Towards reducing traffic congestion using cooperative adaptive cruise control on a freeway with a ramp
Abstract: Purpose: In this
paper, the impact of Cooperative Adaptive Cruise Control (CACC) systems on
traffic performance is examined using microscopic agent-based simulation. Using
a developed traffic simulation model of a freeway with an on-ramp - created to
induce perturbations and to trigger stop-and-go traffic, the CACC system’s
effect on the traffic performance is studied. The previously proposed traffic
simulation model is extended and validated. By embedding CACC vehicles in
different penetration levels, the results show significance and indicate the
potential of CACC systems to improve traffic characteristics and therefore can
be used to reduce traffic congestion. The study shows that the impact of CACC
is positive but is highly dependent on the CACC market penetration. The flow
rate of the traffic using CACC is proportional to the market penetration rate
of CACC equipped vehicles and the density of the traffic.
Design/methodology/approach: This paper uses microscopic simulation
experiments followed by a quantitative statistical analysis. Simulation enables
researchers manipulating the system variables to straightforwardly predict the
outcome on the overall system, giving researchers the unique opportunity to
interfere and make improvements to performance. Thus with simulation, changes
to variables that might require excessive time, or be unfeasible to carry on
real systems, are often completed within seconds.
Findings: The findings of this paper are summarized as follow:
•Provide and validate a platform (agent-based microscopic traffic
simulator) in which any CACC algorithm (current or future) may be evaluated.
•Provide detailed analysis associated with implementation of CACC
vehicles on freeways.
•Investigate whether embedding CACC vehicles on freeways has a
significant positive impact or not.
Research limitations/implications: The main limitation of this research
is that it has been conducted solely in a computer laboratory. Laboratory
experiments and/or simulations provide a controlled setting, well suited for
preliminary testing and calibrating of the input variables. However, laboratory
testing is by no means sufficient for the entire methodology validation. It
must be complemented by fundamental field testing. As far as the simulation
model limitations, accidents, weather conditions, and obstacles in the roads
were not taken into consideration. Failures in the operation of the sensors and
communication of CACC design equipment were also not considered. Additionally,
the special HOV lanes were limited to manual vehicles and CACC vehicles.
Emergency vehicles, buses, motorcycles, and other type of vehicles were not
considered in this dissertation. Finally, it is worthy to note that the human
factor is far more sophisticated, hard to predict, and flexible to be exactly
modeled in a traffic simulation model perfectly. Some human behavior could
occur in real life that the simulation model proposed would fail to model.
Practical implications: A high percentage of CACC market penetration is
not occurring in the near future. Thus, reaching a high penetration will always
be a challenge for this type of research. The public accessibility for such a
technology will always be a major practical challenge. With such a small
headway safety gap, even if the technology was practically proven to be
efficient and safe, having the public to accept it and feel comfortable in
using it will always be a challenge facing the success of the CACC technology.
Originality/value: The literature on the impact of CACC on traffic dynamics
is limited. In addition, no previous work has proposed an open-source
microscopic traffic simulator where different CACC algorithms could be easily
used and tested. We believe that the proposed model is more realistic than
other traffic models, and is one of the very first models to model the behavior
CACC vehicles on freeways.
Keywords: Intelligent
Transportation Systems, Vehicular Ad Hoc Networks, Cooperative Adaptive Cruise
Control, Traffic simulation, agent-based traffic simulation, microscopic simulation
Author: Georges Arnaout,
Shannon Bowling
Journal Code: jptindustrigg110033
