In the urban corridor with a mixed traffic composition of connected and automated vehicles
(CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by
both individual driving behaviors and the presence of signalized intersections. Therefore, the
development of a coordinated control strategy that effectively accommodates these dual factors
becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level
structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor
signal offsets setting. The objective of this structure is to optimize both the average travel time
(ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while
considering driving modes are presented to illustrate the desired driving behaviors of HDVs and
CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is
proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a
multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and
AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting
Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for
corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments
are conducted within a corridor that encompasses three signalized intersections. The performance
of our proposed eco-driving strategy is validated in comparison to the intelligent driver model
(IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation.
Results show that our proposed strategy yields reduced travel time and fuel consumption to both
IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is
validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by
4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3%
maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and
V/C ratios on the optimal ATT and AFC.
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Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment
Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.
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- Award ID(s):
- 1844238
- NSF-PAR ID:
- 10311405
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume:
- 2675
- Issue:
- 7
- ISSN:
- 0361-1981
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/0361198121994580
