A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road
Abstract
:1. Introduction
2. Problem Formulation
2.1. Problem Description
2.2. Notations
3. Analysis of Optimal Travel Time and Departure Sequence
3.1. Optimal Control at ORZ
- ➀
- Vehicle Following: Vehicle i and vehicle i − 1 are in the same lane. Here, i − 1 refers to the vehicle directly in front of vehicle i. This implies that the following relationship between the two vehicles needs to be considered. Hence, the two vehicles are forced to meet the minimum safety headway requirement, i.e., ,.
- ➁
- Vehicle Merging: Vehicle i and vehicle i − 1 come from different roads (directions) and have a merging conflict in MZ. Here, vehicle i − 1 is the preceding vehicle. Then, the two vehicles need to meet the requirements of the safe driving sequences, i.e., .
3.2. Optimal Control at JS
3.3. Optimal Control at DI
- ➀
- Vehicle Following: the minimum safety following time should be met, i.e., ,.
- ➁
- Vehicle Crossing: Vehicle i and vehicle i − 1 come from different directions and have a crossing conflict in the IZ. Here, i − 1 is the proceeding vehicle. These two vehicles must meet the safe passing sequence requirements, i.e., .
3.4. Determination of Optimal Travel Time
4. Vehicle Trajectory Optimization
4.1. Objective Function
4.2. Comparison of Solution Methods
4.3. Solution of Function without Constraints
4.4. Vehicle Velocity Minimum Limit Activated
4.5. Vehicle Following Constraint Activated
5. Parameters Analysis and Simulation Evaluation
5.1. Model Correlation Coefficients Analysis and Solution
5.2. Control Framework
5.3. Simulation Results Evaluation
5.3.1. Selection of Comparison Strategies
- ➀
- Benchmark: among this scenario, vehicles travel along the junction and only following the default driving model of VISSIM, which is used to simulate human driving behavior.
- ➁
- Cooperative Adaptive Cruise Control (CACC): A CACC system is the combination of automated velocity control with a cooperative element, such as vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) communication [47]. It takes advantage of advanced CAV technologies to improve safety, improve traffic flow dynamics by damping disturbances, save energy, and so on, and its effectiveness has been proven by [48]. The model parameters are also borrowed from [48].
5.3.2. Simulation Parameters
5.3.3. Metrics for Evaluation
5.3.4. Computing Efficiency Evaluation
5.3.5. Comparative Analysis of Optimization Results
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Notation | Meaning |
---|---|
Proper noun abbreviation | |
ORZ | Off-Ramp Zone |
JS | Junction Section |
DI | Downstream Intersection |
MZ | Merging Zone |
IZ | Interweaving zone at DI |
Geometric parameters | |
The distance of the vehicle trajectory control zone | |
The distance of the JS | |
The distance of the IZ | |
Time parameters | |
The time that the vehicle i arrives in the control zone of ORZ | |
The time that the vehicle i arrives in MZ | |
The time that the vehicle i leaves MZ | |
The time that the vehicle i arrives at DI | |
The time that the vehicle i arrives in IZ | |
The time that the vehicle i leaves IZ | |
The minimum time that the vehicle i passes the control zone | |
The minimum safe time headway | |
Vehicle parameters | |
i | The current controlled vehicle |
l | The length of the vehicle |
The minimum gap between two vehicles | |
Variables | |
The location of the vehicle i at time t | |
The velocity of the vehicle i at time t | |
The acceleration of the vehicle i at time t | |
The jerk of the vehicle i at time t |
Traffic Demand (veh/h) | Average Computational Time of Each Vehicle (s) | |
---|---|---|
DP | PMP | |
900 | 0.183 | 0.014 |
1100 | 0.516 | 0.032 |
1300 | 1.327 | 0.067 |
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Jiang, Z.; Yu, D.; Zhou, H.; Luan, S.; Xing, X. A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road. Sustainability 2021, 13, 9933. https://doi.org/10.3390/su13179933
Jiang Z, Yu D, Zhou H, Luan S, Xing X. A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road. Sustainability. 2021; 13(17):9933. https://doi.org/10.3390/su13179933
Chicago/Turabian StyleJiang, Zhongtai, Dexin Yu, Huxing Zhou, Siliang Luan, and Xue Xing. 2021. "A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road" Sustainability 13, no. 17: 9933. https://doi.org/10.3390/su13179933