Research on Grid-Based Traffic Simulation Platform

Research on Grid-Based Traffic Simulation Platform

Jiankun Wu, Linpeng Huang, Jian Cao, Minglu Li，Xin Wang Department of Computer Science, Shanghai Jiaotong University, Shanghai, 200240, P.R.China [email protected] { huang-lp,cao-jian, li-ml }@cs.sjtu.edu.cn [email protected]

Abstract

extent has improved greatly.

The solution of traffic

With the improvement of the traffic complexity extent,

problem became more and more severe in reality, so

the solution of traffic problems becomes more and more

traffic simulation becomes an effective tool for analyzing

difficult in the reality, so traffic simulation is an effective

the traffic states and problems[1][18]. Based on the

method for analyzing the traffic states and problems. As

simulation results, it’s useful to make good decisions in

the simulation area becomes larger and more complicate,

traffic project and design effective traffic guidance.

which requires large computing and storage resources, it

Currently, there are some traffic simulation software

can't be resolved by the traditional computing technology.

developed by some organization, such as CORSIM,

Due to grid technology's advantages on such aspects, a

CONTRAM, CORFLO, PARAMICS[3] and so on.

new simulation architecture

which

Although they have advantages on some area, they

implements the HLA's [4] component as grid service and

mostly focus on special traffic area, so it is difficult for

combined with the agent technology is presented in this

users customizing the software with special needs.

named

GHA,

paper. Thus the simulation architecture can offer

As the development of the city traffic, traffic

high-capable computing resources to solve the complex

simulation confronts some problems, which include: (1)

traffic issues with great expansibility and flexibility;

demand of large computing resource, as the simulation

moreover, it also makes solid foundation to the

area become large and complicate, traffic simulation

authenticity of traffic simulation by adopting agent model

requires great computing and storage resources, it

the character of traffic entity.

couldn't be resolved by the traditional computing

At last, a traffic simulation platform is implemented

technology which just could simulate a small area or

based on GHA and the performance tests are given.

little road intersections, (2) requirement of flexible

Key words: Grid, Traffic Simulation, HLA, Agent

simulation styles, as the traffic entity’s behavior became more and more complicate, it needs a new intelligence and flexible technology to simulate the traffic entities, (3)

1.

Introduction

requirement of the coordination between subsystems, with

the

development

of

the

society,

different

With the progress of the society, traffic complexity

organization have developed their simulation software

This paper is supported by ShanghaiGrid grand project of Science

separately, it wastes of a lot money and time to do it,

and Technology Commission of Shanghai Municipality (No.

(4)lack of standard for data interaction, different

05DZ15005).

organization adopt different data format, so it is difficult

Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06) 0-7695-2751-5/06 $20.00 © 2006

for exchanging data between different simulation systems,

simulation; Section 4 presents the traffic entity model

(5) lack of dynamic Fault-tolerant capacity,

when the

and makes analysis; Section 5 the implementation and

simulation site is out of service for some reasons, the

performance test is given; finally, the summary and

simulation procedure will fail and have to restart.

further work plan are given.

Grid technology is aims to solve the resource sharing and coordination dynamically style based on Internet, it collects all the spare computing resource in the internet

2. Grid-Based Analysis

Simulation

Architecture

to satisfy the large computing resource requirement of some application [2]. It could solve some of the problems

2.1 Simulation Architecture

confronted in the traditional simulation system. So it is an effective method to build simulation system based on grid technology [19].

The simulation architecture is composed of three layers. The up layer is traffic simulation grid application

HLA（High Level Architecture）[4]is a high level

layer composed of different subsystems, which provides

simulation architecture that focus on the standardization

the interaction interface to the users that don’t need to

and reusing of the basic simulation components[5][7]. It

know the details of the simulation. The middle layer

provides flexible composition style for the large scale

consists of grid common middleware and simulation

simulation system. Different simulation subsystems

application grid middleware, which is the RTI service

could develop separately as long as they conform to the

implemented by grid service. The bottom layer is grid

specification of HLA, so the skeleton is suitable for the

resource which provides computing resource, storage

complicate development procedure. There are some

resource, agent platform, and some complicate traffic

research combining HLA and Grid, but these researches

computing service such as traffic flow forecasting [12]

just use the HLA for synchronization and can’t solve the

and so on, it supports the up layer by providing basic

preceding problems [6].

resources.

In Microscopic aspect, because the traffic entity’s behaviors are become more and more complicated, the

2.2 Layered Simulation Architecture

traditional simulation tools are not enough to describe the entity’s behavior completely. Agent is an entity that has

From the point of the simulation nature, the simulation

the characteristic of intelligence and autonomy and has

researched in this paper is divided into three layers. The

been used for simulation in some area successfully

basic layer is the models level, all the simulation

[8][9][11], so it is very suitable to describe the traffic

procedure depends on the model.

entity in the traffic environment.

Since the complexity of the simulation model, there

In this paper, we combine the grid technology, HLA

need large computation resources to support it, so above

and agent technology together to build a traffic

the model layer, there is a computation layer for

simulation platform in which grid technology connects

simulation computing and finally, the simulation system

grid resource to support the simulation and HLA

is built as showed in figure 2.

provides the high skeleton and agent simulates the traffic entity.

2.3 Development Procedure Supported by Grid

The remainder of this paper is organized as follows: In section 2 the simulation architecture is presented and some analysis are made; Section 3 discusses the key technology and gives the solution to the problem in the

There are six main steps to develop a simulation application, just as shown in figure 3. The first step is to define federation objective.

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Different developers could online coordinately define it

design the federation coordinately based on the already

through the coordination environment provided by grid.

existed service in grid environment.

The second step is to develop federation conceptual

The fourth step is to develop federation coordinately in

model. In this step, the developer could also adopt the

the grid environment according to the specification

style similar with first step.

defined in the model database.

The third step is to design federation in which

Step fifth is to integrate and test federation. At this step,

developer queries the MDS[15] to search suitable

developer

simulation grid service that had been registered to MDS.

simulation procedure conform to and adopt the

So in the first, we should develop the simulation service

coordinating style described in preceding steps.

should

define

relative

workflow

that

and register them to MDS. Finally, different developers

Fig. 1 Architecture of grid simulation

Fig.2 Layered simulation architecture Step sixth is to execute federation and prepare results.

At this step, execution federation uses the grid computing

Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06) 0-7695-2751-5/06 $20.00 © 2006

resource to execute complicated computing and agent to

uses the GridFTP[15] for high data transportation and

simulate the traffic entity behaviors. Results preparation

grid storage resource to store the simulation result.

Fig. 3 Simulation application development procedure

3. Key Technology and Solution

3.1 Interaction Architecture

...

vehicleAgent

...

...

bicycleAgent

vehicleAgent

...

...

bicycleAgent

... passengerAgent

...

... passengerAgent

Fig. 4 Simulation interaction skeleton For a complex multi-agent based distributed parallel

causality, which is the basic feature of parallel simulation.

traffic simulation system, three major concerns as

The second one is the computational power requirements

follows [17]: The first issue is how to maintain the

are very large and the third one is the communication

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overheads may be too heavy if each agent should

is responsible for which pieces of road so that it can

exchange information with each other. Our solution is

transfer a mobile agent to another one. Another issue is

based on coordination agent, which connects all related

the agents in the border of road pieces affect each other.

entity agents together and also maintains the corrections

Since agents staying in different road pieces can not

of time sequences. Each entity agent obtains the data

exchange information directly, mechanisms should be

from the coordination agent, and then triggers the actions

defined to allow them exchange information through

based on decision rules and reports the renewed status to

their corresponding coordination agents.

the coordination agent. When received the response from

The skeleton of simulation architecture is showed in

all entity agents, it notifies entity agents it manages to

figure 4. In this skeleton, the RTI service is responsible

obtain the information again.

for the synchronization between different coordinate

How to design coordination agent and allocate entity

agents and the corrections of time sequences. As showed

agents to the coordination agent is a very important

in figure 4, the coordinate agents communicate with the

topics. We can divide a road into several pieces and each

RTI service through embedding a RTI ambassador and

piece is allocated to a coordination agent, other agents

federate ambassador in it. Grid resource is responsible

which locate in this piece of road will connect to the

for the time cost and complicate computing such as

corresponding agent. Then a major concern is some

traffic flow predicting, traffic signal time distribution

agents, such as passengers, bikes, vehicles, move from

optimal, and so on.

one place to another. Their behaviors are controlled by themselves. Therefore, each coordination agent should

3.2 Synchronization Mechanism

have a global map and it knows which coordination agent

Fig. 5 Synchronization mechanism of different regions As described in previous section, the synchronization

this paper, we start every step with the RTI evoking the

between different simulation regions is very difficult. In

timeAdwanceGrant() method. And then the coordinate

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agent sends a command to all entity agents for advancing

include random

a step. As the entity agent receives the message, they

negotiation, target following. Social behavior includes

make

competitive, queuing.

behaviors

autonomously

and

report

self

walk,

collision

avoidance,

seek,

The decision rules include

information to the coordinate agent. When the coordinate

voiding collision, walking faster as possible, following

collected all the entity agent information, it requests a

traffic light, et al.

time advance to the RTI service, and at this point, one simulation step complete.

3) Vehicle Agent: The vehicle model is researched extensively and there are already quite a few mathematic models to describe the behaviors of a vehicle.

4.Traffic Unity Model Analysis

What we

have to do is to map these models to the structure of agent. The sensors of vehicle agent will obtain the speed

Within the traffic environment, the entity that has

of itself, the vehicles around itself and traffic signals. The

different behaviors under different situations can be

locomotion actions include driving forward, speeding up,

modeled as a separate agent. These agents include

slowing down and stopping. The steering behaviors

passenger agent, bicycle agent, vehicle agent, road agent,

include collision avoidance, seek, negotiation, target

traffic light agent, police agent and control center agent.

following. The social behaviors include competing,

1) Passenger Agent:

the behaviors of passengers

have a great impact on the traffic flow especially when

queuing. The decision rules include voiding collision, walking faster as possible, following traffic light, et al.

some passengers do not obey the traffic rules. Human

4) Road Agent: this agent is relatively simple because

individuals are different from each other by age, body

it has less intelligence and only act as an information

dimension, motility, and personality. Therefore the

broadcasting place. The road agent may provide some

generator will create the individual passenger randomly

guide information to the vehicles and it also checks the

according to the models defined.

status and reports it to the control center.

The sensor of

passenger agent can obtain the geometrical distance from

5) Traffic Light Agent: traffic light agent can be simple

the intersecting object, and also determine the type of

or complex. For a passive traffic light, it only changes the

object. The actions of passenger agent includes walking

light color according to the predefined rules. But the

forward, running forward, stopping, side-shifting, turning,

traffic light can be complex one, and it can change the

and moving backward. The steering behaviors include

rule according to the commands coming from the control

random walk, collision avoidance, seek, negotiation,

center.

target following, Social behavior includes competitive,

6) Police Agent: Police agent can change the road rules

queuing, herding. The objective behavior includes

according to the commands from the control center. It

passing this area, going to some places in this area. The

affects the behaviors of passengers, bikes and vehicles.

decision rules include avoiding collision, walking faster as possible, following traffic light, et al. 2) Bicycle Agent: Since a large part of people use bike

7) Control Center Agent: The control center will monitor the situation and send commands to the police, traffic light and the road agents. The sensor of this agent

as their transportation tool in China, we should consider

should collect all the data from the road agents.

the affections of bikes. The model of bike is some kind of

complex decision making models can be defined in this

mixture of passenger and vehicle agent models. Some

agent and the user can also change the decisions of this

road has a special lane for bike while some road has no

agent interactively.

this lane. The locomotion actions of bicycle agent include driving forward, speeding, slowing down,

5.Implement and Test

stopping, side-shifting, turning. The steering behaviors

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Some

The simulation platform is based on the grid

the runtime environment and implement the grid service

environment. In our simulation environment, the hard

conform to WSRF[14]. The entity environment is

resource includes the Itanium Servers, IBM690 and IBM

showed in figure 6. When the environment is built, all the

E1350 for complicate computation. And we adopt

resources include the agent platform register to the WS

Globus Toolkit 4.0[15] as the grid middleware to build

MDS [15] server.

Fig. 6 Environment of the traffic simulation Jade [13] platform was adopted to implement the

the synchronization time performance test was given and

traffic entity agent and all the implementations are using

it is showed in figure 7. As showed in figure 7, it is

java programmer language. MPI [20] was used to

satisfied the traffic simulation requirement.

parallelize the complicate traffic algorithms and was run by MPICH-G2[10]. coordinate agent Synchronized through the RTI service and the agent generator generate the traffic entity agent

150 time cost(ms)

The running procedure is as follows: First, different

conforming to the entity model and query the WS MDS

110 90 70 1

for the relative spare simulation node and make such

20

39

58

77

96

系列1

order

agent move to that node and join to corresponding coordinate agent.

130

Fig.7 Synchronization time performance test

When simulation confront complicated computation, it first query the WS MDS for the high performance computing resource information and then distribute the MPI traffic algorithm to it and using GridFTP to

Currently, a simulation prototype system based on the simulation architecture has been built and the simulation results are satisfied to the traffic demands.

transform computing data to the destination. In order to test the architecture proposed in this paper,

6. Summary and future work

Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06) 0-7695-2751-5/06 $20.00 © 2006

In this paper, a grid based simulation architecture

[8]

which combines HLA, agent and grid technology is presented. It provides high reliable, large scale and flexible simulation styles. And in the end a proto

[9]

simulation system is developed and performance test is given. In the future, we will develop more complicate agent model and make such simulation system to reality.

[10] [11]

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