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STmatching_distribution_ver.py
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STmatching_distribution_ver.py
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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()