def FindPointCandidateWay_Grid(csvfilepath, candidatewaypath,
candidatewayname):
"""
:param csvfilepath: csv文件路径 例：H:\TrunksArea\\334e4763-f125-425f-ae42-8028245764fe.csv"
:param candidatewaypath: 轨迹点候选路段保存路径
:param candidatewayname: 候选路段保存的文件名
:return:
"""
if not os.path.isdir(candidatewaypath):
os.mkdir(candidatewaypath)
# 读时间 经纬度 网格编号
#df = pd.read_csv("H:\GPS_Data\Road_Network\BYQBridge\TrunksArea\\334e4763-f125-425f-ae42-8028245764fe.csv",header=None, usecols=[1, 2, 3, 4, 5])
df = pd.read_csv(csvfilepath, header=None, usecols=[1, 2, 3, 4, 5])
points_num = df.shape[0] # 坐标数量
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# print(df.iloc[:,1:4])
drop_list = [] # 要删除的索引列表
for row in range(1, points_num):
if row == points_num:
break
points_dis = Common_Functions.haversine(df.iloc[row, 1], df.iloc[row,
2],
df.iloc[row - 1, 1],
df.iloc[row - 1,
2]) # 相邻坐标点之间的距离
if points_dis < 0.01: # 距离小于10米
drop_list.append(row)
newdf = df.drop(drop_list) # 删除相邻点在10米之内的点
newdf = newdf.reset_index(drop=True)
txtname = candidatewayname + ".txt"
file = open(os.path.join(candidatewaypath, txtname), 'a')
print("本轨迹段共查找坐标点数为：{}".format(newdf.shape[0]))
for row in range(newdf.shape[0]):
dic = {}
#candidatacode = BigGridCode.Neighbor(newdf.iloc[row, 1], newdf.iloc[row, 2]) # 查找附近八个区域
# for subcode in candidatacode:
# if subcode:
# waysets = list(Common_Functions.GetWay_ByGridCode(subcode[1]))
# for subway in waysets:
# dic[subway[0]] = subcode[1]
pointCode = BigGridCode.Encode(newdf.iloc[row, 1], newdf.iloc[row, 2])
waysets = list(Common_Functions.GetWay_ByGridCode(pointCode[1]))
if waysets:
for subway in waysets:
dic[subway[0]] = pointCode[1]
#print(f"{newdf.iloc[row, 1]},{newdf.iloc[row, 2]}CandidateWay>>>{str(dic)}")
if dic:
file.write("PointID-" + str(row + 1) + "CandidateWay>>>" +
str(dic) + "\n")
file.close()
def AllwayConn(waysjsonpath):
"""
对左右的路段进行连通性判断
permutations(Allwayset,2) 二二组合，（A,B,C）->AB AC BA BC CA CB
:param waysjsonpath:所有路段json文件路径
:return:
"""
AllwaysLists = []
with open(waysjsonpath, 'r') as file:
dic = json.loads(file.read())
for key in dic.keys():
AllwaysLists.append(key)
Allwaysset = set(AllwaysLists)
for twowaytuple in permutations(Allwaysset, 2):
try:
print(f"正在查看路段:{twowaytuple[0]}与路段:{twowaytuple[1]}的连通性......")
way1_x, way1_y = Getway_startendnode_coordi(
twowaytuple[0]) # 路段1的起点坐标
way2_x, way2_y = Getway_startendnode_coordi(
twowaytuple[1]) # 路段2的起始坐标
print(way1_x, way1_y, way2_x, way2_y)
if way1_x != 0 and way1_y != 0 and way2_x != 0 and way2_y != 0:
waydis = Common_Functions.haversine(way1_x, way1_y, way2_x,
way2_y)
print(waydis)
if waydis < 3:
connectroute = Common_Functions.InquireConn(
twowaytuple[0], twowaytuple[1], "connects") # 先查表
# connectroute = -1
if connectroute != 0 and connectroute != 1: # 表中没有记录 再用简易导航
connectroute = MapNavigation.waytoway(
twowaytuple[0], twowaytuple[1]) # 为列表
if connectroute:
print(
f"路段{twowaytuple[0]}---->{twowaytuple[1]}的路线：{connectroute}"
)
else:
print(f"路段{twowaytuple[0]}不能到达路段{twowaytuple[1]}")
else:
print("数据库中已存在，跳过")
else:
pass
except Exception as e:
print(e)
def TrajectorySplit(csvfilepath, processpath):
"""
:param csvfilepath: 原始轨迹csv文件
:param processpath: 存储原轨迹分段之后的csv文件
:return:
"""
(path, file) = os.path.split(csvfilepath)
(filename, extension) = os.path.splitext(file)
filesavepath = os.path.join(processpath, filename) #文件分段之后保存的路径
if not os.path.isdir(filesavepath):
os.mkdir(filesavepath)
df = pd.read_csv(csvfilepath,
header=None,
usecols=[1, 2, 3, 4, 5],
names=[0, 1, 2, 3, 4])
df = df.sort_values(by=0) # 时间排序
points_num = df.shape[0] # 坐标数量
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
drop_list = [] # 要删除的索引列表
df.iloc[:, 0] = pd.to_datetime(df.iloc[:, 0], format="%Y%m%d ")
for row in range(1, points_num):
if row == points_num:
break
points_dis = Common_Functions.haversine(df.iloc[row, 1], df.iloc[row,
2],
df.iloc[row - 1, 1],
df.iloc[row - 1,
2]) # 相邻坐标点之间的距离
Time_difference = (df.iloc[row, 0] -
df.iloc[row - 1, 0]).seconds #相邻两个坐标点的时间差,单位：秒
if points_dis < 0.01: # 距离小于10米
drop_list.append(row)
if Time_difference != 0:
speed = points_dis / Time_difference #速度 秒
else:
speed = 0
if speed >= 0.034:
drop_list.append(row) #时速大于120 ，0.034时速为122
newdf = df.drop(drop_list) # 删除相邻点在10米之内的点和时速大于120的异常点
newdf = newdf.reset_index(drop=True)
#Candidate_pointName = filename + ".csv"
#newdf.to_csv(os.path.join(filesavepath, os.path.join()), index=0,header=0, encoding='utf_8_sig') #筛选出的被匹配的轨迹点
#以上为处理所有的相邻点距离小于10米的坐标
#以下循环为分段
start_row = 0 #文件分割的起始行数
endrow = 0 #结束行数
part_num = 1
part_name = "part" + str(part_num) + ".csv"
#tem_df = pd.DataFrame(None)
Is_Save_flag = 0 #记录该轨迹段是否保存
for row in range(1, newdf.shape[0]):
#Time_difference = (newdf.iloc[row, 0] - newdf.iloc[row - 1, 0]).seconds # 相邻两个坐标点的时间差,单位：秒
Adjacent_two_points_dis = Common_Functions.haversine(
newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row - 1, 1],
newdf.iloc[row - 1, 2]) # 相邻坐标点之间的距离
if Adjacent_two_points_dis > 5:
endrow = row - 1
Is_Save_flag = 1
if row == newdf.shape[0] - 1:
#tem_df = newdf.loc[start_row:row+1, :]
endrow = row
Is_Save_flag = 1
if Is_Save_flag == 1:
newdf.loc[start_row:endrow, :].to_csv(os.path.join(
filesavepath, part_name),
index=0,
header=0,
encoding='utf_8_sig')
start_row = endrow + 1
Is_Save_flag = 0
#tem_df.drop(tem_df.index, inplace=True)
part_num += 1
part_name = "part" + str(part_num) + ".csv"
def FindPointCandidateWay(csvfilepath, candidatewaypath, candidatewayname):
"""
处理北野场桥
找出坐标点的候选路段，此部分已经通过角度（大于90）、距离（大于30米）筛除一部分候选路段
:param csvfilepath: csv文件路径 例：H:\TrunksArea\\334e4763-f125-425f-ae42-8028245764fe.csv"
:param candidatewaypath: 轨迹点候选路段保存路径
:param candidatewayname: 候选路段保存的文件名
:return:
"""
if not os.path.isdir(candidatewaypath):
os.mkdir(candidatewaypath)
# 读时间 经纬度 网格编号
#df = pd.read_csv("H:\GPS_Data\Road_Network\BYQBridge\TrunksArea\\334e4763-f125-425f-ae42-8028245764fe.csv",header=None, usecols=[1, 2, 3, 4, 5])
df = pd.read_csv(csvfilepath, header=None, usecols=[1, 2, 3, 4, 5])
points_num = df.shape[0] # 坐标数量
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# print(df.iloc[:,1:4])
drop_list = [] # 要删除的索引列表
for row in range(1, points_num):
if row == points_num:
break
points_dis = Common_Functions.haversine(df.iloc[row, 1], df.iloc[row,
2],
df.iloc[row - 1, 1],
df.iloc[row - 1,
2]) # 相邻坐标点之间的距离
if points_dis < 0.01: # 距离小于10米
drop_list.append(row)
# print(drop_list)
newdf = df.drop(drop_list) # 删除相邻点在10米之内的点
# print(newdf.iloc[:,1:3])
newdf = newdf.reset_index(drop=True)
#file = open("H:\GPS_Data\Road_Network\BYQBridge\CandidateWay\\NewStrategy\\334e4763-f125-425f-ae42-8028245764fe.txt", 'a')
txtname = candidatewayname + ".txt"
file = open(os.path.join(candidatewaypath, txtname), 'a')
print("本轨迹段共查找坐标点数为：{}".format(newdf.shape[0]))
for row in range(newdf.shape[0]):
if row == 0:
#print("处理起始坐标点{}".format([newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3], newdf.iloc[row, 4]]))
dic = Common_Functions.Find_Candidate_Route([
newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3],
newdf.iloc[row, 4]
], [
newdf.iloc[row + 1, 1], newdf.iloc[row + 1, 2],
newdf.iloc[row + 1, 3], newdf.iloc[row + 1, 4]
],
flag=1)
if dic: # 有候选路段才保存
file.write("PointID-" + str(row + 1) + "CandidateWay>>>" +
str(dic) + "\n")
elif row == newdf.shape[0] - 1:
#print("处理终点坐标点{}".format([df.iloc[row - 1, 2], df.iloc[row - 1, 3], df.iloc[row, 2], df.iloc[row, 3]]))
dic = Common_Functions.Find_Candidate_Route([
newdf.iloc[row - 1, 1], newdf.iloc[row - 1, 2],
newdf.iloc[row - 1, 3], newdf.iloc[row - 1, 4]
], [
newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3],
newdf.iloc[row, 4]
],
flag=2)
if dic:
file.write("PointID-" + str(row + 1) + "CandidateWay>>>" +
str(dic) + "\n")
else:
dis1 = Common_Functions.haversine(newdf.iloc[row,
1], newdf.iloc[row,
2],
newdf.iloc[row - 1, 1],
newdf.iloc[row - 1, 2])
dis2 = Common_Functions.haversine(newdf.iloc[row,
1], newdf.iloc[row,
2],
newdf.iloc[row + 1, 1],
newdf.iloc[row + 1, 2])
# 找相邻最近的点做为轨迹方向
if dis2 > dis1:
#print("处理终点坐标点{}".format([newdf.iloc[row - 1, 1], newdf.iloc[row - 1, 2], newdf.iloc[row - 1, 3], newdf.iloc[row - 1, 4]]))
dic = Common_Functions.Find_Candidate_Route([
newdf.iloc[row - 1, 1], newdf.iloc[row - 1, 2],
newdf.iloc[row - 1, 3], newdf.iloc[row - 1, 4]
], [
newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3],
newdf.iloc[row, 4]
],
flag=2)
else:
#print("处理起始坐标点{}".format([newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3], newdf.iloc[row, 4]]))
dic = Common_Functions.Find_Candidate_Route([
newdf.iloc[row, 1], newdf.iloc[row, 2], newdf.iloc[row, 3],
newdf.iloc[row, 4]
], [
newdf.iloc[row + 1, 1], newdf.iloc[row + 1, 2],
newdf.iloc[row + 1, 3], newdf.iloc[row + 1, 4]
],
flag=1)
if dic:
file.write("PointID-" + str(row + 1) + "CandidateWay>>>" +
str(dic) + "\n")
file.close()