Verification of Genetic Algorithm in Dynamic Traffic Light Management Radek Fujdiak, Jiri Misurec, Petr Mlynek, and Tomas Petrak
Abstract—This Article deals with traffic light management in cities. Our solution is based on a sensor network, which should provide enough information for dynamic methods. This solution is about the implementation of genetic algorithms into the system of traffic light control systems to make it more efficient. The article provides a simulation tool for dynamic and static traffic light management simulation, based on multi-agent system and discrete event simulation algorithms. The results of the simulation, provided in this article, should prove the benefits of our dynamic solution for city traffic, which has an indisputable impact on the living standard of citizens in cities. Keywords—Sensor Network, Genetic Algorithm, Traffic Light, Traffic Management, Smart City, Multi-Agent system, Discrete Event Simulation.
I. I NTRODUCTION
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ITIES are growing every year as the number of citizens is growing and also the problems, which the cities have to deal with. The problems might be of different kinds. This article deals with traffic problems, especially with traffic jams and traffic control. Traffic jams have a significant impact on the environment where citizens need to live. Some of these problems include higher noise emissions, slower citytraffic, higher probability of accidents, worse air conditions thanks to bigger environment contamination with pollutants, emission of nitrogen oxides (NOx ), carbon monoxide (CO) and also the impact of the growing greenhouse effect with carbon dioxide (CO2 ), nitrous oxide (N2 O), methane (CH4 ). These and many other problems have a big impact on the living standard of citizens in the respective city and also the economical competitiveness of the specific location [1]. Cities are trying to deal with this situation, worsening yearly, as they can. They build new tunnels, thus bypassing the urban areas and expanding the existing pedestrian routes and other expensive solutions [2] [3]. The planet or the space for going in this way has some limits. It is not possible to only build new and bigger roads for many reasons as for example ground limits or space limits and it is also not a smart solution. Manuscript received February 28, 2015. The research described in this paper was financed by the National Sustainability Program under grant LO1401 and by the Technology Agency of Czech Republic project No. TA02020856. For the research, infrastructure of the SIX Center was used. R. Fujdiak is with the Telecommunication Department, Technical University of Brno, Brno CO 61200 Czech Republic (e-mail:
[email protected]). J. Misurec is with the Telecommunication Department, Technical University of Brno, Brno CO 61200 Czech Republic. P. Mlynek is with the Telecommunication Department, Technical University of Brno, Brno CO 61200 Czech Republic. T. Petrak is with the Telecommunication Department, Technical University of Brno, Brno CO 61200 Czech Republic .
978-1-4799-8498-5/15/$31.00 ©2015 IEEE
Cities are investing millions of dollars to the infrastructure and not every time it has the desired effect. Some of the roads are still busy and some are still quite empty [4]. Modern thinking at first brought to continuous driving of the whole roundabout without waiting at the traffic lights. The next step was completed in the era of growing computing power and many cities used computers for optimizing their city traffic [5]. Today, when every small possible thing gets smarter and people start to think quite differently about the world, it is also time to change the traffic management, because the existing solutions start to be insufficient [6]. The existing solution is a quite good optimize static solution for traffic lights and there is not a much space for doing it better [7]. The possibility might be in the telemetry network where hundreds of millions dollars are invested every year [8]-[13] and which could offer some possibilities how to make the traffic in the cities better. Dynamic traffic light management is one of these possibilities and this article will show one of the possible ways how it could be done. Network telemetry is a solution for future cities [14]. It offers great statistical information about the traffic and the whole situation in the city, which might be used for a dynamic system where the management could react in real time to the real needs of the city. Compared with the static solution of data collection or static traffic management, the dynamic method is a big improvement for the traffic management, for example instead of average data it uses real data, so we might immediately react to the real traffic problems in the city. Simulation tools are the best for early testing where our work is. It is not necessary to have a big grant support or stop for example traffic in some city part, but it is ideal to use some real data and real situations for the simulation. The city of Adelaide in Australia (Fig. 1) was chosen for our work because it is not ordinary and also has special needs. Our proposed method provides an alternative to the traffic light management. We expect an existing sensor network for collecting real-data. These data should be used by genetic algorithms for evaluating the traffic management in the city. We developed a method for this kind of traffic management and we simulated it in the real situation of Adelaide. Adelaide has a specific density or area situation compared to the other bigger cities for example in Europe (Tab. I). This situation is created by the Great Australian Bight on one side and the Adelaide Hills on other side of the city. The fact that in the 500 km neighbourhood around the city lives only 250 thousands citizens [15]. These conditions create a specific traffic situation when every morning people need to come to
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TABLE II S ATISFACTION OF THE CITIZENS WITH THE TRAFFIC SITUATION IN A DELAIDE Year Satisfied citizens [%]
Fig. 1.
The area of Adelaide in Australia
the city and put pressure on the main roads [1]. The traffic around Adelaide is optimized by a click-and-go traffic light system when pedestrians press a button and cross the road. This system is sufficient for the city government and citizens outside the city, but for the roads in the city centre it is highly insufficient. The citizens’ satisfaction of this system in Adelaide is shown in (Tab. II [16]). It is visible that there is a growing satisfaction of citizens, but even today this satisfaction is only around 50% and it has a decelerating character, which could mean that the traffic situation in Adelaide has started to come a standstill. The organization of the article is as following first is the State of the Art where the current situation is described and also presents the main points of our method, followed by the second part, the Dynamic Traffic Light Control System based on Genetic Algorithms where we describe the implementation, the process of simulation in Adelaide, concretely the input data and the main functionality of our algorithm. The last part is left for Simulation Results where we discuss the results of our work and compare it with static solutions. II. S TATE OF THE A RT There are many techniques how to optimize the traffic situation in city. It could be from bike-sharing, efficient public transport solutions to solutions which are more connected with the traffic as for example the car-sharing solution, restrictions, different kinds of traffic management and many other methods. The big part of traffic management in a city is the restriction and limitations which should decrease the number of cars in cities. Cities try to make other travelling options more interesting for the citizens and also try to provide alternatives and a good public transport network. Cities and companies also came up with the possibilities of car and bike sharing, but the main method in the restriction and limitation is traffic calming [17] including different engineering restriction methods or speed limits. These all restrict and hinder traffic and should make driving in the city or suburb less practical, comfortable TABLE I D ENSITY COMPARISON OF A DELAIDE WITH OTHER 1.3 MIL . E UROPEAN CITIES City Density [in/km2 ]
Adelaide 632
Zurich 1142
Nottingham 1821
CITIZENS OF
Prague 2601
2003 40
2007 45
2011 48
2013 49
or more expensive. The existing studies show [17] that these solutions might decrease traffic-related injuries or even deaths, but also mostly increase traffic congestion and problems. This solution might have an impact on the suburban zones where a lot of traffic is not expected. These zones have different needs as for example the safety of citizens (children playing on the street) and with this solution we could increase safety without a bigger impact on the continuous traffic, but this is not a solution for the city centre and highly urbanized zones with heavy traffic and even if we slowly decreased the number of cars in a city, a good city traffic management would still be necessary. There are two solutions for city centres and city-parts with heavy traffic - the static solution and dynamic solution. The static solution means that the city is collecting information about traffic and based on these data the operators in the control centre change the red-green timing in the traffic light at the crossroads. The static solution has many problems and it is slowly losing power and efficiency for traffic management with the growing traffic in cities. The data might be collected by two ways in the static solution. Manual collecting is when the data are averaged or not complete, because the city can manually collect data only in some particular period because it is necessary to pay a salary to the data-collectors (people who collect data). The static solution starts to reach its peak with all these problems and there is not so much space for improving this method in the way that it will have a bigger impact on the traffic results [18]. This is also a reason for searching for new ways for traffic management and with the more and more popular sensor network it has opened the way for the dynamic solution. The dynamic solution should provide real-time reaction and response in any time to the real situation in the city. The dynamic solution is a system, which should operate with a bigger view and also make decisions about traffic management based on the collected data (statistical decision making) but also on the concrete current situation (current situation decision making). This solution presumes and needs an existing sensor network. This method has high initial costs, but it is presumed a quick return of these costs because of low operating costs, less environmental damage, more fluent city traffic and many other influences. The dynamic system is suitable in highly urbanized areas where there are problems with traffic management. Compared to the static solution with modern algorithms and big computing power, it is possible in a short time to make a decision, based on real data, for individual traffic management and deal with the concrete current situation in the city. Our solution proposes a dynamic system, which is based on existing sensor networks for the collection of real data and monitoring the city traffic situation. We want to use genetic
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Fig. 2.
The schematic of (A) static solution and (B) dynamic solution
algorithms implemented into the traffic light management. It should help with making a decision about traffic light timing and optimize the traffic situation in the city. This article resents a simulation test of our future system and also a description of its possible functionality. III. DYNAMIC T RAFFIC L IGHT C ONTROL S YSTEM BASED ON G ENETIC A LGORITHMS Cities with sensor networks provide good conditions and opportunities for dynamic methods. The implementation of our solution is in (Fig. 2), and it is visible that we use the existing sensor network and database, which are connected with the monitoring centre & management. This data will be computed and evaluated by genetic algorithm, which will give options to the centre or which will make decisions and manage the traffic. A. Implementation of the Genetic Algorithm We developed the method for the traffic light simulation in Java language. We needed as objective language for the multiagent system and the whole concept of the simulation system, then we had choices between Java, C++, Objective-C, C# and Delphi. We chose Java as the multi-platform language, which is easy to use and adaptable to different kinds of devices, based also on the practice that the Java language is still staying in the first position as the most commonly used language [24]. Modern genetic algorithms (used scheme is in Fig. 3) provides optimization, a decision algorithm, traffic management and fast reaction to the actual needs. Genetic algorithms are a whole group of algorithms, which try to simulate the natural process of evolution and multiplication. Four basic functions are used: heredity, selection, crossing or mutation. The whole process starts with an initial population. This population is basic for selection, mutation and crossing. These procedures create a new individual that is a new generation represented by this individual. This new formed individual should fill the conditions of the computed fitness function. Our solution used for this problematic the open-source Java Genetic Algorithm Package (JGAP) library provided by Java framework [35]. This library was chosen for easy use, but highly modular for experienced users and it is based on traffic control systems [25] or in general it is used and tested in practice [26]-[29].
function. The individual car delay is the delay of cars at the end of the simulation. The crossroads are defined as a first step. Second, after the car is generated, it is the chosen way for the car (system input, system output). The input value will be not optimized because also in the real situation it is not effectively possible to reduce the incoming cars. The optimization process is about changing the timing of red and green light and also the timing between each traffic light. The genes 0 to 4 are used for the settings of the green light and genes 5 to 9 are used for traffic light timing between each other. The simulation used a multi-agent system for representing the traffic situation. The multi-agent system is used in general for network and grids and it is one of the tools for scientific modelling [30]-[33]. The traffic situation might be transformed to the computer network where the cars are packets and crossroads routers. The routers will create queues, route the packets to the required node and also they will search new possible ways when a problem occurs. The method creates agents as classes and dividing all the system also to the classes as for example Car, Simulation, Optimization and others. This division should help to better understand the whole method concept and algorithms. The integration of the multi-agent system has two basic tasks, which were necessary to fulfil [34]: • create an individual agent, which is able to represent a concrete entity, • create a collection of these agents (networks, which are able to communicate and exchange data between each other). The whole situation model was divided into objects and classes. The objects represent for example the class Car (represents cars), class Road (represents the connection between crossroads), class Crossroad (represents the crossroad situation). The simulation was done as a discrete event simulation. The discrete simulation is a discontinuous process, where in our case delay for example is measured. The events in our solution are represented by classes. The main classes are class Event (representing all the events in the program and it is used for inheriting the attributes), class EventSwitchLights (used for colour switching on the traffic lights), class EventMoveCar (provides car moving) and class DiscreteEventSimulation (provides discrete event tools). The situation could be described as follows: the car went into the system and in the same time it went to the last position of queue on the crossroad where it starts to be counted the One Generation
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Schematic of used genetic algorithm [1]
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Fig. 4.
Crossroads data input and output [1]
delay of the car. The car continues when the green light occurs, cross the crossroads in static 20second and continues to the next crossroads. The situation repeats until the car leaves the system, where its total delay is saved and released from the system. The five crossroads situation in Fig. 5 was used for the simulation [1]. Two system inputs are represented by one highway exit and one fast lane with 4 lanes and three system outputs are represented by three high-loaded crossroads (Fig. 4). The cars going in the Crossroad 1 will randomly choose their way from the system. These are their possible ways: • start from input Crossroad 1 to end Crossroad 3 and out from the system, • start from input Crossroad 1 through the Crossroad 3 to end Crossroad 4 and out from the system, • start from input Crossroad 1 through the Crossroad 3 to end Crossroad 5 and out from the system, and cars going in the Crossroad 3 will randomly choose one of these possible ways for going out of the system: • start from input Crossroad 2 to Crossroad 4 and out from the system, • start from input Crossroad 2 through the Crossroad 4 to end Crossroad 3 and out from the system, • start from input Crossroad 2 through the Crossroad 4 through the Crossroad 3 to end Crossroad 5 and out from the system. We presume in the settings of the simulation with 20 seconds, which the car needs for going out of the crossroads. One from the possible settings is generated at the start of the simulation and this setting stays until the end of the simulation for the objectivity of the results. The static system is optimized for 100% of the traffic situation in this simulation. The dynamic system has 16 types of optimizing, based on the hourly traffic situation. The system measures the whole delay or time, which is needed for a car to leave the system and this time is compared for the static and dynamic system.
2) Data Input: Five main and highly congested crossroads were chosen (Fig. 5), based on data from Adelaide Council [19]. The big traffic jams occur at 8-9 AM and 3-5 PM (Fig. 6) [20]. The Fig. 3 shows how the traffic during the day changes compared to the 100% average. The interval 6AM to 9PM is the time when the traffic lights work, at other times the traffic lights turn to orange-blinking mode and the traffic is managed by the traffic signs. The dynamic solution will not have a big impact on this low-traffic time when the orange mode works. The city of Adelaide collects these data statically. The static data collection have a bigger percentage of errors and it is also more expensive (costs of manual counting and data processing) [21]-[23]. Our method uses a functional telemetry network, which should reduce these costs and also allows data collection each hour of the day, creating better long-time statistics, shows data in real time and reacts also to these data. The static collected data were chosen for our simulation because the city of Adelaide does not have any other data. This should not have any impact on the statistical results of the simulation, because the dynamic system works much better with peaks than with the average value, which means that the dynamic system in our results will have a small disadvantage but in reality it should produce better results. 3) Simulation Results: The results of the simulation are in Tab. III. The results show how the dynamic traffic light management has a big impact on the city traffic. The variance between the delay of the static system (DSS) and delay of the dynamic system (DDS) is on average 13% and it should save
Fig. 5.
Chosen Crossroads in Adelaide City 160 140 120
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The traffic situation in Adelaide from 6AM to 9PM [1]
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TABLE III C OMPARISON OF DYNAMIC AND S TATIC S YSTEM Day Hour [24h] 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Average
Traffic [%] 70 110 150 115 100 95 90 95 105 115 150 115 95 80 65 50 100
DSS 76 121 171 126 108 103 99 103 113 126 171 126 103 86 70 66 111
DDS 71 107 125 112 108 95 88 95 102 112 125 112 95 77 62 63 97
Variance [%] 7,04 13,08 36,80 12,50 0,14 8,42 12,50 8,42 10,78 12,50 36,80 12,50 8,42 11,69 12,90 4,76 13,08
3 million dollars every year, based on Adelaide city council study [36]. DDS shows weakness in the low load time (even 0% compared with DSS) and a big impact on the high load time of traffic (36% compared with DSS). This means that DDS should be really used only in the areas with high traffic loads where normal traffic is not so low and peak traffic is really high (much higher than the normal situation). IV. C ONCLUSION Our proposed method was used for comparing the static and dynamic traffic light management in the city of Adelaide, which is based on a multi-agent system, genetic algorithms and discrete event simulation processes. The dynamic solution shows better results in the city of Adelaide and a big impact on the city traffic. The average value of 13% shows how much effective the dynamic system is or how less the citizens should wait with this system. It should also save around 3 million dollars per year, based on the Adelaide Council approximation study, but it would be necessary to do a bigger financial analysis, which would also take into account the investment to the sensor network and the whole system concept. This article proves the benefits of the dynamic traffic light management for a bigger city and shows its use in practice. Each city has specific needs and the dynamic solution cannot be possible in every situation or it cannot brings some appropriate greater results. Experience shows that people still have an interest in the traffic situation and also that the traffic situation has a big impact on their lives. First problem could be only time consumption of citizens, but the traffic problems ends also often with accident, and it is necessary to put attention to it. R EFERENCES [1] R. Fujdiak, J. Misurec, P. Mlynek and T. Petrak. Verification of Dynamic Traffic Light Control System in Adelaide City. [2] AA Motoring Policy Unit. Going Underground, TUNNELS: What role in town and country?, PA150. Boston MA, USA, June 2001. [3] Orlowski W., Walewski M. and Rytel M. Road Building in Poland: The facts and the myths, experience and perspectives, Report 13410. Poland, 2014. [4] Harris W. How Tunnels Work: Tunnel Construction (Soft Rock and Underwater), How stuffs works. Washington D.C., USA, 2010.
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