object MapMatchingUtil
Util for trajectory related computation
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def
abnormalMovement(t: jTrajectory, configbc: Map[String, String]): (Boolean, String)
this is a rule based abnormal movement detection, the intuition is as follow, that a mapped matched trajectory is very close to the original trajectory, because a map matched sequence of points are falling on the nearest road segment.
this is a rule based abnormal movement detection, the intuition is as follow, that a mapped matched trajectory is very close to the original trajectory, because a map matched sequence of points are falling on the nearest road segment. However this does not hold true when there is bad trajectory, back and forth movement due to bad tracking or in some cases vehicle actually moves in a abnormal way / opposite lane etc. The logic to find the abnormal movement is
use the smoothened trajectory "deviation" = deviation of this trajectory from the mapmatched trajectory, distance between the two trajectory are computed using word mover distance "reverseDeviation" = deviation of this reverse trajectory from the mapmatched trajectory use a configurable "threshold", to check deviation * "threshold" > reverseTrajectoryDeviation
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def
abnormalMovement(t: Trajectory, configbc: Map[String, String]): (Boolean, String)
this is a rule based abnormal movement detection, the intuition is as follow, that a mapped matched trajectory is very close to the original trajectory, because a map matched sequence of points are falling on the nearest road segment.
this is a rule based abnormal movement detection, the intuition is as follow, that a mapped matched trajectory is very close to the original trajectory, because a map matched sequence of points are falling on the nearest road segment. However this does not hold true when there is bad trajectory, back and forth movement due to bad tracking or in some cases vehicle actually moves in a abnormal way / opposite lane etc. The logic to find the abnormal movement is
use the smoothened trajectory "deviation" = deviation of this trajectory from the mapmatched trajectory, distance between the two trajectory are computed using word mover distance "reverseDeviation" = deviation of this reverse trajectory from the mapmatched trajectory use a configurable "threshold", to check deviation * "threshold" > reverseTrajectoryDeviation
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asInstanceOf[T0]: T0
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- def calculateDeviation(t: jTrajectory): (Double, Double, jTrajectory)
- def calculateDeviation(t: Trajectory): (Double, Double, Trajectory)
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def
changeInDirection(t: jTrajectory, configbc: Map[String, String]): (Boolean, Double)
checks if an object has changed direction beyond a certain threshold during the course of traversal
- def changeInDirection(t: Trajectory, configbc: Map[String, String]): (Boolean, Double)
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def
clone(): AnyRef
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- var config: Map[String, String]
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def
consecutiveBearingDiff(t: jTrajectory): (Double, List[Double])
finds the max bearing difference between consecutive segments, during the course of traversal of an object
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def
consecutiveBearingDiff(t: Trajectory): (Double, List[Double])
finds the max bearing difference between consecutive segments, during the course of traversal of an object
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def
isInstanceOf[T0]: Boolean
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- def main(args: Array[String]): Unit
- lazy val mapMatching: MapMatching
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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def
snapToRoadWithEdges(trajectory: List[jLocation], start: Long, end: Long): (List[jLocation], List[String])
returns snappedTrajectory and edges
returns snappedTrajectory and edges
trajectory is map matched with respect to open street map (OSM) , snappedTrajectory represents the new set of points on the OSM edges, the edges associated with trajectory with the trajectory after map matching are also computed for edge/link speed estimation. Link speed speed represents the average traffic speed on road link at a given point of time
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def
snappedEdges(trajectory: List[jLocation], start: Long, end: Long): List[(String, List[jLocation])]
map match the list of Location using openstreetmap, the map matching process returns the all possible assigned edges waypoints/locations for each edge is computed, generates unique edge id for each edge based on the head and tail of the locations unique id is based on murmur hash
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def
snappedToRoadWithEdges(trajectory: List[Location], start: Long, end: Long): (List[Location], List[String])
returns snappedTrajectory and edges
returns snappedTrajectory and edges
trajectory is map matched with respect to open street map (OSM) , snappedTrajectory represents the new set of points on the OSM edges, the edges associated with trajectory with the trajectory after map matching are also computed for edge/link speed estimation. Link speed speed represents the average traffic speed on road link at a given point of time
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- def toJTrajectory(t: Trajectory): jTrajectory
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def
toString(): String
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- def toTrajectory(t: jTrajectory): Trajectory
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def
wait(arg0: Long, arg1: Int): Unit
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- val wayPointGeometryMode: Int