Even though extensive studies have developed various eco-driving strategies for vehicle platoon to travel on urban roads with traffic signals, most of them focus on vehicle-level trajectory planning or speed advisory rather than real-time platoon-level closed-loop control. In addition, majority of existing efforts neglect the traffic and vehicle dynamic uncertainties to avoid the modeling and solution complexity. To make up these research gaps, this study develops a system optimal vehicle platooning control for eco-driving (SO-ED), which can guide a mixed flow platoon to smoothly run on the urban roads and pass the signalized intersections without sudden deceleration or red idling. The SO-ED is mathematically implemented by a hybrid model predictive control (MPC) system, including three MPC controllers and an MINLP platoon splitting switching signal. Based on the features of the system, this study uses active set method to solve the large-scale MPC controllers in real time. The numerical experiments validate the merits of the proposed SO-ED in smoothing the traffic flow and reducing energy consumption and emission at urban signalized intersections.
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This content will become publicly available on February 1, 2025
Multi-objective coordinated control strategy for mixed traffic with partially connected and automated vehicles in urban corridors
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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- Award ID(s):
- 2152258
- NSF-PAR ID:
- 10510373
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Physica A: Statistical Mechanics and its Applications
- Volume:
- 635
- Issue:
- C
- ISSN:
- 0378-4371
- Page Range / eLocation ID:
- 129485
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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