Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
Abstract
:1. Introduction
- In this study, we applied the systematic literature review approach. We used a keyword-based search and systematically identified existing highly relevant studies from the search results.
- This study covers traffic control studies on urban motorways focused on VSL and merging control approaches in mixed traffic flows.
- First, we identified and categorized the control objectives, e.g., improving efficiency or safety, for scenarios with HDV flows. Then, we analyzed different approaches for VSL and merging control in mixed traffic flows. In addition, we identified and summarized papers that analyze the impact of mixed traffic flows on the fundamental diagram without control strategies. Finally, we categorized the main objectives of control approaches in mixed traffic flows.
2. Application of VSL and RM Control in HDV Traffic Flows
2.1. Classical VSL Approaches
2.1.1. Rule-Based Reactive VSL
2.1.2. Open-Loop Based VSL
2.1.3. VSL Based on Negative Feedback Loop
2.2. RL-Based VSL
2.3. Classical RM Approaches
2.4. RL-Based RM
2.5. Impact of VSL and RM on HDV Traffic Flows
2.5.1. VSL Impact on Stable Traffic Flows
2.5.2. VSL Impact on Unstable Traffic Flows
2.5.3. Impact of VSL and RM on Traffic Safety and Emissions
3. Research and Implementation
3.1. Defined Research Questions
- RQ1: What is the impact of AVs and CAVs on the fundamental diagram of mixed traffic flows?
- RQ2: How can the existing methods for VSL and RM be used to control mixed traffic flows?
- RQ3: What types of control algorithms have been proposed for VSL and merging control for mixed traffic flows?
- RQ4: What are the current open problems and what are the prospective research directions?
3.2. Applied Research Method
- Scopus;
- IEEE;
- Web of Science (WoS).
3.3. Impact of AVs and CAVs on the Fundamental Diagram
3.4. VSL in Mixed Traffic Flows
3.4.1. Optimization-Based Methods for VSL
3.4.2. Rule-Based VSL
3.4.3. RL-Based VSL
3.4.4. Other VSL Approaches
3.5. Merging Control Approaches in Mixed Traffic Flows
3.5.1. Optimization-Based Methods for Merging Control
3.5.2. Car-Following and Trajectory-Planning-Based Merging Control Methods
3.5.3. RL-Based Merging Control
3.5.4. Other Merging Control Approaches
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AC | Actor-Critic |
ACC | Adaptive Cruise Control |
AV | Autonomous Vehicle |
Average Travel Time | |
CA | Cellular Automata |
CACC | Cooperative Adaptive Cruise Control |
CAV | Connected and Autonomous Vehicle |
CIDM | Cooperative Intelligent Driver Model |
CLCC | Cooperative Lane Changing Control |
CMC | Cooperative Merging Control |
CTM | Cell-Transmission Model |
CV | Connected Vehicle |
C-VSL | Cooperative Variable Speed Limit |
DVSL | Differential Variable Speed Limit |
DRL | Deep Reinforcement Learning |
DQN | Deep Q-Network |
GA | Genetic Algorithm |
GRU | Gated Recurrent Unit |
HDV | Human-Driven Vehicle |
HERO | Heuristic Ramp-metering Coordination |
IDM | Intelligent Driver Model |
ITS | Intelligent Transport Systems |
I2V | Infrastructure-to-Vehicle |
kNN-TD | k-Nearest Neighbor Temporal Difference |
LC | Lane Change |
MDP | Markov Decision Process |
ML | Machine Learning |
MoE | Measure of Effectiveness |
MPC | Model Predictive Control |
MPDM | Multi-Policy Decision Making |
Mean Travel Time | |
MWR | Mutual Weights Regularization |
NN | Neural Network |
pAC | passive Actor-Critic |
PGM | Probabilistic Graphical Model |
P-VSL | Point Variable Speed Limit |
QL | Q-Learning |
RM | Ramp Metering |
RL | Reinforcement Learning |
RNN | Recurrent Neural Network |
RSU | Road Side Unit |
R-MART | Reinforcement-Markov Average Reward Technique |
RLCA | Reinforcement Learning Control Agent |
SAE | Society of Automotive Engineers |
SWARM | System-Wide Adaptive Ramp Metering |
SZM | Stratified Zone Metering |
SPD-HARM | SPeeD HARMonization |
Time Exposed Time-to-collision | |
Time Integrated Time-to-collision | |
Travel Time | |
Time-to-Collision | |
Total Time Spent | |
Total Travel Time | |
VMS | Variable Message Sign |
VSL | Variable Speed Limit |
V2V | Vehicle-to-Vehicle |
V2R | Vehicle-to-Roadside |
V2I | Vehicle-to-Infrastructure |
V2X | Vehicle-to-Everything |
WoS | Web of Science |
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Paper | Year | Penetration Rate | Obtained Influence |
---|---|---|---|
[76] | 2020 | 0%–70% AVs and CAVs | reduced congestion and conflicts |
[77] | 2019 | 0%–100% CAVs | increased flow, reduced acceleration and speed oscilations |
[78] | 2018 | 0%–100% AVs and CAVs | increased capacity |
[79] | 2018 | 0%–100% AVs | increased value |
[80] | 2017 | 0%–70% CAVs | increased value increased flow |
[81] | 2017 | 0%–100% AVs | increased free-flow speed, increased flow under |
[82] | 2016 | 0%–100% AVs and CVs | increased throughput |
[83] | 2016 | AVs | increased flow |
Paper | Year | Control Strategy | Proposed Method | Penetration Rate | Compared with | Improvements |
---|---|---|---|---|---|---|
[84] | 2020 | DVSL | DRL | CAVs | no control, QL, DQN VSL, VSL-AC | lower , lower emissions |
[85] | 2020 | VSL and LC | GA | CAVs | no control, VSL | lower |
[86] | 2020 | VSL | Multiclass CTM | 0%–100% AVs | no control | lower energy consumption |
[87] | 2019 | Speed harmonization | Optimal control with Hamiltonian function | AVs | no control, VSL, SPD-HARM | lower fuel consumption, lower |
[88] | 2019 | VSL | GA | 0%–10% CAVs | 7 scenarios with no control, VSL, CTM and I2V with V2I | lower , lower delays, lower number of stops, lower emissions |
[89] | 2019 | VSL | Rule-based VSL | 0%–100% AVs | no control | lower , 31% lower fuel consumption |
[90] | 2018 | Centralized VSL | Deep-RL (GRU) | AVs | no control, feedback RM | higher bottleneck throughput |
[91] | 2017 | ACC and VSL | Rule-based VSL | 0%–100% AVs | ACC only, VSL only | lower , lower |
[92] | 2016 | C-VSL | Feedback control | 0%–100% AVs | P-VSL | lower delay time |
[93] | 2015 | VSL | MPC | and CAVs | no control | lower , improved safety, lower fuel consumption |
Paper | Year | Control Strategy | Proposed Method | Penetration Rate | Compared with | Improvements |
---|---|---|---|---|---|---|
[94] | 2019 | LC, merging | rule-based CLCC, event-based CMC | CAVs | no control | lower total delay time, increased speed |
[95] | 2019 | merging | trajectory planning | CAVs | no control | achieved desired merging speed |
[96] | 2019 | LC | decentralized and centralized control | 0%–100% AVs | no control, ALINEA RM | 43%–61% lower |
[97] | 2019 | RM | look-ahead cruise control | AVs | - | queue length optimization |
[98] | 2018 | merging | V2X communication | CAVs | no control | lower , higher average speed |
[99] | 2017 | merging | CIDM safe time gaps | 0%–25% AVs | - | lower speed oscilations, lower |
[100] | 2017 | merging | Hamiltonian analysis | CAVs | no control | lower fuel consumption, lower |
[101] | 2017 | merging | nonlinear optimization | CAVs | no control, gradual speed limit reduction | reduced delay, increased average speed up to 95 km/h |
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Vrbanić, F.; Ivanjko, E.; Kušić, K.; Čakija, D. Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions. Appl. Sci. 2021, 11, 2574. https://doi.org/10.3390/app11062574
Vrbanić F, Ivanjko E, Kušić K, Čakija D. Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions. Applied Sciences. 2021; 11(6):2574. https://doi.org/10.3390/app11062574
Chicago/Turabian StyleVrbanić, Filip, Edouard Ivanjko, Krešimir Kušić, and Dino Čakija. 2021. "Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions" Applied Sciences 11, no. 6: 2574. https://doi.org/10.3390/app11062574
APA StyleVrbanić, F., Ivanjko, E., Kušić, K., & Čakija, D. (2021). Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions. Applied Sciences, 11(6), 2574. https://doi.org/10.3390/app11062574