交通時空大數據_應用

超速車輛

overspeed_vehicles.py
請參考 - 統計每小時的數據量

Image Not Showing Possible Reasons

The image was uploaded to a note which you don't have access to
The note which the image was originally uploaded to has been deleted

設定超速的閾值






# 直接指定
threshold_speed = 60
# 第一四分位數
threshold_speed = data['speed'].quantile(0.25)
# 中位數
threshold_speed = data['speed'].median()

篩選數據
```
# 計算每個小時超速的車輛數量
data[data['speed'] > threshold_speed].groupby('Hour')['car_id'].count()
```
- data[data['speed'] > threshold_speed]：選擇所有速度大於閾值的資料
- .groupby('Hour')：根據小時 (Hour 列) 將資料分組
- ['car_id'].count()：對每個小時的資料進行計數，以得到每個小時超速的車輛數量。

完整程式碼





























import os
import pandas as pd
import matplotlib.pyplot as plt

# 指定資料路徑
input_data = 'merge0509.csv'
data = pd.read_csv(input_data)

# 處理時間
data['_time'] = pd.to_datetime(data['_time'])
data['Hour'] = data['_time'].dt.hour

# 處理速度
# 計算所有車輛速度的第一四分位數作為超速的閾值
threshold_speed = data['speed'].quantile(0.25)  # 第一四分位數

# 計算每個小時超速的車輛數量
hourly_speeding_count = data[data['speed'] > threshold_speed].groupby('Hour')['car_id'].count()

# 繪製圖表
plt.figure(figsize=(8, 4))
plt.plot(hourly_speeding_count.index, hourly_speeding_count.values, 'r-')
plt.xlabel('Hour')
plt.ylabel('Number of Speeding Vehicles')
plt.title('Hourly Speeding Vehicles Count')
plt.xticks(range(24))
plt.grid(True)
plt.show()

出行特徵分析

提取OD數據(有整理過的數據)

將gps資料轉換成以car_id為主的資料格式
每行是gps點 -> 每行是一台車的OD資料

Image Not Showing Possible Reasons

The image was uploaded to a note which you don't have access to
The note which the image was originally uploaded to has been deleted

Learn More →

如果發現資料太多，print時會被省略，可以轉換成字串輸出:print(start_end_points.to_string())

Image Not Showing Possible Reasons

The image was uploaded to a note which you don't have access to
The note which the image was originally uploaded to has been deleted

Learn More →

對每輛車的 GPS 資料進行分組，並提取每組中的第一個和最後一個 GPS 記錄，即為起點和終點


start_end_points = gps_data_sorted.groupby('car_id').agg({'lat': 'first', 'lon': 'first', '_time': 'first'}).reset_index().rename(columns={'lat': 'start_lat', 'lon': 'start_lon', '_time': 'start_time'})
end_points = gps_data_sorted.groupby('car_id').agg({'lat': 'last', 'lon': 'last', '_time': 'last'}).reset_index().rename(columns={'lat': 'end_lat', 'lon': 'end_lon', '_time': 'end_time'})

agg()：聚合函數
'first': 取第一個非空值
'last': 取最後一個非空值

完整程式碼



























import os
import pandas as pd
import matplotlib.pyplot as plt

# 指定資料路徑
input_data = 'C:\\NCKU\\data_analyze\\NSYU_september\\send\\merge0509.csv'
gps_data = pd.read_csv(input_data)

# 將資料中的 timestamp 轉換成 datetime 格式
gps_data['_time'] = pd.to_datetime(gps_data['_time'])

# 對每輛車的 GPS 資料按時間排序
gps_data_sorted = gps_data.sort_values(by=['car_id', '_time'])

# 對每輛車的 GPS 資料進行分組，並提取每組中的第一個和最後一個 GPS 記錄，即為起點和終點
# agg()：聚合函數, 'first': 取第一個非空值, 'last': 取最後一個非空值
start_end_points = gps_data_sorted.groupby('car_id').agg({'lat': 'first', 'lon': 'first', '_time': 'first'}).reset_index().rename(columns={'lat': 'start_lat', 'lon': 'start_lon', '_time': 'start_time'})
end_points = gps_data_sorted.groupby('car_id').agg({'lat': 'last', 'lon': 'last', '_time': 'last'}).reset_index().rename(columns={'lat': 'end_lat', 'lon': 'end_lon', '_time': 'end_time'})

# 合併起點和終點資料
start_end_points['end_lat'] = end_points['end_lat']
start_end_points['end_lon'] = end_points['end_lon']
start_end_points['end_time'] = end_points['end_time']

# 顯示起點和終點資料
# print(start_end_points)
print(start_end_points.to_string())

提取OD數據(未整理過的數據)

計算時間差：直接將end_time - start_time
```
odData['duration'] = odData['end_time'] - odData['start_time']
```
Image Not Showing Possible Reasons
- The image was uploaded to a note which you don't have access to
- The note which the image was originally uploaded to has been deleted
Learn More →
- 有些時間很長 -> 不正常(可能今天這台機車被借1次以上)
- 需先處理、分段od資料

設置時間閾值

如果相鄰 GPS 記錄之間的時間差超過這個閾值，則視為不同的 OD 資料





# 設置時間閾值
time_threshold = pd.Timedelta(minutes=30)

# 根據 'car_id' 分組，並計算每個分組中相鄰 GPS 記錄之間的時間差
gps_data['time_diff'] = gps_data.groupby('car_id')['_time'].diff()

設計判斷條件















od_data = []
current_od = None
for index, row in gps_data.iterrows():
    if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold:
        if current_od is not None: # 前面有資料，要先存起來
            od_data.append(current_od) 
        current_od = {'car_id': row['car_id'], 'start_lat': row['lat'], 'start_lon': row['lon'], 'start_time': row['_time']}
    if current_od is not None: # 更新 current_od 變數的結束資料
        current_od['end_lat'] = row['lat']
        current_od['end_lon'] = row['lon']
        current_od['end_time'] = row['_time']

# 將結束的 OD 資料添加到 od_data 中    
if current_od is not None:
    od_data.append(current_od)

因為不知道哪裡才會出現時間斷點，所以需要逐列判斷
for index, row in gps_data.iterrows() : 可以逐列迭代
每次迭代會產生一對 (index, Series)
- index 是列索引
- Series 是包含列數據的 Pandas Series 對象
if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold：判斷是否為一個新的 OD 資料
- pd.isnull(row['time_diff']) : 第一筆資料
- row['time_diff'] > time_threshold:時間超過閾值
將結束的 OD 資料添加到 od_data 中: 因最後一筆數據就不會再開一個新的OD資料，所以會沒有儲存到最後的資料

將OD資料轉換成 DataFrame，並計算時間



od_df = pd.DataFrame(od_data)

od_df['duration'] = od_df['end_time'] - od_df['start_time']

成果比較

Image Not Showing Possible Reasons

The image was uploaded to a note which you don't have access to
The note which the image was originally uploaded to has been deleted

Learn More →

可以看到 31902415其實是被借了很多次，而不是一次騎了10個小時

時間統計

time_statistics.py

設計x軸為借機車的時間，y軸為使用的分鐘數





# 將持續時間轉換為分鐘
od_df['duration_minutes'] = od_df['duration'].dt.total_seconds() / 60

# 用start_time的時間來統計每台車使用了多久
od_df['Hour'] = pd.to_datetime(od_df['start_time']).dt.hour

繪製箱型圖
這邊使用sns.boxplot的函數繪製













import matplotlib.pyplot as plt
import seaborn as sns

#繪製箱型圖：sns.boxplot  
fig = plt.figure(1,(6,4),dpi = 100)      
ax = plt.subplot(111)  
plt.sca(ax)

sns.boxplot(x="Hour", y = od_df['duration_minutes'], data=od_df,ax = ax)  
plt.ylabel('Order time(minutes)')  
plt.xlabel('Order start time')
plt.ylim(0,120)  
plt.show()

時間統計-完整程式碼






























































import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns 

# 指定資料路徑
input_data = 'merge0509.csv'
gps_data = pd.read_csv(input_data)

# 將資料中的 timestamp 轉換成 datetime 格式
gps_data['_time'] = pd.to_datetime(gps_data['_time'])

# 對每輛車的 GPS 資料按時間排序
gps_data_sorted = gps_data.sort_values(by=['car_id', '_time'])

# 設置時間閾值，如果相鄰 GPS 記錄之間的時間差超過這個閾值，則視為不同的 OD 資料
time_threshold = pd.Timedelta(minutes=15)

# 根據 'car_id' 分組，並計算每個分組中相鄰 GPS 記錄之間的時間差
gps_data['time_diff'] = gps_data.groupby('car_id')['_time'].diff()

# 將 'time_diff' 欄位中時間差大於閾值的記錄分成不同的 OD 資料
od_data = []
current_od = None
for index, row in gps_data.iterrows():
    if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold:
        if current_od is not None:
            od_data.append(current_od)
        current_od = {'car_id': row['car_id'], 'start_lat': row['lat'], 'start_lon': row['lon'], 'start_time': row['_time']}
    if current_od is not None:
        current_od['end_lat'] = row['lat']
        current_od['end_lon'] = row['lon']
        current_od['end_time'] = row['_time']


# 將結束的 OD 資料添加到 od_data 中
if current_od is not None:
    od_data.append(current_od)

# 將 OD 資料轉換成 DataFrame
od_df = pd.DataFrame(od_data)

od_df['duration'] = od_df['end_time'] - od_df['start_time']
# # 顯示 OD 資料
print(od_df.to_string())

# 將持續時間轉換為分鐘
od_df['duration_minutes'] = od_df['duration'].dt.total_seconds() / 60

# 用start_time的時間來統計每台車使用了多久
od_df['Hour'] = pd.to_datetime(od_df['start_time']).dt.hour

#繪製箱型圖：sns.boxplot  
fig = plt.figure(1,(6,4),dpi = 100)      
ax = plt.subplot(111)  
plt.sca(ax)

sns.boxplot(x="Hour", y = od_df['duration_minutes'], data=od_df,ax = ax)  
plt.ylabel('Order time(minutes)')  
plt.xlabel('Order start time')
plt.ylim(0,120)  
plt.show()

可視化

visualization.py

因TransBigData有規定相應格式，故須建立一個新的DataFrame來使用他的繪圖


oddata_tbd = od_df[['car_id','start_time', 'start_lon', 'start_lat','end_time', 'end_lon', 'end_lat']].rename(columns={'car_id':'VehicleNum', 'start_time':'stime', 'start_lon':'slon', 'start_lat':'slat','end_time':'etime', 'end_lon':'elon', 'end_lat':'elat'})

可視化-完整程式碼





















































































import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import transbigdata as tbd

# 指定資料路徑
input_data = 'merge0509.csv'
input_shp = 'shp\\kuohsiung.shp'
gps_data = pd.read_csv(input_data)
sz = gpd.read_file(input_shp)
sz = sz.to_crs(epsg=4326)

# accuracy = 500
# gps_data = tbd.clean_outofshape(gps_data, sz, col=['lon', 'lat'], accuracy = accuracy)

# 將資料中的 timestamp 轉換成 datetime 格式
gps_data['_time'] = pd.to_datetime(gps_data['_time'])

# 對每輛車的 GPS 資料按時間排序
gps_data_sorted = gps_data.sort_values(by=['car_id', '_time'])

# 設置時間閾值，如果相鄰 GPS 記錄之間的時間差超過這個閾值，則視為不同的 OD 資料
time_threshold = pd.Timedelta(minutes=15)

# 根據 'car_id' 分組，並計算每個分組中相鄰 GPS 記錄之間的時間差
gps_data['time_diff'] = gps_data.groupby('car_id')['_time'].diff()

# 將 'time_diff' 欄位中時間差大於閾值的記錄分成不同的 OD 資料
od_data = []
current_od = None
for index, row in gps_data.iterrows():
    if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold:
        if current_od is not None:
            od_data.append(current_od)
        current_od = {'car_id': row['car_id'], 'start_lat': row['lat'], 'start_lon': row['lon'], 'start_time': row['_time']}
    if current_od is not None:
        current_od['end_lat'] = row['lat']
        current_od['end_lon'] = row['lon']
        current_od['end_time'] = row['_time']


# 將結束的 OD 資料添加到 od_data 中
if current_od is not None:
    od_data.append(current_od)

# 將 OD 資料轉換成 DataFrame
od_df = pd.DataFrame(od_data)
od_df['duration'] = od_df['end_time'] - od_df['start_time']
# 將持續時間轉換為分鐘
od_df['duration_minutes'] = od_df['duration'].dt.total_seconds() / 60
# 用start_time的時間來統計每台車使用了多久
od_df['Hour'] = pd.to_datetime(od_df['start_time']).dt.hour


# 因TransBigData有規定相應格式，故須建立一個新的DataFrame來使用他的繪圖
oddata_tbd = od_df[['car_id','start_time', 'start_lon', 'start_lat','end_time', 'end_lon', 'end_lat']].rename(columns={'car_id':'VehicleNum', 'start_time':'stime', 'start_lon':'slon', 'start_lat':'slat','end_time':'etime', 'end_lon':'elon', 'end_lat':'elat'})

#获取栅格化参数
minx, miny, maxx, maxy = sz.total_bounds
bounds = [minx, miny, maxx, maxy]
params = tbd.area_to_params(bounds,accuracy = 1000)

#栅格化OD并集计
od_gdf = tbd.odagg_grid(oddata_tbd,params)
# od_gdf.plot(column = 'count')

#创建图框
fig =plt.figure(1,(8,8),dpi=100)
ax =plt.subplot(111)
plt.sca(ax)
#添加地图底图
# tbd.plot_map(plt,bounds,zoom = 11,style = 11)
sz.plot(ax = ax,edgecolor = (0,0,0,0),facecolor = (0,0,0,0.2),linewidths=0.5)
#绘制colorbar
cax = plt.axes([0.05, 0.33, 0.02, 0.3])
plt.title('Data count')
plt.sca(ax)
#绘制OD
od_gdf.plot(ax = ax,column = 'count',cmap = 'Blues',linewidth = 0.5,vmax = 5,cax = cax,legend = True)
#添加比例尺和指北针
tbd.plotscale(ax,bounds = bounds,textsize = 10,compasssize = 1,accuracy = 2000,rect = [0.9,0.03],zorder = 10)
plt.axis('off')
plt.xlim(bounds[0],bounds[2])
plt.ylim(bounds[1],bounds[3])
plt.show()

使用特徵分析

騎行距離 - 核密度分析

od_data_analysis.py

經緯度的直線距離


import transbigdata as tbd
od_df['distance'] = tbd.getdistance(od_df['start_lon'], od_df['start_lat'], od_df['end_lon'], od_df['end_lat'])

完整程式碼

騎行次數

統計每輛車出現幾次(有幾段OD資料)
```
od_df['car_id'].value_counts()
```

統計使用次數(times)

datatoplot = od_df['car_id'].value_counts().value_counts()

完整程式碼

每次用車時長

trip_analysis.py
核密度分析

篩選出騎乘時間在一小時內的DataFrame

od_df_filtered = od_df[od_df['duration_minutes'] <= 60]

使用sns.kdeplot()繪製核密度圖

sns.kdeplot(od_df_filtered['duration_minutes'])

完整程式碼

每日用車時長

核密度分析

把每車的每段OD資料時間加總

timecount = od_df.groupby('car_id')['duration_minutes'].sum()

使用sns.kdeplot()繪製核密度圖

sns.kdeplot(timecount/60, label = 'Time') # x軸想以小時為單位

核密度完整程式碼































































































































import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns 

# 指定資料路徑
input_data = 'merge0509.csv'
gps_data = pd.read_csv(input_data)

# 將資料中的 timestamp 轉換成 datetime 格式
gps_data['_time'] = pd.to_datetime(gps_data['_time'])

# 對每輛車的 GPS 資料按時間排序
gps_data_sorted = gps_data.sort_values(by=['car_id', '_time'])

# 設置時間閾值，如果相鄰 GPS 記錄之間的時間差超過這個閾值，則視為不同的 OD 資料
time_threshold = pd.Timedelta(minutes=15)

# 根據 'car_id' 分組，並計算每個分組中相鄰 GPS 記錄之間的時間差
gps_data['time_diff'] = gps_data.groupby('car_id')['_time'].diff()

# 將 'time_diff' 欄位中時間差大於閾值的記錄分成不同的 OD 資料
od_data = []
current_od = None
for index, row in gps_data.iterrows():
    if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold:
        if current_od is not None:
            od_data.append(current_od)
        current_od = {'car_id': row['car_id'], 'start_lat': row['lat'], 'start_lon': row['lon'], 'start_time': row['_time']}
    if current_od is not None:
        current_od['end_lat'] = row['lat']
        current_od['end_lon'] = row['lon']
        current_od['end_time'] = row['_time']


# 將結束的 OD 資料添加到 od_data 中
if current_od is not None:
    od_data.append(current_od)

# 將 OD 資料轉換成 DataFrame
od_df = pd.DataFrame(od_data)

od_df['duration'] = od_df['end_time'] - od_df['start_time']

# 將持續時間轉換為分鐘
od_df['duration_minutes'] = od_df['duration'].dt.total_seconds() / 60

# 用start_time的時間來統計每台車使用了多久
od_df['Hour'] = pd.to_datetime(od_df['start_time']).dt.hour

# #距離分析(經緯度的直線距離)
# import transbigdata as tbd
# od_df['distance'] = tbd.getdistance(od_df['start_lon'], od_df['start_lat'], od_df['end_lon'], od_df['end_lat'])
# print(od_df[['car_id', 'duration_minutes', 'distance']].to_string())
# #绘制距离分布的核密度分布（决定数据清洗阈值）
# import numpy as np
# import matplotlib.pyplot as plt
# import seaborn as sns
# fig     = plt.figure(1,(7,7),dpi = 100)   
# ax1      = plt.subplot(411)
# sns.kdeplot(od_df[od_df['distance']<16000]['distance'])
# plt.xlim(0,15000)
# plt.ylabel('Kernel Density')
# ax2      = plt.subplot(412)
# sns.kdeplot(od_df[od_df['distance']<6000]['distance'])
# plt.xlim(0,5000)
# plt.ylabel('Kernel Density')
# ax3      = plt.subplot(413)
# sns.kdeplot(od_df[od_df['distance']<1500]['distance'])
# plt.xlim(0,1000)
# plt.ylabel('Kernel Density')
# ax4      = plt.subplot(414)
# sns.kdeplot(od_df[od_df['distance']<750]['distance'])
# plt.xlim(0,500)
# plt.xlabel('distance traveled(m)')
# plt.ylabel('Kernel Density')
# plt.show()


# #使用次数
# datatoplot = od_df['car_id'].value_counts().value_counts()

# import numpy as np  
# import matplotlib.pyplot as plt  
# import seaborn as sns  
# fig     = plt.figure(1,(7,4),dpi = 150)      
# ax1      = plt.subplot(111)  
# plt.bar(datatoplot.index,datatoplot)  
# plt.xticks(range(0,40,1),range(0,40,1))  
# plt.xlim(0,15)  
# plt.xlabel('times')  
# plt.ylabel('frequency')
# plt.show() 


# 用車時長 - 核密度分析
# # 每次
# import numpy as np
# import matplotlib.pyplot as plt
# import seaborn as sns

# fig = plt.figure(1, (7, 4), dpi=150)
# ax1 = plt.subplot(111)

# od_df_filtered = od_df[od_df['duration_minutes'] <= 60]

# # Plot KDE of trip durations in minutes
# sns.kdeplot(od_df_filtered['duration_minutes'])
# plt.xlabel('Time (mins)')
# plt.ylabel('Kernel Density')
# plt.show()

# 每日
timecount = od_df.groupby('car_id')['duration_minutes'].sum()
#使用时间核密度分布
import matplotlib.pyplot as plt
import seaborn as sns
fig     = plt.figure(1,(7,4),dpi = 150)    
ax1      = plt.subplot(111)
sns.kdeplot(timecount/60, label = 'Time')
plt.legend()

plt.xticks(range(25),range(25))
plt.xlim(0,10)
plt.ylabel('Kernel Density')
plt.xlabel('Time (h)')
plt.show()

停車時長與機車利用率

停車時長短，機車利用的效率越高
utilization_analysis.py

取得本次停車到下次借車的時間
```
next_rent_time = od_df.groupby('car_id').shift(-1)['start_time']
next_rent_time = next_rent_time.fillna(collect_end)

# collect_end = pd.to_datetime(gps_data['_stop'])
```
- .shift(-1) 可取得下一列資料的數據
- 如上圖，將od數據的下一個start_time減去end_time，即可取出停車的時間
- 若沒有下次的資料了，就用GPS資料擷取的最終時間(collect_end)來計算停了多久

計算停車時間

od_df['next_rent'] = next_rent_time
od_df['parking_duration'] = (od_df['next_rent'] - od_df['end_time']).dt.total_seconds()/3600

畫圖














# 顯示 OD 資料
print(od_df[['car_id', 'start_time', 'end_time', 'next_rent','parking_duration']].to_string())

import matplotlib.pyplot as plt
import seaborn as sns
fig     = plt.figure(1,(7,4),dpi = 150)    
ax1      = plt.subplot(111)
sns.kdeplot(od_df['parking_duration'] ,label = 'parking')
plt.legend()
plt.xticks(range(25),range(25))
plt.xlim(0,24)
plt.ylabel('Kernel Density')
plt.xlabel('parking (hr)')
plt.show()

完整程式碼
















































































































import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
import transbigdata as tbd
import seaborn as sns 

# 指定資料路徑
input_data = 'merge0509.csv'
input_shp = 'shp\\kuohsiung.shp'
gps_data = pd.read_csv(input_data)
sz = gpd.read_file(input_shp)
sz = sz.to_crs(epsg=4326)


# 將資料中的 timestamp 轉換成 datetime 格式
gps_data['_time'] = pd.to_datetime(gps_data['_time'])
collect_end = pd.to_datetime(gps_data['_stop'])

# 對每輛車的 GPS 資料按時間排序
gps_data_sorted = gps_data.sort_values(by=['car_id', '_time'])

# 設置時間閾值，如果相鄰 GPS 記錄之間的時間差超過這個閾值，則視為不同的 OD 資料
time_threshold = pd.Timedelta(minutes=15)

# 根據 'car_id' 分組，並計算每個分組中相鄰 GPS 記錄之間的時間差
gps_data['time_diff'] = gps_data.groupby('car_id')['_time'].diff()

# 將 'time_diff' 欄位中時間差大於閾值的記錄分成不同的 OD 資料
od_data = []
current_od = None
for index, row in gps_data.iterrows():
    if pd.isnull(row['time_diff']) or row['time_diff'] > time_threshold:
        if current_od is not None:
            od_data.append(current_od)
        current_od = {'car_id': row['car_id'], 'start_lat': row['lat'], 'start_lon': row['lon'], 'start_time': row['_time']}
    if current_od is not None:
        current_od['end_lat'] = row['lat']
        current_od['end_lon'] = row['lon']
        current_od['end_time'] = row['_time']


# 將結束的 OD 資料添加到 od_data 中
if current_od is not None:
    od_data.append(current_od)

# 將 OD 資料轉換成 DataFrame
od_df = pd.DataFrame(od_data)

# 下一次借車
# .shift(-1) 取的下一列資料的數據
next_rent_time = od_df.groupby('car_id').shift(-1)['start_time']
next_rent_time = next_rent_time.fillna(collect_end)

# 計算停車時間
od_df['next_rent'] = next_rent_time
od_df['parking_duration'] = (od_df['next_rent'] - od_df['end_time']).dt.total_seconds()/3600

# # 顯示 OD 資料
print(od_df[['car_id', 'start_time', 'end_time', 'next_rent','parking_duration']].to_string())

import matplotlib.pyplot as plt
import seaborn as sns
fig     = plt.figure(1,(7,4),dpi = 150)    
ax1      = plt.subplot(111)
sns.kdeplot(od_df['parking_duration'] ,label = 'parking')
plt.legend()
plt.xticks(range(25),range(25))
plt.xlim(0,24)
plt.ylabel('Kernel Density')
plt.xlabel('parking (hr)')
plt.show()

#经纬度小数点保留三位小数  
data_tob = od_df[['start_lon','start_lat','parking_duration']].round(3).copy()

#集计每个小范围内停车时间中位数
data_tob = data_tob.groupby(['start_lon','start_lat'])['parking_duration'].quantile(0.5).reset_index()

#创建图框
import matplotlib as mpl  
import matplotlib.pyplot as plt  
import transbigdata as tbd   
fig     = plt.figure(1,(8,8),dpi = 100)      
ax      = plt.subplot(111)  

#添加地图底图
minx, miny, maxx, maxy = sz.total_bounds
bounds = [minx, miny, maxx, maxy]
sz.plot(ax = ax, edgecolor = (0,0,0,0), facecolor = (0,0,0,0.1), linewidth = 0.5)
plt.sca(ax)

#定义色标colormap
pallete_name = "BuPu"  
colors = sns.color_palette(pallete_name, 3)
colors.reverse()  
cmap = mpl.colors.LinearSegmentedColormap.from_list(pallete_name, colors)  
vmax = data_tob['parking_duration'].quantile(0.95)  
norm = mpl.colors.Normalize(vmin=0, vmax=vmax)  
#绘制散点图 
plt.scatter(data_tob['start_lon'],data_tob['start_lat'],
            s = 0.5,alpha = 1,c = data_tob['parking_duration'],cmap = cmap,norm=norm )  
#添加比例尺和指北针  
tbd.plotscale(ax, bounds = bounds, textsize = 10, compasssize = 1, accuracy = 2000, rect = [0.6, 0.03], zorder = 10)
plt.axis('off')  
plt.xlim(bounds[0],bounds[2])  
plt.ylim(bounds[1],bounds[3])
plt.title('Utilization efficiency')
#绘制colorbar    
cax = plt.axes([0.1, 0.33, 0.02, 0.3])
plt.colorbar(cax=cax)  
plt.title('ToB (min)')
plt.show()

交通時空大數據_應用

超速車輛

完整程式碼

出行特徵分析

提取OD數據(有整理過的數據)

完整程式碼

提取OD數據(未整理過的數據)

成果比較

時間統計

時間統計-完整程式碼

可視化

可視化-完整程式碼

使用特徵分析

騎行距離 - 核密度分析

騎行次數

每次用車時長

每日用車時長

核密度完整程式碼

停車時長與機車利用率

完整程式碼

Read more

TPASS X TDX

交通時空大數據_基礎

原始資料

TPASS