2010 Ninth International Conference on Grid and Cloud Computing
A Novel Intelligent Traffic Light Control Scheme Cheng Hu
Yun Wang
School of Computer Science & Engineering Southeast University Nanjing, China, 210096
[email protected]
School of Computer Science & Engineering Southeast University Nanjing, China, 210096
[email protected]
Abstract—Traffic congestion has become a major problem in today’s society. It may cause a great waste of material and human casualties. We think the fixed-time-state traffic light control scheme is one important reason. An intelligent traffic light control scheme is necessary in order to give a solution to this problem. The paper proposes a model to describe a road cross. In terms of dynamic and real-time traffic information, weights of all the serial lanes are dynamically computed, so as to automatically manage the current status and duration of traffic lights. The proposed novel control scheme consider traffic situations of both upstream lanes and downstream lanes, in order to reduce average vehicle waiting time at a road cross. The paper further performs simulations for the proposed scheme and gives detailed analysis on parameters which have great influence on scheme performance. Results show that the proposed scheme outperforms the fixed-time-state traffic light control scheme on average vehicle waiting time.
apply improved adaptive PI algorithm to calculate the weight of each state, which is the key to determine the next state of a traffic light. If the current state is identical to the expected one, green light period is extended for the current state, otherwise the expected state is switched on, and its green light period is launched. All in all, our scheme aims to reduce the average waiting time of vehicles, thus to ease traffic congestion. The contributions of this paper are summarized as follows: (1) A model describing an road cross is given; (2) A novel traffic control scheme is proposed, in which both the upstream and downstream lane situations are taken into account and traffic light states are dynamically reasoned and adaptively adopt real-time traffic conditions; (3) The control scheme is scalable and easily implemented and maintained. The remainder of the the paper is organized as follows. Section 2 introduces related work. Section 3 addresses the proposed intelligent traffic light control scheme. Section 4 further performs simulations for the proposed scheme and gives detailed analysis on parameters which have great influence on the average vehicle waiting time, and the performance is compared with fixed-time-state scheme. Finally, conclusions and outlook are presented in section 5.
Keywords—Intelligent, Traffic Signal Control, Active Queue Management, Distributed
I. I NTRODUCTION In recent years, more and more countries have established transportation networks in order to improve traffic situation. While transportation networks are increasingly complexity and congestion, recent years’ experience witnesses that relying solely on construction of road infrastructure and adoption of traditional management approaches, is not only expensive, but also inefficient on easing traffic congestion. Therefore, traffic management departments of many developed countries, including the USA, Europe and Japan, have invested a lot of manpower and material resources, aided by advanced technology of computer, information, communication and artificial intelligence, to make extensive study of intelligent transportation systems. The analysis pointed out that one important cause of traffic congestion is unscientific management of traffic light control system [1] .
II. R ELATED W ORK A bunch of literature addresses intelligent traffic light control. Mathematical models were built for traffic lights and then classical algorithms and ideas of computer science were used for traffic light control [1] . Patent[2] comprised a microprocessor, a manual input device, an enforced switching device and an intelligent detecting device, in which a microprocessor was used for controlling traffic lights, a manual input device was used for inputting control parameters of traffic light to the microprocessor, and an enforced switching device was used for carrying a preferentially direct operation. The control system could automatically adjust the traffic light control parameters according to changes of traffic flow in different directions, thereby increasing the traffic efficiency of intersection of roads and achieving a best control for traffic. In reference[3], neural network(NN) was served as the basis for the control law, with NN weight estimation occurring real-time in closedloop mode via the simultaneous perturbation stochastic approximation algorithm. Learning procedure might last for months, or even years. Their simulation results showed a 10% reduction in vehicle waiting time. Tan et al. [4] placed 2 electromagnetic sensors on a road for each lane to count vehicles between them. They proposed a fuzzy logic controller which was responsible for controlling the length of the green time according to traffic conditions, and a state machine to control the sequence of states that the fuzzy traffic controller should cycle through. Reference[5] proposed a new approach to precisely forecast incoming vehicles in a road cross with the help of emerging wireless sensor network technology. WSN nodes could be classified into 3 categories, i.e., control nodes, detector nodes and vehicle nodes. Each node executed a very simple computation because the overall
On the problem of traffic light management, we observe that the fixed-time-state traffic light control scheme further hampers the improvement of traffic capacity. A lot of literature addresses the issue. A control systems usually consists of data collection, communication, data processor and control section, applying methods of fuzzy control, neural networks, multi-agent system and queue management. However, they are still far away from being put into practice. In this paper, we propose a novel intelligent traffic control scheme. Intelligence here means that traffic lights automatically reason their states and compute their durations depending on real-time traffic information, which is obtained by wireless sensors deployed along roads. Therefore, traffic lights are no longer switched on for fixed periods. What’s more, traffic light states of serial lanes are no more symmetric. We model a road cross at first, then discuss all the possible states of a road cross. Regarding lanes as queues, and vehicles as data packets, we 978-0-7695-4313-0/10 $26.00 © 2010 IEEE DOI 10.1109/GCC.2010.78
372
computation was distributed among different nodes, which reduced the complexity of each individual node. Reference[6] put forward a multi-agent approach for traffic light control. The system consisted of agents and their real world consisting of vehicles, road networks, traffic lights, e.t.c. Each of these agents controlled all traffic lights at one road junction by an observe-thinkact cycle, i.e., each agent repeatedly observed the current traffic condition surrounding its junction, and then used this information to reason with condition-action rules to determine in what traffic condition how the agent can efficiently control the traffic flows at its junction, or collaborate with neighbouring agents so that they can efficiently control the traffic flows at their junctions, in such a way that would affect the traffic flows at its junction. Reference[7] used the Adaptive Proportional Integral rate controller, a congestion control algorithm designed for the Internet, to deal with the congestion problem in a road segment of interest that was part of a complex road network. To use this algorithm, they proposed two kinds of roadside units which are installed at the entrance (source roadside unit (S)) and exit (destination roadside unit (D)) of the road segment, the units were used to control the volume of vehicle traffic arriving the road by controlling the traffic lights or by controlling the gates. Reference[8] discussed about an adaptive PI algorithm. We will use the improved weight-calculating method from[8] to calculate the lane weight. In above mentioned research work, wireless sensors or cameras are usually used to capture current traffic network conditions, which are the basic to control traffic lights. On selecting a state of a traffic light, they mainly poll the predefined states and set their time periods depending on upstream traffic conditions. Reference[6] and[7] also takes into account feedback from downstream traffic conditions.
Fig. 1.
A road cross
III. A N I NTELLIGENT T RAFFIC L IGHT C ONTROL S CHEME In this paper, we use a wireless sensor network to collect realtime traffic conditions. Several sensors are deployed along each end of a lane so as to count arriving vehicles. This section firstly builds a model for a road cross. Lanes are treated as queues, and vehicles as data packets. Then weights of possible states are calculated by improved adaptive PI algorithm, which is used to reason the next status of a traffic light. Whether the current state is kept relies on whether the current state is identical to the expected state. Each computation is independent of previous computing. Each selected state will hold the right to turn its green light on for a period. In the computing process, both upstream and downstream traffic conditions are considered. In order to prevent state starvation of red light with lighter traffic load, an ageing algorithm is utilized. Thus, even an upstream lane with low traffic load is permitted through before its red light is on for a too long time. The objective of the scheme is to decrease the average waiting time of vehicles across a road cross.
Fig. 2.
The states of a traffic light
possible state set of a traffic light is denoted by State = (s0 , s1 ...sn ) (n ∈ N ). The shortest duration of a state is t0 . The duration is increased by a time slice ∆t if necessary, record T = (t0 , ∆t).
B. The Model In this paper, we assume that each road of a road cross with twoway 6 lanes, i.e., N = 3, and consider all 3 downstream lanes of a road as a whole. We also assume that a vehicle turns left covering a distance of 7D, go straight covering 6D, and turn right covering 3D. The real-time data provided by wireless sensors are accurate and reliable. How to obtain these data is beyond the scope of this paper. Effects of pedestrians and bicycles are not considered. Hence, turning right is always allowed. Vehicles move strictly according to instructions of traffic lights. According to the model of a road cross, there are totally 12 predefined states of a traffic light, shown in Figure 2. Each state sx allows vehicles in two serial lanes Lij and Lmn to go through, we denote Li and Lm as the upstream of sx , and Lj and Ln as the downstream of sx . Each serial lane is associated with a weight value. The larger of the value means the more urgency to release the corresponding serial lane.
A. A Road Cross Suppose that a generalized road cross connects 4 roads, i.e., A, B, C and D, as shown in figure 1. It is denoted by an 8-tuple Cross = (N, D, Len, F I, F O, Car, State, T ). Each road contains 2N (N is an integer) lanes, including N upstream lanes, represented by L0 ∼ L4N −1 , and N downstream lanes, denoted by L4N ∼ L8N −1 . Each lane’s width is D. The lengths of its connecting 4 roads are denoted as Len = (la , lb , lc , ld ). The arriving vehicle streams of upstream lanes satisfy Poisson distribution with rates of F in = (f in0 , f in1 ...f in4N −1 ), respectively. The leaving vehicle streams of downstream lanes are F out = (f out4N , f out4N +1 ...f out8N −1 ), respectively. All vehicles are supposed to be equivalent vehicles with the same length of D, and the same interval space between consecutive vehicles of y, whenever vehicles stop or move. The speed of a vehicle is at a constant of v when it moves, and acceleration is neglected. To sum up, a vehicle information can be recorded as Car = (x, y, v). Each upstream lane Li with its corresponding downstream lane Lj together forms a serial lane, denoted by Lij . The
C. The Control Scheme 1) Weight of a Serial Lane: When choosing an expected state of a traffic light, the basic principle is to make sure that the upstream of a chosen state contains as many vehicles as possible, and that the downstream contains as few as possible. For a serial lane Lij , its
373
weight is calculated as follows: q = P · (n0 − n(t) + f ink − f outj ) + Q ·
t X
n0 − n(τ )
(1)
τ =0
where n0 indicates the target vehicle number of lane Lj , n(t) represents the current vehicle number of lane Lj , and P and Q are controllable parameters. To avoid congestion in downstream lanes, it must satisfy v · 1 = fout · lmin , where lmin is the average space between vehicles in the perfect state. The perfect state occurs when in a serial lane, the arriving rate equals to its corresponding leaving rate. lmin is related with the both rates and speeds of vehicles. Thus, lj l ·f n0 = lmin = j vout holds. The expression n0 −n(t)+f ink −f outj Pt manifests the current traffic status, and τ =0 (n0 − n(τ )) reflects the historical traffic status. Additionally, if a downstream lane is congested, the current traffic status has more effects on state choosing. If it is empty, the historical status takes more advantage in the computation. P and Q are weight values of considering the current one and the historical one. In fact, P is inversely proportional to Q. Combining these considerations, Q is calculated as follows: Q=
n0 − n(t) n0 · P
Fig. 3. Average waiting time with the same arriving and leaving rates of each lane
maybe controlled by another road cross. Thus both arriving rates and leaving rates of all lanes should be dynamically calculated when the scheme is applied to a traffic network. These messages are shared among related road crosses. As a result, each road cross can apply the scheme independently to control its own traffic. Consequently a distributed control of the traffic network is achieved.
(2)
Related to arriving rate f ini , queue capacity of K and current queue length k, P is calculated as follows:
IV. S IMULATION AND A NALYSIS
λi + k (3) K By integrating formula(1), (2) and (3), the weight of a serial lane is able to be computed. However, if some serial lane’s upstream is always empty and its downstream is always full, certain traffic light states may not be tuned for a long time, which leads to state starvation. In order to prevent from state starvation, red time duration t of each lane is taken into consideration by utilizing an ageing method. The ageing method is expressed as follows: ( 1, t ≤ t0 , (t−t0 ) v(t) = (4) 2 (β·t0 ) , t > t0 P =
In order to further analyze the performance of the proposed control scheme, a simulation program is developed. In this section, the influence of several key parameters on average waiting time is studied. Comparisons are made between fixed-time-state control scheme and proposed control scheme. The simulation program is developed by C++ language on Windows OS, and run on IBM T60, with processor Intel Core(TM) Duo Processor, RAM size 1GB and CPU clock speed 1.67GHz.
A. Effects of Arriving Rate and Leaving Rate Both arriving rate and leaving rate have a significant impact on system performance. If arriving rate is larger than its corresponding leaving rate, congestion may occur. In the first scenario, D=4, Len=(1000, 1000, 1000, 1000), Car=(5, 5, 16), T =(20, 5), β=1.0, and simulation time is 3600s. Figure 3 shows the relationship between fixed system and intelligent system on average waiting time, when all the arriving rates of upstream lanes are roughly the same and all the leaving rates of downstream lanes keep the same. Both curves increase as arriving rates increase. When arriving rates are low and no congestion occurs, average waiting time increases slowly as arriving rates increase. However, when arriving rates are high, which leads to congestion, average waiting time increases dramatically. Comparatively, average waiting time of the proposed scheme is around 13 of that of the fixed scheme when arriving rates are low. When congestion occurs in upstream, comparison is meaningless because arriving vehicle flow no longer satisfy the expected strength of the Poisson distribution and the actual arriving rate of the two systems are different. In the second scenario, the arriving rates are all set to 0.35 at first, then increased according to a vector (0.02, -0.01,-0.01,-0.02,0.03,0.01,0.02,-0.01,-0.01,-0.02,0.03,-0.01) for n times. Figure 4 shows that the average waiting time of both schemes increases as the arriving rates of upstream lanes increase, while the going-up pace of the intelligent scheme is much slower. For instance, the average waiting time of the intelligent scheme is less than 1/4 of that of the other at the beginning. The ratio reaches to at most 1/2 when the arriving rate is as 4 times large as that at first. Therefore, we conclude that, suppose a given sum of all the arriving rates, the greater difference of the arriving rates of upstream lanes, the more benefits of the intelligent control scheme can bring.
where t0 stands for the minimum green time, and β is a positive real number. Multiplying vt with q, the final weight of a serial lane is obtained as following: w = q · v(t)
(5)
2) Selection and Duration of Traffic Light States: When the duration of state s(t) comes to an end, a new state is launched, which holds the largest value among those of all the serial lanes at present. Due to that each state si contains two serial lanes Lmn and Lpq , and their final weights are wmn and wpq , in terms of formula Wi = wmn + wpq , si is assigned to a weight Wi . A state with the largest weight is always pick up as the next state, i.e., s(t + 1) = M ax si . i=0..11 If s(t) = s(t + 1), a time slice ∆t is increased to the current state’s duration. Otherwise, the current state is switched into s(t + 1), and its duration is set to t0 . Suppose that a vehicle enters an upstream lane at time tin , and leaves at time tout , then its waiting time at the road cross is twait = tout − tin . If totally M vehicles passed through all upstream lanes in T time, and the total waiting time of them is tsum , then the average waiting time at the road cross is tavg = tsum . M 3) Further Discussion: What we have discussed is traffic light control of a single road cross. The scheme can be easily extended to a traffic network. Firstly, downstream lanes are separately under consideration, i.e., each vehicle may turn to any of the three lanes of a downstream lane at a road cross. Additionally, each downstream lane may also be an upstream lane of another road cross, which actually controls its leaving rate. Similarly, arriving rate of each upstream lane
374
Fig. 4.
Fig. 5.
Average waiting time with varied arriving rates
Effects of vehicle speeds
TABLE I C OMPARISON ON AVERAGE WAITING TIME BETWEEN FIXED - TIME - STATE SCHEME AND THE INTELLIGENT SCHEME In Rate (0∼11) 0.23,0.37,0.3,0.49,0.41,0.45, 0.25,0.45,0.1,0.47,0.48,0.19 0.35,0.33,0.58,0.12,0.53,0.48, 0.35,0.27,0.18,0.39,0.39,0.23 0.18,0.36,0.25,0.21,0.52,0.45, 0.52,0.14,0.57,0.16,0.4,0.43 0.11,0.1,0.53,0.18,0.49,0.13, 0.47,0.28,0.45,0.59,0.49,0.37 0.46,0.46,0.13,0.57,0.27,0.56, 0.14,0.4,0.39,0.13,0.33,0.37 0.35,0.12,0.28,0.48,0.49,0.33, 0.34,0.44,0.34,0.21,0.33,0.5 0.35,0.29,0.28,0.59,0.25,0.55, 0.52,0.2,0.15,0.46,0.23,0.34 0.25,0.49,0.23,0.22,0.54,0.59, 0.52,0.4,0.16,0.23,0.46,0.11 0.55,0.45,0.33,0.59,0.23,0.42, 0.19,0.22,0.23,0.21,0.4,0.38 0.3,0.56,0.37,0.33,0.43,0.16, 0.39,0.2,0.32,0.4,0.35,0.38
Out Rate (12∼15)
Average Waiting Time Fixed Intelligent
Improvement (%)
1.23,1.13,1.39,0.84
68.68
55.93
18.56
0.72,1.42,1.3,1.16
38.58
29.60
23.28
0.88,1.27,1.07,1.37
44.49
31.07
30.16
0.93,1.59,0.92,1.15
66.33
37.64
43.25
1.44,0.7,1.25,1.22
55.71
35.97
35.43
1.52,1.05,0.76,1.28
50.62
29.54
41.64
1.23,1.13,1.4,0.85
55.70
41.63
25.26
0.83,1.31,1.41,1.05
74.29
57.47
22.64
1.29,1.02,1.18,1.11
57.41
48.94
14.75
1.01,1.21,1.22,1.15
46.69
39.39
15.64
Fig. 6.
Effects of vehicle lengths plus their margins
green light duration, fewer vehicles pass through a road cross, thus the total waiting time and average waiting time go up. A set of arriving rates of upstream lanes is randomly generated as shown in the first column in Table I. The result shows that the intelligent scheme outperforms the fixed system on an average of about 1/4. In addition, if a serial lane synchronously keeps very high or very low arriving and leaving rates, performance benefits of the intelligent scheme will be more dramatic.
C. Effects of Minimum Green Light Duration In the scenario, D=4, Len=(1000, 1000, 1000, 1000), Car=(5, 5, 16), ∆t=5, β=1.0, F O=(0.88, 1.27, 1.07, 1.37), F I=(0.18, 0.36, 0.25, 0.21, 0.52, 0.45, 0.52, 0.14, 0.57, 0.16, 0.4, 0.43), and simulation time is 3600s. Figure 7 shows the influence of minimum green time on average waiting time. With the increase of the minimum green time, the average waiting time increases. This is because in any state, only two upstream lanes are released, while the other six are waiting, thus the total waiting time and average waiting time increase. Furthermore, due to the impact of acceleration and some other factors are ignored, the declining curve may not be realistic.
B. Effects of Vehicle Speed, Vehicle Length, Vehicle Margin and Lane Length The ratio of vehicle length plus vehicle margin and lane length is regarded as a parameter, which reflects the queue capacity. In this section we discuss the influence of this parameter and vehicle speed on system performance. In the scenario, D=4, Len=(1000, 1000, 1000, 1000), T =(20, 5), β=1.0, F O=(0.88, 1.27, 1.07, 1.37), F I=(0.18, 0.36, 0.25, 0.21, 0.52, 0.45, 0.52, 0.14, 0.57, 0.16, 0.4, 0.43), and simulation time is 3600s. Figure 5 shows the influence of vehicle speeds on average waiting time. As vehicle speed increases, the average waiting time reduces. This is because in a given green light duration, more vehicles can pass through a road cross if their speeds are higher, thus reduce both the total waiting time and average waiting time. We observe that a lower bound exists due to the limit of arriving rates and minimum green light duration. In particular, if vehicle speed closes to infinite, average arriving rate is λ, and the minimum green time is the t, the lower bound 34 · λ · t is able to be reached. Figure 6 shows the influence of vehicle lengths plus their margins on average waiting time. Because of changing lane lengths may lead to a variation of travel time on the lane, we only change the sum of lane lengths in the scenario. In figure 6, with the increase of the sum, the average waiting time also increases. The reason is in a certain
Fig. 7.
375
Effects of minimum duration of green light
[6] V. Hirankitti, J. Krohkaew, and C. J. Hogger, “A multi-agent approach for intelligent traffic-light control,” in World Congress on Engineering, 2007, pp. 116–121. [7] B. Mohandas, R. Liscano, and O. Yang, “Vehicle traffic congestion management in vehicular ad-hoc networks,” in LCN, 2009, pp. 655–660. [8] Y. Hong and O. W. W. Yang, “Design of adaptive pi rate controller for best-effort traffic in the internet based on phase margin,” IEEE Trans. Parallel Distrib. Syst., vol. 18, no. 4, pp. 550–561, 2007.
Fig. 8.
Effects of β
D. Effects of Beta In the scenario, D=4, Len=(1000, 1000, 1000, 1000), Car=(5, 5, 16), T =(20, 5), F O=(0.88, 1.27, 1.07, 1.37), F I=(0.18, 0.36, 0.25, 0.21, 0.52, 0.45, 0.52, 0.14, 0.57, 0.16, 0.4, 0.43), and simulation time is 3600s. Figure 8 shows the effects of β on average waiting time. The average waiting time reduces when β increases. This is because the larger β is, the greater impact of current traffic status has on choosing the next state. For the same reason, there is a smaller average waiting time but also a greater maximum waiting time. On the contrary, the smaller β is, the greater impact of red light lasting time has on choosing the next state. For the same reason, there is a smaller maximum waiting time but also a greater average waiting time.
V. C ONCLUSION AND F UTURE W ORK In this paper, thanks to wireless sensor technique, we propose an intelligent traffic light control scheme. We describe a model of a road cross, which is the base of the control scheme. Simulation experiments are done to explore the scheme’s features. In general, the average wait time of the scheme is significantly less than that of the fixed-time-state scheme. In future studies, the influence of acceleration, pedestrian and e.t.c shall be taken into consideration. By considering downstream lanes separately, the scheme may be extended to control a traffic network, in which each road cross runs the same program. Thus, we are able to realize a distributed control of entire traffic network. In addition, with the development of VANET research, wireless sensors may be replaced by equipments in vehicles and roadside units, and further optimization may be achieved.
ACKNOWLEDGMENT The research work in the paper is partially supported by the Natural Science Foundation of China under grant No:60973122, and the 973 Program under grant No:2009CB320705 in China. Thanks to all the anonymous reviewers for their valuable comments.
R EFERENCES [1] F. Zhang and Y. Fan, Tr. China Railway Publishing House, 2005. [2] “Intelligent traffic light control system.” [Online]. Available: http://www.freshpatents.com/-dt20090702ptan20090167561.php [3] R. H. Smith and D. C. Chin, “Evaluation of an adaptive traffic control technique with underlying system changes,” in Winter Simulation Conference, 1995, pp. 1124–1130. [4] K. Kok, K. Marzuki, and Y. Rubiyah, “Intelligent traffic lights control by fuzzy logic,” Malaysian Journal of Computer Science, vol. 9, no. 2, pp. 29–35, 1996. [5] W. Chen, L. Chen, Z. long Chen, and S. liang Tu, “A realtime dynamic traffic control system based on wireless sensor network,” in ICPP Workshops, 2005, pp. 258–264.
376
