Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments

Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce co...

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Veröffentlicht in:	Mathematics (Basel) Jg. 11; H. 6; S. 1540
Hauptverfasser:	Agafonov, Anton, Yumaganov, Alexander, Myasnikov, Vladislav
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.03.2023
Schlagworte:	Adaptive algorithms Adaptive control adaptive traffic signal control Air pollution Artificial neural networks Autonomous vehicles Communication Computer simulation connected and automated vehicle Connectivity Control Control algorithms Control methods Control theory Cooperative control Efficiency Energy consumption Flow control Fuel consumption GDP Gross Domestic Product Infrastructure Intelligent vehicle-highway systems Interchanges and intersections Internet of Things Mathematics Methods Neural networks Optimization Predictive control R&D Research & development Roads Sensors Simulation Smart cities Traffic accidents & safety Traffic congestion Traffic control Traffic flow Traffic signals Traffic signs and signals Trajectory control Transportation systems Travel time Russia United Kingdom > UK United States > US
ISSN:	2227-7390, 2227-7390
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Abstract	Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.
AbstractList	Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods.
Audience	Academic
Author	Myasnikov, Vladislav Agafonov, Anton Yumaganov, Alexander
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Cites_doi	10.1109/TITS.2021.3058193 10.1016/j.trb.2019.03.002 10.1109/TCYB.2019.2904742 10.1007/978-981-16-6289-8 10.1016/j.procs.2018.04.008 10.1109/TSC.2020.3025993 10.1016/j.trc.2021.103416 10.3390/math10162832 10.1080/0305215X.2014.995644 10.1109/TITS.2021.3107258 10.3390/math10030388 10.1109/TVT.2021.3069921 10.1109/TITS.2022.3195221 10.1007/BF00992698 10.1109/IVS.2017.7995947 10.1109/SUMMA57301.2022.9973827 10.1109/TITS.2008.2006812 10.3182/20120912-3-BG-2031.00044 10.1109/TVT.2021.3056457 10.1109/TITS.2020.3008612 10.3390/math10020282 10.3390/math9162001 10.1016/j.trc.2022.103918 10.1109/TITS.2013.2277737 10.1109/LRA.2020.3014010 10.1016/j.trb.2016.06.010 10.1016/j.grets.2022.100002 10.1007/978-1-4614-6243-9 10.1007/s13177-023-00346-4 10.1109/ITSC.2018.8569938 10.1016/j.trb.2016.05.007 10.3103/S8756699022050016 10.3103/S1060992X19030081 10.1109/ICISS49785.2020.9315970 10.3390/app10248933 10.1109/SICE.2016.7749173 10.1109/TIV.2022.3181330 10.1109/TITS.2017.2741507 10.1007/978-1-4471-5113-5 10.3390/math10193507 10.3390/su14031542 10.3103/S1060992X2203002X 10.1109/TITS.2014.2376772 10.3390/math10091599 10.1109/ICTLE55577.2022.9901894 10.1109/CCDC52312.2021.9601529 10.1109/TITS.2019.2901791 10.3182/20080706-5-KR-1001.02208 10.1109/JPROC.2003.819610
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References	Guo (ref_60) 2021; 132 Agafonov (ref_11) 2019; 28 Chu (ref_50) 2019; 21 ref_54 ref_53 ref_19 ref_18 ref_16 Genders (ref_52) 2018; 130 ref_15 ref_59 Tajalli (ref_62) 2022; 23 Wang (ref_14) 2023; 8 Agafonov (ref_45) 2022; 31 Little (ref_22) 1981; 795 Papageorgiou (ref_23) 2003; 91 Tan (ref_40) 2020; 50 ref_25 Haydari (ref_8) 2022; 23 ref_66 ref_21 ref_65 ref_20 ref_64 ref_63 Lv (ref_12) 2023; 1 ref_29 ref_28 ref_27 ref_26 Zhou (ref_57) 2017; 95 Agafonov (ref_17) 2022; 58 Yu (ref_55) 2019; 122 ref_36 ref_35 Gupta (ref_13) 2022; 15 ref_32 Li (ref_34) 2014; 15 Aslani (ref_49) 2019; 172 ref_39 ref_37 Watkins (ref_38) 1992; 8 Wang (ref_56) 2022; 145 Balid (ref_3) 2018; 19 Ribeiro (ref_24) 2016; 48 Ma (ref_51) 2022; 23 Yazici (ref_31) 2008; 41 Kamal (ref_30) 2012; 45 ref_46 ref_44 Ma (ref_58) 2017; 95 ref_42 Gama (ref_9) 2015; 16 Sarvi (ref_10) 2008; 9 ref_41 Du (ref_61) 2021; 70 ref_1 ref_2 Shen (ref_33) 2020; 5 ref_48 Boukerche (ref_43) 2022; 71 ref_5 ref_4 Zhu (ref_47) 2022; 20 ref_7 ref_6
References_xml	– volume: 23 start-page: 6525 year: 2022 ident: ref_62 article-title: Traffic Signal Timing and Trajectory Optimization in a Mixed Autonomy Traffic Stream publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2021.3058193 – volume: 122 start-page: 416 year: 2019 ident: ref_55 article-title: Managing Connected and Automated Vehicles at Isolated Intersections: From Reservation- to Optimization-Based Methods publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2019.03.002 – volume: 50 start-page: 2687 year: 2020 ident: ref_40 article-title: Cooperative Deep Reinforcement Learning for Large-Scale Traffic Grid Signal Control publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2904742 – ident: ref_37 doi: 10.1007/978-981-16-6289-8 – volume: 130 start-page: 26 year: 2018 ident: ref_52 article-title: Evaluating Reinforcement Learning State Representations for Adaptive Traffic Signal Control publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2018.04.008 – volume: 15 start-page: 1912 year: 2022 ident: ref_13 article-title: Secure V2V and V2I Communication in Intelligent Transportation Using Cloudlets publication-title: IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2020.3025993 – volume: 132 start-page: 103416 year: 2021 ident: ref_60 article-title: DRL-TP3: A Learning and Control Framework for Signalized Intersections with Mixed Connected Automated Traffic publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2021.103416 – ident: ref_29 doi: 10.3390/math10162832 – ident: ref_65 – ident: ref_39 – volume: 48 start-page: 199 year: 2016 ident: ref_24 article-title: The Fully Actuated Traffic Control Problem Solved by Global Optimization and Complementarity publication-title: Eng. Optim. doi: 10.1080/0305215X.2014.995644 – volume: 23 start-page: 11789 year: 2022 ident: ref_51 article-title: A Deep Reinforcement Learning Approach to Traffic Signal Control with Temporal Traffic Pattern Mining publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2021.3107258 – ident: ref_53 doi: 10.3390/math10030388 – ident: ref_1 – ident: ref_35 – volume: 71 start-page: 1187 year: 2022 ident: ref_43 article-title: A Novel Reinforcement Learning-Based Cooperative Traffic Signal System Through Max-Pressure Control publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3069921 – ident: ref_28 doi: 10.1109/TITS.2022.3195221 – volume: 8 start-page: 279 year: 1992 ident: ref_38 article-title: Q-Learning publication-title: Mach. Learn. doi: 10.1007/BF00992698 – ident: ref_15 doi: 10.1109/IVS.2017.7995947 – ident: ref_16 doi: 10.1109/SUMMA57301.2022.9973827 – volume: 9 start-page: 580 year: 2008 ident: ref_10 article-title: Using ITS to Improve the Capacity of Freeway Merging Sections by Transferring Freight Vehicles publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2008.2006812 – volume: 45 start-page: 221 year: 2012 ident: ref_30 article-title: Control of Traffic Signals in a Model Predictive Control Framework publication-title: IFAC Proc. Vol. doi: 10.3182/20120912-3-BG-2031.00044 – volume: 70 start-page: 2089 year: 2021 ident: ref_61 article-title: A Coupled Vehicle-Signal Control Method at Signalized Intersections in Mixed Traffic Environment publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3056457 – volume: 23 start-page: 11 year: 2022 ident: ref_8 article-title: Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2020.3008612 – ident: ref_54 doi: 10.3390/math10020282 – ident: ref_6 doi: 10.3390/math9162001 – volume: 145 start-page: 103918 year: 2022 ident: ref_56 article-title: Connected Automated Vehicle Trajectory Optimization along Signalized Arterial: A Decentralized Approach under Mixed Traffic Environment publication-title: Transp. Res. Part C Emerg. Technol. doi: 10.1016/j.trc.2022.103918 – volume: 15 start-page: 425 year: 2014 ident: ref_34 article-title: A Survey of Traffic Control with Vehicular Communications publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2013.2277737 – ident: ref_41 – volume: 795 start-page: 40 year: 1981 ident: ref_22 article-title: MAXBAND: A Program for Setting Signals on Arteries and Triangular Networks publication-title: Transp. Res. Rec. J. Transp. Res. Board – volume: 5 start-page: 6247 year: 2020 ident: ref_33 article-title: Cooperative Comfortable-Driving at Signalized Intersections for Connected and Automated Vehicles publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2020.3014010 – ident: ref_66 – volume: 95 start-page: 421 year: 2017 ident: ref_58 article-title: Parsimonious Shooting Heuristic for Trajectory Design of Connected Automated Traffic Part II: Computational Issues and Optimization publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2016.06.010 – volume: 1 start-page: 100002 year: 2023 ident: ref_12 article-title: Impacts of Intelligent Transportation Systems on Energy Conservation and Emission Reduction of Transport Systems: A Comprehensive Review publication-title: Green Technol. Sustain. doi: 10.1016/j.grets.2022.100002 – ident: ref_26 doi: 10.1007/978-1-4614-6243-9 – ident: ref_42 doi: 10.1007/s13177-023-00346-4 – volume: 20 start-page: 734 year: 2022 ident: ref_47 article-title: Intelligent Traffic Light via Policy-Based Deep Reinforcement Learning publication-title: Int. J. Intell. Transp. Syst. Res. – ident: ref_18 doi: 10.1109/ITSC.2018.8569938 – volume: 95 start-page: 394 year: 2017 ident: ref_57 article-title: Parsimonious Shooting Heuristic for Trajectory Design of Connected Automated Traffic Part I: Theoretical Analysis with Generalized Time Geography publication-title: Transp. Res. Part B Methodol. doi: 10.1016/j.trb.2016.05.007 – ident: ref_7 – volume: 58 start-page: 503 year: 2022 ident: ref_17 article-title: Adaptive Traffic Signal Control Based on Neural Network Prediction of Weighted Traffic Flow publication-title: Optoelectron. Instrum. Data Process. doi: 10.3103/S8756699022050016 – volume: 28 start-page: 222 year: 2019 ident: ref_11 article-title: Bus Arrival Time Prediction Using Recurrent Neural Network with LSTM Architecture publication-title: Opt. Mem. Neural Netw. doi: 10.3103/S1060992X19030081 – volume: 172 start-page: 289 year: 2019 ident: ref_49 article-title: Developing Adaptive Traffic Signal Control by Actor–Critic and Direct Exploration Methods publication-title: Proc. Inst. Civ. Eng.-Transp. – ident: ref_27 doi: 10.1109/ICISS49785.2020.9315970 – ident: ref_20 doi: 10.3390/app10248933 – ident: ref_32 doi: 10.1109/SICE.2016.7749173 – volume: 8 start-page: 583 year: 2023 ident: ref_14 article-title: An Event-Triggered Scheme for State Estimation of Preceding Vehicles under Connected Vehicle Environment publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2022.3181330 – volume: 19 start-page: 1784 year: 2018 ident: ref_3 article-title: Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2017.2741507 – ident: ref_25 doi: 10.1007/978-1-4471-5113-5 – ident: ref_63 – ident: ref_4 doi: 10.3390/math10193507 – ident: ref_44 – ident: ref_21 – ident: ref_59 doi: 10.3390/su14031542 – volume: 31 start-page: 277 year: 2022 ident: ref_45 article-title: Hybrid Prediction-Based Approach for Traffic Signal Control Problem publication-title: Opt. Mem. Neural Netw. doi: 10.3103/S1060992X2203002X – volume: 16 start-page: 1636 year: 2015 ident: ref_9 article-title: Improving Mass Transit Operations by Using AVL-Based Systems: A Survey publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2014.2376772 – ident: ref_5 doi: 10.3390/math10091599 – ident: ref_2 – ident: ref_46 – ident: ref_48 doi: 10.1109/ICTLE55577.2022.9901894 – ident: ref_64 – ident: ref_36 doi: 10.1109/CCDC52312.2021.9601529 – volume: 21 start-page: 1086 year: 2019 ident: ref_50 article-title: Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2019.2901791 – ident: ref_19 – volume: 41 start-page: 13058 year: 2008 ident: ref_31 article-title: A Model Predictive Control Approach for Decentralized Traffic Signal Control publication-title: IFAC Proc. Vol. doi: 10.3182/20080706-5-KR-1001.02208 – volume: 91 start-page: 2043 year: 2003 ident: ref_23 article-title: Review of Road Traffic Control Strategies publication-title: Proc. IEEE doi: 10.1109/JPROC.2003.819610
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SubjectTerms	Adaptive algorithms Adaptive control adaptive traffic signal control Air pollution Artificial neural networks Autonomous vehicles Communication Computer simulation connected and automated vehicle Connectivity Control Control algorithms Control methods Control theory Cooperative control Efficiency Energy consumption Flow control Fuel consumption GDP Gross Domestic Product Infrastructure Intelligent vehicle-highway systems Interchanges and intersections Internet of Things Mathematics Methods Neural networks Optimization Predictive control R&D Research & development Roads Sensors Simulation Smart cities Traffic accidents & safety Traffic congestion Traffic control Traffic flow Traffic signals Traffic signs and signals Trajectory control Transportation systems Travel time
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Title	Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments
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