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A Distributed Traffic Signal Control Scheme Based on Digital Hormone Model Xueyong Yu Minghui Wu Ting Wu School of Computer and Computing Science, Zhejiang University City College HangZhou, China e-mail: [email protected] Chunyan Yu College of Mathematics and Computer Science Fuzhou University Fuzhou ,China AbstractBased on the digital hormone model, this paper established a new distributed coordination control scheme for traffic signals, which adopts some hormonal information. The experiments with a traffic network of 8 junctions shows that this new control method is obviously superior to the timing control, sensing control and real-time control. Keywords-digital hormone model; computing particles; traffic signal control I. INTRODUCTION In general, hundreds or even thousands of intersections are included in the traffic network of big or medium-sized cities, which is in great need of traffic signal control to make the traffic flow in order. Being different from the single intersection, the traffic signal control of multi- intersection network would be mutual-influenced by other intersection’s traffic flow. The mere consideration of the single intersection’s traffic could not enable the communication to an optimal condition. The key to the distributed signal control of the traffic network is to improve the coordination among intersections for increasing the traffic capacity of the whole network under the unimpeded condition of every intersection. First of all, it takes great time and effort to constitute the traffic model which needs a great deal of the road network geometrical data and traffic flow information. Secondary, the problem of sub-district division has not been solved yet. Third, the calibration of the saturation flow rate needs to be automatic immediately. People begin to explore and discuss the problem of the distributed control, because of the deficiency of the traffic network’s control method, combined with the development of the solving method and multi-intelligence technology of the distributed problem [1]. Hakim Laichour [2], Jeffrey L. Adler [3] and John France [4] have put forward several distributed traffic control conception models based on multi-intelligence. Ying Li [5] has constituted the distributed traffic signal control conception model and simulated the simple traffic system made up of two intersections, which proved distributed control method is superior to the traditional one. Lingxi Li [6] has established and simulated hierarchical fuzzy control model of two neighboring intersections, and it proved its advantage over the traditional control method. In this paper, we have established a distributed coordination control of road traffic signals, according to the basis of hormonal information, together combining with the digital hormone model, and experimented with the traffic network of 8 junctions. The final result shows that this new control method is obviously superior to the timing control, sensing control and real-time control. II. DIGITAL HORMONE MODEL A. The digital hormone model’s major components Digital hormone model includes three parts: dynamic network, conduct rules of computing particles, hormone reaction-diffusion rules [10] [11]. Dynamic network reflects N equivalent topology among computing particles[7]. There exist physical or logical links among particles, which constitute the entire network topology. The link concept is very broad: In the super-compute network, a link means a channel to connect the adjacent nods; In the wireless network, the link of nodes is used for communication channel; In its own reconfigurable machine, the link means physical robot joints connecting the different components. Digital Hormone Model DHM network topology can be expressed as: ( , ) t t t DNSR N E { 1 Where t DNSR is the topology when DHM network is in time of t; t N is the collection for computing particles in the network ; t E is the collection for the links among particles. The topology of DHM network is uncertain and t N and t E in the formula(1) are both dynamically variable. DHM allows computing particles to be under an external damage and also to increase them by the external forces. Meanwhile, the links among particles are not stable, dynamically vary accompanying the movement of particles. The dynamic network is the foundation for the communication among computing particles, and computing particles receive or send hormones via the network topology. The dynamic network has some properties: 2010 International Conference on Artificial Intelligence and Computational Intelligence 978-0-7695-4225-6/10 $26.00 © 2010 IEEE DOI 10.1109/AICI.2010.295 277 2010 International Conference on Artificial Intelligence and Computational Intelligence 978-0-7695-4225-6/10 $26.00 © 2010 IEEE DOI 10.1109/AICI.2010.295 277

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Page 1: [IEEE 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI) - Sanya, China (2010.10.23-2010.10.24)] 2010 International Conference on Artificial

A Distributed Traffic Signal Control Scheme Based on Digital Hormone Model

Xueyong Yu Minghui Wu Ting Wu School of Computer and Computing Science, Zhejiang

University City College HangZhou, China

e-mail: [email protected]

Chunyan Yu College of Mathematics and Computer Science

Fuzhou University Fuzhou ,China

Abstract—Based on the digital hormone model, this paper established a new distributed coordination control scheme for traffic signals, which adopts some hormonal information. The experiments with a traffic network of 8 junctions shows that this new control method is obviously superior to the timing control, sensing control and real-time control.

Keywords-digital hormone model; computing particles; traffic signal control

I. INTRODUCTION In general, hundreds or even thousands of intersections

are included in the traffic network of big or medium-sized cities, which is in great need of traffic signal control to make the traffic flow in order. Being different from the single intersection, the traffic signal control of multi-intersection network would be mutual-influenced by other intersection’s traffic flow. The mere consideration of the single intersection’s traffic could not enable the communication to an optimal condition. The key to the distributed signal control of the traffic network is to improve the coordination among intersections for increasing the traffic capacity of the whole network under the unimpeded condition of every intersection.

First of all, it takes great time and effort to constitute the traffic model which needs a great deal of the road network geometrical data and traffic flow information. Secondary, the problem of sub-district division has not been solved yet. Third, the calibration of the saturation flow rate needs to be automatic immediately. People begin to explore and discuss the problem of the distributed control, because of the deficiency of the traffic network’s control method, combined with the development of the solving method and multi-intelligence technology of the distributed problem [1]. Hakim Laichour [2], Jeffrey L. Adler [3] and John France [4] have put forward several distributed traffic control conception models based on multi-intelligence. Ying Li [5] has constituted the distributed traffic signal control conception model and simulated the simple traffic system made up of two intersections, which proved distributed control method is superior to the traditional one. Lingxi Li [6] has established and simulated hierarchical fuzzy control model of two neighboring intersections, and it proved its advantage over the traditional control method.

In this paper, we have established a distributed coordination control of road traffic signals, according to the basis of hormonal information, together combining with the digital hormone model, and experimented with the traffic network of 8 junctions. The final result shows that this new control method is obviously superior to the timing control, sensing control and real-time control.

II. DIGITAL HORMONE MODEL

A. The digital hormone model’s major components Digital hormone model includes three parts: dynamic

network, conduct rules of computing particles, hormone reaction-diffusion rules [10] [11].

Dynamic network reflects N equivalent topology among computing particles[7]. There exist physical or logical links among particles, which constitute the entire network topology. The link concept is very broad: In the super-compute network, a link means a channel to connect the adjacent nods; In the wireless network, the link of nodes is used for communication channel; In its own reconfigurable machine, the link means physical robot joints connecting the different components.

Digital Hormone Model DHM network topology can be expressed as:

( , )t t tDNSR N E 1

Where tDNSR is the topology when DHM network is in time of t;

tN is the collection for computing particles in the network ;

tE is the collection for the links among particles.

The topology of DHM network is uncertain and tN and

tE in the formula(1) are both dynamically variable. DHM allows computing particles to be under an external damage and also to increase them by the external forces. Meanwhile, the links among particles are not stable, dynamically vary accompanying the movement of particles.

The dynamic network is the foundation for the communication among computing particles, and computing particles receive or send hormones via the network topology. The dynamic network has some properties:

2010 International Conference on Artificial Intelligence and Computational Intelligence

978-0-7695-4225-6/10 $26.00 © 2010 IEEE

DOI 10.1109/AICI.2010.295

277

2010 International Conference on Artificial Intelligence and Computational Intelligence

978-0-7695-4225-6/10 $26.00 © 2010 IEEE

DOI 10.1109/AICI.2010.295

277

Page 2: [IEEE 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI) - Sanya, China (2010.10.23-2010.10.24)] 2010 International Conference on Artificial

Each link is a self-organization computing particle; Each computing particle has a finite number of

interfaces and a pair of them are connected to form a link; The network topology is dynamic and the link among

the computing particles is uncertain, dynamically generated according to environment changes;

Hormone spreads through the links among computing particles;

Calculating particles neither know the size and information of the entire network nor have fixed ID.

DHM’s second part is the specification for computing particles behavior. Each computing particle in the network selects its B action according to function P of the same probability. The formula is shown as follows:

P B|C S V H 2 From the formula, we can see: Movement of particles is

determined by four factors, namely the link information C, sensor information S, the value of local variables V, as well as the received hormonal information H; In the DHM network, each particle dynamically creates a link to adjacent particles, and C means the information of those links; At the same time, DHM has also set up many sensors for each particle and each sensor corresponds to the adjacent particles as well as itself. S represents the above information; V represents the local particle information; H, the most crucial factor for the particle movement, means all the received hormones values of the current particle.

The third part is hormone response and the proliferation specifications. Hormone concentration is a equation concerning the space and time, demonstrated by function C (x, y), in which x and y respectively represent x-axis and y-axis. Then the reaction-diffusion equation of controlling hormone can be expressed as follows:

2 2

1 2 22( )C C C R bCa a

t yx 3

The first statement in the formula on the right represents the hormones proliferation in the space, and a1 and a2 respectively stand for the proliferation rate for small x and y directions. Function R is reaction function controlling C, determined by all other hormone concentrations. Constant b is the dissipation rate.

B. The basic rules and steps of digital hormone model The digital hormone model provides a strong

coordination mechanism for computing particles, via which the computing particles of a large number of uncertain behaviors can form an established global schema. The specific style of the global model is uncertain, and it is entirely dependent on low-level computings particles; however, the emergence of the global model is uncertain, and the uncertain behavior among computing particles is bound to emerge a bottom-up global schema. At a certain point, computing particles perceive the surrounding environment information, according to inner conduct rules, select and implement one or more actions. These rules can

be either deterministic or probabilistic. Given network, computing particles, hormones, behavior and rules, a single computing particle implements basic control asynchronously. Like the below circulation, inter-particles are through the following cycle to reach the overall style [8]: (1) The computing particle chooses behaviors determined by its conduct rules; (2) The computing particle implements the actions which it has selected; (3) The computing particles movement causes changes in local hormone levels; (4) Hormones spread from local to linked adjacent particles; (5) Go to step 1.

In the digital hormone model, two simple conduct rules control the computing particles’ behavior [9]. The first rule is: "In every step the computing particle secrets attracting factors and inhibitory factors", which means that each computing particle changes in the various statuses accompanied by corresponding changes in hormone concentration. The second rule is : "The computing particle determines the mobile location based on the hormone concentration in different locations ", that is, the possibility of a computing particle moving to a specific location (including the stay in its current location) is proportional to the attracting factors concentration in the corresponding position and is inversely proportional to the inhibitory factors concentration. Because hormone concentration changes with time, so the mobile location is unpredictable.

According to the above conduct rules, in the initial state, the computing particle is randomly distributed in a dynamic network. The computing particle firstly accept its corresponding information to select its behavior, combining with some of its own data , according to the formula (2), and to respond, move to an appropriate location. Because the location of the computing particle changes, immediately causing variance in local hormone concentration, hormones spread out with the space and time, according to the formula (3) ,resulting changes in hormone concentration of linked adjacent particles. And gradually it affects the entire network. The behavior of the computing particle is shown in Figure 1:

Figure 1 schematic diagram of the computing particle behaviors

There is a link among computing particles in the DHM, and the hormone information spread through the links among particles. But the link is uncertain, for each particle's movement will possibly change the whole network topology. Links among particles are connected by some characteristics,

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not determined by their identifiers, so the links change dynamically.

III. DISTRIBUTED CONTROL SIMULATION BASED ON DIGITAL HORMONE MODEL

A. The simulation experiment The simulation experiment's grid pattern uses Poisson

distribution: where is the average arrival rate of vechials; t is the duration time of inrerval counter.Poisson distribution suits to the situation where the flow capacity is not too fast leading slignt influence among the vehicals and veichals randomly arriving freely move on.Because Poisson distribution can well simulate the randomness of the starting and target point of actual vehicals, it considerably applies to the simulation system.

This experiment uses HSpice simulation software to simulate traffic network consisting of 8 intersections (Figure 2), and each section’s upper and lower rows have two lanes; Signal machines of Junctions 1,2,4,5,7,8 are set by three phases, and Junctions 3,6 are by set four phases[10]. Each Junction’s road conditions and signal control phase set are shown in Figure 3 and Figure4.

Figure 2 schematic diagram of a distributed traffic control example

of the road

The signals of Junctions 1,2,4,5,7,8 (Figure 3) are set through three phases. Phase 1 controls lanes 1,2; Phase 2 controls lanes 3,4; Phase 3 controls lanes 5,6.

Figure 3 schematic diagram of

road conditions at road junctions 1,2,4,5,7,8

Figure 4 schematic diagram of road conditions at road junctions 3,6 The signals of Junctions 3,6 (Figure 4) are set by four

phases. Phase 1 controls lanes 2,3; Phase 2 controls lanes 1,4; Phase 3 controls lanes 5,8; Phase 4 controls lanes 6,7. This experiment uses methods of similar step length, in which step time is 1 / 15 second and simulation time is 3 hours. In the condition of 8 different traffics, the types of traffic signal control are timing point control mode, sensing point control method, real-time control method on genetic algorithm [10], distributed control of digital hormone model. The experiment results are shown in Table 1.

Table 1 the control effect comparison chart for several traffic signal control in the distributed intersections

comparison chart traffic volume (vehicles / hour) 1080 1540 2230 2700 timing point of control vehicle delay (seconds) 115320 147657 175643 201088

sensor point control vehicle delay (seconds) 83292 104503 130046 154374

real-time control of vehicle delay (seconds) 67264 87029 115650 138390

distributed control of vehicle delay (seconds) 53064 72497 99924 120376

improvements of distributed over timing point control 53.99% 50.90% 43.11% 40.14%

improvements of distributed over sensor point control 36.29% 30.63% 23.16% 22.02%

improvements of distributed over real-time control 21.11% 16.70% 13.60% 13.02%

traffic volume (vehicles / hour) 3220 3740 4480 5400 Timing point of control vehicle delay (seconds) 217457 233564 252851 277265

sensor point control vehicle delay (seconds) 171099 185652 206382 235365

real-time control of vehicle delay (seconds) 158024 167824 186340 213524

distributed surface-controlled vehicle delay (seconds) 142194 155904 174948 201280

improvements of distributed over timing point control 34.61% 33.25% 30.81% 27.41%

improvements of distributed over sensor point control 16.89% 16.02% 15.23% 14.48%

improvements of distributed over sensor point control 10.02% 7.10% 6.11% 5.73%

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Figure 5 Distributed Traffic Information Simulation Effect Diagram Based on Digital Hormone

B. Conclusion

Figure 6 Control effect curve comparison diagram of various control

modes

In Figure 6, abscissa 1-8 represent 1080, 1540, 2230, 2700, 3220, 3740, 4480, 5400 (vehicles /hour) 8 different traffic types . As can be seen from the Figure 6, for the total delay generated by multi-intersection traffic network control traffic under a distributed traffic signal control method based on digital hormone model is significantly smaller than that under regular point of control, sensor control and real-time control point approach. From the average calculated data in Table 1, we can see that the gross vehicle delay under the distributed control method based on the digital hormone model is reduced by an average 39.28% than that of timing point, also by average of 21.84% compared with induction-point control, and is averagely 11.67% less than real-time control based on genetic algorithms. So it is a remarkable improvement in the control effect.

IV. CONCLUSION This paper presents a distributed traffic signal control scheme. Firstly, it demonstrated the basic thought rule and procedure of DHM, pointing out a newly optimized method for signal lights arranging the time based on the distributed algorithm of DHM. Finally, it simulated this theory applied to the signal lights control. Intelligent transportation systems research aims at globally optimizing the overall city traffic signal control: single cross control obeys the global scheduling and local optimization obeys global optimization. DHM and its coordination with other algorithm are still the focus of the future research.

REFERENCES

[1] Gerhard Weiss. Multiagent systems: a modern approach to distributed artificial intelligence [A]. Cambridge, Mass. MIT Press, 1999.

[2] Hakim Laichour, etc. Traffic control assistance in connection nodes: multi-agent applications in urban transport systems. [A]International Workshop on Intelligent Data Application and Advanced Computing System: Technology and Application. 1-4 July 2001, Foros, Ukraine: 133-137.

[3] Jeffrey L Adler, Victor J Blue. A cooperative multi-agent transportation management and route guidance system [J].Transportation Research Part C, 2002, 10: 433-454.

[4] John France, Ali A Ghorbani. A Multiagent System for Optimizing Urban Traffic [C]. Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology (IAT’03), 2003 IEEE.

[5] Li Ying. Study on the agent2based forecast and traffic control system [D]. Dissertation of PhD of Tianjing University, 2000. 12.

[6] Li Lingxi, Gao Haijun, Chen Long, et al. Fuzzy signal control of two adjacent intersections using hierarchy methods [J]. China Journal of Highway and Transport, 2002 , 15(4) : 66 - 68.

[7] Fang Liang-song, Yu Chun-yan. Digital Hormones Model-Based Optimal Time Assignment of Traffic Signal Cycle. Computer Technology and Development (China). vo1.19 No1.2009

[7] Steffen Staab. Neurons, Viscose Fluids, Freshwater Polyp Hydra and Self-Organizing Information Systems Published by the IEEE Computer Society. 2003 IEEE IEEE INTELLIGENT SYSTEMS, pp.72-74.

[9] Wei-min Shen, Cheng-ming Chuong, Peter Will Simulating Self-Organization for Multi-Robot Systems. In Proc. 2002 IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, pp. 2776–2781, Switzerland, 2002.

[10] WEI-MIN SHEN, PETER WILL AND ARAM GALSTYAN. Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms [J]. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA 90292, USA 2004:93-105

[11] Cheng Xiangjun , He Zhenhuan , Yang Zhaoxia. Machine learning traffic signal control approach based on genetic algorithm [J]. System Engineering - Theory & Practice, 2004, 24(8): 130 - 135.

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