Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network
Abstract
:1. Introduction
2. Related Work
3. Study Scenario and Data Description
4. Definition and Formulation of the ISTP
- Vehicle control delay at signalized intersections
- Conflict delay of right-turning vehicles
- Traffic capacity
- Construction of the optimization objective model
5. Methodology
5.1. General Denoising Autoencoder
5.2. Improving the NSGA-III Algorithm Embedded with a DAE
6. Test Verification and Analysis of Results
- Analysis of Pareto solutions
- Influence of the conflict delay
- Comparison of performance indices with another algorithm
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Dadashova, B.; Li, X.; Turner, S.; Koeneman, P. Multivariate Time Series Analysis of Traffic Congestion Measures in Urban Areas as They Relate to Socioeconomic Indicators. Socio-Econ. Plan. Sci. 2021, 75, 100877. [Google Scholar] [CrossRef]
- Wang, J.; Lu, L.L.; Peeta, S. Real-Time Deployable and Robust Cooperative Control Strategy for a Platoon of Connected and Autonomous Vehicles by Factoring Uncertain Vehicle Dynamics. Transp. Res. Part B Methodol. 2022, 163, 88–118. [Google Scholar] [CrossRef]
- Wang, J.; Wang, W.; Ren, G.; Yang, M. Worst-Case Traffic Assignment Model for Mixed Traffic Flow of Human-Driven Vehicles and Connected and Autonomous Vehicles by Factoring in the Uncertain Link Capacity. Transp. Res. C Emerg. Technol. 2022, 140, 103703. [Google Scholar] [CrossRef]
- Lana, I.; Del Ser, J.; Velez, M.; Vlahogianni, E.I. Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intell. Transp. Syst. Mag. 2018, 10, 93–109. [Google Scholar] [CrossRef]
- Manual on Uniform Traffic Control Devices for Streets and Highways: MUTCD NPA Webinar Recordings. Available online: https://mutcd.fhwa.dot.gov/mutcd_news.htm#dec_17_20 (accessed on 17 April 2023).
- Chen, P.; Zheng, F.; Lu, G.; Wang, Y. Comparison of Variability of Individual Vehicle Delay and Average Control Delay at Signalized Intersection. Transp. Res. Rec. J. Transp. Res. Board 2016, 2553, 128–137. [Google Scholar] [CrossRef]
- Traffic Signal Timing & Operations Strategies. Available online: https://ops.fhwa.dot.gov/arterial_mgmt/tst_ops.htm (accessed on 18 March 2023).
- Elena, S.P.; Roger, P.R. The Highway Capacity Manual: A Conceptual and Research History Volume 2: Signalized and Unsignalized Intersections; Springer: Cham, Switzerland, 2020; ISBN 978-3-030-34478-8. [Google Scholar]
- Zhao, D.; Dai, Y.; Zhen, Z. Computational Intelligence in Urban Traffic Signal Control: A Survey. IEEE Trans. Syst. Man Cybern. Part C. Appl. Rev. 2012, 42, 485–494. [Google Scholar] [CrossRef]
- Jalili, S.; Nallaperuma, S.; Keedwell, E.C.; Dawn, A.; Oakes-Ash, L. Application of Metaheuristics for Signal Optimisation in Transportation Networks: A Comprehensive Survey. Swarm Evol. Comput. 2021, 63, 100865. [Google Scholar] [CrossRef]
- Li, Z.; Yu, H.; Zhang, G.; Dong, S.; Xu, C. Network-Wide Traffic Signal Control Optimization using A Multi-agent Deep Reinforcement Learning. Transp. Res. C Emerg. Technol. 2021, 125, 103059. [Google Scholar] [CrossRef]
- Abdoos, M. A Cooperative Multi-Agent System for Traffic Signal Control Using Game Theory and Reinforcement Learning. IEEE Intell. Transp. Syst. Mag. 2020, 13, 6–16. [Google Scholar] [CrossRef]
- Rouphail, N.; Park, B.; Sacks, J. Direct Signal Timing Optimization: Strategy Development and Results. In Proceedings of the XI Pan American Conference in Traffic and Transportation Engineering, Gramado, Brazil, 1 January 2000. [Google Scholar]
- Garcia-Nieto, J.; Olivera, A.C.; Alba, E. Optimal Cycle Program of Traffic Lights with Particle Swarm Optimization. IEEE Trans. Evol. Comput. 2013, 17, 823–839. [Google Scholar] [CrossRef] [Green Version]
- Gao, K.; Zhang, Y.; Sadollah, A.; Su, R. Optimizing Urban Traffic Light Scheduling Problem using Harmony Search with Ensemble of Local Search. Appl. Soft Comput. 2016, 48, 359–372. [Google Scholar] [CrossRef]
- Jia, H.; Lin, Y.; Luo, Q.; Li, Y.; Miao, H. Multi-Objective Optimization of Urban Road Intersection Signal Timing Based on Particle Swarm Optimization Algorithm. Adv. Mech. Eng. 2019, 11, 168781401984249. [Google Scholar] [CrossRef] [Green Version]
- Mou, J. Intersection Traffic Control Based on Multi-objective Optimization. IEEE Access 2020, 8, 61615–61620. [Google Scholar] [CrossRef]
- Li, Y.; Yu, L.; Tao, S.; Chen, K. Multi-objective Optimization of Traffic Signal Timing for Oversaturated Intersection. Math. Probl. Eng. 2013, 2013, 182643. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Fan, X.; Yu, S.; Shan, A.; Fan, S.; Xiao, Y.; Dang, F. Intersection Signal Timing Optimization: A Multi-Objective Evolutionary Algorithm. Sustainability 2022, 14, 1506. [Google Scholar] [CrossRef]
- Li, X.; Sun, J.-Q. Signal Multiobjective Optimization for Urban Traffic Network. IEEE Trans. Intell. Transp. Syst. 2018, 19, 3529–3537. [Google Scholar] [CrossRef]
- Zhao, H.; Han, G.; Niu, X. The Signal Control Optimization of Road Intersections with Slow Traffic Based on Improved PSO. Mob. Netw. Appl. 2020, 25, 623–631. [Google Scholar] [CrossRef]
- Roshandeh, A.M.; Levinson, H.S.; Li, Z.; Patel, H.; Zhou, B. New Methodology for Intersection Signal Timing Optimization to Simultaneously Minimize Vehicle and Pedestrian Delays. J. Transp. Eng. 2014, 140, 4014009. [Google Scholar] [CrossRef]
- Li, M.; Yao, L.; Wang, Y. Analysis of the Macro Control Efficiency of Intersection Group Signals. J. Cent. South. Univ. 2020, 51, 1451–1460. (In Chinese) [Google Scholar]
- Liu, J.; Gong, M.; Miao, Q.; Wang, X.; Li, H. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization. IEEE Trans. Neural Netw. Learn. Syst. 2018, 29, 2450–2463. [Google Scholar] [CrossRef]
- Mouassa, S.; Bouktir, T. Multi-objective Ant Lion Optimization Algorithm to Solve Large-scale Multi-objective Optimal Reactive Power Dispatch Problem. COMPEL-Int. J. Comput. Math. Electr. Electron. Eng. 2019, 38, 304–324. [Google Scholar] [CrossRef]
- Yang, W.; Xu, K.; Ma, C.; Lian, J.; Jiang, X.; Zhou, Y.; Bin, L. A Novel Multi-objective Optimization Framework to Allocate Support Funds for Flash Flood Reduction Based on Multiple Vulnerability Assessment. J. Hydrol. 2021, 603, 127144. [Google Scholar] [CrossRef]
- Cao, C.J.; Li, C.D.; Yang, Q.; Liu, Y.; Qu, T. A Novel Multi-objective Programming Model of Relief Distribution for Sustainable Disaster Supply Chain in Large-scale Natural Disasters. J. Clean. Prod. 2018, 174, 1422–1435. [Google Scholar] [CrossRef]
- Aquino-Brítez, D.; Ortiz, A.; Ortega, J.; León, J.; Formoso, M.; Gan, J.Q.; Escobar, J.J. Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms. Sensors 2021, 21, 2096. [Google Scholar] [CrossRef] [PubMed]
- Mosallanezhad, B.; Chouhan, V.K.; Paydar, M.M.; Hajiaghaei-Keshteli, M. Disaster Relief Supply Chain Design for Personal Protection Equipment During the COVID-19 Pandemic. Appl. Soft Comput. 2021, 112, 107809. [Google Scholar] [CrossRef]
- Xiao, J.; Li, J.J.; Hong, X.X.; Huang, M.M.; Hu, X.M.; Tang, Y.; Huang, C.Q. An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization. Complexity 2018, 2018, 1–16. [Google Scholar] [CrossRef]
- Zhou, B.; Wu, Q. Decomposition-based Bi-objective Optimization for Sustainable Robotic Assembly Line Balancing Problems. J. Manuf. Syst. 2020, 55, 30–43. [Google Scholar] [CrossRef]
- Gu, Z.M.; Wang, G.G. Improving NSGA-III Algorithms with Information Feedback Models for Large-scale Many-objective Optimization. Future Generat. Comput. Syst. 2020, 107, 49–69. [Google Scholar] [CrossRef]
- Tian, Y.; Lu, C.; Zhang, X.; Tan, K.; Jin, Y. Solving Large-Scale Multiobjective Optimization Problems with Sparse Optimal Solutions via Unsupervised Neural Networks. IEEE Trans. Cybern. 2021, 51, 3115–3128. [Google Scholar] [CrossRef]
- Cao, L.; Xu, L.; Goodman, E.D.; Bao, C.; Zhu, S. Evolutionary Dynamic Multiobjective Optimization Assisted by a Support Vector Regression Predictor. IEEE Trans. Evol. Comput. 2020, 24, 305–319. [Google Scholar] [CrossRef]
- Guo, R.; Liu, J.; Qi, Y. An Innovative Signal Timing Strategy for Implementing Contraflow Left-Turn Lanes at Signalized Intersections with Split Phasing. Sustainability 2021, 13, 6307. [Google Scholar] [CrossRef]
- Jiang, X.; Yao, L.; Jin, Y.; Wu, R. Signal Control Method for Through and Left-Turn Shared Lane by Setting Left-Turn Waiting Area at Signalized Intersections. Sustainability 2021, 13, 13154. [Google Scholar] [CrossRef]
- Deb, K.; Jain, H. An Evolutionary Many-objective Optimization Algorithm using Reference Point-based Non Dominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Trans. Evol. Comput. 2014, 18, 577–601. [Google Scholar] [CrossRef]
- Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P.A. Extracting and Composing Robust Features with Denoising Autoencoders. In Proceedings of the 25th International Conference (ICML2008) on Machine Learning, Helsinki, Finland, 5–9 June 2008. [Google Scholar]
- Akcxelik, R.; Rouphail, N.M. Estimation of Delays at Traffic Signals for Variable Demand Conditions. Transp. Res. Part B Methodol. 1993, 27, 109–131. [Google Scholar] [CrossRef]
- Noroozi, R.; Hellinga, B. Distribution of Delay in Signalized Intersections: Day-to-day Variability in Peak-hour Volumes. J. Transp. Eng. 2012, 138, 1123–1132. [Google Scholar] [CrossRef]
- Hu, M. Empirical Study on the Statistical Distribution of Vehicle Arrival at Intersections. Road Traffic Saf. 2009, 2, 10–15. (In Chinese) [Google Scholar]
- Hinton, G.E.; Osindero, S.; Teh, Y.W. A Fast Learning Algorithm for Deep Belief Nets. Neural Comput. 2006, 18, 1527–1554. [Google Scholar] [CrossRef]
- Bengio, Y.; Lamblin, P.; Popovici, D.; Larochelle, H. Greedy Layer-wise Training of Deep Networks. Advances in Neural Information Processing Systems 19. In Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 4–7 December 2006. [Google Scholar]
- Das, I.; Dennis, J.E. Normal-boundary intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems. SIAM J. Optim. 1998, 8, 631–657. [Google Scholar] [CrossRef] [Green Version]
- Al-Turki, M.; Jamal, A.; Al-Ahmadi, H.M.; Al-Sughaiyer, M.A.; Zahid, M. On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia. Sustainability 2020, 12, 7394. [Google Scholar] [CrossRef]
- Zitzler, E.; Thiele, L. Multi-objective optimization using evolutionary algorithms: A comparative case study. In Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), Amsterdam, The Netherlands, 27–30 September 1998; pp. 292–301. [Google Scholar]
- Wu, J.; Azarm, S. Metrics for Quality Assessment of a Multi-objective Design Optimization Solution. J. Mech. Des. 2001, 123, 18–25. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.; Wang, C.; Liu, B.; He, C.; Cong, L.; Wan, S. Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles. IEEE Trans. Intell. Transp. Syst. 2023. early access. [Google Scholar] [CrossRef]
- Chen, C.; Yao, G.; Liu, L.; Pei, Q.; Song, H.; Dustdar, S. A Cooperative Vehicle-Infrastructure System for Road Hazards Detection with Edge Intelligence. IEEE Trans. Intell. Transp. Syst. 2023, 24, 5186–5198. [Google Scholar] [CrossRef]
Phase Sequence | ||||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
I1, I2 | E 1&W 2-ST 3 | E&W-L 4 | S&N-ST | S&N-L | --- | --- |
I3 | E-ST | W-ST | W-R 5 | W-L | S&N-ST | S&N-L |
I4 | E&W-ST | E&W-L | S&N-ST | --- | --- | --- |
No | Motor Vehicle Flow (veh/15 min) | Non-Motor Vehicle Flow (veh/15 min) | |||||||
---|---|---|---|---|---|---|---|---|---|
East | West | South | North | East | West | South | North | ||
I1 | straight | 88 | 113 | 104 | 157 | 55 | 28 | 210 | 142 |
left | 24 | 4 | 57 | 127 | 7 | 10 | 16 | 20 | |
right | 128 | 28 | 11 | 23 | 75 | 20 | 17 | 15 | |
I2 | straight | 121 | 128 | 142 | 155 | 46 | 80 | 114 | 137 |
left | 101 | 55 | 63 | 60 | 18 | 35 | 78 | 49 | |
right | 27 | 59 | 16 | 27 | 32 | 69 | 26 | 22 | |
I3 | straight | 53 | 78 | 105 | 154 | 48 | 33 | 140 | 243 |
left | 32 | 69 | 25 | 73 | 46 | 37 | 13 | 59 | |
right | 63 | 47 | 5 | 38 | 64 | 16 | 1 | 60 | |
I4 | straight | 130 | 170 | 164 | 123 | 85 | 77 | 154 | 158 |
left | 37 | 45 | 59 | 29 | 21 | 16 | 28 | 43 | |
right | 16 | 17 | 6 | 19 | 15 | 7 | 15 | 13 |
Notation | Meaning |
---|---|
phase index at intersections, ; | |
approach index at intersections, ; | |
intersection index, ; | |
the total vehicle delay in a signal cycle; | |
the signal cycle of the intersection; | |
the effective green time of the phase; | |
the motor traffic volume of the approach at the phase; | |
the traffic volume of straight non-motor vehicles at the approach of the phase; | |
the lane saturation flow of the lane at the phase; | |
the traffic saturation of the lane at the phase; | |
The safe time interval, and the value here is 5 s; | |
The number of vehicles in a motor vehicle fleet that can be accommodated in a right-turn lane. | |
The minimum headway of a right-turning vehicle passing the conflict point, which is 2 s; | |
The duration of the random dissipation process of the approach of the phase, ; | |
The duration of the centralized dissipation process of the approach of the phase, . |
or | Figure 4a | Figure 4b | Figure 4c | Figure 5a | Figure 5b | Figure 5c |
---|---|---|---|---|---|---|
(delay 1) | 198.54 | × | × | 465.62 | × | × |
(delay 1) | 842.17 | × | × | 924.94 | × | × |
(Capacity) | 166.27 | × | × | 618.34 | × | × |
(Capacity) | 1049.28 | × | × | 1388.86 | × | × |
(delay 2) | × | 4.701 | × | × | 18.765 | × |
(delay 2) | × | 15.223 | × | × | 30.851 | × |
5.231 × 105 | 3.390 × 104 | 4.798 × 103 | 9.347 × 105 | 1.043 × 104 | 9.003 × 103 | |
3.008 × 105 | 4.732 × 103 | 4.270 × 103 | 5.601 × 105 | 7.451 × 103 | 1.228 × 104 |
PI 4 | Current Scheme | The Proposed Method | ||
---|---|---|---|---|
Actual | Assumed | |||
delay 1 | 525.95 | 180.13 | 244.10 | |
I1 | capacity | 524.00 | 704.00 | 400.00 |
delay 2 | 5.48 | 2.14 | 0.93 | |
delay 1 | 471.54 | 263.24 | 214.94 | |
I2 | capacity | 528.00 | 400.00 | 760.00 |
delay 2 | 3.5900 | 0.61 | 0.61 | |
delay 1 | 379.14 | 160.78 | 209.09 | |
I3 | capacity | 756.25 | 1015.00 | 515.00 |
delay 2 | 4.59 | 0.47 | 0.98 | |
delay 1 | 393.08 | 214.38 | 135.75 | |
I4 | capacity | 493.42 | 300 | 890.00 |
delay 2 | 4.24 | 0.32 | 3.26 | |
delay 1 | 1769.71 | 818.53 | 803.88 | |
Itotal 3 | capacity | 2301.67 | 2419.00 | 2565.00 |
delay 2 | 17.9 | 3.54 | 5.78 |
PI 3 | CS | HCNSGA-III | NSGAIII-DAE | RPD(%) HCNSGA-III | RPD(%) NSGAIII-DAE | ||||
---|---|---|---|---|---|---|---|---|---|
Opt | Ave | Opt | Ave | Opt | Ave | Opt | Ave | ||
delay 1 | 1769.7 | 1372.7 | 1889.2 | 776.2 | 826.3 | −22.4 | 6.8 | −56.1 | −53.3 |
capacity | 2301.7 | 2998.7 | 2698.9 | 2566.8 | 2292.8 | 30.3 | 17.3 | 11.5 | −0.4 |
delay 2 | 17.9 | 7.7 | 13.7 | 2.1 | 4.26 | −57.0 | −23.5 | −88.3 | −76.2 |
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Zhang, X.; Fan, X.; Yu, S.; Shan, A.; Men, R. Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network. Sensors 2023, 23, 6303. https://doi.org/10.3390/s23146303
Zhang X, Fan X, Yu S, Shan A, Men R. Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network. Sensors. 2023; 23(14):6303. https://doi.org/10.3390/s23146303
Chicago/Turabian StyleZhang, Xinghui, Xiumei Fan, Shunyuan Yu, Axida Shan, and Rui Men. 2023. "Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network" Sensors 23, no. 14: 6303. https://doi.org/10.3390/s23146303
APA StyleZhang, X., Fan, X., Yu, S., Shan, A., & Men, R. (2023). Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network. Sensors, 23(14), 6303. https://doi.org/10.3390/s23146303