transbigdata lab

原创于 2025-03-14 21:26:22 发布

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import pandas as pd
import numpy as np
import geopandas as gpd
import transbigdata as tbd
import networkx as nx
import warnings
warnings.filterwarnings('ignore')
line,stop = tbd.getbusdata('昆明市',['1号线','2号线','3号线','4号线'])
line.plot()
stop.plot()
metroline_splited = tbd.split_subwayline(line,stop)
metroline_splited.plot(column = pd.Series(metroline_splited.index))

#Modeling for subway network topology
line['speed'] = 60 #operation speed 55km/h
line['stoptime'] = 50 #stop time at each stations 50s
G = tbd.metro_network(line,stop, transfertime=50)
nx.draw(G,node_size=200)

path = tbd.get_shortest_path(G,stop,'','')
path

Obtaining city id: 昆明市success
Get bus data: 1号线
地铁1号线(环城南路-大学城南) success
地铁1号线(大学城南-环城南路) success
地铁1号线(昆明南火车站-环城南路) success
地铁1号线(环城南路-昆明南火车站) success
Get bus data: 2号线
地铁2号线(环城南路-北部汽车站) success
地铁2号线(北部汽车站-环城南路) success
地铁2号线二期(环城南路-海东公园) success
地铁2号线二期(海东公园-环城南路) success
Get bus data: 3号线
地铁3号线(东部汽车站-西山公园) success
地铁3号线(西山公园-东部汽车站) success
观鸥三号线(拓东体育馆(东风东路)-海埂大坝车场) success
观鸥三号线(海埂大坝车场-拓东体育馆(东风东路)) success
Get bus data: 4号线
地铁4号线(金川路-昆明南火车站) success
地铁4号线(昆明南火车站-金川路) success
观鸥四号线(官渡广场(国贸路)-海埂大坝车场) success
观鸥四号线(海埂大坝车场-官渡广场(国贸路)) success



---------------------------------------------------------------------------

IndexError                                Traceback (most recent call last)

Cell In[1], line 20
     17 G = tbd.metro_network(line,stop, transfertime=50)
     18 nx.draw(G,node_size=200)
---> 20 path = tbd.get_shortest_path(G,stop,'','')
     21 path


File D:\python\3.12版本\Lib\site-packages\transbigdata\metro.py:213, in get_shortest_path(G, stop, ostation, dstation)
    191 '''
    192 Obtain the travel path of shortest path from the metro nextwork
    193 
   (...)
    210     travel path: list of station names
    211 '''
    212 import networkx as nx
--> 213 o = stop[stop['stationnames'] == ostation]['line'].iloc[0]+ostation
    214 d = stop[stop['stationnames'] == dstation]['line'].iloc[0]+dstation
    215 return nx.shortest_path(G, source=o, target=d, weight='weight')


File D:\python\3.12版本\Lib\site-packages\pandas\core\indexing.py:1153, in _LocationIndexer.__getitem__(self, key)
   1150 axis = self.axis or 0
   1152 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1153 return self._getitem_axis(maybe_callable, axis=axis)


File D:\python\3.12版本\Lib\site-packages\pandas\core\indexing.py:1714, in _iLocIndexer._getitem_axis(self, key, axis)
   1711     raise TypeError("Cannot index by location index with a non-integer key")
   1713 # validate the location
-> 1714 self._validate_integer(key, axis)
   1716 return self.obj._ixs(key, axis=axis)


File D:\python\3.12版本\Lib\site-packages\pandas\core\indexing.py:1647, in _iLocIndexer._validate_integer(self, key, axis)
   1645 len_axis = len(self.obj._get_axis(axis))
   1646 if key >= len_axis or key < -len_axis:
-> 1647     raise IndexError("single positional indexer is out-of-bounds")


IndexError: single positional indexer is out-of-bounds

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

transbigdata

import pandas as pd
import numpy as np
import geopandas as gpd
import transbigdata as tbd
import networkx as nx
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['fangsong']  # 设置中文字体为仿宋
plt.rcParams['axes.unicode_minus'] = False     # 用来正常显示负号

km = gpd.read_file(r'C:\Users\Redmi\Desktop\GIS\课程作业\全国公交路线信息\云南\km.json')
km.crs = None
km.head()
fig = plt.figure(1, (13, 5), dpi=150)
ax1 = plt.subplot(111)
km.plot(ax=ax1)
plt.title('昆明市json图(ruomu hu)')
plt.xticks([], fontsize=10)
plt.yticks([], fontsize=10);

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

Transbigdata 练习

import osmnx as ox
import matplotlib.pyplot as plt
streets_graph = ox.graph_from_place('云南, 昆明', network_type='drive')#address：Los Angeles城市名，California
streets_graph = ox.projection.project_graph(streets_graph)

streets = ox.graph_to_gdfs(ox.get_undirected(streets_graph), nodes=False, edges=True,
                                   node_geometry=False, fill_edge_geometry=True)
f, ax = plt.subplots(figsize=(10, 10))
streets.plot(ax=ax, linewidth=0.2)
ax.set_axis_off()
plt.show()

Osmnx

import osmnx as ox
import networkx as nx

#根据点和半径获取地图
wurster_hall = (24.86,102.86)
one_mile = 1500  # meters
G = ox.graph_from_point(wurster_hall, dist=one_mile, network_type="drive")
fig, ax = ox.plot_graph(G, node_size=0)

#获取地点中的具体设施，可视化
G = ox.graph_from_place(
    "New York, New York, USA",
    retain_all=False,
    truncate_by_edge=True,
    simplify=True,
    custom_filter='["railway"~"subway"]',
)
fig, ax = ox.plot_graph(G, node_size=0, edge_color="w", edge_linewidth=0.2)

#获取设施的几何，可视化
place = "Civic Center, Los Angeles, California"
tags = {"building": True}
gdf = ox.geometries_from_place(place, tags)
fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))

#根据地点获取路网，并可视化
G = ox.graph_from_place("kunming, yunnan, china", network_type="drive")
fig, ax = ox.plot_graph(G)

#将图结构的路网转变成geopandas格式的节点和边
gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)
gdf_nodes.head()

# 将geopandas格式的边和节点转变成Networkx的图
G2 = ox.graph_from_gdfs(gdf_nodes, gdf_edges, graph_attrs=G.graph)

# 计算边的中心度，赋予边权重，并渲染出来
edge_centrality = nx.closeness_centrality(nx.line_graph(G))
nx.set_edge_attributes(G, edge_centrality, "edge_centrality")
edge_centrality = nx.closeness_centrality(nx.line_graph(G))
nx.set_edge_attributes(G, edge_centrality, "edge_centrality")

#最短路径搜索
#为网络添加速度和旅行时间
G = ox.speed.add_edge_speeds(G)
G = ox.speed.add_edge_travel_times(G)
# 将节点匹配到网络的最近路段上
orig = ox.distance.nearest_nodes(G, X=-122.245846, Y=37.828903)
dest = ox.distance.nearest_nodes(G, X=-122.215006, Y=37.812303)
# 生成最短路径并渲染
route = ox.shortest_path(G, orig, dest, weight="travel_time")
fig, ax = ox.plot_graph_route(G, route, node_size=0)
#路径长度计算
edge_lengths = ox.utils_graph.get_route_edge_attributes(G, route, "length")
round(sum(edge_lengths))

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

---------------------------------------------------------------------------

TimeoutError                              Traceback (most recent call last)

File D:\python\3.12版本\Lib\site-packages\urllib3\connection.py:174, in HTTPConnection._new_conn(self)
    173 try:
--> 174     conn = connection.create_connection(
    175         (self._dns_host, self.port), self.timeout, **extra_kw
    176     )
    178 except SocketTimeout:


File D:\python\3.12版本\Lib\site-packages\urllib3\util\connection.py:95, in create_connection(address, timeout, source_address, socket_options)
     94 if err is not None:
---> 95     raise err
     97 raise socket.error("getaddrinfo returns an empty list")


File D:\python\3.12版本\Lib\site-packages\urllib3\util\connection.py:85, in create_connection(address, timeout, source_address, socket_options)
     84     sock.bind(source_address)
---> 85 sock.connect(sa)
     86 return sock


TimeoutError: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。


During handling of the above exception, another exception occurred:


ConnectTimeoutError                       Traceback (most recent call last)

File D:\python\3.12版本\Lib\site-packages\urllib3\connectionpool.py:715, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
    714 # Make the request on the httplib connection object.
--> 715 httplib_response = self._make_request(
    716     conn,
    717     method,
    718     url,
    719     timeout=timeout_obj,
    720     body=body,
    721     headers=headers,
    722     chunked=chunked,
    723 )
    725 # If we're going to release the connection in ``finally:``, then
    726 # the response doesn't need to know about the connection. Otherwise
    727 # it will also try to release it and we'll have a double-release
    728 # mess.


File D:\python\3.12版本\Lib\site-packages\urllib3\connectionpool.py:404, in HTTPConnectionPool._make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
    403 try:
--> 404     self._validate_conn(conn)
    405 except (SocketTimeout, BaseSSLError) as e:
    406     # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.


File D:\python\3.12版本\Lib\site-packages\urllib3\connectionpool.py:1060, in HTTPSConnectionPool._validate_conn(self, conn)
   1059 if not getattr(conn, "sock", None):  # AppEngine might not have  `.sock`
-> 1060     conn.connect()
   1062 if not conn.is_verified:


File D:\python\3.12版本\Lib\site-packages\urllib3\connection.py:363, in HTTPSConnection.connect(self)
    361 def connect(self):
    362     # Add certificate verification
--> 363     self.sock = conn = self._new_conn()
    364     hostname = self.host


File D:\python\3.12版本\Lib\site-packages\urllib3\connection.py:179, in HTTPConnection._new_conn(self)
    178 except SocketTimeout:
--> 179     raise ConnectTimeoutError(
    180         self,
    181         "Connection to %s timed out. (connect timeout=%s)"
    182         % (self.host, self.timeout),
    183     )
    185 except SocketError as e:


ConnectTimeoutError: (<urllib3.connection.HTTPSConnection object at 0x0000012860BA48F0>, 'Connection to nominatim.openstreetmap.org timed out. (connect timeout=180)')


During handling of the above exception, another exception occurred:


MaxRetryError                             Traceback (most recent call last)

File D:\python\3.12版本\Lib\site-packages\requests\adapters.py:486, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    485 try:
--> 486     resp = conn.urlopen(
    487         method=request.method,
    488         url=url,
    489         body=request.body,
    490         headers=request.headers,
    491         redirect=False,
    492         assert_same_host=False,
    493         preload_content=False,
    494         decode_content=False,
    495         retries=self.max_retries,
    496         timeout=timeout,
    497         chunked=chunked,
    498     )
    500 except (ProtocolError, OSError) as err:


File D:\python\3.12版本\Lib\site-packages\urllib3\connectionpool.py:801, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
    799     e = ProtocolError("Connection aborted.", e)
--> 801 retries = retries.increment(
    802     method, url, error=e, _pool=self, _stacktrace=sys.exc_info()[2]
    803 )
    804 retries.sleep()


File D:\python\3.12版本\Lib\site-packages\urllib3\util\retry.py:594, in Retry.increment(self, method, url, response, error, _pool, _stacktrace)
    593 if new_retry.is_exhausted():
--> 594     raise MaxRetryError(_pool, url, error or ResponseError(cause))
    596 log.debug("Incremented Retry for (url='%s'): %r", url, new_retry)


MaxRetryError: HTTPSConnectionPool(host='nominatim.openstreetmap.org', port=443): Max retries exceeded with url: /search?format=json&polygon_geojson=1&dedupe=0&limit=50&q=New+York%2C+New+York%2C+USA (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x0000012860BA48F0>, 'Connection to nominatim.openstreetmap.org timed out. (connect timeout=180)'))


During handling of the above exception, another exception occurred:


ConnectTimeout                            Traceback (most recent call last)

Cell In[5], line 11
      8 fig, ax = ox.plot_graph(G, node_size=0)
     10 #获取地点中的具体设施，可视化
---> 11 G = ox.graph_from_place(
     12     "New York, New York, USA",
     13     retain_all=False,
     14     truncate_by_edge=True,
     15     simplify=True,
     16     custom_filter='["railway"~"subway"]',
     17 )
     18 fig, ax = ox.plot_graph(G, node_size=0, edge_color="w", edge_linewidth=0.2)
     20 #获取设施的几何，可视化


File D:\python\3.12版本\Lib\site-packages\osmnx\graph.py:379, in graph_from_place(query, network_type, simplify, retain_all, truncate_by_edge, which_result, buffer_dist, clean_periphery, custom_filter)
    375 # create a GeoDataFrame with the spatial boundaries of the place(s)
    376 if isinstance(query, (str, dict)):
    377     # if it is a string (place name) or dict (structured place query),
    378     # then it is a single place
--> 379     gdf_place = geocoder.geocode_to_gdf(
    380         query, which_result=which_result, buffer_dist=buffer_dist
    381     )
    382 elif isinstance(query, list):
    383     # if it is a list, it contains multiple places to get
    384     gdf_place = geocoder.geocode_to_gdf(query, buffer_dist=buffer_dist)


File D:\python\3.12版本\Lib\site-packages\osmnx\geocoder.py:138, in geocode_to_gdf(query, which_result, by_osmid, buffer_dist)
    136 gdf = gpd.GeoDataFrame()
    137 for q, wr in zip(query, which_result):
--> 138     gdf = pd.concat([gdf, _geocode_query_to_gdf(q, wr, by_osmid)])
    140 # reset GeoDataFrame index and set its CRS
    141 gdf = gdf.reset_index(drop=True)


File D:\python\3.12版本\Lib\site-packages\osmnx\geocoder.py:179, in _geocode_query_to_gdf(query, which_result, by_osmid)
    157 """
    158 Geocode a single place query to a GeoDataFrame.
    159 
   (...)
    175     a GeoDataFrame with one row containing the result of geocoding
    176 """
    177 limit = 50 if which_result is None else which_result
--> 179 results = _nominatim._download_nominatim_element(query, by_osmid=by_osmid, limit=limit)
    181 # choose the right result from the JSON response
    182 if not results:
    183     # if no results were returned, raise error


File D:\python\3.12版本\Lib\site-packages\osmnx\_nominatim.py:64, in _download_nominatim_element(query, by_osmid, limit, polygon_geojson)
     61         raise TypeError(msg)
     63 # request the URL, return the JSON
---> 64 return _nominatim_request(params=params, request_type=request_type)


File D:\python\3.12版本\Lib\site-packages\osmnx\_nominatim.py:106, in _nominatim_request(params, request_type, pause, error_pause)
    104 # transmit the HTTP GET request
    105 utils.log(f"Get {prepared_url} with timeout={settings.timeout}")
--> 106 response = requests.get(
    107     url,
    108     params=params,
    109     timeout=settings.timeout,
    110     headers=_downloader._get_http_headers(),
    111     **settings.requests_kwargs,
    112 )
    114 # handle 429 and 504 errors by pausing then recursively re-trying request
    115 if response.status_code in {429, 504}:  # pragma: no cover


File D:\python\3.12版本\Lib\site-packages\requests\api.py:73, in get(url, params, **kwargs)
     62 def get(url, params=None, **kwargs):
     63     r"""Sends a GET request.
     64 
     65     :param url: URL for the new :class:`Request` object.
   (...)
     70     :rtype: requests.Response
     71     """
---> 73     return request("get", url, params=params, **kwargs)


File D:\python\3.12版本\Lib\site-packages\requests\api.py:59, in request(method, url, **kwargs)
     55 # By using the 'with' statement we are sure the session is closed, thus we
     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
     57 # cases, and look like a memory leak in others.
     58 with sessions.Session() as session:
---> 59     return session.request(method=method, url=url, **kwargs)


File D:\python\3.12版本\Lib\site-packages\requests\sessions.py:589, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
    584 send_kwargs = {
    585     "timeout": timeout,
    586     "allow_redirects": allow_redirects,
    587 }
    588 send_kwargs.update(settings)
--> 589 resp = self.send(prep, **send_kwargs)
    591 return resp


File D:\python\3.12版本\Lib\site-packages\requests\sessions.py:703, in Session.send(self, request, **kwargs)
    700 start = preferred_clock()
    702 # Send the request
--> 703 r = adapter.send(request, **kwargs)
    705 # Total elapsed time of the request (approximately)
    706 elapsed = preferred_clock() - start


File D:\python\3.12版本\Lib\site-packages\requests\adapters.py:507, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    504 if isinstance(e.reason, ConnectTimeoutError):
    505     # TODO: Remove this in 3.0.0: see #2811
    506     if not isinstance(e.reason, NewConnectionError):
--> 507         raise ConnectTimeout(e, request=request)
    509 if isinstance(e.reason, ResponseError):
    510     raise RetryError(e, request=request)


ConnectTimeout: HTTPSConnectionPool(host='nominatim.openstreetmap.org', port=443): Max retries exceeded with url: /search?format=json&polygon_geojson=1&dedupe=0&limit=50&q=New+York%2C+New+York%2C+USA (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x0000012860BA48F0>, 'Connection to nominatim.openstreetmap.org timed out. (connect timeout=180)'))

数据分析

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

# 设置图片清晰度
plt.rcParams['figure.dpi'] = 300

# 正常显示中文
plt.rcParams['font.sans-serif'] = ['fangso']

# 正常显示负号
plt.rcParams['axes.unicode_minus'] = False

# 加载数据
data = []
with open(r'C:\Users\Redmi\Desktop\数据集\交通流.txt', encoding='utf-8') as file:
    for line in file:
        obj = pd.json_normalize(eval(line))
        # 对 stats 列进行处理，将其展开并与原数据合并
        stats_df = pd.json_normalize(obj['stats'].explode())
        combined_df = pd.concat([obj.drop('stats', axis=1).reset_index(drop=True), stats_df], axis=1)
        data.append(combined_df)

# 将所有 DataFrame 合并成一个
df = pd.concat(data, ignore_index=True)
df

	genTime	nodeId.region	nodeId.nodeId	statType.interval.interval	mapElementType	ptcType	vehicleType	timestamp	density	mapElement.laneStatInfo.laneId	...	mapElement.laneStatInfo.linkStatInfo.nodeStatInfo.nodeId.nodeId	mapElement.laneStatInfo.extId	congestion	delay	speedArea	queueLength	volume	mapElement.movementStatInfo.turnDirection	mapElement.movementStatInfo.nodeStatInfo.nodeId.region	mapElement.movementStatInfo.nodeStatInfo.nodeId.nodeId
0	1739279283	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739279283465	3359.0	1.032088e+09	...	10.0	3079293404110025631	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	1739279283	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739279283466	4135.0	3.767599e+09	...	10.0	3079293404110055961	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	1739279283	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739279283466	NaN	1.367934e+09	...	10.0	3079293404110025619	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	1739279283	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739279283466	NaN	1.032088e+09	...	10.0	3079293404110025631	NaN	2625.0	NaN	NaN	NaN	NaN	NaN	NaN
4	1739279283	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739279283466	NaN	1.032088e+09	...	10.0	3079293404110025631	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
86424	1739290198	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739290198463	569.0	3.432194e+09	...	10.0	3079293404110055955	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
86425	1739290198	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739290198463	NaN	3.432194e+09	...	10.0	3079293404110055955	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
86426	1739290199	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739290199467	NaN	3.432194e+09	...	10.0	3079293404110055955	NaN	1320.0	NaN	NaN	NaN	NaN	NaN	NaN
86427	1739290199	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739290199468	569.0	3.432194e+09	...	10.0	3079293404110055955	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
86428	1739290199	1	10	1	MAP_ELEMENT_TYPE_LANE	OBJECTTYPE_MOTOR	PASSENGER_VEHICLE_TYPE_UNKNOWN	1739290199468	NaN	3.432194e+09	...	10.0	3079293404110055955	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN

86429 rows × 22 columns

轨迹

import transbigdata as tbd
import pandas as pd

#Read_data
data = pd.read_csv(r'C:\Users\Redmi\Desktop\数据集\第6章-出租车 GPS 数据-时空大数据处理基础\第6章-出租车 GPS 数据-时空大数据处理基础\data\TaxiData-Sample.csv') 
data.columns = ['VehicleNum','Time','Lng','Lat','OpenStatus','Speed']
data

	VehicleNum	Time	Lng	Lat	OpenStatus	Speed
0	34745	20:24:07	113.809898	22.627399	0	0
1	34745	20:24:27	113.809898	22.627399	0	0
2	34745	20:22:07	113.811348	22.628067	0	0
3	34745	20:10:06	113.819885	22.647800	0	54
4	34745	19:59:48	113.820213	22.674967	0	23
...	...	...	...	...	...	...
544993	28265	21:35:13	114.321503	22.709499	0	18
544994	28265	09:08:02	114.322701	22.681700	0	0
544995	28265	09:14:31	114.336700	22.690100	0	0
544996	28265	21:19:12	114.352600	22.728399	0	0
544997	28265	19:08:06	114.137703	22.621700	0	0

544998 rows × 6 columns

from leuvenmapmatching.matcher.distance import DistanceMatcher
from leuvenmapmatching.map.inmem import InMemMap
from leuvenmapmatching import visualization as mmviz

#obtain OD from trajectory data
oddata = tbd.taxigps_to_od(data,col = ['VehicleNum','Time','Lng','Lat','OpenStatus'])
#extract deliver and idle trip trajectories
data_deliver,data_idle = tbd.taxigps_traj_point(data,oddata,col=['VehicleNum', 'Time', 'Lng', 'Lat', 'OpenStatus'])
# obtain road network
import osmnx as ox
bounds = [113.75, 22.4, 114.62, 22.86]
north, south, east, west = bounds[3], bounds[1], bounds[2], bounds[0]
G = ox.graph_from_bbox(north, south, east, west, network_type='drive')
oddata

C:\Users\Redmi\AppData\Local\Temp\ipykernel_18932\84526281.py:9: FutureWarning: The `north`, `south`, `east`, and `west` parameters are deprecated and will be removed in the v2.0.0 release. Use the `bbox` parameter instead.
  G = ox.graph_from_bbox(north, south, east, west, network_type='drive')

	VehicleNum	stime	slon	slat	etime	elon	elat	ID
19999	22396	00:19:41	114.013016	22.664818	00:23:01	114.021400	22.663918	0
20093	22396	00:41:51	114.021767	22.640200	00:43:44	114.026070	22.640266	1
20216	22396	00:45:44	114.028099	22.645082	00:47:44	114.030380	22.650017	2
19092	22396	01:08:26	114.034897	22.616301	01:16:34	114.035614	22.646717	3
19711	22396	01:26:06	114.046021	22.641251	01:34:48	114.066048	22.636183	4
...	...	...	...	...	...	...	...	...
183651	36805	22:49:12	114.114365	22.550632	22:50:40	114.115501	22.557983	5811
183036	36805	22:52:07	114.115402	22.558083	23:03:12	114.118484	22.547867	5812
180331	36805	23:03:45	114.118484	22.547867	23:20:09	114.133286	22.617750	5813
181803	36805	23:36:19	114.112968	22.549601	23:43:12	114.089485	22.538918	5814
180898	36805	23:46:14	114.091217	22.540768	23:53:36	114.120354	22.544300	5815

5816 rows × 8 columns

#save road network
ox.save_graphml(G,'shenzhen.graphml')

# Read the road network
import osmnx as ox
filepath = "shenzhen.graphml"
G = ox.load_graphml(filepath)
#Obtain the road GeoDataFrame and road centroid
nodes, edges = ox.graph_to_gdfs(G, nodes=True, edges=True)
edges['lon'] = edges.centroid.x
edges['lat'] = edges.centroid.y
#convert to projection coordinates
G_p = ox.project_graph(G, to_crs=2416)
nodes_p, edges_p = ox.graph_to_gdfs(G_p, nodes=True, edges=True)
edges_p.plot()

C:\Users\Redmi\AppData\Local\Temp\ipykernel_18932\1208862378.py:7: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  edges['lon'] = edges.centroid.x
C:\Users\Redmi\AppData\Local\Temp\ipykernel_18932\1208862378.py:8: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  edges['lat'] = edges.centroid.y





<Axes: >

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

# create road network
map_con = InMemMap(name='pNEUMA', use_latlon=False) # , use_rtree=True, index_edges=True)

for node_id, row in nodes_p.iterrows():
    map_con.add_node(node_id, (row['y'], row['x']))
for node_id_1, node_id_2, _ in G_p.edges:
    map_con.add_edge(node_id_1, node_id_2)

# Extract one of the trajectory using transbigdata
import geopandas as gpd
tmp_gdf = data_deliver[data_deliver['ID'] == 27].sort_values(by='Time')
# trajectory densify
tmp_gdf = tbd.traj_densify(
    tmp_gdf, col=['ID', 'Time', 'Lng', 'Lat'], timegap=15)
# convert coordinate
tmp_gdf['geometry'] = gpd.points_from_xy(tmp_gdf['Lng'], tmp_gdf['Lat'])
tmp_gdf = gpd.GeoDataFrame(tmp_gdf)
tmp_gdf.crs = {'init': 'epsg:4326'}
tmp_gdf = tmp_gdf.to_crs(2416)
# obtain trajectoies
path = list(zip(tmp_gdf.geometry.y, tmp_gdf.geometry.x))
# create mapmatcher
matcher = DistanceMatcher(map_con,
                          max_dist=500,
                          max_dist_init=170,
                          min_prob_norm=0.0001,
                          non_emitting_length_factor=0.95,
                          obs_noise=50,
                          obs_noise_ne=50,
                          dist_noise=50,
                          max_lattice_width=20,
                          non_emitting_states=True)
# mapmatching
states, _ = matcher.match(path, unique=True)
# plot the result
mmviz.plot_map(map_con, matcher=matcher,
               show_labels=False, show_matching=True,  # show_graph=True,
               filename=None)

D:\python\3.12版本\Lib\site-packages\transbigdata\traj.py:530: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  data[Time] = pd.to_datetime(data[Time])
D:\python\3.12版本\Lib\site-packages\pyproj\crs\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)
Searching closeby nodes with linear search, use an index and set max_dist





(<Figure size 2000x1179.53 with 1 Axes>, <Axes: xlabel='X', ylabel='Y'>)

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

#Obtain the path GeoDataFrame
pathdf = pd.DataFrame(matcher.path_pred_onlynodes,columns = ['u'])
pathdf['v'] = pathdf['u'].shift(-1)
pathdf = pathdf[-pathdf['v'].isnull()]
pathgdf = pd.merge(pathdf,edges_p.reset_index())
pathgdf = gpd.GeoDataFrame(pathgdf)
pathgdf.plot()
pathgdf.crs = {'init':'epsg:2416'}
pathgdf_4326 = pathgdf.to_crs(4326)

D:\python\3.12版本\Lib\site-packages\pyproj\crs\crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6
  in_crs_string = _prepare_from_proj_string(in_crs_string)

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

#Visualize with road network
import matplotlib.pyplot as plt

fig     = plt.figure(1,(8,8),dpi = 100)
ax      = plt.subplot(111)
plt.sca(ax)
fig.tight_layout(rect = (0.05,0.1,1,0.9))
#visualization bounds
bounds = pathgdf_4326.unary_union.bounds
gap = 0.003
bounds = [bounds[0]-gap,bounds[1]-gap,bounds[2]+gap,bounds[3]+gap]
#plot the matched path
pathgdf_4326.plot(ax = ax,zorder = 1)
#plot the road network geometry
tbd.clean_outofbounds(edges,bounds,col = ['lon','lat']).plot(ax = ax,color = '#111',lw = 0.05)
#plot the trajectory points
tmp_gdf.to_crs(4326).plot(ax = ax,color = 'red',markersize = 8,zorder = 12)

plt.axis('off')
plt.xlim(bounds[0],bounds[2])
plt.ylim(bounds[1],bounds[3])
plt.show()

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传

!pip install plot_map -i https://mirrors.cloud.tencent.com/pypi/simple

Looking in indexes: https://mirrors.cloud.tencent.com/pypi/simple
Requirement already satisfied: plot_map in d:\python\3.12版本\lib\site-packages (0.3.7)
Requirement already satisfied: geopandas in d:\python\3.12版本\lib\site-packages (from plot_map) (0.14.3)
Requirement already satisfied: matplotlib in d:\python\3.12版本\lib\site-packages (from plot_map) (3.9.2)
Requirement already satisfied: fiona>=1.8.21 in d:\python\3.12版本\lib\site-packages (from geopandas->plot_map) (1.9.5)
Requirement already satisfied: packaging in d:\python\3.12版本\lib\site-packages (from geopandas->plot_map) (23.2)
Requirement already satisfied: pandas>=1.4.0 in d:\python\3.12版本\lib\site-packages (from geopandas->plot_map) (2.1.4)
Requirement already satisfied: pyproj>=3.3.0 in d:\python\3.12版本\lib\site-packages (from geopandas->plot_map) (3.6.1)
Requirement already satisfied: shapely>=1.8.0 in d:\python\3.12版本\lib\site-packages (from geopandas->plot_map) (2.0.3)
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Requirement already satisfied: colorama in d:\python\3.12版本\lib\site-packages (from click~=8.0->fiona>=1.8.21->geopandas->plot_map) (0.4.6)

间隔

import transbigdata as tbd
import pandas as pd
import geopandas as gpd
#read data
data = pd.read_csv(r'C:\Users\Redmi\Desktop\数据集\第6章-出租车 GPS 数据-时空大数据处理基础\第6章-出租车 GPS 数据-时空大数据处理基础\data\TaxiData-Sample.csv')
data.columns = ['VehicleNum','time','slon','slat','OpenStatus','Speed']
#encode geohash
data['geohash'] = tbd.geohash_encode(data['slon'],data['slat'],precision=6)
data['geohash']
#Aggregate
dataagg = data.groupby(['geohash'])['VehicleNum'].count().reset_index()
dataagg['lon_geohash'],dataagg['lat_geohash'] = tbd.geohash_decode(dataagg['geohash'])
dataagg['geometry'] = tbd.geohash_togrid(dataagg['geohash'])
dataagg = gpd.GeoDataFrame(dataagg)
dataagg

	geohash	VehicleNum	lon_geohash	lat_geohash	geometry
0	w3uf3x	1	108.001099	10.280457	POLYGON ((107.99561 10.27771, 107.99561 10.283...
1	webzz6	12	113.878784	22.469788	POLYGON ((113.87329 22.46704, 113.87329 22.472...
2	webzz7	21	113.878784	22.475281	POLYGON ((113.87329 22.47253, 113.87329 22.478...
3	webzzd	1	113.889771	22.469788	POLYGON ((113.88428 22.46704, 113.88428 22.472...
4	webzzf	2	113.900757	22.469788	POLYGON ((113.89526 22.46704, 113.89526 22.472...
...	...	...	...	...	...
2022	ws1d9u	1	114.691772	22.964172	POLYGON ((114.68628 22.96143, 114.68628 22.966...
2023	ws1ddh	6	114.702759	22.964172	POLYGON ((114.69727 22.96143, 114.69727 22.966...
2024	ws1ddj	2	114.702759	22.969666	POLYGON ((114.69727 22.96692, 114.69727 22.972...
2025	ws1ddm	4	114.713745	22.969666	POLYGON ((114.70825 22.96692, 114.70825 22.972...
2026	ws1ddq	7	114.713745	22.975159	POLYGON ((114.70825 22.97241, 114.70825 22.977...

2027 rows × 5 columns

import matplotlib.pyplot as plt
import plot_map
bounds = [113.6,22.4,114.8,22.9]

fig =plt.figure(1,(8,8),dpi=480)
ax =plt.subplot(111)
plt.sca(ax)
tbd.plot_map(plt,bounds,zoom = 12,style = 4)
cax = plt.axes([0.05, 0.33, 0.02, 0.3])
plt.title('count')
plt.sca(ax)
dataagg.plot(ax = ax,column = 'VehicleNum',cax = cax,legend = True)
tbd.plotscale(ax,bounds = bounds,textsize = 6,compasssize = 1,accuracy = 2000,rect = [0.06,0.03],zorder = 10)
plt.axis('off')
plt.xlim(bounds[0],bounds[2])
plt.ylim(bounds[1],bounds[3])
plt.show()

[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'

[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
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[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'
[WinError 183] 当文件已存在时，无法创建该文件。: 'C:\\Users\\Redmi\\Desktop\\出租车轨迹数据处理tileimg'

外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传