# American Institute of Mathematical Sciences

## 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:

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##### References:
Intersection set model
Four phase of intersection signal
The combination of East and West can be combined
Fuzzy control algorithm structure
Controller structure
Average vehicle delay time curve
Traffic flow distribution
Comparison of average waiting vehicle length
Comparison of average waiting time of vehicles
Comparison of average waiting time for pedestrians
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 $\ldots$ $\ldots$ $\ldots$ $\ldots$ $\ldots$ $\ldots$
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 $\ldots$ $\ldots$ $\ldots$ $\ldots$ $\ldots$
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 $\ldots$ $\ldots$ $\ldots$ $\ldots$
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%
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
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
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
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