-
Notifications
You must be signed in to change notification settings - Fork 25
/
STmatching_distribution_ver.py
189 lines (147 loc) · 9.5 KB
/
STmatching_distribution_ver.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
import numpy as np
import pandas as pd
from itertools import tee
import networkx as nx
from operator import itemgetter
import yaml
pd.options.mode.chained_assignment = None
np.seterr(divide='ignore')
# 100m grid
loninter = 0.000976
latinter = 0.0009
def haversine(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2
c = 2 * np.arcsin(np.sqrt(a))
return c * 6367 * 1000
def pairwise(iterable):
a, b = tee(iterable)
next(b, None)
return zip(a, b)
def network_data():
config = yaml.safe_load(open('config.yaml'))
dataset = str(config["dataset"])
nx_vertice = pd.read_csv('./data/{}/road/node.csv'.format(dataset), usecols=['node', 'lng', 'lat'])
vertice_dict = nx_vertice.set_index('node').T.to_dict('list')
nx_edge = pd.read_csv('./data/{}/road/edge.csv'.format(dataset), usecols=['edge', 's_node', 'e_node', 's_lng', 's_lat', 'e_lng', 'e_lat', 'c_lng', 'c_lat'])
edge_dict = nx_edge.set_index('edge').T.to_dict('list')
rdnetwork = pd.read_csv('./data/{}/road/edge_weight.csv'.format(dataset), usecols=['section_id', 's_node', 'e_node', 'length'])
edge_dist = rdnetwork[['s_node', 'e_node', 'length', 'section_id']]
edge_dist['idx'] = list(zip(edge_dist['s_node'], edge_dist['e_node']))
edge_dist = edge_dist[['idx', 'length', 'section_id']]
edge_dist_dict = edge_dist.set_index('idx').T.to_dict('list')
edge_dist = edge_dist[['section_id', 'length']].set_index('section_id')['length'].to_dict()
roadnetwork = nx.DiGraph()
for row in rdnetwork.values:
roadnetwork.add_edge(int(row[1]), int(row[2]), distance=row[-1])
return nx_vertice, nx_edge, vertice_dict, edge_dict, edge_dist, edge_dist_dict, roadnetwork
def get_traj2edge_distance(traj_point, sub_nx_edge):
sub_nx_edge['a'] = haversine(traj_point[0], traj_point[1], sub_nx_edge['s_lng'], sub_nx_edge['s_lat'])
sub_nx_edge['b'] = haversine(traj_point[0], traj_point[1], sub_nx_edge['e_lng'], sub_nx_edge['e_lat'])
sub_nx_edge['c'] = haversine(sub_nx_edge['s_lng'], sub_nx_edge['s_lat'], sub_nx_edge['e_lng'], sub_nx_edge['e_lat'])
indexer1 = sub_nx_edge['b']**2 > sub_nx_edge['a']**2 + sub_nx_edge['c']**2
indexer2 = sub_nx_edge['a']**2 > sub_nx_edge['b']**2 + sub_nx_edge['c']**2
sub_nx_edge.loc[indexer1, 'shortest_dist'] = sub_nx_edge.loc[indexer1, 'a']
sub_nx_edge.loc[indexer1, 'matched_nd'] = sub_nx_edge.loc[indexer1, 's_node']
sub_nx_edge.loc[indexer2, 'shortest_dist'] = sub_nx_edge.loc[indexer2, 'b']
sub_nx_edge.loc[indexer2, 'matched_nd'] = sub_nx_edge.loc[indexer2, 'e_node']
sub_nx_edge['l'] = (sub_nx_edge['a'] + sub_nx_edge['b'] + sub_nx_edge['c'])/2
sub_nx_edge['s'] = np.sqrt(sub_nx_edge['l'] * np.abs(sub_nx_edge['l'] - sub_nx_edge['a']) * np.abs(sub_nx_edge['l'] - sub_nx_edge['b']) * np.abs(sub_nx_edge['l'] - sub_nx_edge['c']))
indexer3 = pd.isnull(sub_nx_edge['shortest_dist'])
sub_nx_edge.loc[indexer3, 'shortest_dist'] = 2 * sub_nx_edge.loc[indexer3, 's'] / sub_nx_edge.loc[indexer3, 'c']
return sub_nx_edge[['edge', 'shortest_dist', 'matched_nd']]
def get_candidates(row):
traj_point = [row['LON'], row['LAT']]
sub_nx_edge = nx_edge[((nx_edge['s_lng'] >= traj_point[0]-loninter) & (nx_edge['s_lng'] <= traj_point[0]+loninter) & (nx_edge['s_lat'] >= traj_point[1]-latinter) & (nx_edge['s_lat'] <= traj_point[1]+latinter)) | ((nx_edge['e_lng'] >= traj_point[0]-loninter) & (nx_edge['e_lng'] <= traj_point[0]+loninter) & (nx_edge['e_lat'] >= traj_point[1]-latinter) & (nx_edge['e_lat'] <= traj_point[1]+latinter)) | ((nx_edge['c_lng'] >= traj_point[0]-loninter) & (nx_edge['c_lng'] <= traj_point[0]+loninter) & (nx_edge['c_lat'] >= traj_point[1]-latinter) & (nx_edge['c_lat'] <= traj_point[1]+latinter))]
cand_edges = get_traj2edge_distance(traj_point, sub_nx_edge)
cand_edges = cand_edges[(cand_edges['shortest_dist'] <= 35) & pd.notnull(cand_edges['shortest_dist'])]
cand_edges['shortest_dist'] = round(cand_edges['shortest_dist'])
if not cand_edges.empty:
return cand_edges['edge'].tolist(), cand_edges['matched_nd'].tolist(), cand_edges['shortest_dist'].tolist()
else:
return -1, -1, -1
def observation_probability(row):
cand_nd_df = np.array(row['CAND_ND_DIS'])
cand_nd_df = 1 / (np.sqrt(2 * np.pi) * 20) * np.exp(-cand_nd_df ** 2 / 800)
return list(cand_nd_df)
def transmission_probability(traj):
v_list = [[]]
for row1, row2 in pairwise(traj.values):
d = haversine(row1[0], row1[1], row2[0], row2[1])
row_v_list = []
for idx1, nd1 in enumerate(row1[-2]):
temp_list = []
for idx2, nd2 in enumerate(row2[-2]):
try: # nd1 and nd2 are not connected
if pd.notnull(nd1) and pd.notnull(nd2):
temp_list.append(d / nx.astar_path_length(roadnetwork, nd1, nd2, weight='distance'))
elif pd.notnull(nd1):
nd2_back_node = edge_dict[row2[-3][idx2]][0]
nd2_back_node_cor = vertice_dict[nd2_back_node]
temp_list.append(d / (nx.astar_path_length(roadnetwork, nd1, nd2_back_node, weight='distance') + np.sqrt(np.abs(haversine(row2[0], row2[1], nd2_back_node_cor[0], nd2_back_node_cor[1])**2 - row2[-4][idx2]**2))))
elif pd.notnull(nd2):
nd1_forward_node = edge_dict[row1[-3][idx1]][1]
nd1_forward_node_cor = vertice_dict[nd1_forward_node]
temp_list.append(d / (nx.astar_path_length(roadnetwork, nd1_forward_node, nd2, weight='distance') + np.sqrt(np.abs(haversine(row1[0], row1[1], nd1_forward_node_cor[0], nd1_forward_node_cor[1]) ** 2 - row1[-4][idx1] ** 2))))
else:
nd1_forward_node = edge_dict[row1[-3][idx1]][1]
nd1_forward_node_cor = vertice_dict[nd1_forward_node]
nd2_back_node = edge_dict[row2[-3][idx2]][0]
nd2_back_node_cor = vertice_dict[nd2_back_node]
temp_list.append(d / (nx.astar_path_length(roadnetwork, nd1_forward_node, nd2_back_node, weight='distance') + np.sqrt(np.abs(haversine(row1[0], row1[1], nd1_forward_node_cor[0], nd1_forward_node_cor[1]) ** 2 - row1[-4][idx1] ** 2)) + np.sqrt(np.abs(haversine(row2[0], row2[1], nd2_back_node_cor[0], nd2_back_node_cor[1]) ** 2 - row2[-4][idx2] ** 2))))
except:
temp_list.append(0)
row_v_list.append(temp_list)
v_list.append(row_v_list)
return v_list
def spatial_analysis(row):
return [[n_i * v_i[i] if not np.isinf(v_i[i]) else n_i for v_i in row[-1]] for i, n_i in enumerate(row[-2])]
def candidate_graph(traj):
max_f = max([max([max(f) for f in f_list]) for f_list in traj['F'].tolist()[1:]])
cand_graph = nx.DiGraph()
idx = 0
for row1, row2 in pairwise(traj.values):
for i, nd2 in enumerate(row2[-5]):
for j, nd1 in enumerate(row1[-5]):
cand_graph.add_edge(str(idx) + '-' + str(nd1), str(idx + 1) + '-' + str(nd2), distance=max_f - row2[-1][i][j])
idx += 1
return cand_graph
def trajectory_matching(traj):
traj_id = list(traj.keys())[0]
traj_list = list(traj.values())[0]
traj = pd.DataFrame(traj_list, columns=['LON', 'LAT'])
traj.drop_duplicates(['LON', 'LAT'], inplace=True)
results = traj.apply(get_candidates, axis=1)
traj['CAND_ND_DIS'] = [x[2] if x != -1 else -1 for x in results]
traj['CAND_EG'] = [x[0] if x != -1 else -1 for x in results]
traj['CAND_ND'] = [x[1] if x != -1 else -1 for x in results]
traj = traj[traj['CAND_EG'] != -1]
if traj.shape[0] > 1: # not enough candidates
traj['N'] = traj.apply(observation_probability, axis=1)
traj['V'] = transmission_probability(traj)
traj['F'] = traj.apply(spatial_analysis, axis=1)
cand_graph = candidate_graph(traj)
try:
cand_path_dict = {nx.shortest_path_length(cand_graph, '0-' + str(s_node), str(traj.shape[0]-1) + '-' + str(e_node), weight='distance'): nx.shortest_path(cand_graph, '0-' + str(s_node), str(traj.shape[0]-1) + '-' + str(e_node), weight='distance') for e_node in traj.iloc[-1]['CAND_EG'] for s_node in traj.iloc[0]['CAND_EG']}
except:
return pd.DataFrame([[traj_id, -1, -1]], columns=['TRAJ_ID', 'MATCHED_EDGE', 'MATCHED_NODE'])
matched_path = min(cand_path_dict.items(), key=itemgetter(0))[1]
matched_path = [int(x[x.index('-') + 1:]) for x in matched_path]
cand_node_list = traj['CAND_ND'].tolist()
cand_edge_list = traj['CAND_EG'].tolist()
matched_node = [cand_node_list[idx][cand_edge_list[idx].index(me)] for idx, me in enumerate(matched_path)]
return pd.DataFrame([[traj_id, matched_path, matched_node]], columns=['TRAJ_ID', 'MATCHED_EDGE', 'MATCHED_NODE'])
else:
return pd.DataFrame([[traj_id, -1, -1]], columns=['TRAJ_ID', 'MATCHED_EDGE', 'MATCHED_NODE'])
def data_convert(taxigps_day):
def thread_task(df):
return list(zip(df['LON'], df['LAT']))
traj_task = pd.DataFrame(taxigps_day.groupby('TRAJ_ID').apply(thread_task), columns=['TRAJ_LIST'])
traj_task.reset_index(level=['TRAJ_ID'], inplace=True)
traj_task_list = []
for row in traj_task.values:
traj_task_list.append({row[0]: row[1]})
return traj_task_list
# nx_vertice, nx_edge, vertice_dict, edge_dict, edge_dist, edge_dist_dict, roadnetwork = network_data()