304
Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
of the object. For example, using a threshold of 500 m, the average time between updates is increased from 141 to 203 s with the INFATI data and from 148 to 158 s with
the AKTA data. The lower benefit from using acceleration profiles for the AKTA data
is likely to be due to congestion (see Fig. 13.8) as well as the majority of routes being
on the highway where speed patterns are not so clear.
We note that with acceleration profiles, we outperform the previously introduced
theoretical technique that is optimal only under the assumption of constant-speed
prediction.
In closing, it is also worth considering a few alternatives for the speed modeling and some implications of the alternative presented. In reality, the travel speed
associated with a road segment varies during the day and different drivers may well
negotiate the same segment with different speeds. By associating acceleration profiles with routes that are specific to individual drivers, we capture the variation among
drivers. And because the same route (e.g. from home to work or from work to home)
is typically used during the same time of the day, the variation of speeds during the
day is also taken into account fairly well. Next, if significant variations exist within
the observations based on which the acceleration profile of a route is constructed,
it is possible to create several speed profiles, for example, so that rush-hour and
non-rush-hour profiles are available.
13.7 Conclusions
This chapter presents and empirically evaluates a range of techniques for the tracking
of moving objects, including point-, vector-, and basic segment-based tracking. The
proposed techniques are robust and generally applicable; they function even if no
underlying road network is available or if map matching is not unsuccessful, and
they apply to mobile objects with even stringent memory restrictions.
The performance of basic segment-based tracking is sensitive to the segmentation
of the road network representation used and to the speed variations of the moving objects. Based on these observations, the chapter describes several techniques that aim
to reduce the number of updates needed for segment-based tracking with accuracy
guarantees. They are the following:
• Road Network Modification. The segment-based representation of the underlying road network used in segment-based tracking is modified with the goal of
arriving at a segmentation that enables objects to use as few segments as possible as they move in the road network. This then reduces the number of updates
caused by segment changes.
• Use of Routes. A route is a polyline constructed from (partial) road network segments that capture an object’s entire movement from a source to a destination.
As segments are themselves polylines, segment-based tracking readily accommodates the use of routes. Routes are specific to individual moving objects, and
the use of routes is expected to reduce the number of updates caused by segment
changes.
Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
of the object. For example, using a threshold of 500 m, the average time between updates is increased from 141 to 203 s with the INFATI data and from 148 to 158 s with
the AKTA data. The lower benefit from using acceleration profiles for the AKTA data
is likely to be due to congestion (see Fig. 13.8) as well as the majority of routes being
on the highway where speed patterns are not so clear.
We note that with acceleration profiles, we outperform the previously introduced
theoretical technique that is optimal only under the assumption of constant-speed
prediction.
In closing, it is also worth considering a few alternatives for the speed modeling and some implications of the alternative presented. In reality, the travel speed
associated with a road segment varies during the day and different drivers may well
negotiate the same segment with different speeds. By associating acceleration profiles with routes that are specific to individual drivers, we capture the variation among
drivers. And because the same route (e.g. from home to work or from work to home)
is typically used during the same time of the day, the variation of speeds during the
day is also taken into account fairly well. Next, if significant variations exist within
the observations based on which the acceleration profile of a route is constructed,
it is possible to create several speed profiles, for example, so that rush-hour and
non-rush-hour profiles are available.
13.7 Conclusions
This chapter presents and empirically evaluates a range of techniques for the tracking
of moving objects, including point-, vector-, and basic segment-based tracking. The
proposed techniques are robust and generally applicable; they function even if no
underlying road network is available or if map matching is not unsuccessful, and
they apply to mobile objects with even stringent memory restrictions.
The performance of basic segment-based tracking is sensitive to the segmentation
of the road network representation used and to the speed variations of the moving objects. Based on these observations, the chapter describes several techniques that aim
to reduce the number of updates needed for segment-based tracking with accuracy
guarantees. They are the following:
• Road Network Modification. The segment-based representation of the underlying road network used in segment-based tracking is modified with the goal of
arriving at a segmentation that enables objects to use as few segments as possible as they move in the road network. This then reduces the number of updates
caused by segment changes.
• Use of Routes. A route is a polyline constructed from (partial) road network segments that capture an object’s entire movement from a source to a destination.
As segments are themselves polylines, segment-based tracking readily accommodates the use of routes. Routes are specific to individual moving objects, and
the use of routes is expected to reduce the number of updates caused by segment
changes.