# MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION IN TRAFFIC SYSTEM BASED ON GENETIC ALGORITHM. Received February 2011; revised June 2011

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1 International Journal of Innovative Computing, Information and Control ICIC International c 2012 ISSN Volume 8, Number 5(A), May 2012 pp MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION IN TRAFFIC SYSTEM BASED ON GENETIC ALGORITHM Wenge Ma 1, Dayong Geng 1 and Yan Yan 2 1 Institute of Electrical Engineering Liaoning University of Technology No. 169, Shiying Street, Jinzhou , P. R. China 2 Department of Automation Liaoning Petrochemical Vocational and Technology College No. 4, Beijing Road, Jinzhou , P. R. China Received February 2011; revised June 2011 Abstract. Single intersection in the traffic system is a complex system which has the nonlinear and uncertain characteristics. If the traffic states in the single intersection become more complex, the traditional timing control of the traffic lights is not suitable for the traffic control. A multi-phase fuzzy controller of the single intersection is proposed in this paper, by using the integration of the multi-phase control, and incorporating genetic algorithm into fuzzy control design to optimize fuzzy membership functions. The proposed control method is suitable for the different traffic states in the different times, and can achieve satisfactory control performance. The simulation experiment is implemented to prove the effectiveness of the proposed multi-phase fuzzy controller. Keywords: Single intersection, Membership function, Fuzzy control, Genetic algorithm 1. Introduction. Single intersection is an important problem in the traffic system. The single intersection has the complex characteristics, such as the strong nonlinearity, uncertainty, and thus, it is difficult to be described by an accurate mathematical model [1]. The traditional timing control of the traffic signal light is not applied to the current traffic flow. In order to achieve the satisfactory control performance of traffic flow in the single intersection, some intelligent control methods have been developed in the traffic system. For example, Papp first used the fuzzy set theory to control the traffic flow in the intersection in 1977 [2]. Afterwards, several fuzzy control methods have been proposed for the single intersection of the traffic system [3-6]. However, in these fuzzy controllers, the fuzzy rules and membership functions are fixed and are determined only by using the experts experience, and therefore, these traditional fuzzy controllers have some disadvantages in the traffic flow control of the single intersection. For example, if the number of the membership functions in the single intersection is large, it is difficult to determine the appropriate membership functions by using the experts experience. Moreover, since the traffic flow is different in the different time, the fixed membership functions cannot be suitable for the variations of the traffic flow in the different time [7]. In recent years, phase sequence control is also considered in the traffic system [8-12]. The integration of the phase sequence and the intelligent control becomes one of the effective control methods to handle the single intersection problem. A multi-phase controller with two neural networks of the single intersection is proposed in [12], and the traffic flow can be controlled with the satisfactory performance. However, the initial weights of the 3387

2 3388 W. MA, D. GENG AND Y. YAN neural networks are difficult to be determined, which makes this method difficult to be suitable for the frequent variety of the traffic flow. In this paper, a multi-phase fuzzy control of the single intersection is proposed. In order to overcome the disadvantages of the traditional fuzzy control mentioned above, the Genetic Algorithm (GA) is utilized to optimize the membership functions in IF-THEN rules. The membership functions can be adjusted according to the traffic states in the different time, and the proposed fuzzy control is suitable for the variations of the traffic states. There are two novel and different characteristics in the proposed control method compared with the existing control methods of the single section: the integration of the multi-phase control and the fuzzy control is utilized to control the traffic flow, and the optimization of the membership functions is implemented by using GA according to the different traffic state. The satisfactory control performance of the traffic system can be obtained by using the multi-phase fuzzy control proposed in this paper. Finally, the simulation experiment is implemented to prove the effectiveness of the proposed control method. 2. Initial Fuzzy Controller of the Single Intersection. Traffic flow distribution of the single intersection in the traffic system is shown in Figure 1. The intersection is composed of two streets that are intersected each other. Figure 1. Intersection description In this section, we designed a fuzzy controller of the intersection. In this initial fuzzy controller, the fuzzy rules and membership functions are fixed. In the next section, we used GA to optimize the membership functions of the fuzzy controller. The structure of the fuzzy control is shown in Figure 2. There are two input linguistic variables. One is L i, and the other is L i+1. L i is the number of the delay vehicles in the current phase, and L i+1 is the number of the delay vehicles in the subsequent phase. The output linguistic variable of the fuzzy control is t i, where t i is the delay time of the green traffic light in the current phase. The numerical values of the input and output variables are shown in Table 1. In Table 1, VS denotes the fuzzy set named very small. S denotes the fuzzy set named small. PS denotes the fuzzy set named positive small. M denotes the fuzzy set named medium. PL denotes the fuzzy set named positive large. L denotes the fuzzy set named large. VL denotes the fuzzy set named very large. The fuzzification of L i is shown in Table 2. The fuzzification of L i+1 is shown in Table 3. The fuzzification of t i is shown in Table 4.

3 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION 3389 Figure 2. The structure of the fuzzy controller Table 1. The input and output variables in the fuzzy control variables domain linguistic variables L i, {0,1,2,3,4,5,6,7,8,9,10} {VS,S,PS,M,PL,L,VL} L i+1 {0,1,2,3,4,5,6,7,8,9,10} {VS,S,PS,M,PL,L,VL} t i {0,1,2,3,4,5,6,7,8,9,10} {VS,S,PS,M,PL,L,VL} Table 2. The fuzzification of L i VS S P M PL L VL Table 3. The fuzzification of L i VS S PS M PL L VL Table 4. The fuzzification of t i VS S PS M PL L VL

4 3390 W. MA, D. GENG AND Y. YAN From Table 1, we know that there are 7 7 = 49 fuzzy rules in the fuzzy control. The initial fuzzy rules are determined by using the experts experience. The initial fuzzy rules are shown in Table 5. Table 5. Fuzzy rules L i L i+1 VL L PL M PS S VS VL L PL M PS S VS L L PL M PS S VS PL VL PL PL PS PS S VS M VL L PL PL PS S VS PS VL L PL PL PS PS S S VL VL L PL M PS S VS VL VL L PL M PS S By using the singleton fuzzifier, max-min reasoning and weighted mean defuzzification [13,14], the query table of the fuzzy control can be expressed by Table 6. Table 6. Query table of the fuzzy control L i 0 L i Optimization of the Membership Functions in the Fuzzy Controller. Traffic flow is varying in the different time. If the membership functions shown in Section 2 can be adjusted according to the difference of the traffic flow, the fuzzy control can be more suitable for the different traffic states, and the delay time of the vehicle in the single intersection can also be decreased. Genetic Algorithm (GA) is a kind of artificial intelligence technology with the selforganization, self-adaptation, global searching and colony characteristics. Genetic Algorithm can find the globally optimal solution or closely optimal solution by using the colony search strategy and the information exchange between the individuals. In this section, GA is utilized to adjust and optimize the membership functions of the fuzzy control described in Section 2. The adjustment process of the membership functions is shown in Figure 3. At first, the initial membership functions are determined by using the expert s experience. The initial fuzzy controller can also be obtained, which is described in Section 2. The initial fuzzy controller is utilized to control the traffic flow and the traffic flow parameters are storied

5 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION 3391 in the historical database at the same time. After a period of time, Genetic Algorithm is utilized to optimize and update the membership functions according to the traffic flow parameters in the historical database. In the optimization process, the main steps of Genetic Algorithm include the coding scheme, generation of the initial colony, design of the fitness function, genetic manipulation. Figure 3. The optimization process of the fuzzy control 3.1. Coding scheme. Usually, for the multi-dimensional high-accuracy continuous problem, real number coding is widely utilized in Genetic Algorithm [15,16]. Its advantages are shown as follows: 1. For the membership function, real number coding is easy to decode. 2. Comparing to the binary coding, real number coding can decrease the coding length and the operation complexity. 3. Interval limit is utilized in the real number coding, which is more suitable for the fuzzy control. 4. The real number coding can express the larger value range. Contrarily, if we adopted the binary coding to express the larger value range, we must increase the coding length which can decrease the accuracy. For the advantages of the real number coding, we utilized the real number coding in the optimization process of the membership functions. Triangle membership functions are adopted in the fuzzy control of the single intersection. Each membership function can be expressed by its three vertices of the triangle. In this paper, P 1, P 2 and P 3 denote these vertices of the triangle. So, P α = [P1 α, P2 α, P3 α ] denote the membership function of the fuzzy set. In order to avoid the out-of-control point and overlay region in the adjacent fuzzy sets [6], we defined that each fuzzy set is into its limited region, i.e., P a i [P i, P + i ] (i = 1, 2,, n) Generation of the initial colony. The number of colony is an important problem in Genetic Algorithm. If the number is too small, the optimization space will be reduced and the earlier convergence will be induced. If the number is too big, the computational complexity will be increased and the efficiency of Genetic Algorithm will be decreased. Usually, the number of the initial colony is In this paper, the number of the initial colony is 50. From Tables 2-4, we can know that there are 56 parameters should be adjusted in the fuzzy control of the single intersection. A random matrix is given, which has 50 rows and 56 columns. The number of the rows denotes the number of the initial colony, and the number of the columns denotes the number of the coding bits.

6 3392 W. MA, D. GENG AND Y. YAN 3.3. Design of the fitness function. Fitness function in Genetic Algorithm can be determined according to the object function. In the optimization process of the single intersection, the object function is the delay time of the vehicles. There are two kinds of delay time in the traffic system. One is the total delay time, and the other is the average delay time [17,18]. For the multi-phases single intersection, the total delay time D is: D = n d i q i (1) i=1 In Equation (1), di is the average delay time of each vehicle in the ith phase. q i is the average traffic flow in the ith phase. According to the computer simulation and the actual survey of the traffic states, the computation formula of the average delay time is: ( ) c(1 λ) d = 2(1 λx) + x 2 1/3 c 2q(1 x) 0.65 x (2+5λ) (2) q 2 In Equation (2), d is the average delay time in the appointed entrance. c is the signal period. q is the average traffic flow of the studied phase. λ is the green ratio of the studied phase. x is the saturation of the studied phase. Delay expression of the intersection is: D = sqr2 2(s q) = qr2 2(1 λ) In Equation (3), s is the ratio of the arrival and departure in the saturated state. Average delay time of each vehicle in the entrance is: (3) d = D qc = R 2 x = q s (c R) λ = c 2c(1 x) = (1 λ)2 2(1 x) c (4) In Equation (4), q is the vehicle arrival ratio. R is the duration time of the red traffic light including the losing time. c is the period. If the vehicle arrival submits to the Poisson distribution, the average delay time of each vehicle is (1 g)2 d = 2(1 λ) c + x 2 (7) 2q(1 x) In this paper, we take the average delay time of each vehicle as the object function. So, the fitness function is the inverse of the object function, which is shown in Equation (8). (5) (6) f = 1 d (8) In Equation (8), f is the fitness function. From Equation (8), we know if the delay time of the vehicles d is small, the fitness function f is big. When the optimization process is over, we can obtain the biggest fitness function, and the least delay time of the vehicles is also given.

7 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION Genetic manipulation. (1) Selecting operator By using the selecting operator, individual characteristic of the parent can be inherited to the next generation in Genetic Algorithm. So, the gene losing can be avoided, moreover, the global convergence and computational efficiency can also be increased. There are many methods to determine the selecting operator. In these methods, fitness proportion method is widely utilized. In this selecting method, the selected probability of the individual is proportion to its fitness. The fitness proportion method is described in Equation (9): P si = f i m (9) f j In Equation (9), m is the colony size. f i is the fitness of the ith individual. P si is the selected probability of the ith individual. In Genetic Algorithm, the new individual is generated by the crossover and mutation. For the random of the crossover and mutation, the new individual may destroy the best individual of the current colony. In order to solve this problem, the selecting operator method in this paper is the combination of the fitness proportion selecting and the optimal reservation selecting. Firstly, we use the fitness proportion selecting method in the selecting process. We copy the individual structure with the highest fitness to the next colony. So, when the search process of Genetic Algorithm is finished, the finally obtained individual is the best one which has the highest fitness throughout the whole search process. (2) Crossover operator Genetic Algorithm uses the crossover operator to produce the new individual [19]. In this paper, the linear recombination of the real number shown in Figure 4 is adopted during the crossover process. j Figure 4. Linear recombination In the linear recombination of the real number, the generation of the new individual is shown as follows: New individual = parent individual + a (parent individual new individual) (10) In Equation (10), a is the proportional factor. Usually, a is (3) Mutation operator

8 3394 W. MA, D. GENG AND Y. YAN By using the mutation operator, Genetic Algorithm can substitute the other genes for some genes in one chromosome, and a new individual can be generated [18]. In this paper, X a = [x a 1, x a 2,, x a n] is the chromosome which is selected to participate in the mutation. The definition interval of x a i is [x i, x+ i ]. We assume that the mutated chromosome is Y a = [y1, a y2, a, yn], a then yi a can be expressed as follows [20]: { x yi a a = i + aθ ( ) x i x + i λ < 0.5 x a i βθ ( ) x i x + (11) i λ 0.5 In Equation (11), α, β and γ are the random numbers in [0, 1]. θ is a constant in [0, 1]. 4. Simulation Simulation flow. We developed the simulation program based on the optimization strategy described in Section 3. The flow chart of the simulation program is shown in Figure 5. Figure 5. Flow chart of the simulation program

9 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION Parameter determination in GA. 1. N is the number of the colony. In the simulation, N = The initial crossover probability is The initial mutation probability is In the traditional GA, the crossover probability and the mutation probability are fixed. These unchanged crossover and mutation probability can decrease the optimization performance. In this paper, the non-uniformity crossover manipulation is adopted, which can overcome the disadvantages of the unchanged crossover and mutation probability. In the first fifty times of the evolution, the crossover and mutation probability are constants. After the fifty times, the crossover probability become to be bigger and the mutation probability become to be smaller. When the evolution time is 100, both crossover probability and mutation probability are constants. 3. The final evolution time in the simulation is Simulation results. In the simulation, there are four phases in the single intersection. The average traffic flows in these phases are 0.38, 0.16, 0.32 and The final results of the optimization process that denote the parameters of the membership functions are shown in Table 7. The performance curve of the average delay time during the optimization process is shown in Figure 6. Table 7. Final optimization results of the parameters of the membership functions Columns Figure 6. Performance curve of the average delay time

10 3396 W. MA, D. GENG AND Y. YAN In the same traffic state, we adopted three methods to control the traffic flow in the single intersection. These three methods are the timing control of the traffic signal, the traditional fuzzy control and the proposed fuzzy control with the optimal membership functions in this paper. The simulation results of the control performances are shown in Table 8. Table 8. Simulation results Average Average delay time(s) Sequence ratio Timing Traditional Fuzzy control number of of the control of fuzzy with the optimal the phase arrival the traffic control membership vehicles signal functions If we adopted the traditional fuzzy control, the average delay time is seconds in the first phase. If we adopted the optimized fuzzy controller, the average delay time is seconds in the first phase. So, from Table 8, we can know that the average delay time is decreased by using the optimized fuzzy controller. Simulation results show that the combination of the fuzzy control and GA is more suitable for the changeable traffic state, and the better control performance can be obtained. 5. Conclusions. Traditional fuzzy control can not be suitable for the changeable traffic state. A new multi-phase fuzzy control with the optimization of the membership functions is proposed to control the single intersection in the traffic system. By using GA, the membership functions can be adjusted according to the traffic state in the different time. The simulation experiment is implemented and proves the effectiveness of the proposed fuzzy control of the single intersection. Acknowledgment. This work is partially supported by Educational Department of Liaoning Province (No. 2008T087). REFERENCES [1] W. G. Ma, Signal Control Method and Algorithm Research Based on Fuzzy Control for Single Intersection, Dalian Maritime University, [2] C. P. Pappis and E. H. Mamdani, A fuzzy logic controller for a traffic junction, IEEE Transactions on Systems Man and Cybernetics, vol.7, no.10, pp , [3] J. Favila, A. Machion and F. Gomide, Fuzzy traffic control: Adaptive strategies, Proc. of the 2nd IEEE International Conference on Fuzzy Systems, San Francisco, pp , [4] R. Hoyer and U. Jumar, Fuzzy control of traffic lights, Proc. of the 3rd IEEE International Conference on Fuzzy Systems, Orlando, pp , [5] J. Kim, A fuzzy logic control simulator for adaptive traffic management, Proc. of the 6th IEEE International Conference on Fuzzy Systems, Barcelona, pp , [6] M. B. Trabia, M. S. Kaseko and M. Ande, A two-stage fuzzy logic controller for traffic signals, Transportation Research, Part C, vol.7, no.6, pp , [7] S. F. Chen, H. Chen and J. Q. Xu, Two level fuzzy control and simulation for urban traffic single intersection, Journal of System Simulation, vol.10, no.2, pp.35-40, [8] J. Qiao and H. Y. Xuan, A pass through need degree-based isolated intersection fuzzy control algorithm, Systems Engineering, vol.22, no.10, pp.59-64, 2004.

11 MULTI-PHASE FUZZY CONTROL OF SINGLE INTERSECTION 3397 [9] Z. P. Liu and Q. Han, A six-phase fuzzy control method used for a single intersection in the city, Computer Engineering and Applications, vol.22, no.2, pp , [10] J. X. Gao, J. G. Li and Y. Z. Chen, Probe into fuzzy control and simulation in multi-phase isolated intersection, Computer and Communications, vol.22, no.2, pp.84-87, [11] W. G. Ma, Design of signal fuzzy controller of single intersection in intelligence transportation system, International Journal of Innovative Computing, Information and Control, vol.3, no.4, pp , [12] X. P. Liu, Z. Y. Liu and J. P. Wu, An approach of intersection traffic signal control, Information and Control, vol.30, no.1, pp.76-79, [13] S. C. Tong and Y. Li, Direct adaptive fuzzy backstepping control for a class of nonlinear systems, International Journal of Innovative Computing, Information and Control, vol.3, no.4, pp , [14] S. C. Tong, W. Wang and L. J. Qu, Decentralized robust control for uncertain T-S fuzzy large-scale systems with time-delay, International Journal of Innovative Computing, Information and Control, vol.3, no.3, pp , [15] W. G. Ma, Y. Yan and D. Y. Geng, Traffic flow forecasting and signal timing research based on ant colony algorithm, ICIC Express Letters, vol.4, no.5(b), pp , [16] T. Kalganova, G. Russell and A. Gumming, Multiple traffic signal control using a genetic algorithm, IEEE Transactions on Systems Man and Cybernetics, vol.28, no.10, pp , [17] C. Q. Shao and J. Rong, Further study on the relation between control delay and stop delay, Journal of Beijing Polytechnic University, vol.28, no.1, pp.18-21, [18] Y. W. Yuan and L. Y. Dong, Models and algorithm of delay in vehicle motion at intersections with left-turning traffic lights, Journal of Shanghai University (Natural Science Edition), vol.8, no.4, pp.82-86, [19] J. W. Kim and B. M. Kim, Genetic algorithm approach to generate rules and membership, IEEE International Conference of Fuzzy Systems, Australia, pp , [20] Z. H. Zhang, W. Zhang and L. Zhao, Method of optimizing the traffic control system using genetic algorithm, Computer Engineering, vol.12, no.7, pp.53-55, [21] W. W. Li and H. Wang, Two-stage fuzzy logic controller based on genetic algorithm for urban traffic intersection, Journal of Central South University (Science and Technology), vol.34, no.4, pp , 2003.

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