- Email: [email protected]
Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait
Expert Systems with Applications 38 (2011) 9434–9441
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait Abdel Nasser H. Zaied a,⇑, Woroud Al Othman b a b
College of Computers and Informatics, Zagazig University, Egypt Ministry of Public Works, State of Kuwait
a r t i c l e
i n f o
Keywords: Traffic control Fuzzy logic Fuzzy logic traffic system
a b s t r a c t In a conventional traffic signals controller, the lights change at constant cycle time. In many cities, automatic traffic signals are often based on a constant green-to-red cycle. The time period for green light (or red light) to be on is determined based on a stochastic model. The traditional vehicle-actuated control of isolated intersections attempts continuously to adjust green times. The decision to change green light duration involves fuzzy factors that cannot be precisely determined. The main objective of this paper is to develop a fuzzy logic traffic system that considers the two two-way intersections and is able to adjust changes in time intervals of a traffic signal based on traffic situation level. The proposed system has been applied and tested using real data collected from signalized intersection in Hawalli governorate in the State of Kuwait. Twenty-seven iterations have been done; the results show that the proposed fuzzy logic traffic system provides better performance in terms of total waiting time, total moving time, and vehicle queue. Finally, it can be observed from the results that the proposed system can be used to accelerate the cycle time and to give other phases the chance to gain more benefit from the green time lost. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Different techniques have been used to control traffic performance and to minimize traffic delay. Applications with fuzzy logic in controlling traffic signals have been used since the 1970s. The strength of fuzzy logic lies in its capability of simulating the decision-making process of a human, a process that is often difficult to define with traditional mathematical methods. Fuzzy set theory represents a methodology for dealing with the phenomena that are too complex to be analyzed by the conventional means and it is an extremely suitable concept with which to combine subjective knowledge (linguistic information) and objective knowledge (formulae and equations). The essence of fuzzy logic lies in its ability to handle linguistic information by representing it as a fuzzy set. Another aspect that is worth examining when comparing fuzzy control with conventional control is robustness and adaptivity (Niittymäki & Pursula, 2000). There are many misconceptions about fuzzy logic. Fuzzy logic is much more than a logical system. It has many facets. The principal facets are: logical, fuzzy-set-theoretic, epistemic and
⇑ Corresponding author. Tel.: +20 108388588; fax: +20 552345452. E-mail address: [email protected] (A.N.H. Zaied). 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.01.130
relational. Most of the practical applications of fuzzy logic are associated with its relational facet (Zadeh 2008).
2. Traffic fuzzy logic systems In the past several decades a variety of deterministic and/or stochastic models have been developed to solve complex traffic and transportation engineering problems. In this section, various models of traffic fuzzy logic control which have appeared in the literature are reviewed. The goal of these revisions is to see how the issues of traffic fuzzy logic control are actually formalized in the proposed models. Hegyi et al. (2001) presented a fuzzy decision support system that can be used in traffic control centers to provide a limited list of appropriate combinations of traffic control measures for a given traffic situation. Whereas, Wei, Yong, Xuanqin, and Yan (2001) presented new concepts that called main urgent phase and minor urgent phase and a set of fuzzy control rules were developed to control the phases and delay of traffic lights. Chou and Teng (2002) proposed a fuzzy logic based traffic junction signal controller (FTJSC) by considering the number of consecutive junctions, the number of lanes, the lengths of vehicles, and the widths of streets. Moreover, Kuo and Lin (2002) developed a new procedure to calculate the change and clearance intervals of
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
a traffic signal from a rule-based fuzzy logic system. This procedure was based on the theory that ‘‘the driver’s decision making at signalized intersections is based on imprecise or fuzzy information’’. Kosonen (2003) presented a traffic signal control system based on fuzzy signal control. It was an algorithm chosen to provide the control agents with a decision-making capability. Khalid, See, and Yusof (2004) proposed a fuzzy traffic lights control system based on six phases in a four-way intersection. Three modules were proposed in the design of the fuzzy traffic lights controller Next Phase, a Green Phase and a Decision Modules. The system allowed communications with neighboring controllers and managed phase sequences and phase lengths adaptively according to traffic density, waiting time of vehicles and congestion. Akiyama and Okushima (2006) modified the inflow traffic controller with continuous variables to optimize parameters for linguistic expression in fuzzy reasoning and proposed an advanced fuzzy traffic control as an extension of conventional inflow control traffic management to reduce the traffic congestion effectively on urban expressways in Japan. Bagheri, Ensan, Faizy, and Behnia (2007) proposed a fuzzy control of signal timing as a subclass of advanced traffic management systems, using the Mamdani inference engine to shortening the average waiting time and queue length. Also, Hu, Thomas, and Stonier (2007) constructed fuzzy logic control system to control
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Table 1 Sample of fuzzy rules. R No.
Fuzzy rules statement
R21
IF L1 Full AND IF L2 No AND IF L5 Full THEN Open L1 AND L5 for P1 Full Strategy IF L1 Full AND IF L2 No AND IF L5 Half THEN Open L1 AND L5 for P1 Full Strategy IF L1 Full AND IF L2 No AND IF L5 No THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 Full THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 Half THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 No THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Full THEN Open L1 AND L2 for P1 Full Strategy IF L1 Half AND IF L2 No AND IF L5 Full THEN Open L1 AND L5 for P1 Half Strategy IF L1 Half AND IF L2 No AND IF L5 Half THEN Open L1 AND L5 for P1 Half Strategy IF L1 Half AND IF L2 No AND IF L5 No THEN Open L1 AND L2 for P1 Half Strategy
R22 R23 R24 R25 R26 R27 R28 R29 R30
the green time length for a real single intersection that consisted of five approaches, 14 lanes including 6 turns, and two two-way pedestrian crossings. An evolutionary algorithm was employed to
Fig. 1. Proposed FLTSystem structure and process.
Fig. 2. Different possible movement scenarios.
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Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
generate the fuzzy logic rule base, using real statistical traffic data of the intersection. Zhang and Ye (2008) proposed a fuzzy logic system methodology to forecast traffic flow using dual-loop detector. The forecast-
ing results showed that the fuzzy logic system produces more accurate and stable predictions. It was also more robust as it is able to forecast flow under various traffic and detector operation conditions.
Fig. 3. The proposed FLTSystem.
Fig. 4. The membership function for input (L1).
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
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3. Proposed fuzzy logic traffic system (FLTSystem)
3.1. System design
Traffic control system is generally divided into two problems, the choice of signal sequences, and optimizing the relative lengths of green lights.
The first step in designing the fuzzy logic control system is determining the system inputs. The inputs of the proposed system include traffic situation level (car location inside detected zone),
Fig. 5. The membership function for output (11).
Fig. 6. The rule editor of the fuzzy rules.
9438
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
Phase sequence plan (P1 tp P4): (90 s): No (N) = 0–0–22.5 s, Semi-Half (SH) = 0–22.5–45 s, Half (H) = 22.5–45–67.5 s, SemiFull (SF) = 45–67.5–90 s and Full (F) = 67.5–90–90 s
phases sequence level (sequence of green lights) and phases strategies (traffic light duration time). 3.2. System assumptions
3.5. Fuzzy rules
The following are list of the major assumptions currently attributed to the proposed system:
The basic principles of these rules are to adjust cycle time and to minimize unused green light times of phases. The fuzzy rules de-
The system dose not concern the lengths of vehicles, distance between vehicles. The system used the pre-set signal timings used in the existing system in the State of Kuwait. The system designed for signalized isolated intersection with multiple lanes in each street and four directions in the intersection and follows the phases sequence P1–P2–P3–P4 (anticlockwise). 3.3. System structure and process The proposed FLTSystem is very simple; it receives the data, and maps it to the appropriate membership functions and truth-values (fuzzification). The system then combines the results of the values (inference mechanism). The Mamdani model and centroid method are used to build the fuzzy system by establishing relations between the inputs and outputs fuzzy regions in the form of if–then rules (Jamshidi, Titli, Zadeh, & Boverie 1997). The combined results converted back into specific warning statements or actions (defuzzification). In this work, mean of maximum (MOM) method is used. The structure of the proposed FLTSystem is shown in Fig. 1.
Fig. 8. Existing and proposed cycle length for seven discrete cycles.
3.4. Fuzzy sets definition Fuzzy logic starts with the concept of a fuzzy set. The proposed FLTSystem has eight inputs and sixteen outputs. All input and output linguistic variables defined as No, Semi-Half, Half, Semi-Full and Full. The membership functions that represent the values of input and output degrees are defined as follows: Directions (L1–L8): No (N) = 0–0–25 m, Semi-Half (SH) = 0– 25–50 m, Half (H) = 25–50–75 m, Semi-Full (SF) = 50–75– 100 m and Full (F) = 75–100–100 m
Fig. 9. Acceleration percentage of cycle length for seven discrete cycles.
Fig. 7. Detective sensors locations.
Table 2 Seven discrete cycles. System
Cycle no. 1
2
3
4
5
6
7
Cycle length (s) Existing FLTSystem
109 105
101 97
109 106
107 104
114 111
117 111
81 75
Acceleration %
4
4
3
3
3
5
7
Delay/cycle (s) Existing FLTSystem
496.9 431.7
271.3 259.4
632.1 528.6
292.5 283.2
317.8 307.9
327.0 306.4
274.4 237.1
Delay improvement %
13
4
16
3
3
6
14
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Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
fined different cases and different scenarios as shown in Fig. 2 and Table 1. 3.6. System building MATLAB fuzzy logic used to build the proposed FLTSystem, the system consists of 78 fuzzy rules as shown in Fig. 3. The membership functions for inputs and outputs are shown in Figs. 4 and 5. The rule editor of the fuzzy rules is shown in Fig. 6. 4. Case study In this study, twenty-seven iterations have been done. Data was collected using detective sensors located as shown in Fig. 7. Data
was entered to MATALB and the system output was used to simulate the system behaviors. Delay values are calculated by using Aashtiani and Iravani (1999) model. Comparisons are done by considering three scenarios: seven discrete cycles, ten continuous cycles and system robustness. 4.1. Comparison using seven discrete cycles Seven actual discrete cycles were taken from historical data; the results show that the proposed FLTSystem gives good results compared with the existing system as shown in Table 2. The cycle length proposed by FLTSystem is less than that proposed by the existing system as shown in Fig. 8, it means that the proposed FLTSystem accelerates the traffic cycle with percentage ranged between 3% and 7% as shown in Fig. 9. Also, delay per cycle produced by applying the proposed FLTSystem is always less than that produced by the existing system as shown in Fig. 10. The proposed system minimizes delay (improve cycle) with percentage ranged 4–16% as shown in Fig. 11.
Fig. 10. Existing and proposed cycle delay for seven discrete cycles. Fig. 12. Existing and proposed cycle length for ten continuous cycles.
Fig. 11. Improving percentage of cycle delay for seven discrete cycles.
Fig. 13. Acceleration percentage of cycle length for ten continuous cycles.
Table 3 Ten continuous cycles. System
Cycle no. 1
2
3
4
5
6
7
8
9
10
Cycle length (s) Existing FLTSystem
86 71
91 70
88 67
69 57
96 79
80 66
83 53
86 54
81 57
86 60
Acceleration %
17
23
24
17
18
18
36
37
30
30
Delay/cycle (s) Existing FLTSystem
285.3 231.3
309.6 231.1
288.2 219.5
198.6 164.5
332.4 219.7
238.8 162
248.3 159.7
281.3 170.6
242.5 174.3
268.6 184.7
Delay improvement %
19
25
24
17
34
32
36
39
28
31
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Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
4.2. Comparison using ten continuous cycles Ten continuous cycles were taken; the cycle length and delay per cycles were calculated as shown in Table 3. The proposed FLTSystem gives good results with continuous cycles compared with discrete cycles as shown in Fig. 12. The results show that the proposed FLTSystem accelerates the traffic cycle by 17–37% as shown in Fig. 13. Also, the delay per cycle improved by 17–39% compared with the existing system as shown in Figs. 14 and 15, it means that the proposed FLTSystem improves intersection performance.
275–300%. The queue is zero during the intervals from 25% to 125%, the queue start formulating during the interval 150% and increased from 3 vehicles to 156 in interval 300%, whereas queue increased for the existing system from 3 to 190 vehicles in interval 300% as shown in Fig. 18.
4.3. Robustness test To examine the robustness of the proposed FLTSystem, the system tested by increasing the traffic flow up to 300% and the cycle time and delay were calculated as shown in Table 4. The proposed FLTSystem shows good results, it minimizes the delay per cycle by 22.8% in the first cycle and the percentage decreased by increasing the flow as shown in Figs. 16 and 17. The proposed FLTSystem gives the same results as the existing system during the intervals
Fig. 16. Existing and proposed cycle time when flow increased.
Fig. 17. Improving percentage when flow increased. Fig. 14. Existing and proposed cycle delay for ten continuous cycles.
Fig. 15. Improving percentage of cycle delay for ten continuous cycles.
Fig. 18. Existing and proposed cycle queue when flow increased.
Table 4 Ten cycles with increasing flow. Item
Cycle time (s)
Percentage of increasing flow
Existing system FLTSystem
Delay improvement % Queue (vehicle) Existing system FLTSystem
25
50
75
100
125
150
175
200
225
250
275
300
100 87
115 101
132 116
184 129
164 134
176 152
179 156
180 160
182 165
183 170
184 184
185 184
2.07 123 88
0 157 114
0 190 156
22.8 0 0
9.32 0 0
8.31 0 0
6.6 0 0
5.44 0 0
11.1 3 3
8.35 24 24
5.8 54 35
5.61 88 66
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
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5. Conclusion
References
The analysis of the existing system that was conducted as part of this research revealed that the current methods for estimating delays, number of vehicle stops and queue lengths at signalized intersections depend on a predetermined fixed time strategy. This confirms the pressing need to develop a new system that can address the limitations in the existing system. The importance of developing a new system came from the fact that isolated signalized intersection analysis is a part of almost every traffic analysis. In this study, a fuzzy logic traffic system (FLTSystem) is developed. The proposed FLTSystem validation is investigated by comparing traffic delay accrued when using existing and proposed FLTSystem. From the results, it can be observed that the proposed FLTSystem is almost acts as the existing traffic system when considering three second strategy and little traffic volumes. It provides good results compared to the existing system when considering heavy traffic volumes due to reducing the unused green time and accelerates the phase’s sequences. When considered system robustness (by increasing the traffic flow), the proposed FLTSystem minimizes the delay per cycle and vehicle queue is zero until the traffic volume reached 125% from the initial value, it means that the proposed FLTSystem can work in a proper way to manage any access flow up to 150% from the initial flow. Finally, it can be observed that the proposed FLTSystem implementation is found to be useful in reducing vehicle delay and saving time up to 16% for discrete cycles and up to 36% for continuous cycles compared to the existing system and gives other phases the chances to gain more benefit from the green time lost.
Aashtiani, H., & Iravani, H. (1999). Use of intersection delay functions to improve reliability of traffic assignment model. In 14th annual international EMME/2 conference, Chicago, Illinois (pp. 1–6). Akiyama, T., & Okushima, M. (2006). Advanced fuzzy traffic controller for urban expressways. International Journal of Innovative Computing, Information and Control, 2(2), 339–355. Bagheri, E., Ensan, F., Faizy, M., & Behnia, F. (2007). A novel fuzzy control model of traffic light timing at an urban intersection. In 3rd information and knowledge technology conference, Mashhad, Iran (pp. 1–5). Chou, C., & Teng, J. (2002). A fuzzy logic controller for traffic junction signals. Information Sciences, 143, 73–97. Hegyi, A., De Schutter, B., Babuska, R., Hoogendoorn, S., Zuylen, H., & Schuurman, H. (2001). A fuzzy decision support system for traffic control centers. In Proceedings of the 2001 IEEE intelligent transportation systems conference (ITSC’01), Oakland, California (pp. 358–363). Hu, Y., Thomas, P., & Stonier, R. (2007). Fuzzy control of traffic signals accompanying pedestrian crossings. In: Proceedings of the 2007 WSEAS international conference on computer engineering and applications, Gold Coast, Australia, January 17–19 (pp. 288–292). Jamshidi, M., Titli, A., Zadeh, L., & Boverie, S. (1997). Applications of fuzzy logictowards high machine intelligence quotient systems. Prentice Hall. Khalid, M., See, C., & Yusof, R. (2004). Control of a complex traffic junction using fuzzy inference. In 5th Asian control conference, 20–23 July 2004 (Vol. 3, pp. 1544–1551). Available from: Accessed 15.07.08. Kosonen, I. (2003). Multi-agent fuzzy signal control based on real-time simulation. Transportation Research, Part C–Emerging Technologies, 1, 389–403. Kuo, K., & Lin, J. (2002). Application of fuzzy set theory to the change intervals at a signalized intersection. Fuzzy Sets and Systems, 129, 387–403. Niittymäki, J., & Pursula, M. (2000). Signal control using fuzzy logic. Fuzzy Sets and Systems, 116(1), 11–22. Wei, H., Yong, W., Xuanqin, M., & Yan, W. (2001). A cooperative fuzzy control method for traffic lights. In IEEE intelligent transportation systems conference proceedings, Oakland (CA), USA (pp. 185–188). Zadeh, L. (2008). Is there a need for fuzzy logic? Information Sciences, 178, 2751–2779. Zhang, Y., & Ye, Z. (2008). Short-term traffic flow forecasting using fuzzy logic system methods. Journal of Intelligent Transportation Systems, 12(3), 102–112.
Acknowledgments The authors wish to express the warmest appreciation to Arabian Gulf University in Bahrain and the Traffic Department, Ministry of Interior in the State of Kuwait for their help and support.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
Development of a fuzzy logic traffic system for isolated signalized intersections in the State of Kuwait Abdel Nasser H. Zaied a,⇑, Woroud Al Othman b a b
College of Computers and Informatics, Zagazig University, Egypt Ministry of Public Works, State of Kuwait
a r t i c l e
i n f o
Keywords: Traffic control Fuzzy logic Fuzzy logic traffic system
a b s t r a c t In a conventional traffic signals controller, the lights change at constant cycle time. In many cities, automatic traffic signals are often based on a constant green-to-red cycle. The time period for green light (or red light) to be on is determined based on a stochastic model. The traditional vehicle-actuated control of isolated intersections attempts continuously to adjust green times. The decision to change green light duration involves fuzzy factors that cannot be precisely determined. The main objective of this paper is to develop a fuzzy logic traffic system that considers the two two-way intersections and is able to adjust changes in time intervals of a traffic signal based on traffic situation level. The proposed system has been applied and tested using real data collected from signalized intersection in Hawalli governorate in the State of Kuwait. Twenty-seven iterations have been done; the results show that the proposed fuzzy logic traffic system provides better performance in terms of total waiting time, total moving time, and vehicle queue. Finally, it can be observed from the results that the proposed system can be used to accelerate the cycle time and to give other phases the chance to gain more benefit from the green time lost. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Different techniques have been used to control traffic performance and to minimize traffic delay. Applications with fuzzy logic in controlling traffic signals have been used since the 1970s. The strength of fuzzy logic lies in its capability of simulating the decision-making process of a human, a process that is often difficult to define with traditional mathematical methods. Fuzzy set theory represents a methodology for dealing with the phenomena that are too complex to be analyzed by the conventional means and it is an extremely suitable concept with which to combine subjective knowledge (linguistic information) and objective knowledge (formulae and equations). The essence of fuzzy logic lies in its ability to handle linguistic information by representing it as a fuzzy set. Another aspect that is worth examining when comparing fuzzy control with conventional control is robustness and adaptivity (Niittymäki & Pursula, 2000). There are many misconceptions about fuzzy logic. Fuzzy logic is much more than a logical system. It has many facets. The principal facets are: logical, fuzzy-set-theoretic, epistemic and
⇑ Corresponding author. Tel.: +20 108388588; fax: +20 552345452. E-mail address: [email protected] (A.N.H. Zaied). 0957-4174/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2011.01.130
relational. Most of the practical applications of fuzzy logic are associated with its relational facet (Zadeh 2008).
2. Traffic fuzzy logic systems In the past several decades a variety of deterministic and/or stochastic models have been developed to solve complex traffic and transportation engineering problems. In this section, various models of traffic fuzzy logic control which have appeared in the literature are reviewed. The goal of these revisions is to see how the issues of traffic fuzzy logic control are actually formalized in the proposed models. Hegyi et al. (2001) presented a fuzzy decision support system that can be used in traffic control centers to provide a limited list of appropriate combinations of traffic control measures for a given traffic situation. Whereas, Wei, Yong, Xuanqin, and Yan (2001) presented new concepts that called main urgent phase and minor urgent phase and a set of fuzzy control rules were developed to control the phases and delay of traffic lights. Chou and Teng (2002) proposed a fuzzy logic based traffic junction signal controller (FTJSC) by considering the number of consecutive junctions, the number of lanes, the lengths of vehicles, and the widths of streets. Moreover, Kuo and Lin (2002) developed a new procedure to calculate the change and clearance intervals of
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
a traffic signal from a rule-based fuzzy logic system. This procedure was based on the theory that ‘‘the driver’s decision making at signalized intersections is based on imprecise or fuzzy information’’. Kosonen (2003) presented a traffic signal control system based on fuzzy signal control. It was an algorithm chosen to provide the control agents with a decision-making capability. Khalid, See, and Yusof (2004) proposed a fuzzy traffic lights control system based on six phases in a four-way intersection. Three modules were proposed in the design of the fuzzy traffic lights controller Next Phase, a Green Phase and a Decision Modules. The system allowed communications with neighboring controllers and managed phase sequences and phase lengths adaptively according to traffic density, waiting time of vehicles and congestion. Akiyama and Okushima (2006) modified the inflow traffic controller with continuous variables to optimize parameters for linguistic expression in fuzzy reasoning and proposed an advanced fuzzy traffic control as an extension of conventional inflow control traffic management to reduce the traffic congestion effectively on urban expressways in Japan. Bagheri, Ensan, Faizy, and Behnia (2007) proposed a fuzzy control of signal timing as a subclass of advanced traffic management systems, using the Mamdani inference engine to shortening the average waiting time and queue length. Also, Hu, Thomas, and Stonier (2007) constructed fuzzy logic control system to control
9435
Table 1 Sample of fuzzy rules. R No.
Fuzzy rules statement
R21
IF L1 Full AND IF L2 No AND IF L5 Full THEN Open L1 AND L5 for P1 Full Strategy IF L1 Full AND IF L2 No AND IF L5 Half THEN Open L1 AND L5 for P1 Full Strategy IF L1 Full AND IF L2 No AND IF L5 No THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 Full THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 Half THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Half AND IF L5 No THEN Open L1 AND L2 for P1 Full Strategy IF L1 Full AND IF L2 Full THEN Open L1 AND L2 for P1 Full Strategy IF L1 Half AND IF L2 No AND IF L5 Full THEN Open L1 AND L5 for P1 Half Strategy IF L1 Half AND IF L2 No AND IF L5 Half THEN Open L1 AND L5 for P1 Half Strategy IF L1 Half AND IF L2 No AND IF L5 No THEN Open L1 AND L2 for P1 Half Strategy
R22 R23 R24 R25 R26 R27 R28 R29 R30
the green time length for a real single intersection that consisted of five approaches, 14 lanes including 6 turns, and two two-way pedestrian crossings. An evolutionary algorithm was employed to
Fig. 1. Proposed FLTSystem structure and process.
Fig. 2. Different possible movement scenarios.
9436
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
generate the fuzzy logic rule base, using real statistical traffic data of the intersection. Zhang and Ye (2008) proposed a fuzzy logic system methodology to forecast traffic flow using dual-loop detector. The forecast-
ing results showed that the fuzzy logic system produces more accurate and stable predictions. It was also more robust as it is able to forecast flow under various traffic and detector operation conditions.
Fig. 3. The proposed FLTSystem.
Fig. 4. The membership function for input (L1).
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
9437
3. Proposed fuzzy logic traffic system (FLTSystem)
3.1. System design
Traffic control system is generally divided into two problems, the choice of signal sequences, and optimizing the relative lengths of green lights.
The first step in designing the fuzzy logic control system is determining the system inputs. The inputs of the proposed system include traffic situation level (car location inside detected zone),
Fig. 5. The membership function for output (11).
Fig. 6. The rule editor of the fuzzy rules.
9438
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
Phase sequence plan (P1 tp P4): (90 s): No (N) = 0–0–22.5 s, Semi-Half (SH) = 0–22.5–45 s, Half (H) = 22.5–45–67.5 s, SemiFull (SF) = 45–67.5–90 s and Full (F) = 67.5–90–90 s
phases sequence level (sequence of green lights) and phases strategies (traffic light duration time). 3.2. System assumptions
3.5. Fuzzy rules
The following are list of the major assumptions currently attributed to the proposed system:
The basic principles of these rules are to adjust cycle time and to minimize unused green light times of phases. The fuzzy rules de-
The system dose not concern the lengths of vehicles, distance between vehicles. The system used the pre-set signal timings used in the existing system in the State of Kuwait. The system designed for signalized isolated intersection with multiple lanes in each street and four directions in the intersection and follows the phases sequence P1–P2–P3–P4 (anticlockwise). 3.3. System structure and process The proposed FLTSystem is very simple; it receives the data, and maps it to the appropriate membership functions and truth-values (fuzzification). The system then combines the results of the values (inference mechanism). The Mamdani model and centroid method are used to build the fuzzy system by establishing relations between the inputs and outputs fuzzy regions in the form of if–then rules (Jamshidi, Titli, Zadeh, & Boverie 1997). The combined results converted back into specific warning statements or actions (defuzzification). In this work, mean of maximum (MOM) method is used. The structure of the proposed FLTSystem is shown in Fig. 1.
Fig. 8. Existing and proposed cycle length for seven discrete cycles.
3.4. Fuzzy sets definition Fuzzy logic starts with the concept of a fuzzy set. The proposed FLTSystem has eight inputs and sixteen outputs. All input and output linguistic variables defined as No, Semi-Half, Half, Semi-Full and Full. The membership functions that represent the values of input and output degrees are defined as follows: Directions (L1–L8): No (N) = 0–0–25 m, Semi-Half (SH) = 0– 25–50 m, Half (H) = 25–50–75 m, Semi-Full (SF) = 50–75– 100 m and Full (F) = 75–100–100 m
Fig. 9. Acceleration percentage of cycle length for seven discrete cycles.
Fig. 7. Detective sensors locations.
Table 2 Seven discrete cycles. System
Cycle no. 1
2
3
4
5
6
7
Cycle length (s) Existing FLTSystem
109 105
101 97
109 106
107 104
114 111
117 111
81 75
Acceleration %
4
4
3
3
3
5
7
Delay/cycle (s) Existing FLTSystem
496.9 431.7
271.3 259.4
632.1 528.6
292.5 283.2
317.8 307.9
327.0 306.4
274.4 237.1
Delay improvement %
13
4
16
3
3
6
14
9439
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
fined different cases and different scenarios as shown in Fig. 2 and Table 1. 3.6. System building MATLAB fuzzy logic used to build the proposed FLTSystem, the system consists of 78 fuzzy rules as shown in Fig. 3. The membership functions for inputs and outputs are shown in Figs. 4 and 5. The rule editor of the fuzzy rules is shown in Fig. 6. 4. Case study In this study, twenty-seven iterations have been done. Data was collected using detective sensors located as shown in Fig. 7. Data
was entered to MATALB and the system output was used to simulate the system behaviors. Delay values are calculated by using Aashtiani and Iravani (1999) model. Comparisons are done by considering three scenarios: seven discrete cycles, ten continuous cycles and system robustness. 4.1. Comparison using seven discrete cycles Seven actual discrete cycles were taken from historical data; the results show that the proposed FLTSystem gives good results compared with the existing system as shown in Table 2. The cycle length proposed by FLTSystem is less than that proposed by the existing system as shown in Fig. 8, it means that the proposed FLTSystem accelerates the traffic cycle with percentage ranged between 3% and 7% as shown in Fig. 9. Also, delay per cycle produced by applying the proposed FLTSystem is always less than that produced by the existing system as shown in Fig. 10. The proposed system minimizes delay (improve cycle) with percentage ranged 4–16% as shown in Fig. 11.
Fig. 10. Existing and proposed cycle delay for seven discrete cycles. Fig. 12. Existing and proposed cycle length for ten continuous cycles.
Fig. 11. Improving percentage of cycle delay for seven discrete cycles.
Fig. 13. Acceleration percentage of cycle length for ten continuous cycles.
Table 3 Ten continuous cycles. System
Cycle no. 1
2
3
4
5
6
7
8
9
10
Cycle length (s) Existing FLTSystem
86 71
91 70
88 67
69 57
96 79
80 66
83 53
86 54
81 57
86 60
Acceleration %
17
23
24
17
18
18
36
37
30
30
Delay/cycle (s) Existing FLTSystem
285.3 231.3
309.6 231.1
288.2 219.5
198.6 164.5
332.4 219.7
238.8 162
248.3 159.7
281.3 170.6
242.5 174.3
268.6 184.7
Delay improvement %
19
25
24
17
34
32
36
39
28
31
9440
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
4.2. Comparison using ten continuous cycles Ten continuous cycles were taken; the cycle length and delay per cycles were calculated as shown in Table 3. The proposed FLTSystem gives good results with continuous cycles compared with discrete cycles as shown in Fig. 12. The results show that the proposed FLTSystem accelerates the traffic cycle by 17–37% as shown in Fig. 13. Also, the delay per cycle improved by 17–39% compared with the existing system as shown in Figs. 14 and 15, it means that the proposed FLTSystem improves intersection performance.
275–300%. The queue is zero during the intervals from 25% to 125%, the queue start formulating during the interval 150% and increased from 3 vehicles to 156 in interval 300%, whereas queue increased for the existing system from 3 to 190 vehicles in interval 300% as shown in Fig. 18.
4.3. Robustness test To examine the robustness of the proposed FLTSystem, the system tested by increasing the traffic flow up to 300% and the cycle time and delay were calculated as shown in Table 4. The proposed FLTSystem shows good results, it minimizes the delay per cycle by 22.8% in the first cycle and the percentage decreased by increasing the flow as shown in Figs. 16 and 17. The proposed FLTSystem gives the same results as the existing system during the intervals
Fig. 16. Existing and proposed cycle time when flow increased.
Fig. 17. Improving percentage when flow increased. Fig. 14. Existing and proposed cycle delay for ten continuous cycles.
Fig. 15. Improving percentage of cycle delay for ten continuous cycles.
Fig. 18. Existing and proposed cycle queue when flow increased.
Table 4 Ten cycles with increasing flow. Item
Cycle time (s)
Percentage of increasing flow
Existing system FLTSystem
Delay improvement % Queue (vehicle) Existing system FLTSystem
25
50
75
100
125
150
175
200
225
250
275
300
100 87
115 101
132 116
184 129
164 134
176 152
179 156
180 160
182 165
183 170
184 184
185 184
2.07 123 88
0 157 114
0 190 156
22.8 0 0
9.32 0 0
8.31 0 0
6.6 0 0
5.44 0 0
11.1 3 3
8.35 24 24
5.8 54 35
5.61 88 66
Abdel Nasser H. Zaied, W. Al Othman / Expert Systems with Applications 38 (2011) 9434–9441
9441
5. Conclusion
References
The analysis of the existing system that was conducted as part of this research revealed that the current methods for estimating delays, number of vehicle stops and queue lengths at signalized intersections depend on a predetermined fixed time strategy. This confirms the pressing need to develop a new system that can address the limitations in the existing system. The importance of developing a new system came from the fact that isolated signalized intersection analysis is a part of almost every traffic analysis. In this study, a fuzzy logic traffic system (FLTSystem) is developed. The proposed FLTSystem validation is investigated by comparing traffic delay accrued when using existing and proposed FLTSystem. From the results, it can be observed that the proposed FLTSystem is almost acts as the existing traffic system when considering three second strategy and little traffic volumes. It provides good results compared to the existing system when considering heavy traffic volumes due to reducing the unused green time and accelerates the phase’s sequences. When considered system robustness (by increasing the traffic flow), the proposed FLTSystem minimizes the delay per cycle and vehicle queue is zero until the traffic volume reached 125% from the initial value, it means that the proposed FLTSystem can work in a proper way to manage any access flow up to 150% from the initial flow. Finally, it can be observed that the proposed FLTSystem implementation is found to be useful in reducing vehicle delay and saving time up to 16% for discrete cycles and up to 36% for continuous cycles compared to the existing system and gives other phases the chances to gain more benefit from the green time lost.
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Acknowledgments The authors wish to express the warmest appreciation to Arabian Gulf University in Bahrain and the Traffic Department, Ministry of Interior in the State of Kuwait for their help and support.