ITE Journal - July 2021 - 43

With the development of Intelligent Transportation System (ITS),
higher resolution data (i.e., probe vehicle data) became available.7-9
Probe vehicle data are usually collected in research to find critical
points in a FD for capturing the traffic states. With the emergence
of CV technology, this line of research can be moved one step
forward.10, 11
On one hand, the data collected in CV trajectories
provide more information. Beyond fixed-point data collector
systems (i.e., loop) or roadside collector system (i.e., radar), CV
trajectory is continuously available in time and space. On the other
hand, the CV trajectory is defined as higher-resolution event-based
data, which include information like vehicle interaction status and
network communicated driving status, along with those traditional
probe vehicle data (i.e., location, speed, and acceleration).12, 13
Moreover, compared to probe vehicle information-which focuses
on the mobility of the detection for ITS traffic management
purposes-CV not only provides this information, but also obtains
network-wide data via communications with other CVs or the
infrastructure. For instance, in this study, Signal Phase and Timing
(SpaT) messages are incorporated into the trajectory of CVs.
Given that collected data are from microscopic detections and
the FD is a macroscopic level demonstration, shock waves play as a
connection between two representations.14-16
That is to say, in this
study, shock waves are the transition zones between traffic states
that move through a traffic region.
By 2025, more than 400 million vehicles on our roadways will
have basic connected technology onboard (i.e., adaptive cruise
control).17
It is meaningful to take advantage of their massive
trajectory data and explore a method to build a macroscopic FD
from microscopic data (i.e., CV trajectory data). Hence, the primary
objective of this study is to estimate a FD using CV trajectory at a
signalized intersection. This study is designed to reveal traffic flow
dynamics for signalized intersections through the vehicular interactions.
A CV trajectory method constructs a FD with three-step,
data filtering and categorization, critical point extraction and state
identification, and shock wave formation. The method is then
validated through an experimental design with VISSIM trajectory
data to prove the concept. The potential applications of this study
with emerging connected vehicle technologies will benefit traffic flow
modeling and the development of traffic management strategies.
Method Development
This proposed trajectory-based method is to reveal traffic flow
dynamics for signalized intersections by constructing a FD using
CV data. With the development of CV technologies and the
increasing market penetrations of CV on road, CV trajectory data
is collectible by either roadside units or a traffic operation center, or
both. This study explores eight dynamic properties/states of traffic
at a signalized intersection:
1. Approaching State,
2. Queue Formation State,
3. Stopped Queue State,
4. No Vehicle State,
5. Queue Dissipation State,
6. Capacity State,
7. Following State, and
8. Free Flow State.
These macroscopic states of traffic are continuous and bounded by
shock waves. The Queue Formation State and the Queue Dissipation
State, are essential to describe traffic flow at an intersection, but they
have been simplified or neglected in the previous research.18, 19
In this
study, the authors aim to observe only a portion of the trajectory data
(i.e., CV trajectory data at a certain penetration rate), form a shock
waves through those observed CV interactions, and estimate a FD.
At a single intersection, the authors first assumed that a set of
connected vehicles approach the intersection where traffic flow is
under-saturated. Each connected vehicle is then assumed to provide
GPS coordinates, time step, speed, acceleration, and driving status
from its trajectories. The variables-GPS position, timing, and
speed-have been used widely in previous research.20, 21
Driving
status is a relatively novel variable in the study of vehicle trajectory
sets. In this study, the authors took the recommendation in the 2010
Highway Capacity Manual (HCM) and set the driving status as a
binary variable.22
That is, the driving status is " in queue, " when the
speed of a vehicle is lower than 5 miles per hour (mph) (8 kilometers
per hour [km/hr]); otherwise, the driving status is not " in queue. "
In this CV trajectory method development section, the method
includes the following steps, 1) data filtering and categorization, 2)
critical point extraction and state identification, and 3) shock wave
formation and structure formation of FD.
Step 1 - Filtering and Categorization
A prior filtering process is needed on CV trajectory dataset, and the
process applies the following assumptions within the analysis zone:
ƒ Single vehicle type, that is passenger car only,
ƒ No turning vehicles,
ƒ No lane changing, and
ƒ No flow interruptions turning from minor streets before,
after and at the intersection.
After trajectory filtering, the method then categorizes vehicle
trajectory data into queued and non-queued vehicle sets based
on the driving state (i.e., " in queue " or not). It is important to
categorize as queued and non-queued vehicle sets, rather than
other factors (i.e., green duration, red duration, etc.) This is because
vehicle trajectory is not bound by time and space. It is a continuous
record of an individual vehicle in the analysis zone. Once one
vehicle's trajectory identifies itself as queued or non-queued, it is
then assigned into its belonged cycle by its starting time as entering
the analysis zone (i.e., about 750 feet [228.6 meters] upstream from
www.ite.org July 2021 43
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