doi: 10.3934/dcdss.2020251

Traffic signal fuzzy control algorithm for urban intersection based on vehicle-road cooperative environment

School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430022, China

* Corresponding author: Jianhua Zhang

Received  March 2019 Published  January 2020

Traditionally, when using simple fuzzy control algorithm to control traffic signals at urban intersections, phase sequence optimization control is not carried out in view of vehicle-road coordination environment, and traffic lights cannot be reasonably arranged, resulting in a long queue of vehicles at intersections, resulting in traffic congestion. Therefore, a fuzzy control algorithm of urban intersection traffic signal is proposed under the vehicle-road cooperative environment. Based on the traffic signal model under the vehicle-road cooperative environment, the fuzzy control algorithm based on phase sequence optimization is obtained by combining the fuzzy control algorithm with the phase sequence optimization algorithm, and the fuzzy control algorithm steps are controlled reasonably according to the phase sequence optimization. The experimental results show that the average waiting time of vehicles controlled by the proposed algorithm is only 21s and 23s in the afternoon rush hours, which is an effective traffic signal control algorithm for urban intersections.

Citation: Jianhua Zhang. Traffic signal fuzzy control algorithm for urban intersection based on vehicle-road cooperative environment. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020251
References:
[1]

Y. Ahmad, U. Ali and M. Bilal, e. al., Some new standard graphs labeled by 3-total edge product cordial labeling, Applied Mathematics and Nonlinear Sciences, 2 (2017), 61-72. doi: 10.21042/AMNS.2017.1.00005.  Google Scholar

[2]

M. CollottaL. L. Bello and G. Pau, A novel approach for dynamic traffic lights management based on wireless sensor networks and multiple fuzzy logic controllers, Expert Systems with Applications, 42 (2015), 5403-5415.  doi: 10.1016/j.eswa.2015.02.011.  Google Scholar

[3]

W. Gao and W. F. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones, Journal of Difference Equations and Applications, 23 (2017), 100-109.  doi: 10.1080/10236198.2016.1197214.  Google Scholar

[4]

W. Gao, L. Zhu and Y. Guo, e. al., Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent and Fuzzy Systems, 33 (2017), 3153-3163. Google Scholar

[5]

M. Ghorbani and F. N. Larki, On the spectrum of finite cayley graphs, Journal of Discrete Mathematical Sciences and Cryptography, 21 (2018), 83-112.  doi: 10.1080/09720529.2018.1449797.  Google Scholar

[6]

S. M. Hosamani, B. B. Kulkarni and R. G. Boli, e. al., Qspr analysis of certain graph theocratical matrices and their corresponding energy, Applied Mathematics and Nonlinear Sciences, 2 (2017), 131-150. doi: 10.21042/AMNS.2017.1.00011.  Google Scholar

[7]

K. JangH. Kim and I. G. Jang, Traffic signal optimization for oversaturated urban networks: Queue growth equalization, IEEE Transactions on Intelligent Transportation Systems, 16 (2015), 2121-2128.  doi: 10.1109/TITS.2015.2398896.  Google Scholar

[8]

C. J. Jin, W. Wang and R. Jiang, Four-phase or two-phase signal plan? a study on four-leg intersection by cellular automaton simulations, International Journal of Modern Physics C, 27 (2016), 1650032, 14 pp. doi: 10.1142/S0129183116500327.  Google Scholar

[9]

P. Jin and J. Li, Bldc fuzzy pid control system based on improved genetic algorithm, Automation and Instrumentation. Google Scholar

[10]

Q. LongJ. F. Zhang and Z. M. Zhou, Multi-objective traffic signal control model for traffic management, Transportation Letters, 7 (2015), 196-200.  doi: 10.1179/1942787515Y.0000000002.  Google Scholar

[11]

S. M. Odeh, A. M. Mora and M. N. Moreno, A hybrid fuzzy genetic algorithm for an adaptive traffic signal system, Advances in Fuzzy Systems, 2015 (2015), Article ID 378156, 11 pages. doi: 10.1155/2015/378156.  Google Scholar

[12]

P. PaschalidisJ. Nuckelt and K. Mahler, Investigation of mpc correlation and angular characteristics in the vehicular urban intersection channel using channel sounding and ray tracing, IEEE Transactions on Vehicular Technology, 65 (2016), 5874-5886.  doi: 10.1109/TVT.2015.2476512.  Google Scholar

[13]

M. Salman, S. Ozdemir and F. Celebi, Fuzzy traffic control with vehicle-to-everything communication, Sensors, 18 (2018), 368. doi: 10.3390/s18020368.  Google Scholar

[14]

N. Sharma and S. Sahu, Review of traffic signal control based on fuzzy logic, International Journal of Computer Applications, 145 (2016), 18-22.  doi: 10.5120/ijca2016910869.  Google Scholar

[15]

M. J. S. Shiri and H. R. Maleki, Maximum green time settings for traffic-actuated signal control at isolated intersections using fuzzy logic, International Journal of Fuzzy Systems, 19 (2017), 247-256.  doi: 10.1007/s40815-016-0143-7.  Google Scholar

[16]

M. J. Shirvani and H. R. Maleki, Enhanced variable bandwidth progression optimisation model in arterial traffic signal control, Iet Intelligent Transport Systems, 10 (2016), 396-405.  doi: 10.1049/iet-its.2015.0061.  Google Scholar

[17]

A. VanithaA. Subramani and P. Balamurugan, Game theory and fuzzy based back off algorithm for mac protocol for multi traffic flows in ad hoc networks, Wireless Networks, 23 (2017), 1993-2004.   Google Scholar

[18]

C. WenZ. Hui and L. Tao, Intelligent traffic signal controller based on type-2 fuzzy logic and nsgaii, Journal of Intelligent and Fuzzy Systems, 29 (2015), 2611-2618.  doi: 10.3233/IFS-151964.  Google Scholar

[19]

D. Yu, H. Zhu and W. Han, e. al., Dynamic multi agent-based management and load frequency control of pv/fuel cell/ wind turbine/ chp in autonomous microgrid system, Energy, 173 (2019), 554-568. doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

L. I. Yuze, X. Pei and H. Wang, Parallel control strategy for aviation static converters based on virtual negative resistance, Journal of Power Supply. Google Scholar

[21]

Z. Zahreddine, On positive para-odd and complex discrete reactance functions, Journal of Interdisciplinary Mathematics, 21 (2018), 243-251.  doi: 10.1080/09720502.2017.1367525.  Google Scholar

[22]

N. T. Zhang, K. L. Zhao and G. L. Liu, Thought on constructing the integrated space-terrestrial information network, Journal of China Academy of Electronics and Information Technology. Google Scholar

show all references

References:
[1]

Y. Ahmad, U. Ali and M. Bilal, e. al., Some new standard graphs labeled by 3-total edge product cordial labeling, Applied Mathematics and Nonlinear Sciences, 2 (2017), 61-72. doi: 10.21042/AMNS.2017.1.00005.  Google Scholar

[2]

M. CollottaL. L. Bello and G. Pau, A novel approach for dynamic traffic lights management based on wireless sensor networks and multiple fuzzy logic controllers, Expert Systems with Applications, 42 (2015), 5403-5415.  doi: 10.1016/j.eswa.2015.02.011.  Google Scholar

[3]

W. Gao and W. F. Wang, The fifth geometric-arithmetic index of bridge graph and carbon nanocones, Journal of Difference Equations and Applications, 23 (2017), 100-109.  doi: 10.1080/10236198.2016.1197214.  Google Scholar

[4]

W. Gao, L. Zhu and Y. Guo, e. al., Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent and Fuzzy Systems, 33 (2017), 3153-3163. Google Scholar

[5]

M. Ghorbani and F. N. Larki, On the spectrum of finite cayley graphs, Journal of Discrete Mathematical Sciences and Cryptography, 21 (2018), 83-112.  doi: 10.1080/09720529.2018.1449797.  Google Scholar

[6]

S. M. Hosamani, B. B. Kulkarni and R. G. Boli, e. al., Qspr analysis of certain graph theocratical matrices and their corresponding energy, Applied Mathematics and Nonlinear Sciences, 2 (2017), 131-150. doi: 10.21042/AMNS.2017.1.00011.  Google Scholar

[7]

K. JangH. Kim and I. G. Jang, Traffic signal optimization for oversaturated urban networks: Queue growth equalization, IEEE Transactions on Intelligent Transportation Systems, 16 (2015), 2121-2128.  doi: 10.1109/TITS.2015.2398896.  Google Scholar

[8]

C. J. Jin, W. Wang and R. Jiang, Four-phase or two-phase signal plan? a study on four-leg intersection by cellular automaton simulations, International Journal of Modern Physics C, 27 (2016), 1650032, 14 pp. doi: 10.1142/S0129183116500327.  Google Scholar

[9]

P. Jin and J. Li, Bldc fuzzy pid control system based on improved genetic algorithm, Automation and Instrumentation. Google Scholar

[10]

Q. LongJ. F. Zhang and Z. M. Zhou, Multi-objective traffic signal control model for traffic management, Transportation Letters, 7 (2015), 196-200.  doi: 10.1179/1942787515Y.0000000002.  Google Scholar

[11]

S. M. Odeh, A. M. Mora and M. N. Moreno, A hybrid fuzzy genetic algorithm for an adaptive traffic signal system, Advances in Fuzzy Systems, 2015 (2015), Article ID 378156, 11 pages. doi: 10.1155/2015/378156.  Google Scholar

[12]

P. PaschalidisJ. Nuckelt and K. Mahler, Investigation of mpc correlation and angular characteristics in the vehicular urban intersection channel using channel sounding and ray tracing, IEEE Transactions on Vehicular Technology, 65 (2016), 5874-5886.  doi: 10.1109/TVT.2015.2476512.  Google Scholar

[13]

M. Salman, S. Ozdemir and F. Celebi, Fuzzy traffic control with vehicle-to-everything communication, Sensors, 18 (2018), 368. doi: 10.3390/s18020368.  Google Scholar

[14]

N. Sharma and S. Sahu, Review of traffic signal control based on fuzzy logic, International Journal of Computer Applications, 145 (2016), 18-22.  doi: 10.5120/ijca2016910869.  Google Scholar

[15]

M. J. S. Shiri and H. R. Maleki, Maximum green time settings for traffic-actuated signal control at isolated intersections using fuzzy logic, International Journal of Fuzzy Systems, 19 (2017), 247-256.  doi: 10.1007/s40815-016-0143-7.  Google Scholar

[16]

M. J. Shirvani and H. R. Maleki, Enhanced variable bandwidth progression optimisation model in arterial traffic signal control, Iet Intelligent Transport Systems, 10 (2016), 396-405.  doi: 10.1049/iet-its.2015.0061.  Google Scholar

[17]

A. VanithaA. Subramani and P. Balamurugan, Game theory and fuzzy based back off algorithm for mac protocol for multi traffic flows in ad hoc networks, Wireless Networks, 23 (2017), 1993-2004.   Google Scholar

[18]

C. WenZ. Hui and L. Tao, Intelligent traffic signal controller based on type-2 fuzzy logic and nsgaii, Journal of Intelligent and Fuzzy Systems, 29 (2015), 2611-2618.  doi: 10.3233/IFS-151964.  Google Scholar

[19]

D. Yu, H. Zhu and W. Han, e. al., Dynamic multi agent-based management and load frequency control of pv/fuel cell/ wind turbine/ chp in autonomous microgrid system, Energy, 173 (2019), 554-568. doi: 10.1016/j.energy.2019.02.094.  Google Scholar

[20]

L. I. Yuze, X. Pei and H. Wang, Parallel control strategy for aviation static converters based on virtual negative resistance, Journal of Power Supply. Google Scholar

[21]

Z. Zahreddine, On positive para-odd and complex discrete reactance functions, Journal of Interdisciplinary Mathematics, 21 (2018), 243-251.  doi: 10.1080/09720502.2017.1367525.  Google Scholar

[22]

N. T. Zhang, K. L. Zhao and G. L. Liu, Thought on constructing the integrated space-terrestrial information network, Journal of China Academy of Electronics and Information Technology. Google Scholar

Figure 1.  Intersection set model
Figure 2.  Four phase of intersection signal
Figure 3.  The combination of East and West can be combined
Figure 4.  Fuzzy control algorithm structure
Figure 5.  Controller structure
Figure 6.  Average vehicle delay time curve
Figure 7.  Traffic flow distribution
Figure 8.  Comparison of average waiting vehicle length
Figure 9.  Comparison of average waiting time of vehicles
Figure 10.  Comparison of average waiting time for pedestrians
Table 1.  Partial rules of phase sequence optimization module
Ncar Rtime Stime FNCar Urgency
1 L Z
2 VL L S VH
3 L M S H
4 M S H
5 M L S Z
6 S S S M
7 Z Z S M
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
Ncar Rtime Stime FNCar Urgency
1 L Z
2 VL L S VH
3 L M S H
4 M S H
5 M L S Z
6 S S S M
7 Z Z S M
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
Table 2.  Part rule of green light judgement module
RNCar Orate FNCar SDGP
1 L Y
2 Z Z S Y
3 S H S M
4 L H S N
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
RNCar Orate FNCar SDGP
1 L Y
2 Z Z S Y
3 S H S M
4 L H S N
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
Table 3.  Partial rules of phase switching module
SDGP Urgency Decision
1 N H N
2 M M N
3 Y M Y
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
SDGP Urgency Decision
1 N H N
2 M M N
3 Y M Y
$ \ldots $ $ \ldots $ $ \ldots $ $ \ldots $
Table 4.  Comparison of simulation results
Simulation order Average vehicle flow in North and South East West average traffic volume Average vehicle delay time
(s)
Result comparison
(%)
Curve (1) Curve (2)
(1) 1 vehicle /3s 1 vehicle /2.5s 6.24 5.23 16.20%
(2) 1 vehicle /3s 1 vehicle /3s 6.05 5.10 15.70%
(3) 1 vehicle /4s 1 vehicle /3s 5.80 5.15 11.20%
(4) 1 vehicle /5s 1 vehicle /3s 5.83 5.05 13.40%
Simulation order Average vehicle flow in North and South East West average traffic volume Average vehicle delay time
(s)
Result comparison
(%)
Curve (1) Curve (2)
(1) 1 vehicle /3s 1 vehicle /2.5s 6.24 5.23 16.20%
(2) 1 vehicle /3s 1 vehicle /3s 6.05 5.10 15.70%
(3) 1 vehicle /4s 1 vehicle /3s 5.80 5.15 11.20%
(4) 1 vehicle /5s 1 vehicle /3s 5.83 5.05 13.40%
Table 5.  Vehicle flow data at 29 intersection of Nanshan District, Shenzhen
University crossing (vehicle / hour) East import South import West import North import
Left turn 141 161 160 192
Straight line 558 433 594 651
Right turn 126 52 131 86
Small-scale 618 484 664 697
Medium-sized 124 97 133 139
Large 83 64 88 93
University crossing (vehicle / hour) East import South import West import North import
Left turn 141 161 160 192
Straight line 558 433 594 651
Right turn 126 52 131 86
Small-scale 618 484 664 697
Medium-sized 124 97 133 139
Large 83 64 88 93
Table 6.  Traffic volume data of Taoyuan intersection in Nanshan District, Shenzhen in December 29th
Taoyuan intersection (vehicle / hour) East import South import West import North import
Left turn 0 80 0 153
Straight line 113 476 123 599
Right turn 170 176 165 171
Small-scale 212 549 216 693
Medium-sized 43 110 43 138
Large 28 73 29 92
Taoyuan intersection (vehicle / hour) East import South import West import North import
Left turn 0 80 0 153
Straight line 113 476 123 599
Right turn 170 176 165 171
Small-scale 212 549 216 693
Medium-sized 43 110 43 138
Large 28 73 29 92
Table 7.  Comparison of two algorithms
Control mode Saturation Average delay (s / vehicle) Maximum queue length (vehicle) Average parking rate Fuel consumption (ml/ vehicles)
Timing control algorithm 1.145 40.73 15 0.842 8.961
Algorithm in this paper 0.913 34.48 15 0.594 6.585
Control mode Saturation Average delay (s / vehicle) Maximum queue length (vehicle) Average parking rate Fuel consumption (ml/ vehicles)
Timing control algorithm 1.145 40.73 15 0.842 8.961
Algorithm in this paper 0.913 34.48 15 0.594 6.585
[1]

Paola Goatin. Traffic flow models with phase transitions on road networks. Networks & Heterogeneous Media, 2009, 4 (2) : 287-301. doi: 10.3934/nhm.2009.4.287

[2]

Sarah Ibri. An efficient distributed optimization and coordination protocol: Application to the emergency vehicle management. Journal of Industrial & Management Optimization, 2015, 11 (1) : 41-63. doi: 10.3934/jimo.2015.11.41

[3]

Yinfei Li, Shuping Chen. Optimal traffic signal control for an $M\times N$ traffic network. Journal of Industrial & Management Optimization, 2008, 4 (4) : 661-672. doi: 10.3934/jimo.2008.4.661

[4]

Lino J. Alvarez-Vázquez, Néstor García-Chan, Aurea Martínez, Miguel E. Vázquez-Méndez. Optimal control of urban air pollution related to traffic flow in road networks. Mathematical Control & Related Fields, 2018, 8 (1) : 177-193. doi: 10.3934/mcrf.2018008

[5]

Mauro Garavello, Francesca Marcellini. The Riemann Problem at a Junction for a Phase Transition Traffic Model. Discrete & Continuous Dynamical Systems - A, 2017, 37 (10) : 5191-5209. doi: 10.3934/dcds.2017225

[6]

Yuanjia Ma. The optimization algorithm for blind processing of high frequency signal of capacitive sensor. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1399-1412. doi: 10.3934/dcdss.2019096

[7]

Li Gang. An optimization detection algorithm for complex intrusion interference signal in mobile wireless network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1371-1384. doi: 10.3934/dcdss.2019094

[8]

Francesca Marcellini. Existence of solutions to a boundary value problem for a phase transition traffic model. Networks & Heterogeneous Media, 2017, 12 (2) : 259-275. doi: 10.3934/nhm.2017011

[9]

Mohamed Benyahia, Massimiliano D. Rosini. A macroscopic traffic model with phase transitions and local point constraints on the flow. Networks & Heterogeneous Media, 2017, 12 (2) : 297-317. doi: 10.3934/nhm.2017013

[10]

Wei Xu, Liying Yu, Gui-Hua Lin, Zhi Guo Feng. Optimal switching signal design with a cost on switching action. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-19. doi: 10.3934/jimo.2019068

[11]

Jiao-Yan Li, Xiao Hu, Zhong Wan. An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-18. doi: 10.3934/jimo.2018200

[12]

Yuhe Du, Jianwei Ji, Yu Liao, Yichu Liu. Design of energy storage coordination optimization algorithm for distributed power distribution network operation planning. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020206

[13]

Hartmut Schwetlick, Daniel C. Sutton, Johannes Zimmer. On the $\Gamma$-limit for a non-uniformly bounded sequence of two-phase metric functionals. Discrete & Continuous Dynamical Systems - A, 2015, 35 (1) : 411-426. doi: 10.3934/dcds.2015.35.411

[14]

Xinwen Luo, Weize Liu, Zhiyi Huo, Dayong Xu. Modeling and control algorithm design for power-assisted steering stability of electric vehicle. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020205

[15]

Wen-Li Li, Jing-Jing Wang, Xiang-Kui Zhang, Peng Yi. A novel road dynamic simulation approach for vehicle driveline experiments. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1035-1052. doi: 10.3934/dcdss.2019071

[16]

Tien-Fu Liang, Hung-Wen Cheng. Multi-objective aggregate production planning decisions using two-phase fuzzy goal programming method. Journal of Industrial & Management Optimization, 2011, 7 (2) : 365-383. doi: 10.3934/jimo.2011.7.365

[17]

Andrzej Swierniak, Jaroslaw Smieja. Analysis and Optimization of Drug Resistant an Phase-Specific Cancer. Mathematical Biosciences & Engineering, 2005, 2 (3) : 657-670. doi: 10.3934/mbe.2005.2.657

[18]

Ji Li, Tie Zhou. Numerical optimization algorithms for wavefront phase retrieval from multiple measurements. Inverse Problems & Imaging, 2017, 11 (4) : 721-743. doi: 10.3934/ipi.2017034

[19]

Sheng Wang, Xue An, Chen Yang, Long Liu, Yongchang Yu. Design and experiment of seeding electromechanical control seeding system based on genetic algorithm fuzzy control strategy. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020210

[20]

Junjie Peng, Ning Chen, Jiayang Dai, Weihua Gui. A goethite process modeling method by asynchronous fuzzy cognitive Network based on an improved constrained chicken swarm optimization algorithm. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2020021

2018 Impact Factor: 0.545

Article outline

Figures and Tables

[Back to Top]