13 Tracking of Moving Objects with Accuracy Guarantees
307
means that position update can be generated by the moving object entering or leaving
a certain region, for example, when leaving the city limits or a prespecified route or
when getting into a certain range of a point of interest.
It should be noted that none of the products described in Table 13.1 provides
efficient accuracy guarantees or support accuracy-based tracking. Advanced options
such as Spatial events are usually supported by solutions involving large, custommade terminals.
13.8.2 Related Academic Contributions
When predicting the future position of an object, the notion of a trajectory is typically used [12, 17, 25], where a trajectory is defined in a three-dimensional [17] or
four-dimensional [21] space. The dimensions are a two-dimensional “geographical”
space, a time dimension, and (possibly) an uncertainty threshold dimension. A point
in this space then indicates, for a point in time, the location of an object and the
uncertainty of the location. Such points may be computed using speed limits and
average speeds on specific road segments belonging to a trajectory.
Wolfson et al. [25] have recently investigated how to incorporate travel-speed
prediction in a database. They assume that sensors that can send up-to-date speed
information are installed in the roads, and they use average real-time speeds reported
every 5 minutes by such in-road sensors. This contrasts the techniques covered in this
chapter that use GPS records (termed floating-car data) received from an individual
object for predicting that object’s movement.
Wolfson et al. [23] propose tracking techniques that offer accuracy guarantees.
These assume that objects move on predefined routes already known to the objects,
and route selection is done on the client side. If an object changes its route, it sends
a position update with information about the new route to the server. The techniques
described in this chapter go further by accommodating objects with memory restrictions, and they also work in cases where routes are not known or where map matching
fails.
Lam et al. [13] present an adaptive monitoring method that takes into consideration the update, deviation, and uncertainty costs associated with tracking. The
method also takes into account the cost of providing incorrect results to queries, during the process of determining when to issue updates. With this method, the moving
objects that fall into a query region need close monitoring, and a small accuracy
threshold is used for them. Objects not inside a query region may have big thresholds. The techniques presented in this chapter are applicable to this scenario, as they
allow different objects to have different thresholds and allow thresholds to change
dynamically.
A proposal for trajectory prediction by Karimi and Liu [12] assigns probabilities
to the roads emanating from an intersection according to how likely it is that an
object entering the intersection will proceed on them. The subroad network within
a circular area around an object is extracted, and the most probable route within
this network is used for prediction. When the object leaves the current subnetwork,
a new subnetwork is extracted, and the procedure is repeated. In this proposal, the
307
means that position update can be generated by the moving object entering or leaving
a certain region, for example, when leaving the city limits or a prespecified route or
when getting into a certain range of a point of interest.
It should be noted that none of the products described in Table 13.1 provides
efficient accuracy guarantees or support accuracy-based tracking. Advanced options
such as Spatial events are usually supported by solutions involving large, custommade terminals.
13.8.2 Related Academic Contributions
When predicting the future position of an object, the notion of a trajectory is typically used [12, 17, 25], where a trajectory is defined in a three-dimensional [17] or
four-dimensional [21] space. The dimensions are a two-dimensional “geographical”
space, a time dimension, and (possibly) an uncertainty threshold dimension. A point
in this space then indicates, for a point in time, the location of an object and the
uncertainty of the location. Such points may be computed using speed limits and
average speeds on specific road segments belonging to a trajectory.
Wolfson et al. [25] have recently investigated how to incorporate travel-speed
prediction in a database. They assume that sensors that can send up-to-date speed
information are installed in the roads, and they use average real-time speeds reported
every 5 minutes by such in-road sensors. This contrasts the techniques covered in this
chapter that use GPS records (termed floating-car data) received from an individual
object for predicting that object’s movement.
Wolfson et al. [23] propose tracking techniques that offer accuracy guarantees.
These assume that objects move on predefined routes already known to the objects,
and route selection is done on the client side. If an object changes its route, it sends
a position update with information about the new route to the server. The techniques
described in this chapter go further by accommodating objects with memory restrictions, and they also work in cases where routes are not known or where map matching
fails.
Lam et al. [13] present an adaptive monitoring method that takes into consideration the update, deviation, and uncertainty costs associated with tracking. The
method also takes into account the cost of providing incorrect results to queries, during the process of determining when to issue updates. With this method, the moving
objects that fall into a query region need close monitoring, and a small accuracy
threshold is used for them. Objects not inside a query region may have big thresholds. The techniques presented in this chapter are applicable to this scenario, as they
allow different objects to have different thresholds and allow thresholds to change
dynamically.
A proposal for trajectory prediction by Karimi and Liu [12] assigns probabilities
to the roads emanating from an intersection according to how likely it is that an
object entering the intersection will proceed on them. The subroad network within
a circular area around an object is extracted, and the most probable route within
this network is used for prediction. When the object leaves the current subnetwork,
a new subnetwork is extracted, and the procedure is repeated. In this proposal, the