288
Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
Having received this update, the server determines which tracking technique and
threshold to be used for the object (these are predefined), and it stores the information
received from the object in the database. If segment-based tracking is to be used,
the server also uses map matching to determine on which road segment the object is
moving. The server then sends its representation, or prediction, of the object’s current
and future position to the object.
Having received this information from the server, the object again obtains its
actual, current location information from the GPS receiver. It then calculates its predicted position using the representation received from the server, and it compares this
to the GPS position. If the difference between these two exceeds the given threshold, the client issues an update to the server. If not, a new comparison is made. This
procedure continues until it is terminated by the object. Although the server may
also initiate and terminate the tracking, we assume for simplicity that the object is in
control. This aspect has no impact on the chapter’s exposition.
13.2.2 Data Description
As mentioned, we assume that GPS is used for positioning of the moving objects.
In making this assumption, we note that Galileo-based positioning [19] and hybrid
GPS/Galileo-based positioning are likely to work even better when they become
available. In experiments, the results of which will be reported upon throughout the
chapter, we used two data sets of GPS logs. Both were obtained by installing GPS
receivers together with small computers in a number of vehicles. The positions of
the vehicles were recorded every second while the vehicles were driving. Positions
were not recorded for a vehicle when its engine was turned off.
The first GPS data set stems from a Danish intelligent speed adaptation project
called “INFATI” [10]. A total of 20 GPS equipped cars were participating in the
project, and their positions were recorded during a period of approximately 8 weeks.
Cars were driving in Aalborg, Denmark area, an area with a population of about
140,000 inhabitants. This data set represents the behavior of vehicles traveling in
semiurban surroundings. Here, the average trip length is 9.5 km (continuous driving
ignoring pauses shorter than 5 minutes is considered to be one trip). The part of the
INFATI data set used in the experiments reported in this chapter consists of about
500,000 GPS records, and the total trip length is about 9,000 km.
The second GPS data set stems from a road-pricing project called “AKTA” [16].
Here, the participating cars were driving in the Copenhagen, Denmark area. This
data set represents the behavior of vehicles traveling in a larger urban area. Here,
the average trip length is 17.9 km, and the part of the data set used consists of about
4,000,000 GPS records, corresponding to a total trip length of about 67,000 km.
For the experiments, we also used digital road networks obtained from both
projects. The initial road networks were composed of sets of segments, where each
segment corresponds to some part of a road between two consecutive intersections
or and intersection and a dead end. A segment consists of a sequence of coordinates,
that is, it is a polyline. Further, the road networks are partitioned into named roads or
Alminas ˇ
Civilis, Christian S. Jensen, and Stardas Pakalnis
Having received this update, the server determines which tracking technique and
threshold to be used for the object (these are predefined), and it stores the information
received from the object in the database. If segment-based tracking is to be used,
the server also uses map matching to determine on which road segment the object is
moving. The server then sends its representation, or prediction, of the object’s current
and future position to the object.
Having received this information from the server, the object again obtains its
actual, current location information from the GPS receiver. It then calculates its predicted position using the representation received from the server, and it compares this
to the GPS position. If the difference between these two exceeds the given threshold, the client issues an update to the server. If not, a new comparison is made. This
procedure continues until it is terminated by the object. Although the server may
also initiate and terminate the tracking, we assume for simplicity that the object is in
control. This aspect has no impact on the chapter’s exposition.
13.2.2 Data Description
As mentioned, we assume that GPS is used for positioning of the moving objects.
In making this assumption, we note that Galileo-based positioning [19] and hybrid
GPS/Galileo-based positioning are likely to work even better when they become
available. In experiments, the results of which will be reported upon throughout the
chapter, we used two data sets of GPS logs. Both were obtained by installing GPS
receivers together with small computers in a number of vehicles. The positions of
the vehicles were recorded every second while the vehicles were driving. Positions
were not recorded for a vehicle when its engine was turned off.
The first GPS data set stems from a Danish intelligent speed adaptation project
called “INFATI” [10]. A total of 20 GPS equipped cars were participating in the
project, and their positions were recorded during a period of approximately 8 weeks.
Cars were driving in Aalborg, Denmark area, an area with a population of about
140,000 inhabitants. This data set represents the behavior of vehicles traveling in
semiurban surroundings. Here, the average trip length is 9.5 km (continuous driving
ignoring pauses shorter than 5 minutes is considered to be one trip). The part of the
INFATI data set used in the experiments reported in this chapter consists of about
500,000 GPS records, and the total trip length is about 9,000 km.
The second GPS data set stems from a road-pricing project called “AKTA” [16].
Here, the participating cars were driving in the Copenhagen, Denmark area. This
data set represents the behavior of vehicles traveling in a larger urban area. Here,
the average trip length is 17.9 km, and the part of the data set used consists of about
4,000,000 GPS records, corresponding to a total trip length of about 67,000 km.
For the experiments, we also used digital road networks obtained from both
projects. The initial road networks were composed of sets of segments, where each
segment corresponds to some part of a road between two consecutive intersections
or and intersection and a dead end. A segment consists of a sequence of coordinates,
that is, it is a polyline. Further, the road networks are partitioned into named roads or