def calInfAccuray(modeChains, gpsTraces):
timeTotal, timeInferred, distTotal, distInferred = 0, 0, 0, 0
segTotal, segInferred, segWalkInfNonWalk, segNonWalkInfWalk = 0, 0, 0, 0
for modeChain in modeChains:
segTotal += 1
walk, nonWalk, activity = 0, 0, 0
for i in range(modeChain[0], modeChain[1]):
timeTotal += ((gpsTraces[i+1][1] - gpsTraces[i][1])/1000.0)
distTotal += (tripActivitySeparator.calDistance(gpsTraces[i][2:4], gpsTraces[i+1][2:4])/1609.34)
if gpsTraces[i][10] == 'Trip' and gpsTraces[i][11] == 'Walk':
walk += 1
elif gpsTraces[i][10] == 'Trip':
nonWalk += 1
else:
activity += 1
if ((modeChain[-1] == 1 and gpsTraces[i][10] == 'Trip' and gpsTraces[i][11] == 'Walk') or
(modeChain[-1] == 0 and gpsTraces[i][10] == 'Trip' and gpsTraces[i][11] != 'Walk')):
timeInferred += ((gpsTraces[i+1][1] - gpsTraces[i][1])/1000.0)
distInferred += (tripActivitySeparator.calDistance(gpsTraces[i][2:4], gpsTraces[i+1][2:4])/1609.34)
if ((max(walk, nonWalk, activity) == walk and modeChain[-1] == 1)
or (max(walk, nonWalk, activity) == nonWalk and modeChain[-1] == 0)):
segInferred += 1
elif (max(walk, nonWalk, activity) == walk and modeChain[-1] == 0):
segWalkInfNonWalk += 1
elif (max(walk, nonWalk, activity) == nonWalk and modeChain[-1] == 1):
segNonWalkInfWalk += 1
return (timeTotal, timeInferred, distTotal, distInferred,
segTotal, segInferred, segWalkInfNonWalk, segNonWalkInfWalk)
def inferModeChain(gpsTraces, trip, maxWalkSpeed, maxWalkAcceleration,
minSegmentDuration, minSegmentLength, gpsAccuracyThreshold):
# Step 1: Label GPS points as walk points or non-walk points
walkDummy = {}
i = trip[0]
while i < trip[1]:
start, end = i, i
while end < trip[1] and (gpsTraces[end][4] > gpsAccuracyThreshold
or gpsTraces[end + 1][4] > gpsAccuracyThreshold
or gpsTraces[end + 2][4] > gpsAccuracyThreshold):
end += 1
if start == end:
features = determineFeatures(gpsTraces, i)
if features['Acceleration'] <= 945:
if features['Heading Change'] <= 0.0000:
walkDummy[i] = 0
elif features['Speed'] <= 8.0205:
walkDummy[i] = 1
else:
walkDummy[i] = 0
else:
walkDummy[i] = 0
i += 1
else:
distance = tripActivitySeparator.calDistance(gpsTraces[start][2:4], gpsTraces[end][2:4])
time = (gpsTraces[end][1] - gpsTraces[start][1]) / 1000.0
speed = 2.23694 * (float(distance) / time)
dummy = int(speed < maxWalkSpeed)
while i < end:
walkDummy[i] = dummy
i += 1
print walkDummy
print
# Step 2: Identify walk and non-walk segments as consecutive walk or non-walk points
modeChains = []
beginSegment = trip[0]
currentPoint = trip[0] + 1
while currentPoint < trip[1]:
if walkDummy[currentPoint] != walkDummy[beginSegment]:
modeChains.append([beginSegment, currentPoint, int(walkDummy[beginSegment] != 0)])
beginSegment = currentPoint
currentPoint += 1
modeChains.append([beginSegment, currentPoint, int(walkDummy[beginSegment] != 0)])
print modeChains
print
# Step 3: If the time span of a segment is greater than minSegmentDuration milliseconds, label it
# as certain. If it is less than minSegmentDuration milliseconds, and its backward segment is certain,
# merge it with the backward segment. If no certain backward segment exists, label the segment as
# uncertain, and save it as an independent segment.
newModeChains = []
for i in range(0, len(modeChains)):
if gpsTraces[modeChains[i][1]][1] - gpsTraces[modeChains[i][0]][1] >= minSegmentDuration:
modeChains[i].append(1)
newModeChains.append(modeChains[i])
elif newModeChains and newModeChains[-1][-1] == 1:
newModeChains[-1][1] = modeChains[i][1]
else:
modeChains[i].append(0)
newModeChains.append(modeChains[i])
modeChains = newModeChains
print modeChains
print
# Step 4: Merge consecutive uncertain segments into a single certain segment. Calculate average
# speed over segment and compare it against maxWalkSpeed to determine whether walk or non-walk.
# Check if this segment exceeds minSegmentDuration milliseconds. If it doesn't, and there exists
# a certain forward segment, merge the new segment with this forward segment.
newModeChains, i = [modeChains[0][0:-1]], 1
while i < len(modeChains) and modeChains[i][-1] == 0:
i += 1
if i > 1:
newModeChains[0][1] = modeChains[i-1][1]
distance = tripActivitySeparator.calDistance(gpsTraces[newModeChains[0][0]][2:4], gpsTraces[newModeChains[0][1]][2:4])
time = (gpsTraces[newModeChains[0][1]][1] - gpsTraces[newModeChains[0][0]][1]) / 1000.0
speed = 2.23694 * (float(distance) / time)
newModeChains[0][-1] = int(speed < maxWalkSpeed)
if i < len(modeChains) and modeChains[0][-1] == 0:
time = (gpsTraces[newModeChains[0][1]][1] - gpsTraces[newModeChains[0][0]][1])
if time < minSegmentDuration:
modeChains[i][0] = trip[0]
newModeChains = []
while i < len(modeChains):
newModeChains.append(modeChains[i][:-1])
i += 1
modeChains = newModeChains
print modeChains
print
# Step 5: Merge consecutive walk segments and consecutive non-walk segments
newModeChains = [modeChains[0]]
for i in range(1, len(modeChains)):
if modeChains[i][2] == newModeChains[-1][2]:
newModeChains[-1][1] = modeChains[i][1]
else:
newModeChains.append(modeChains[i])
modeChains = newModeChains
return modeChains
def lengthPoint(gpsTraces, j): return tripActivitySeparator.calDistance(gpsTraces[j][2:4], gpsTraces[j+1][2:4])