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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Traffic signal control in mixed traffic environment based on advance decision and reinforcement learning
Reinforcement learning-based traffic signal control systems (RLTSC) can enhance dynamic adaptability, save vehicle travelling time and promote intersection capacity. However, the existing RLTSC methods do not consider the driver's response time requirement, so the systems often face efficiency limitations and implementation difficulties. We propose the advance decision-making reinforcement learning traffic signal control (AD-RLTSC) algorithm to improve traffic efficiency while ensuring safety in mixed traffic environment. First, the relationship between the intersection perception range and the signal control period is established and the trust region state (TRS) is proposed. Then, the scalable state matrix is dynamically adjusted to decide the future signal light status. The decision will be displayed to the human-driven vehicles (HDVs) through the bi-countdown timer mechanism and sent to the nearby connected automated vehicles (CAVs) using the wireless network rather than be executed immediately. HDVs and CAVs optimize the driving speed based on the remaining green (or red) time. Besides, the Double Dueling Deep Q-learning Network algorithm is used for reinforcement learning training; a standardized reward is proposed to enhance the performance of intersection control and prioritized experience replay is adopted to improve sample utilization. The experimental results on vehicle micro-behaviour and traffic macro-efficiency showed that the proposed AD-RLTSC algorithm can simultaneously improve both traffic efficiency and traffic flow stability.
more » « less- PAR ID:
- 10383109
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Transportation Safety and Environment
- Volume:
- 4
- Issue:
- 4
- ISSN:
- 2631-4428
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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