop empty columns 
ttc_stations = ttc_stations[['stop_id', 'stop_code', 'stop_name', 'stop_lat', 'stop_lon', 'wheelchair_boarding', 'geometry']]

# Add coordinate system EPSG 4326. 
ttc_stations.crs = {'init': 'epsg:4326'}

print('There are {} TTC stations'.format(len(ttc_stations)))

ttc_stations.head()

There are 9496 TTC stations


ax = neighbourhoods.plot(figsize = (15, 8), edgecolor='w', alpha=0.75)
ttc_stations.plot(ax = ax, color='red', label='Subway Stations', markersize = 2)
bikeshare_stations.plot(ax = ax, color='green', edgecolor = 'k', label='Bike Stations', markersize = 10)


plt.legend(fontsize=16, loc=2)
plt.xlabel('East, meters', fontsize=18)
plt.ylabel('North, meters', fontsize=18)
plt.show()


# Create a 200m buffer around each subway station. 
ttc_stations_buffer = ttc_stations.to_crs(epsg=26917).geometry.buffer(200)

# Combine all buffers into one multipolygon for simplicity. 
ttc_stations_union = ttc_stations_buffer.geometry.unary_union

# Convert to GeoDataFrame
ttc_stations_union = gpd.GeoDataFrame(geometry=[ttc_stations_union], crs='EPSG:26917')


# See if each bikeshare station has TTC stop access. 
bikeshare_station_data['TTC Access'] = bikeshare_station_data.geometry.apply(
    lambda x: ttc_stations_union.contains(x))

print('There are {} bikeshare stations with TTC access.'.format(sum(bikeshare_station_data['TTC Access'])))
print('{} % of bikeshare stations have TTC access'.format(
    100 * sum(bikeshare_station_data['TTC Access']) / len(bikeshare_station_data['TTC Access'])))

There are 545 bikeshare stations with TTC access.
89.34426229508196 % of bikeshare stations have TTC access


# Group so each row coresponds to a day. Create new dataframe with the count of total, annual, and casual rides taken each day.  
daily_data = trips_data.groupby(trips_data['Start Time'].dt.floor('D')).agg(
    rides = ('Trip Id', 'count'),
    Annual_members = ('User Type', lambda x : sum(x == 'Annual Member')),
    Casual_members = ('User Type', lambda x : sum(x == 'Casual Member')))

daily_data.head()


# Take the rolling mean for the previous 7 days to simplify plotting. 
data = daily_data.rolling(7).mean()

#plt.figure()
fig, ax = plt.subplots(figsize = (8, 5))
ax = sns.lineplot(data = data, dashes = False)
ax.set_title('Daily Ridership from 2017 to 2020', size = 14)
ax.set_xlabel('Date-Time', size = 14)
ax.set_ylabel('Daily Rides', size = 14)
ax.legend(['Total Rides', 'Annual members', 'Casual members'])
plt.show()


# Look at the yearly total rides by user type. 
# Group by year of start time to find total annual trips. 
yearly_data = daily_data.groupby(daily_data.index.year).sum()

# Drop incomplete data from 2016.
yearly_data = yearly_data[yearly_data.index != 2016]

yearly_data.head()


# Plot yearly data. 
fig, ax = plt.subplots(figsize = (7, 5))
ax = sns.lineplot(data = yearly_data, dashes = False, linewidth = 2)
ax.set_title('Annual Ridership from 2017 to 2020', size = 14)
ax.set_xlabel('Year', size = 14)
ax.set_ylabel('Total Rides', size = 14)
ax.legend(['Total Rides', 'Annual members', 'Casual members'])
plt.show()


# Plot distribution of trip duration, split by user type. 
daily_rides = sns.distplot(trips_data.loc[trips_data['User Type'] == 'Casual Member', 'Trip Duration (mins)'], label = 'Casual')
daily_rides = sns.distplot(trips_data.loc[trips_data['User Type'] == 'Annual Member', 'Trip Duration (mins)'], label = 'Annual')

daily_rides.set_title('Distribution of Trip Duration by User Type')
daily_rides.set_xlabel('Trip Duration (Minutes)')
daily_rides.set_ylabel('Probability Density')
daily_rides.legend()

<matplotlib.legend.Legend at 0x7fade01f0f40>


# Find average trip durations. 
mean_duration_casual = trips_data[trips_data['User Type'] == 'Casual Member']['Trip Duration (mins)'].mean()
mean_duration_annual = trips_data[trips_data['User Type'] == 'Annual Member']['Trip Duration (mins)'].mean()

print('Casual Members mean trip duration is {} minutes'.format(mean_duration_casual))

print('Annual Members mean trip duration is {} minutes'.format(mean_duration_annual))

Casual Members mean trip duration is 17.989849864716618 minutes
Annual Members mean trip duration is 11.586441197600228 minutes


# Add colum that is TRUE if the day is a weekday. 
daily_data['workday'] = daily_data.index.weekday <= 5


# Plot working and non working days for annual and casual members. 
f, ax = plt.subplots(figsize = (10, 5))

working_days = sns.scatterplot(x = 'Casual_members', y = 'Annual_members', hue = 'workday', size = 'rides', sizes = (2, 60), alpha = 0.7, data = daily_data, ax = ax)

# Set labels and format
working_days.set_title('Comparison of casual members and annual members on working and non-working days')
working_days.set_xlabel('Casual Membership')
working_days.set_ylabel('Annual Membership')
plt.xlim(0, )
working_days.legend()

<matplotlib.legend.Legend at 0x7fad76e3a2b0>


# Plot 2019
f, ax = plt.subplots(figsize = (10, 5))

working_days = sns.scatterplot(x = 'Casual_members', y = 'Annual_members', hue = 'workday', size = 'rides', sizes = (2, 60), alpha = 1.0, 
                                data = daily_data[(daily_data.index.year == 2019)], 
                                ax = ax)

# Set labels and format
working_days.set_title('Comparison of casual members and annual members on working and non-working days in 2019')
working_days.set_xlabel('Casual Membership')
working_days.set_ylabel('Annual Membership')
plt.xlim(0, )
working_days.legend()

<matplotlib.legend.Legend at 0x7fad76e3aa30>


# Plot 2020
f, ax = plt.subplots(figsize = (10, 5))

working_days = sns.scatterplot(x = 'Casual_members', y = 'Annual_members', hue = 'workday', sizes = (2, 60), alpha = 1.0, 
                                data = daily_data[(daily_data.index.year == 2020)], 
                                ax = ax)

# Set labels and format
working_days.set_title('Comparison of casual members and annual members on working and non-working days in 2020')
working_days.set_xlabel('Casual Membership')
working_days.set_ylabel('Annual Membership')
plt.xlim(0, )
working_days.legend(['Workday', 'Weekend'])

<matplotlib.legend.Legend at 0x7fad757533a0>


# Take daily_data from previous section. Add column that for the day of the week where Monday = 0. Add another column that is True if date was running the FRW initiative. 
daily_data['Weekday'] = daily_data.index.weekday
daily_data['FRW Day'] = (daily_data['Weekday'] == 2) & (
    ((daily_data.index.year == 2020) & (daily_data.index.month == 9)) | ((daily_data.index.year == 2019) & (daily_data.index.month == 8)) | 
    ((daily_data.index.year == 2018) & (daily_data.index.month == 6)) | ((daily_data.index.year == 2017) & (daily_data.index.month == 7))
    )

# Check that Wednesdays durring the initiatives in these four years are accounted for. 
print('There are {} days running the Free Ride Wednesday Initiative with data available.'.format(sum(daily_data['FRW Day'] == True)))

There are 17 days running the Free Ride Wednesday Initiative with data available.


# Groupy by the day of the week and if the day is running the FRW initiative. Return the average number of daily trips taken on each day of the week. 
mean_rides = daily_data.groupby(['Weekday', 'FRW Day'])['rides'].mean()
print(mean_rides)

Weekday  FRW Day
0        False       5456.010000
1        False       5861.875000
2        False       5717.021858
         True       13070.294118
3        False       5892.405000
4        False       6033.365000
5        False       5536.741294
6        False       5046.700000
Name: rides, dtype: float64


fig, ax = plt.subplots(figsize=(10, 5))

ax = sns.scatterplot(x = daily_data.index, y = "rides",
                hue = "FRW Day", size = 'FRW Day', sizes = (30, 10),
                data = daily_data, ax = ax)

ax.set_title('Daily ridership in from 2017 to 2020')

Text(0.5, 1.0, 'Daily ridership in from 2017 to 2020')


daily_data_2020 = daily_data.loc[(daily_data.index.year == 2020) & (daily_data.index.month > 5) & (daily_data.index.weekday <= 5)]

fig, ax = plt.subplots(figsize=(10, 5))

ax = sns.scatterplot(x = daily_data_2020.index, y = "rides", hue = "FRW Day", 
                data = daily_data_2020, ax = ax)

ax.set_title('Daily ridership in 2020')

Text(0.5, 1.0, 'Daily ridership in 2020')


# Identify free ride weekend in 2020. 
free_weekend = daily_data[(daily_data.index.date == date(2020, 2, 29)) | (daily_data.index.date == date(2020, 3, 1))]

# Look at only first half of 2020. 
free_ride_period = daily_data[(daily_data.index.year == 2020) & (daily_data.index.month < 12) & (daily_data.index.weekday > 4)]

# Plot ridership on days running free ride weekends and those without.
fig, ax = plt.subplots(figsize=(8, 5))

# Plot all rides. 
ax = sns.scatterplot(x = free_ride_period.index, y = "rides",
                sizes = (30, 30),
                data = free_ride_period, ax = ax, color='b')

# Plot rides running 
ax = sns.scatterplot(x = free_weekend.index, y = "rides",
                sizes = (30, 30), data = free_weekend, ax = ax, color = 'r')                

ax.set_title('Daily Ridership on Weekends')
ax.legend(['Regular Day', 'Free Ride Weekend'])
plt.show()

	Trip Id	Start Time	End Time	Trip Duration	Start Station Id	Start Station Name	End Station Id	End Station Name	User Type	Subscription Id	...	Wind Spd Flag	Visibility (km)	Visibility Flag	Stn Press (kPa)	Stn Press Flag	Hmdx	Hmdx Flag	Wind Chill	Wind Chill Flag	Weather
0	712431	2016-12-31 23:43:00-05:00	2016-12-31 23:51:00-05:00	494	7163	Yonge St / Wood St	7634	University Ave / Gerrard St W (West Side)	Annual Member	NaN	...	NaN	16.1	NaN	99.81	NaN	NaN	NaN	NaN	NaN	Clear
1	712432	2016-12-31 23:43:00-05:00	2016-12-31 23:50:00-05:00	425	7163	Yonge St / Wood St	7634	University Ave / Gerrard St W (West Side)	Annual Member	NaN	...	NaN	16.1	NaN	99.81	NaN	NaN	NaN	NaN	NaN	Clear
2	712433	2016-12-31 23:44:00-05:00	2016-12-31 23:50:00-05:00	388	7163	Yonge St / Wood St	7634	University Ave / Gerrard St W (West Side)	Annual Member	NaN	...	NaN	16.1	NaN	99.81	NaN	NaN	NaN	NaN	NaN	Clear
3	712435	2016-12-31 23:48:00-05:00	2017-01-01 00:02:00-05:00	851	7284	University Ave / King St W - SMART	7046	Niagara St / Richmond St W	Annual Member	NaN	...	NaN	16.1	NaN	99.81	NaN	NaN	NaN	NaN	NaN	Clear
4	712436	2016-12-31 23:48:00-05:00	2017-01-01 00:00:00-05:00	693	7070	25 York St – Union Station South	7172	Strachan Ave / Princes' Blvd	Annual Member	NaN	...	NaN	16.1	NaN	99.81	NaN	NaN	NaN	NaN	NaN	Clear

	Start Time	End Time	Trip Duration (mins)	Year	Month	Day	Time
0	2016-12-31 23:43:00-05:00	2016-12-31 23:51:00-05:00	8.233333	2017	1	1	00:00
1	2016-12-31 23:43:00-05:00	2016-12-31 23:50:00-05:00	7.083333	2017	1	1	00:00
2	2016-12-31 23:44:00-05:00	2016-12-31 23:50:00-05:00	6.466667	2017	1	1	00:00
3	2016-12-31 23:48:00-05:00	2017-01-01 00:02:00-05:00	14.183333	2017	1	1	00:00
4	2016-12-31 23:48:00-05:00	2017-01-01 00:00:00-05:00	11.550000	2017	1	1	00:00

Holidays	Date
New Year's Day	January 1
Family Day	Third Monday in February
Good Friday	Friday before Easter Sunday
Victoria Day	Last Monday before May 25
Canada Day	July 1
Labour Day	First Monday in September
Thanksgiving	Second Monday in October
Christmas Day	December 25
Boxing Day	December 26

	Trips_Count	Weather	Temp	Rel_Hum	Wind_Spd	Visibility
Start Time
2016-12-31 23:00:00-05:00	9	Clear	1.5	69.0	39.0	16.1
2017-01-01 00:00:00-05:00	18	Clear	1.5	69.0	39.0	16.1
2017-01-01 01:00:00-05:00	13	Clear	1.5	68.0	35.0	16.1
2017-01-01 02:00:00-05:00	15	Clear	1.2	68.0	37.0	16.1
2017-01-01 03:00:00-05:00	10	Clear	1.3	67.0	37.0	16.1

	STATION	LINE	geometry
0	Kipling	Bloor-Danforth	POINT (-79.53583 43.63734)
1	Islington	Bloor-Danforth	POINT (-79.52462 43.64537)
2	Royal York	Bloor-Danforth	POINT (-79.51133 43.64825)
3	Old Mill	Bloor-Danforth	POINT (-79.49510 43.65010)
4	Jane	Bloor-Danforth	POINT (-79.48446 43.64978)

CIV1498 - Introduction to Data Science¶

Team https://xkcd.com/1838/¶

Setup Notebook¶

Read the cleaned dataset¶

1.2 Distribution of trip durations¶

2.2 Average trip duration for each month¶

6.1 Type of weather¶

6.2 Temperature¶

6.3 Humidity¶

6.4 Wind speed¶

6.5 Visibility¶

7. Spatial Analysis¶

7.1 Neighbourhood differences¶

7.2 Proximity to Transit¶

7.3 Proximity to TTC Stations¶

8. Ridership Programs and Initiatives¶

8.1 Annual and Casual Membership¶

8.2 FREE RIDE WEDNESDAYS¶

8.3 Free Ride Weekends¶

	Date	Holiday
0	2017-01-01	NY
1	2017-02-20	FD
2	2017-04-14	GF
3	2017-05-22	VD
4	2017-07-01	CA

	Station Id	Station Name	lat	lon	capacity	geometry
0	7000	Fort York Blvd / Capreol Ct	43.639832	-79.395954	35	POINT (-79.39595 43.63983)
1	7001	Lower Jarvis St / The Esplanade	43.647830	-79.370698	15	POINT (-79.37070 43.64783)
2	7002	St. George St / Bloor St W	43.667333	-79.399429	19	POINT (-79.39943 43.66733)
3	7003	Madison Ave / Bloor St W	43.667158	-79.402761	15	POINT (-79.40276 43.66716)
4	7004	University Ave / Elm St	43.656518	-79.389099	11	POINT (-79.38910 43.65652)

	name	geometry	Departures	Arrivals
0	Wychwood	POLYGON ((-79.43592 43.68015, -79.43492 43.680...	45089	30790
1	Yonge-Eglinton	POLYGON ((-79.41096 43.70408, -79.40962 43.704...	28606	24093
2	Yonge-St.Clair	POLYGON ((-79.39119 43.68108, -79.39141 43.680...	6005	4631
3	York University Heights	POLYGON ((-79.50529 43.75987, -79.50488 43.759...	5292	5255
4	Yorkdale-Glen Park	POLYGON ((-79.43969 43.70561, -79.44011 43.705...	0	0

	STATION	LINE	geometry	Bikeshare Access
0	Kipling	Bloor-Danforth	POINT (618101.613 4832636.300)	False
1	Islington	Bloor-Danforth	POINT (618990.613 4833544.113)	False
2	Royal York	Bloor-Danforth	POINT (620056.496 4833882.764)	False
3	Old Mill	Bloor-Danforth	POINT (621361.678 4834111.901)	False
4	Jane	Bloor-Danforth	POINT (622220.664 4834091.381)	True

	stop_id	stop_code	stop_name	stop_lat	stop_lon	wheelchair_boarding	geometry
0	262	662	DANFORTH RD AT KENNEDY RD	43.714379	-79.260939	2	POINT (-79.26094 43.71438)
1	263	929	DAVENPORT RD AT BEDFORD RD	43.674448	-79.399659	1	POINT (-79.39966 43.67445)
2	264	940	DAVENPORT RD AT DUPONT ST	43.675511	-79.401938	2	POINT (-79.40194 43.67551)
3	265	1871	DAVISVILLE AVE AT CLEVELAND ST	43.702088	-79.378112	1	POINT (-79.37811 43.70209)
4	266	11700	DISCO RD AT ATTWELL DR	43.701362	-79.594843	1	POINT (-79.59484 43.70136)

	rides	Annual_members	Casual_members
Start Time
2016-12-31 00:00:00-05:00	9	9	0
2017-01-01 00:00:00-05:00	482	412	70
2017-01-02 00:00:00-05:00	826	756	70
2017-01-03 00:00:00-05:00	871	853	18
2017-01-04 00:00:00-05:00	1395	1361	34

	rides	Annual_members	Casual_members
Start Time
2017	1392020	1142621	249399
2018	1840223	1556369	283854
2019	2338532	1834708	503824
2020	2468582	1633170	835412

CIV1498 - Introduction to Data Science¶

Project - Toronto Bike Share¶

Team https://xkcd.com/1838/¶

Setup Notebook¶

Read the cleaned dataset¶

1. Annual Pattern of Bike Share Usage¶

1.1 Daily numbers of bike share trips¶

1.2 Distribution of trip durations¶

2. Seasonal Trend in Bike Share Usage¶

2.1 Monthly average of bike share trips¶

2.2 Average trip duration for each month¶

3. Statutory Holidays and Bike Share Demand¶

4. Weekly Pattern of Bike Share Usage¶

5. Daily Pattern of Bike Share Usage¶

6. Weather Condition and Bike Share Demand¶

6.1 Type of weather¶

6.2 Temperature¶

6.3 Humidity¶

6.4 Wind speed¶

6.5 Visibility¶

7. Spatial Analysis¶

7.1 Neighbourhood differences¶

7.2 Proximity to Transit¶

7.3 Proximity to TTC Stations¶

8. Ridership Programs and Initiatives¶

8.1 Annual and Casual Membership¶

8.2 FREE RIDE WEDNESDAYS¶

8.3 Free Ride Weekends¶