Air Pollution Assessment through aMultiAgent-Based Traffic Simulation

Jesus Domınguez1, Luis M. Fernandez2, Jose L. Aguirre3, Leonardo Garrido4,and Ramon Brena5

1 A00779374@itesm.mx2 A00789695@itesm.mx3 jlaguirre@itesm.mx

4 leonardo.garrido@itesm.mx5 ramon.brena@itesm.mx

Center for Intelligent SystemsTecnologico de Monterrey, Campus Monterrey

Monterrey, Mexico

Abstract. The present document explores how air pollution can be as-sessed from a multiagent point of view. In order to do so, a traffic systemwas simulated using agents as a way to measure if air pollution levelsgo down when the traffic lights employ a multigent cooperative systemthat negotiates the green light duration of each traffic light, in orderto minimize the time a car has to wait to be served in an intersection.The findings after running some experiments where lanes of each direc-tion are congested incrementally, showed that using this technique, thereis a significant decrease in air pollution over the simulated area, whichmeans that traffic lights controlled by the multiagent system do improvethe levels of air pollution.

1 Introduction

The control of vehicle traffic in big cities is an important problem in modern life.Aiming at finding a solution to this problem, several computer-based applicationshave been developed being traffic simulators the most common ones. A trafficsimulator has the purpose of modeling, in a virtual environment, the conditions oftraffic in a city so that, from the behaviours observed in the simulation, decisionscan be made in order to generate better conditions for the involved entities [7].

In the last years an increasing number of simulators based on agents havebeen proposed. Unlike the conventional simulators where the modeling of thesystem is done at a macro level, in the simulators based on agents the modelingis performed at a micro level. This means that the system is made of severalentities (agents) and, for each one, it is possible to define specific behavioursthat indicate how those entities will respond to the events that happen in theenvironment in which they are immersed [1]. The behaviour of the system isnot defined beforehand, but it comes out (i.e. it emerges) from the interactionsamong the agents of the system.

An important part of a vehicle traffic system is the operation of the trafficlights. Traditionally, each traffic light is assigned static periods of time, in whichthe lights of different colors (red, green and yellow) are turned on. An intelli-gent system should, however, handle those periods of time based on the presentconditions of the traffic. Consequently, it is feasible to think that the vehicles’waiting time could be reduced if such traffic lights are handled intelligently. Asa result of this, some other aspects, like for example air pollution levels, couldbe reduced.

Due to the increasing interest in mantaining good air quality in big cities [9]and the apparent relation between the vehicles’ waiting time and air pollutionlevels, this work shows how air pollution levels diminish when the time assignedto a traffic light, when in green, is the outcome of a negotiation process that triesto diminish the vehicles’ waiting time. The proposed system follows a multiagentapproach aiming at reducing pollution levels.

The structure of the document is the following one: Section 2 comments aboutsome related works. Section 3 describes the multiagent system and the simulatorused. Section 4 shows the conducted experiments along with the obtained results.Finally, in section 5, the conclusions and future work are mentioned.

2 Related works

Eissfeldt et. al. [2] used a model (called Q-model) to measure the air pollutionlevels generated in a traffic simulation. The links in the network of the simulatedarea are represented by priorities and output queues, each characterized by theirmaximum flow (capacity), length and maximal velocity allowed. The approachis not explicitly based on agents, but allows the vehicles to have individualcharacteristics like route destination. The work focusses on the measurement ofair pollution and not in pollution reduction strategies.

Gualtieri et al. [4] presented a system that uses simulation as a way to fore-cast air quality on an urban area. The system uses a series of mathematicalmodels as the basis for traffic, gas emission and gas dispersion simulation. Allthe models are integrated in a Geographic Information System (GIS) that allowsthe description of the urban areas, road networks, and pollutants distributionsin the atmosphere. The work also focusses on the measurement of air pollutionbut from a macrolevel perspective.

France et. al. [3] presented a multiagent system for optimizing urban trafficnot only in one intersection but in several. They use an interesting hierarchicalorganization of agents, ranging from agents that control the light patterns inone intersection to agents that coordinate groups of intersections. The work isinteresting because it could be a basis for studying reductions in air pollutionwhen there is more than one intersection. Unfortunately, they model the carsas a quantity (traffic flow) difficulting the simulation of different gas emissionfactors per car, which is a more realistic situation.

The work presented here is distinguished from the previous ones in that itis not only focussed in the measurement of air pollution but also on how air

pollution could be reduced. Besides, each vehicle is modelled as an agent, thatpermits to associate individual characteristics to each one, like for example, theemission factor of the vehicles.

3 Proposed solution

Based on the assumption that a reduction in waiting time produces a reductionon air pollution levels, our solution extends the ideas from the work of [5]. Onthat paper, a cooperative multiagent system was proposed for the traffic lightcontrol on an intersection in order to reduce the waiting time of a vehicle. Wefirst describe briefly the simulator and the cooperative system used on that work,and then the extensions that are being proposed to assess gas emission.

3.1 Reducing waiting time

Simulator. In [5] a traffic simulator built on the synchronous engine of theMadKit Multiagent platform [6] is presented. It represents a world of a n ×mgrid. In this world appears an intersection of various lanes in every direction(North, East, South, West). The behaviour of cars and traffic lights is simulatednot under typical units (i.e. meters, seconds) but rather as environment states(i.e. execution cycle, position on the grid). The agents of the simulator are:

Car: It is purely reactive. It possesses a “vision” limit where it can detect in itsforward direction other cars and the states of lights. It can stop, decelerate,accelerate and turn direction.

Traffic Light: This agent has several internal states that represent the greenand red traffic light colors.

LightSet: Every traffic light agent belongs to a single traffic LightSet. This setis in charge of changing the state of its aggregated traffic lights. It has startand finish service times, which represent the times when the traffic lights aregoing to be on green state.

Traffic Manager: This agent is in charge of coordinating the different trafficLightSets in the simulation.

Source: This agent spawns cars with a predefined probability λ into theposition where the agent is placed. If that position is occupied by any othervehicle, the car will not be spawned.

Cooperative system. In order to diminish the time a car has to wait tobe served, they proposed a cooperative multiagent system that works in thefollowing abstract way:

1. The LightSet checks for the worst case of congestion (i. e. the Light Agentwho has the highest arrival rate of vehicles) among its aggregated lights.Then, it calculates the actual utilization rate. If this rate is greater thana “tolerance utilization rate” then it sends an open proposal message to

neighbor LightSets, asking them for time that they may not need. It alsocreates a cooperative group called coop group, for which it is the managerand has the role of leader.

2. When the other LightSets receive the call for proposals, they subscribe tocoop group with the role of providers. They propose a time quota (∆τ) tothe leader in function of their actual utilization state. The proposal is sendthrough a reply proposal message.

3. The leader keeps track of the proposals, then it determines which of theproposals is the best once a Light cycle T has passed (The Light cycle is thesum of the service time of all the LightSets). After that, it announces to thegroup who was the winner, it increases its service time by an amount of ∆τand it closes the group.

4. The winner decreases its service time by ∆τ .

3.2 Reducing gas emission

Extended simulator. The following additions where made to the simulator:

1. Every Car agent can now emit a constant quantity of CO (Carbon monoxide)gas in each execution step (i.e. the emission factor). This constant quantityis assigned to the Car Agent by the Source Agent at the moment of creationof the Car. Every emission factor has the same probability of being assignedto a new Car Agent. Every Car Agent can have a different emission factor.

2. A gas emission variable is present in each cell of the entire grid. When a CarAgent emits its gas quantity, this quantity is summed over the quantity thatis already present in the cell where the Car Agent is located. The emissionvariable is initialized with the zero value when the simulation starts.

3. The gas emission variable diffuminates over adjacent cells at a constant rate.It also evaporates at a constant rate.

4. A Gas Observer Agent is able to measure the value of the gas emissionvariable in each cell. Thus, it can compute the total value of the CO emissionper execution cycle. It also computes the CO emission average until thatmoment. This agent graphs and writes to a file, both the absolute value ata particular moment and the average value until that particular moment ofthe total CO emission variable.

Extended cooperative system. The cooperative system presented in lastsection only works when there is only one direction congested (i.e. the coopera-tive system is triggered only by one LightSet Agent); the reason of this is thatwhenever more than one direction is starting to congest, each LightSet Agentin charge of the congested direction starts a cooperating group concurrently asthe algorithm dictates; when this occurs, due to the concurrent execution of theagents, some rare situations can occur.

Let’s suppose the situation represented in figure 1. In this situation, thereare four LightSet Agents engaged in two concurrent cooperative groups. In thefirst group, the leader is A. In the second group, the leader is B. The circle

represents the Light cycle T and it must be read counterclockwise starting fromA. The start time and finish time (i.e. this interval defines the service time ofthe LightSet) are represented as a triangle and a square respectively for eachLightSet Agent. Let’s suppose that A emits the open proposal message. Only Canswers with the reply proposal message offering to A the time marked with thedotted arrow. Inmediately after that, B emits the open proposal message. Again,C answers with the reply proposal message offering to B the time marked withthe dotted arrow. Then, A announces to its group that C is the winner and closesits cooperative group. C adjust its finish time as indicated with the dotted arrow.A adjust its start time in the same quantity. The service time of B is movedto the left accordingly. But, after that, B announces to its group that C is thewinner and closes its cooperative group. C adjust its finish time in the samequantity as the first time, but in this situation, its start time will be after itsfinish time. B adjust its start time accordingly. The result is that C will mantainits green light at the same moment when D, A and B reach their green state.

Fig. 1. Two concurrent cooperating groups using the original cooperative system thatreach an akward result: at most two LightSet Agents will be in green state at the sametime.

As we can see, the problem arises from the fact that the Light cycle T is ashared resource, similar to the problem of the shared resource of a multithreadedsystem. Then, we need some kind of monitor that ensures that the resource isbeing accesed by only one cooperative group at a time. Instead of blocking andactivating cooperative groups, we use the simple mechanism of a negotiationtoken. A LightSet Agent can only start a cooperative group if he has the nego-tiation token.

So, the extensions to the cooperating system are as follow:

– The LightManager Agent is also in charge of passing the negotiation token toevery LightSet Agent of the intersection. The LightManager Agent assignsthe token sequentially to the LightSet Agents. When the negotiation tokenhas passed over all LightSet Agents, the LightManager will start assigningit again.

– A LightSet Agent can only start a cooperative group if it has the negotiationtoken. Once the LightSet Agent closes its cooperative group, it announcesto the LightManager to pass the token.

– If a LightSet Agent recieves the token, and it doesn’t need to start a cooper-ative group (i.e. its actual utilization rate is less than its critical utilizationrate) it announces to the LightManager to pass the token.

There will never be more than one cooperating group working at the sametime. Each LightSet Agent is going to have at least one opportunity to start acooperative group.

4 Experimentation

4.1 Simulation environment

The objective of the experiments is to compare the total CO emission when theLight Agents at the intersection use the extended cooperative system (we callthis: negotiation policy) with respect to the situation where the Light Agentsuse a constant time green duration (we call this: no negotiation policy). We wantto know if using the negotiation policy produces a significant decrease in totalCO emission.

The experiments where run over the following environment:

– A grid of 135× 135 cells.– One intersection consisting of 2 lanes in each direction (i.e. North, South,

East, West).– One Light Agent per lane; so there are 2 Light Agents in each LightSet.– One Source Agent at the start of each lane. (For example, the two lanes

headed to North have their two Source Agents at the South side of thelanes).

– Each Source Agent belonging to the same direction has the same spawnprobability.

– Table 1 shows the CO emission factors used for the vehicles. These emissionfactors are based on a vehicular park study made by Rogers [8] on Monterreycity in the year 2003. The units are interpreted qualitatively (i.e. one minuteis equivalent to one execution step in the simulator). This allowed us to use asimple real variable that represents the grams of CO emitted by the vehicles.

– The diffussion factor of the CO emission variable is set to 0.5 (or 0.5 gramsper execution step). The evaporation factor of the same variable is set to0.005 (or 0.005 grams per execution step).

– The initial green time of each LightSet is of 10 execution steps, with 2execution steps between a change in LightSets. This initial green time definesthe constant time green duration in the no negotiation policy.

– There is only one LightSet in green time at the corresponding moment, this isbecause the cars are able to turn right, left or go straight at the intersection.

Table 1. Average CO emission factors for some vehicle trademarks in Monterrey City.Units are: gr/(vehicle × min)

Particular vehicles

Ford GM Chrysler Dodge Chevrolet0.7148 0.696262 0.711 0.54931 0.51

Taxis

Chevrolet Volkswagen Nissan Ford Chrysler Dodge2.4392 2.7532 2.47834 2.65486 3.20426 2.038581

4.2 Experimental results

Using each policy (negotiation and no negotiation), we started congesting incre-mentally one direction at a time: North, West, South and East, in that order.By congesting a direction we mean setting the same value to the spawn proba-bility ps of the Source Agents at the given direction in order to full completelyall the lanes of that direction with cars (before the intersection) when the nonegotiation policy is used. Experimenting different values of λ, we found that asuitable value for congesting all the lanes of a direction is λ = 0.2 = λc and asuitable value for maintaining a direction with a minimum quantity of vehiclesis λ = 0.01 = λm.

The absolute value of the total CO emission (in simulated grams) with respectto execution steps (or simulated minutes) is shown in Figures 2(a), 3(a), 4(a) and5(a). The average CO emission (in simulated grams) with respect to executionsteps is shown in Figures 2(b), 3(b), 4(b) and 5(b). In each figure, the resultsof the negotiation policy are shown with a solid line, and the results of theno negotiation policy are shown with a dotted line. Each congested directionhas a spawn probability of λc, the other directions are left free with a spawnprobability of λm.

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Fig. 5. Results when all directions are congested.

The graphs show that after a considerable large number of execution cyclesthe system tends to stabilize over the average value. So, we took the averagevalue at the end of each experiment as an indicator of the air pollution levelthe system was able to achieve. Taking into account this point, the results foreach congestion experiment are summarized in table 2 for each of the involvedpolicies. From this, we can calculate the percentages of reduction in CO emissionthat the negotiation policy was able to achieve with respect to the no negotiationpolicy: For one congested direction 20.95%, for two congested directions 14.71%,for three congested directions 5.12% and all directions congested 1.21%.

Table 2. Average values of the CO emission in each congestion experiment with respectto each policy. The value was taken at the 30,000 execution step in all the experiments.

Final average CO pollution level

Congestion experiment Negotiation policy No negotiation policy

One direction 18,301 gr 23,150 gr

Two directions 47,510 gr 55,704 gr

Three directions 84,180 gr 88,725 gr

Four directions 108,500 gr 109,820 gr

As we can see the extended cooperative system has the highest CO reductionwhen there is only one congested direction. The amount of this reduction de-creases if we add more congested directions; the improvement is almost nothingwhen all the directions are congested. The reason for this is that when moreLightSet Agents are making petitions for more time, there are less LightSetAgents who can made offers. The situation is at an extreme when all the direc-tions are congested: No LightSet Agent wants to give away green time, so it onlyasks for more time.

5 Conclusions and future work

This work presented some extensions to a multiagent cooperative system thathandles vehicles’ waiting time on an intersection through a negotiation processbetween traffic lights. The extensions allow to handle more than one congesteddirection using a negotiation policy between the agents that represent trafficlights; they also allow to measure vehicle CO emissions in order to assess airpollution levels. The experimental results using the extended cooperative system,showed that attempts in reducing waiting time of vehicles in an intersectiondo improve the air quality in the area. The extended cooperative system hasthe advantage of its simplicity and modular design that permits it to be easilyaugmented.

More experiments can be done using another assignment policy on the nego-tiation process. For example, the LightManager Agent, responsible of assigninga negotiation token to the LightSet agents, which represents a set of Traffic

Lights, could use other assignment policies instead of the sequential one used inthis work in order to improve the probability of a LightSet Agent to recieve timeoffers from other LightSet agents when it needs to start a cooperative group. So,the LightManager Agent needs to have some kind of feedback from the LightSetAgents and to be able to do prediction based on its probabilities model.

Also, experiments can be conducted with different values for the diffusion andevaporation factors of the CO emission variable to see their influence on the airpollution levels. Other emission gases will be incorporated in the simulator to seeif the overall behaviour stays the same but with different reduction percentagesfor each gas. The initial green duration of the Light Agents could also be changedto explore if it produces different reduction percentages. Finally, in order toexperiment with global reduction in air pollution levels, more intersections can beadded to the environment where all intersections coordinate among themselves(i.e. global negotiation), or they can have their own independent cooperativesystem (i.e. local negotiation).

References

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