Probe vehicles, like mobile sensors, can provide rich information about
traffic conditions in transportation networks. The rapid development of
connected vehicle technology and the emergence of ride-hailing services have
enabled the collection of a huge amount of trajectory data of the probe
vehicles. Attribute to the scale and the accessibility, the trajectory data
have become a potential substitute for the widely used fixed-location sensors
in terms of the performance measures of the transportation networks. There has
been some literature estimating traffic volume and queue length at signalized
intersections using the trajectory data. However, some of the existing methods
require the prior information about the distribution of queue lengths and the
penetration rate of the probe vehicles, which might vary a lot both spatially
and temporally and usually are not known in real life. Some other methods can
only work when the penetration rate of the probe vehicles is sufficiently high.
To overcome the limitations of the existing literature, this paper proposes a
series of novel methods for queue length and traffic volume estimation. The
validation results show that the methods are accurate enough for mid-term and
long-term performance measures and traffic signal control, even when the
penetration rate is very low. Therefore, the methods are ready for large-scale
real-field applications.Comment: Transportation network sensing using probe vehicle trajectorie
