<p>Connected and autonomous
vehicle (CAV) technologies provide disruptive and transformational
opportunities for innovations toward intelligent transportation systems.
Compared with human driven vehicles (HDVs), the CAVs can reduce reaction time
and human errors, increase traffic mobility and will be more knowledgeable due
to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. CAVs’
potential to reduce traffic accidents, improve vehicular mobility and promote
eco-driving is immense. However, the new characteristics and capabilities of
CAVs will significantly transform the future of transportation, including the dissemination
of traffic information, traffic flow dynamics and network equilibrium flow.
This dissertation seeks to realize and enhance the application of CAVs by
specifically advancing the research in three connected topics: (1) modeling and
controlling information flow propagation within a V2V communication
environment, (2) designing a real-time deployable cooperative control mechanism
for CAV platoons, and (3) modeling network equilibrium flow with a mix of CAVs
and HDVs. </p>
<p>Vehicular traffic
congestion in a V2V communication environment can lead to congestion effects
for information flow propagation due to full occupation of the communication
channel. Such congestion effects can impact not only whether a specific
information packet of interest is able to reach a desired location, but also
the timeliness needed to influence traffic system performance. This dissertation
begins with exploring spatiotemporal information flow propagation under
information congestion effects, by introducing a two-layer macroscopic model
and an information packet relay control strategy. The upper layer models the information
dissemination in the information flow regime, and the lower layer model
captures the impacts of traffic flow dynamics on information propagation.
Analytical and numerical solutions of the information flow propagation wave
(IFPW) speed are provided, and the density of informed vehicles is derived
under different traffic conditions. Hence, the proposed model can be leveraged
to develop a new generation of information dissemination strategies focused on
enabling specific V2V information to reach specific locations at specific
points in time.</p>
<p>In a V2V-based system,
multiclass information (e.g., safety information, routing information, work
zone information) needs to be disseminated simultaneously. The application
needs of different classes of information related to vehicular reception ratio,
the time delay and spatial coverage (i.e., distance it can be propagated) are
different. To meet the application needs of multiclass information under
different traffic and communication environments, a queuing strategy is
proposed for each equipped vehicle to disseminate the received information. It
enables control of multiclass information flow propagation through two
parameters: 1) the number of communication servers and 2) the communication
service rate. A two-layer model is derived to characterize the IFPW under the
designed queuing strategy. Analytical and numerical solutions are derived to
investigate the effects of the two control parameters on information
propagation performance in different information classes. </p>
<p>Third, this dissertation
also develops a real-time implementable cooperative control mechanism for CAV
platoons. Recently, model predictive control (MPC)-based platooning strategies
have been developed for CAVs to enhance traffic performance by enabling
cooperation among vehicles in the platoon. However, they are not deployable in
practice as they require anembedded optimal control problem to be solved
instantaneously, with platoon size and prediction horizon duration compounding
the intractability. Ignoring the computational requirements leads to control
delays that can deteriorate platoon performance and cause collisions between
vehicles. To address this critical gap, this dissertation first proposes an
idealized MPC-based cooperative control strategy for CAV platooning based on
the strong assumption that the problem can be solved instantaneously. It then
develops a deployable model predictive control with first-order approximation
(DMPC-FOA) that can accurately estimate the optimal control decisions of the
idealized MPC strategy without entailing control delay. Application of the
DMPC-FOA approach for a CAV platoon using real-world leading vehicle trajectory
data shows that it can dampen the traffic oscillation effectively, and can lead
to smooth deceleration and acceleration behavior of all following vehicles.</p>
<p>Finally, this dissertation
also develops a multiclass traffic assignment model for mixed traffic flow of
CAVs and HDVs. Due to the advantages of CAVs over HDVs, such as reduced value
of time, enhanced quality of travel experience, and seamless situational
awareness and connectivity, CAV users can differ in their route choice behavior
compared to HDV users, leading to mixed traffic flows that can significantly
deviate from the single-class HDV traffic pattern. However, due to a lack of
quantitative models, there is limited knowledge on the evolution of mixed
traffic flows in a traffic network. To partly bridge this gap, this dissertation
proposes a multiclass traffic assignment model. The multiclass model captures
the effect of knowledge level of traffic conditions on route choice of both
CAVs and HDVs. In addition, it captures the characteristics of mixed traffic
flow such as the difference in value of time between
HDVs and CAVs and the asymmetry in their driving interactions, thereby
enhancing behavioral realism in the modeling. New solution algorithms will be
developed to solve the multiclass traffic assignment model. The study results can
assist transportation decision-makers to design effective planning and
operational strategies to leverage the advantages of CAVs and manage traffic
congestion under mixed traffic flows.</p>
<p>This dissertation deepens
our understanding of the characteristics and phenomena in domains of traffic
information dissemination, traffic flow dynamics and network equilibrium flow in
the age of connected and autonomous transportation. The findings of this dissertation
can assist transportation managers in designing effective traffic operation and planning
strategies to fully exploit the potential
of CAVs to improve
system performance related to traffic safety, mobility and energy consumption. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7411844 |
| Date | 17 January 2019 |
| Creators | Jian Wang (5930372) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/System_modeling_for_connected_and_autonomous_vehicles/7411844 |
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