Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
Abstract
:1. Introduction
- Overview of relevant research papers on the topic of urban TSC and mixed traffic flows with a systematic overview of conventional TSC strategies, and intersection state estimation methods.
- Analyzed the impact of mixed vehicle traffic flows on TSC.
- Suggestions for further research steps on the topic TSC of mixed traffic flows in urban environments.
2. Research Methodology
2.1. Open Questions and Scope Definition
- RQ1: What are the intersection state estimation approaches?
- RQ2: What is the impact of mixed traffic flows on urban traffic dynamics?
- RQ3: How to process large amounts of data quickly and efficiently using the potential of CVs and CAVs as mobile sensors?
2.2. Applied Research Method
- Scopus;
- IEEE;
- Web of Science (WoS).
3. Technological Background
3.1. Intelligent Transportation System Overview
3.2. Traffic Signal Control Importance
3.3. Characteristics of CVs and CAVs
4. Conventional Traffic Signal Control Strategies
4.1. Signal Program Parameters
- Green time: The time duration in seconds, during which a given traffic movement at signalized intersection proceeds at a saturation flow rate.
- Cycle length: The amount of time it takes a signal to complete the signal cycle.
- Phase sequence: The sequence in which the signal program phases occur throughout a signal cycle.
- Change interval: Also known as a Clearance interval, a short time period to provide clearance before the green time for conflicting traffic movements.
- Offset: The relationship in time between the beginning of the signal cycle between two or more consecutive intersections.
4.2. Fixed Time Traffic Signal Control
4.3. Adaptive Traffic Signal Control
4.3.1. Conventional Adaptive Traffic Signal Control Strategies
4.3.2. New Adaptive Traffic Signal Control Strategies
5. Intersection State Estimation
5.1. Model-Driven Methods
5.2. Data-Driven Methods
5.3. Streaming Data-Driven Methods
6. Impact of Mixed Traffic Flows on Traffic Signal Control
7. Connected and Autonomous Vehicles Based Traffic Signal Control
8. Discussion
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACC | Adaptive Cruise Control |
ANN | Artificial Neural Network |
ATSC | Adaptive Traffic Signal Control |
CAVs | Connected Autonomous Vehicles |
CNN | Convolutional Neural Network |
CVs | Connected Vehicles |
DP | Dynamic Programming |
FTSC | Fixed Time Signal Control |
GCN | Graph Convolutional Network |
GPS | Global Positioning System |
HDV | Human Driven Vehicle |
ITS | Intelligent Transportation Systems |
kNN | k-Nearest Neighbors |
LIDAR | Light Detection and Ranging |
MILP | Mixed-integer Linear Programming |
NMF | Non-negative Matrix Factorization |
RADAR | Radio Detection and Ranging |
RL | Reinforcement Learning |
SAE | Society of Automotive Engineers |
STM | Speed Transition Matrix |
SVM | Support Vector Machine |
TSC | Traffic Signal Control |
V2I | Vehicle-to-Infrastructure |
V2V | Vehicle-to-Vehicle |
V2X | Vehicle-to-everything |
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Paper | Year | Data Source | Type | Applied Method | Impact | Benchmark |
---|---|---|---|---|---|---|
[42] | 2022 | Simulation, FCD | Streaming-data driven method | Bottleneck Probability Estimation Using STM | Bottleneck probability estimated on the simulated motorway traffic scenarios | Evaluated on four different simulated motorway traffic scenarios |
[43] | 2021 | Simulation, FCD | Streaming-data driven method | Intersection state estimation using STM and Fuzzy logic | Intersection Traffic State Estimation using STM | Crossvalidation |
[44] | 2022 | Simulation, FCD | Streaming-data driven method | Statistical analysis | Analysis of impact CVs on STM accuracy | Statistical validation |
[45] | 2021 | GPS, FCD | Data-driven method | Data mining | Estimation of congestion zones and time-varying travel time indexes | Real travel times, historical dataset, and state-of-the-art method |
[46] | 2021 | FCD | Model-driven method | Directed graph | Estimation of vehicle density in every road section | Realistic simulation |
[47] | 2021 | Camera | Streaming-data driven method | CNN | Robust approach for queue length estimation | Comparative analysis to Yolo v3,4,5 |
[48] | 2021 | GPS | Data-driven method | Clustering | Congestion identification on global scale | Real data |
[49] | 2020 | Street view imagery | Model-driven method | GCN model | Model based prediction of congestion | Compared to Taxi GPS dataset |
[50] | 2020 | GPS | Data-driven method | Schatten p-norm model for speed-matrix completion | Monitor and visualize traffic dynamics via stochastic congestion maps | kNN, NMF |
[51] | 2020 | GPS | Data-driven method | Classification, STM | Traffic state estimation on citywide scale | Cross-validation |
[52] | 2019 | Camera | Streaming-data driven method | Background difference and AdaBoost classifier | Fast video-based queue length detection | Compared to traditional Adaboost-based method |
[53] | 2019 | GPS | Model-driven | Adaptive multi-kernel support vector machine | Short-term traffic flow prediction | Real data |
[54] | 2019 | GPS | Data-driven method | Classification | Turn-level congestion | Cross-validation with labeled data |
[55] | 2018 | GPS | Data-driven method | Data mining | Method for queue length, level of service and control delay estimation | None |
[56] | 2018 | Sensors, floating car data | Streaming-data driven method | Data fusion | Robust traffic state estimation approach | Realistic simulation |
[57] | 2017 | GPS | Data-driven method | SVM model | Short-term traffic prediction | Historical data-based model, moving average data-based model, ANN model, and k-NN model |
[58] | 2017 | GPS | Data-driven method | Classification | Detecting traffic congestion and incidents from real-time GPS traces | Cross-validation |
Paper | Year | Data Source | Type | Applied Method | Impact | Benchmark |
---|---|---|---|---|---|---|
[60] | 2019 | CVs, loop detectors | Model-driven method | Gipps’ car-following model-based CV signal control | Estimation vehicle position and speed | Intersection capacity utilization, EPICS |
[62] | 2020 | Simulation, CV | Data-driven method | TSE algorithms for partially connected networks | Better overall performance compared to existing signal plan | Real-world Vissim simulation |
[21] | 2018 | Simulation | Model-driven method | Two-lane cellular automation | Road capacity growth rate is determined by CAV characteristics | Validation against real-world dataset |
Paper | Year | Data Source | Type | Applied Method | Impact | Benchmark |
---|---|---|---|---|---|---|
[66] | 2020 | Simulation, real-world data | Model-driven method | Traffic signal coordination formulated as MINLP | Improved traffic signal performance | Compared to existing actuated signal timing |
[69] | 2018 | Real-world data | Model-driven method | Back-pressure based ATSC | Reduced average vehicle travelling time | Fixed-cycle and Backpressure algorithms |
[67] | 2017 | Simulation | Model-driven method | Distributed-coordinated methodology for signal timing optimization | The algorithm controlled queue length, maximized intersection throughput and reduced travel time | Tested on different scenarios in simulation |
[65] | 2017 | Real-world data | Model-driven method | Two-level coordination algorithm | Offset optimization along corridor | Actuated-coordinated signal control by Vissim |
[71] | 2017 | Simulation | Data-driven method | Platoon-based intersection scheduling algorithm | Reduce average delay per vehicle by up to | Evaluation in simulated environment |
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Majstorović, Ž.; Tišljarić, L.; Ivanjko, E.; Carić, T. Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review. Appl. Sci. 2023, 13, 4484. https://doi.org/10.3390/app13074484
Majstorović Ž, Tišljarić L, Ivanjko E, Carić T. Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review. Applied Sciences. 2023; 13(7):4484. https://doi.org/10.3390/app13074484
Chicago/Turabian StyleMajstorović, Željko, Leo Tišljarić, Edouard Ivanjko, and Tonči Carić. 2023. "Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review" Applied Sciences 13, no. 7: 4484. https://doi.org/10.3390/app13074484