def lengthPoint(gpsTraces, j):
return tripActivitySeparatorMongo.calDistance(
gpsTraces[j]["gpsReading"]["location"]["coordinates"], gpsTraces[j + 1]["gpsReading"]["location"]["coordinates"]
)
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]["gpsReading"]["gpsAccuracy"] > gpsAccuracyThreshold
or gpsTraces[end + 1]["gpsReading"]["gpsAccuracy"] > gpsAccuracyThreshold
or gpsTraces[end + 2]["gpsReading"]["gpsAccuracy"] > 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 = tripActivitySeparatorMongo.calDistance(
gpsTraces[start]["gpsReading"]["location"]["coordinates"],
gpsTraces[end]["gpsReading"]["location"]["coordinates"],
)
time = (gpsTraces[end]["epochTime"] - gpsTraces[start]["epochTime"]) / 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]]["epochTime"] - gpsTraces[modeChains[i][0]]["epochTime"] >= 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 = tripActivitySeparatorMongo.calDistance(
gpsTraces[newModeChains[0][0]]["gpsReading"]["location"]["coordinates"],
gpsTraces[newModeChains[0][1]]["gpsReading"]["location"]["coordinates"],
)
time = (gpsTraces[newModeChains[0][1]]["epochTime"] - gpsTraces[newModeChains[0][0]]["epochTime"]) / 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]]["epochTime"] - gpsTraces[newModeChains[0][0]]["epochTime"]
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 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]):
try:
timeTotal += (gpsTraces[i + 1]["epochTime"] - gpsTraces[i]["epochTime"]) / 1000.0
distTotal += (
tripActivitySeparatorMongo.calDistance(
gpsTraces[i]["gpsReading"]["location"]["coordinates"],
gpsTraces[i + 1]["gpsReading"]["location"]["coordinates"],
)
/ 1609.34
)
if gpsTraces[i]["groundTruth"]["label"] == "Trip" and gpsTraces[i]["groundTruth"]["mode"] == "Walk":
walk += 1
elif gpsTraces[i]["groundTruth"]["label"] == "Trip":
nonWalk += 1
else:
activity += 1
if (
modeChain[-1] == 1
and gpsTraces[i]["groundTruth"]["label"] == "Trip"
and gpsTraces[i]["groundTruth"]["mode"] == "Walk"
) or (
modeChain[-1] == 0
and gpsTraces[i]["groundTruth"]["label"] == "Trip"
and gpsTraces[i]["groundTruth"]["mode"] != "Walk"
):
timeInferred += (gpsTraces[i + 1]["epochTime"] - gpsTraces[i]["epochTime"]) / 1000.0
distInferred += (
tripActivitySeparatorMongo.calDistance(
gpsTraces[i]["gpsReading"]["location"]["coordinates"],
gpsTraces[i + 1]["gpsReading"]["location"]["coordinates"],
)
/ 1609.34
)
except:
print "Unexpected error while calculating inference accuracy:", sys.exc_info()[0]
pass
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,
)
