introducing driving behavior model into large-scale...

Master Thesis

Introducing Driving Behavior Modelinto Large-scale Multi-agent Traffic

Simulation

Supervisor Professor Toru Ishida

Department of Social Informatics

Graduate School of Informatics

Kyoto University

Yoshiyuki NAKAI

February 2, 2010

i

大規模マルチエージェント交通シミュレーションへの運転行動モデルの導入

中井喜之

内容梗概

エージェントベースシミュレーションが社会システムと社会現象を理解する

ために有用な技術であると認識されている．社会シミュレーションにリアリティ

を持たせるためには，仮想社会を構成するエージェントに実際の人間の行動を

割り当てることが有効である．また，社会現象を再現するためには実際の社会

と同じ規模でシミュレーションを行うことが望ましい．このように，大規模マ

ルチエージェントシミュレーションは世の中から求められていて期待できる技

術である．

日本の道路交通は運転者の高齢化や新交通システムの普及により大きく変わ

ろうとしている．将来の道路交通に起こりうる問題を予想したり，その問題を解

決する交通政策を検討したりするために，新しい技術が必要とされている．交

通シミュレーションは現実世界での社会実験と異なり，条件を変えて何度でも

交通政策の効果を検証できるという点で有用である．

交通シミュレーションには，2つの主要なアプローチがある．1つ目のアプ

ローチは，ミクロなモデルによる車両一台ごとの挙動の再現を目指すものであ

る．各車両が，走行速度の決定，車線変更，交差点での右左折，路上駐車，障

害物の回避などを再現できるようなモデルを持っている．2つ目のアプローチ

は，大規模な道路ネットワーク上で渋滞や各車両の静的／動的経路選択を再現

することを目指すものである．各道路リンクが，交通量–密度特性から車両移動

の計算をするようなマクロなモデルを持っている．

交通は，多数の運転者の個々の運転行動の蓄積で発生する社会現象である．ゆ

えに都市規模の交通政策を検討するためには，都市全体の交通流を見るマクロ

的な視点と同時に，政策の影響を直接受ける各運転者の行動に注目するミクロ

的な視点が必要である．そこで本研究では，下記の 2つの課題に取り組む．

1. 運転行動モデルに基づく大規模交通シミュレーションプラットフォームの

構築

1つ目の課題は，ユーザーが都市全体の交通流を見るマクロ的な視点と同時

に，各運転者の行動に注目するミクロ的な視点を持つことを可能にするシ

ii

ミュレーションプラットフォームを構築することである．プラットフォーム

は大規模交通をミクロレベルで再現できなければならない．プラットフォー

ムの各運転者エージェントはそれぞれの運転行動モデルを持ち，環境を認

識して車両を運転することができる．

2. 例題となるシミュレーションによるプラットフォームの検証

2つ目の課題は例題となるシミュレーションによってプラットフォームの可

能性を示すことである．例題の設定は道路ネットワークとエージェントの

設定からなる．道路ネットワークは道路と交差点の集合である．エージェ

ントの設定はOD（起終点）と運転行動モデルである．

本研究の貢献は下記のとおりである．

1. ミクロモデルとマクロモデルを統合するエージェントと道路リンクの設計

1つ目の貢献はプラットフォームのためのエージェントと道路リンクの設

計をすることである．エージェントは経路選択のレイヤと運転行動のレイ

ヤの 2つからなる．経路選択レイヤは抽象的な道路ネットワークにおける

経路を決定し，運転行動レイヤは運転行動モデルに基づいて道路リンク上

の車両を運転する．各道路リンクは 2次元平面上の道路を持っていて，そ

の上を車両が通過する．プラットフォームはMATSim-Tという名前のツー

ルキットを用いて実装された．MATSim-Tは大規模エージェントベース交

通シミュレーションを効率的に実行できる．

2. 運転行動モデル導入の検証とシミュレーション実行時間の検証

プラットフォームは検証のため，2つの例題に適用された．1つ目の例題は

単純であり結果を予測することが可能で，プラットフォームが正しく動作

していることを確認できた．2つ目の例題は複雑であり，プラットフォーム

が運転行動モデルに基づいて大規模シミュレーションを実行する能力を持

つことを示せた．

本研究は運転行動モデルを大規模交通シミュレーションに統合する方法を提

案し，その設計を実現するための実装を行った．シミュレーションを行ったと

ころ，いくつかの場合において，異なる運転行動を持つエージェントは異なる

経路を選択した．プラットフォームは大規模シミュレーションをミクロレベル

で実行できる．

iii

Introducing Driving Behavior Model into Large-scale

Multi-agent Traffic Simulation

Yoshiyuki NAKAI

Abstract

Agent-based simulation is recognized as a useful technology for understanding

social systems and social phenomena. In order to make a social simulation

have more realism, it is effective to assign a real human behavior to each agent

which makes up the virtual society. In addition, it is desirable to conduct a

simulation on as a large scale as the social phenomenon to reproduce. Thus

large-scale multi-agent simulation is a promising technology which modern so-

ciety demands.

There are so many changes in Japan’s road traffic such as aging of the

drivers or the widespread use of new traffic systems. New technologies are

needed for predicting the problems in the future road traffic or consideration of

a road traffic policy to solve the problems. Unlike pilot programs in real world,

simulations are useful in examining the effect of a road traffic policy as many

times as the policymaker wants.

There are two major approaches to a traffic simulation. One approach is to

reproduce the behavior of each vehicle driven by the microscopic model. Each

vehicle has the model which can determine its driving speed, lane change, turn-

ing left or right, street parking, and how to avoid obstacles. Another approach

is to reproduce traffic jams or routing of drivers on a large-scale road network.

Each road link has the model which can calculate the position of vehicles on

the link according to the relationship between traffic volume and density.

Road traffic is a social phenomenon in which a number of individual drivers

participate. Thus both a macroscopic point of view and a microscopic point of

view are needed when the policymaker examine the policy. The challenges of

this study are as follows.

1. Development of large-scale traffic simulation platform based on

driving behavior model

The first challenge is to develop a simulation platform which allows users

to see urban traffic from a microscopic point of view and a macroscopic

iv

point of view. The platform must be able to reproduce large-scale traffic

at the micro level. Each driver agent in the platform has its own driving

behavior model to recognize the environment and drive its vehicle.

2. Platform verification by example simulations

The second challenge is to show the potential of the platform by examples.

The configuration of an example simulation contains a road network and

configurations of agents. A road network consists of roads and intersections.

An agent configuration includes an OD (Origin-Destination) and a driving

behavior model.

The contributions of this study are as follows.

1. Design of an agent architecture and a road link architecture to

integrate micro model into macro model

First contributions is to design an agent architecture and a road link ar-

chitecture. The agent consists of two layers: a layer of routing and driving

behavior. The routing layer determine the route on the abstract road net-

work and the driving behavior layer drives the vehicle based on driving

behavior model on a road link. Each road link has a two-dimensional sur-

face in which vehicles run through. The platform is implemented using

a toolkit named MATSim-T which can effectively run a large-scale agent-

based traffic simulation.

2. Verification of introducing driving behavior model and execution

time of simulation

The platform was applied to two examples for verification. First example

was simple enough to predict the results of the simulation and confirm

that the platform worked properly. Second example was complex enough

to show the capability of running a large-scale simulation based on driving

behavior models.

This study proposed how to integrate a driving behavior model into a large-

scale traffic simulation and implemented the platform to realize the proposed

design. In some cases, agents with different driving behavior models choose

different routes during the simulation. The platform is able to run a large-scale

simulation at the micro level.

Introducing Driving Behavior Model into Large-scale

Multi-agent Traffic Simulation

Contents

Chapter 1 Introduction 1

Chapter 2 Traffic simulation models: micro and macro model 5

2.1 Traffic simulation based on micro model . . . . . . . . . . . . . . . . . 5

2.2 Traffic simulation based on macro model . . . . . . . . . . . . . . . . 8

Chapter 3 Platform for Large-scale traffic simulation based on

driving behavior model 11

3.1 Design of driver agent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Design of road link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Process and data flow of the platform . . . . . . . . . . . . . . . . . . 18

3.4 Data used inside the platform . . . . . . . . . . . . . . . . . . . . . . . . 21

Chapter 4 Simulations for platform verification 26

4.1 Introduced driving behavior model . . . . . . . . . . . . . . . . . . . . . 26

4.2 Two routes selection simulation without driving behavior model 27

4.3 Two routes selection simulation based on driving behavior model 31

4.4 Simulation of traffic in a real road network . . . . . . . . . . . . . . . 38

Chapter 5 Conclusion 44

Acknowledgments 46

References 47

Chapter 1 Introduction

Agent-based simulation is recognized as a useful technology for understanding

social systems and social phenomena. In order to make a social simulation

have more realism, it is effective to assign a real human behavior to each agent

which makes up the virtual society. In addition, it is desirable to conduct a

simulation on as a large scale as the social phenomenon to reproduce. Thus

large-scale multi-agent simulation is a promising technology which modern so-

ciety demands.

Japan is an aged society and has a great percentage of elderly drivers. Ac-

cording to the white paper on traffic safety in Japan 2009 1), people aged 65 and

older had the largest number of fatalities of any age group, 2,499, accounting

for more than 48.5% of all fatalities. This was the sixteenth year in a row that

this segment has remained at the top. In terms of the mode of transporta-

tion used by the accident victims aged 65 and older, pedestrians accounted for

47.7% of all, and the second largest number was of automobile, accounting for

23.5%. Compared with the past decade, injuries sharply decreased for the 16-24

year-old (70% down), but rose for the 65 and older (18% up).

Ministry of Land, Infrastructure, Transport and Tourism is promoting the

diffusion of Intelligent Transport Systems 2)(ITS) to improve the traffic envi-

ronment in Japan. ITS is a new traffic system which solves various problems

such as traffic accidents or traffic jams by connecting human driver and roads

and vehicles using leading-edge information and communication technologies.

For example, VICS sends road traffic information about traffic jams or traffic

regulations in real time so that the information is displayed on a car navigation

equipment. As of 2009 September, over 37.1 million units of car navigation sys-

tems have been sold, and the number of VICS on-board devices sold in Japan

has reached 25.1 million units. Electronic Toll Collection System (ETC) is a

system which aims to eliminate the delay on toll roads by collecting tolls elec-

tronically. The number of vehicles with transponder devices was only 51,6000,

1) http://www8.cao.go.jp/koutu/taisaku/h21kou haku/2) http://www.mlit.go.jp/road/ITS/

1

accounting for 0.9% of all vehicles at December 2001, but increased to 5,890,600,

accounting for 82.3%, at December 2009.

A century after the appearance of automobiles, switching from gasoline cars

to eco-friendly cars like hybrid gas-and-electric car is an inevitable worldwide

trend. Major automakers has began to sell their gas-and-electric cars and elec-

tric cars, and new entry into the market is increasing. Those eco-friendly cars

are so popular among consumers in Japan. According to the ranking of the new

vehicle sales volume 2009 1) released by Japan automobile dealers association,

Toyota’s hybrid car “Prius” has reached the top for the first time, and Honda’s

hybrid car “Insight” has reached eighth place.

There are so many changes in Japan’s road traffic as above. New technologies

are needed for predicting the problems in the future road traffic or consideration

of a road traffic policy to solve the problems. Unlike pilot programs in real world,

simulations are useful in examining the effect of a road traffic policy as many

times as the policymaker wants.

There are two major approaches to a traffic simulation. One approach is to

reproduce the behavior of each vehicle driven by the microscopic model. Each

vehicle has the model which can determine its driving speed, lane change, turn-

ing left or right, street parking, and how to avoid obstacles. Another approach

is to reproduce traffic jams or routing of drivers on a large-scale road network.

Each road link has the model which can calculate the position of vehicles on

the link according to the relationship between traffic volume and density.

Road traffic is a social phenomenon in which a number of individual drivers

participate. Thus both a macroscopic point of view and a microscopic point of

view are needed when the policymaker examine the policy.

Figure 1 shows how to realize traffic simulation based on driving behavior

model obtained from human subjects. Driver modeling step is to obtain driving

behavior model. Based on the method of participatory design [1], a method of

obtaining a driving behavior model from a human subject operating a driving

simulator was proposed[2, 3]．Simulation and analysis step is to conduct a

1) http://www.jada.or.jp/contents/data/ranking/pdf/ranking2009.pdf

2

Driving Simulator

DomainKnowledge

Driving Log

Traffic Simulator

DrivingBehavior

Model

Driver Modeling Simulation and Analysis

Subjects Traffic Policy Maker

Figure 1: Traffic simulation based on driving behavior model obtained from

human subjects

simulation based on driving behavior model and analyze the results by a traffic

policy maker.

In the driver modeling step, driving log can be acquired from the driving

simulator and domain knowledge from the subject by interview. Subjects are

asked reasons and their state of mind for each operation while driving the car. a

driving behavior model, which is able to drive a car recognizing the environment,

is made from driving log and domain knowledge. In the simulation and analysis

step, obtained driving behavior model is used for a large-scale traffic simulation.

Thus a large-scale traffic simulator based on driving behavior model is needed

for this step.

The challenges of this study are as follows.

1. Development of large-scale traffic simulation platform based on

driving behavior model

The first challenge is to develop a simulation platform which allows users

to see urban traffic from a microscopic point of view and a macroscopic

point of view. The platform must be able to reproduce large-scale traffic

at the micro level. Each driver agent in the platform has its own driving

behavior model to recognize the environment and drive its vehicle.

2. Platform verification by example simulations

The second challenge is to show the potential of the platform by examples.

3

The configuration of an example simulation contains a road network and

configurations of agents. A road network consists of roads and intersections.

An agent configuration includes an OD (Origin-Destination) and a driving

behavior model.

Chapter 2 introduces models and purposes of existing simulators which be-

long to either microscopic approach or macroscopic approach. Chapter 3 de-

scribes the design of the platform for large-scale traffic simulation based on

driving behavior model. Chapter 4 shows simulation results and reviews for

verification of the developed platform. Chapter 5 is the conclusion of this the-

sis.

4

Chapter 2 Traffic simulation models: micro and

macro model

There are two major approaches to a traffic simulation: a microscopic approach

and a macroscopic approach. “Driving behavior model” and “Large-scale traffic

simulation” are the key words of this study. Driving behavior model has been

studied in the area of traffic simulation based on micro model. Large-scale

traffic simulation has been studied in the area of traffic simulation based on

macro model.

This chapter introduces models and purposes of existing simulators which

belong to either microscopic approach or macroscopic approach.

2.1 Traffic simulation based on micro model

This approach aims to reproduce the behavior of each vehicle driven by the

microscopic model.

HESTIA

To address traffic problems, we generally build more roads, but an alternative

solution is to increase existing roads’ capacity by intelligent transportation sys-

tems (ITS) infrastructure. ITS allows autonomous vehicles to run cooperatively

with each other.

In a collaborative driving system (CDS), the coordination of each vehicle

controller’s actions must be resolved. To solve the problem, each vehicle be-

longs to a platoon of more than ten vehicles. Each platoon has the leader and

other vehicles follow him. The important task in a collaborative driving sys-

tem is to coordinate vehicles during the entrance and exit of vehicles from the

platoon. CDS should communicate information about obstacles or high decel-

eration ahead, thus allowing platoon members to follow each other at a closer

distance. These platoons of automated vehicles increase traffic throughput and

lowering fuel consumption due to an average higher cruising velocity and lower

accelerations[4].

[4] proposed a driver model which consists of three layers as shown in figure

2 for CDS. Guidance layer is the bottom layer which contains environment

5

Traffic Control Layer

Management Layer

desired state

criteria

actuatorssensors

otheragents

otheragents

communicationsoutput

communicationsinput

sensed data

communicationswith road

Guidance Layer

Agent

Figure 2: Layered architecture of driving agent for collaborative driving system

sensors and operates an accelerator and a decelerator and a steering wheel.

Management layer is the middle layer which communicates with surrounding

vehicles and decides how to run cooperatively. Traffic control layer is the top

layer which exists on the road and constrains lower layer to obey traffic signs,

traffic signals and observe traffic rules. Traffic layer gives criteria to management

layer and get vehicle information from management layer. Management layer

gives desired state to guidance layer and get sensed data from guidance layer.

This model was implemented and evaluated on a simulator named HES-

TIA developed by Universite Laval. A vehicle in HESTIA includes longitudinal

and lateral vehicle dynamics, wheel model dynamics, engine dynamics, torque

converter model, automatic gear shifting, throttle/brake actuators and steering

wheel actuator. Each vehicle has an interface that allows a driver to control

accelerator/brake actuators, steering wheel actuator, external and internal sen-

sors, and communication receiver/transmitter.

INSPECTOR

INSPECTOR is a microsfopic traffic flow simulator of an urban road network

developed by Nagoya University. In INSPECTOR1), Each vehicle has a model

which can reproduce the speed of itself, lane change, turning left or right at

1) http://www.genv.nagoya-u.ac.jp/ge1/nakamura/research/INSPECTOR/

6

intersections, street parking, and avoiding obstacles. This model can reproduce

not only each vehicle’s behavior but also traffic jams caused by vehicles waiting

for loading, street parking, or vehicles searching for parking lots in urban traffic.

Each vehicle has not only a vehicle behavior model but also a user behavior

model. The user model is able to decide where to park and select the route etc.

A driver stochastically chooses the route from three possible routes considering

the travel time to the goal, the number of lanes in the route, and the number

of times turning left or right. The user model can reproduce each driver’s

selection of parking place, the duration of parking, and the route selection.

INSPECTOR is applicable to the evaluation of traffic policies such as a control

policy of street parking in urban streets, street improvement, and information

service about traffic jams.

The amount of traffic at intersections, the travel time in arterial roads or nar-

row streets. Barometers such as the number of loading and unloading vehicles,

street parking vehicles on each street were reproducible results. INSPECTOR

was used for the verification of the policy that introduces transit mall into the

section of the street between Hirokoji-fushimi intersection and Sakae intersec-

tion in Nagoya City. The simulation time was five hours from noon to 5 pm

using the road network with 358 nodes and 857 links. Policies like one-way

street regulations, street parking regulations, intersection improvement were

examined.

tiss-NET

tiss-NET [5] is a local traffic simulator developed by Saitama University. Roads

are modeled as a sequence of meshes. The length of a mesh is five meters.

Vehicles run based on car following model through the meshes. Vehicles also

includes route selection model. Thus the simulator is applicable to a network

of roads.

tiss-NET is suitable for a simulation of an urban road network and can be

used for the prior evaluation of traffic policies like intersection improvements,

street improvements, traffic regulations, traffic signal controls, transportation

planning around large-scale retail store, and street parking regulations.

7

tiss-NET 2006 1) is a release version of tiss-NET developed by LITEC Corpo-

ration. tiss-NET 2006 can load GIS data and create the network for simulation.

Attributes like the number of lanes, speed limit, signal control, one-way regu-

lation can be assigned to the network.

Statistics on average travel time of vehicles by the section between selected

nodes can be displayed. Each vehicles’s information about vehicle type, travel

time, travel distance, and route are output. It takes two and a half minutes

to run a simulation with 23 links, 24 nodes, 40 minutes simulation time, and

3400 vehicles on the machine which has Pentium 4 2.4GHz processor and 2GB

memory.

2.2 Traffic simulation based on macro model

This approach aims to reproduce traffic jams or routing of each vehicle on a

large-scale road network.

SOUND

SOUND[6] is a traffic flow simulator for a wide area road network. SOUND/4U2)

is a release version of SOUND. In SOUND/4U, each road link has a macro model

which calculates the position of vehicles on the link based on the relationship

between traffic volume and capacity. The computational load of this model is

low enough to apply the model to a large-scale network. Each vehicle has also

a dynamic/static route selection model. Those two models work together to

move the vehicles.

Figure 3 shows how each road link hold vehicles on it. Vehicles on a link

exists in either a running queue or a waiting queue. A vehicle coming to a

new link is added to the running queue first. When the elapsed time since the

entrance is longer than free speed travel time, the car is moved to the waiting

queue. The upper limit of the number of vehicles on the road link is jam density.

In order to strictly manage the density based on the relationship between traffic

volume and capacity, Simplified Kinematic Wave[7] algorithm was implemented

in addition to queue based calculation.

1) http://www.litec.co.jp/product/tiss/tiss-2006.html2) http://www.i-transportlab.jp/products/sound/

8

RoadNode

RoadLink

RunningVehicles

WaitingVehicles

RoadLink

RunningVehicles

WaitingVehicles

Figure 3: Road link as two FIFO queues

Least cost route selection model or logit choice model is available for each

vehicle. Vehicles have to choose their routs when they depart, enter a road link,

and the cost of road link is updated. The cost of a road link is the sum of the

length of the link, the current travel time, the number of times turning left or

right, and the toll fare. The costs of road links are updated once every five to

fifteen minutes.

SOUND/4U is able to examine the traffic policies like change of traffic reg-

ulations such as right turn prohibition, parameters of signal control, temporary

road blocking or entrance restriction, or to recommend drivers to change routes

and take detours by information supplement.

MATSim-T

MATSim-T1) is a toolkit for implementing an agent based large-scale traffic

simulation. MATSim-T is a open source software developed by Universitat

Berlin and Swiss Federal Institute of Technology Zurich. MATSim-T is superior

to other traffic simulators as follows[8]．

• Computational savings when compared to the calculation and storage of

large multidimensional probability arrays.

• Larger range of output options, from overall statistics to information about

each synthetic traveler in the simulation.

• Explicit modeling of the individuals’ decision-making processes.

MATSim-T is used for a traffic simulation of the entire area of Switzerland

in the morning rush hour. The simulation was conducted on the road network

1) http://www.matsim.org/

9

of the entire Switzerland using ODs1) provided by the regional planning division

in Switzerland. The results of the simulation was compared with instrumental

data of the real traffic and found to be at least as good as existing methods[9]．

The primary objective of MATSim is to simulate traffic flows over extensive

areas and to optimize travel demands. Within a simulation, agents learn their

route running through following steps:

1. Search the initial route using road travel time based on free speed.

2. Move along the road network along the route.

3. Score the executed route based on the actual travel time.

4. Decide the next route by choosing from executed routes with scores or

searching the route using road travel time in the previous simulation.

5. Return to step 2.

A road network is implemented as the network of FIFO queues like figure

3. This implementation is so simple that large-scale agent based simulation can

be realized efficiently, but it cannot run realistic microscopic traffic simulations.

For example, there is no way to consider roads’ alignments or interactions among

vehicles, which are needed for modeling local driving behaviors.

1) Origin-destination of agents

10

Chapter 3 Platform for Large-scale traffic sim-

ulation based on driving behavior

model

Vehicles in a simulation based on microscopic model consists of layers and is

capable of complex decision as shown in figure 2. Vehicles in a simulation based

on macroscopic model move from a queue to another queue as shown in figure

3. The queue based model is simple enough to run a large-scale simulation.

In this study, the platform which allows users to analyze a traffic flow from

both the microscopic and macroscopic points of view was developed. Users

can analyze the traffic focusing on the behaviors of the drivers and viewing the

entire city at once. Such a platform requires integration of a micro model into

a macro model.

In this chapter, the design of a driver agent and the design of a road link

required for introducing driving behavior model into large-scale multi-agent

traffic simulation are proposed. The process and input/output of the platform

is also described.

The platform is implemented using MATSim described in Chapter 2 as a

macroscopic traffic simulation. MATSim-T can run large-scale traffic simulation

efficiently.

3.1 Design of driver agent

An agent of this platform is a driver of a vehicle and called driver agent. A

driver agent is independent from a vehicle so the driver agent can drive any

other vehicle. Figure 4 shows the architecture of a driver agent which consists

of a layer for route selection and a layer for driving behavior. The design of

layered architecture comes from traffic simulations based on microscopic model

in Chapter 2.

The routing layer gets a road travel time from the driving behavior layer.

The road travel time is the required time for going through a certain interval of

the road. A driver agent communicate with other driver agents and send/receive

11

Routing Layer

route

from otheragents

to otheragents

traveltime

operation

traveltime

travel time

Driving Behavior Layerfromenvironment

Driver Agent

road

other vehicles

to itsvehicle

Figure 4: Driver agent composed of routing layer and driving behavior layer

the road travel times. The communication of the road travel times is done by

routing layer. The routing layer uses road travel times to generate the least

cost path in terms of the whole travel time from the origin to the destination.

The least cost path is sent to the driving behavior layer.

The driving behavior layer gets a route from the routing layer. The route

is defined as a set of road links. When the driver agent arrived at the end of a

road link, the agent is moved to the next link as defined in the route. In the

road link, each vehicle exists on the two-dimensional road. Since vehicles on

the two-dimensional road can pass the other cars and run at the desired speed,

which is different from the case of the queue model, vehicles do not leave the

link in order of arrival.

The routing model which has an important role in the routing layer and the

driving behavior model which has an important role in the driving are described

below. An agent has a routing model for selecting the path on the wide-are road

network and a driving behavior model for driving the vehicle in the local traffic

conditions.

Routing model of agent

Each agent has a routing model. There are four types of routing model of the

agent in this platform. Types of agents’ routing models are as follows:

1. ReRoute

An agent search the new least cost path. The cost of each link for search

12

is the road travel times in the previous simulation. The road travel time is

the required time running through the road, which is updated every fifteen

minutes. The new least cost path is the next plan. Even if the route is

the same route in existing plans, it is viewed as the different plan since

the route was found based on the different road link costs. In the initial

routing, the road travel time is calculated as dividing the length of the road

link by the free speed of the vehicle since the road travel time is unknown

before the first iteration.

2. BestScore

An agent chooses the highest scored plan from existing plans.

3. KeepLastSelected

An agent chooses the same plan as it chooses in the previous iteration.

4. Random

An agent randomly chooses a plan from the existing plans.

All agents have the four types of routing model described above, and each

agent randomly uses a routing model from the four types at each iteration. The

platform configures the percentage of the number of agents selecting each plan.

For example, the configuration is like that the percentage of the agents using

ReRoute is 10% and BestScore is 90 %.

Driving behavior model of agent

Each agent has a driving behavior model. This platform allows each agent

to have an individual driving behavior model. The driving behavior model of

each agent has common three steps as described below. Agents recognize the

environment, interpret the situation and decide what to do next, and operate

the vehicles.

1. Recognition

An agent recognizes the environment like the condition of its vehicle, the

condition of the vehicles around it, and the road alignments. The condi-

tions of its vehicle, which are available to the agent, are the position, the

velocity, the acceleration, and the current lane. The conditions of the other

vehicles, which are available to the agent, are the position, the velocity, the

acceleration. The agent can get the information of the nearest vehicles in

13

front of it, on the left, on the right, or behind it. The agent can get the

information about the current road link. Each road link has attributes like

length, width, the number of lanes, curvature, and slope as shown in the

table 3.

2. Determination

An agent decides what to do next from the information acquired during

the recognition step. Rules of decision making can be implemented by

users of the platform. The combination of conditions acquired during the

recognition step can define a new condition. For example, “Right lane is

empty” can be defined as “The difference between the position of the car

in front on the right and the position of its car is more than 20[m].” and

“The difference between the position of the car in front on the left and the

position of its car is more than 20[m].”

3. Operation

An agent operates the accelerator pedal, the brake pedal, and the steering

wheel of the vehicle based on the results of the determination. An agent in

this platform is not a vehicle but a driver. Operation is the step in which

drivers drive the vehicle.

3.2 Design of road link

Vehicles driven by agents with driving behavior model run through the flat

surface road. The two-dimensional road exists in each road link. The two-

dimensional road in a road link is independent from the road of another road

link during the calculation of each simulation step.

Figure 5 shows vehicles on the road link. Running vehicles exist on the

two-dimensional road. Vehicles near the intersection exist in the queue. The

queue based design comes from traffic simulators based on macroscopic model.

If the next road link is full of entering vehicles, then those vehicles have to wait

in the queue until the next road link is able to accept them. The queue allows

the platform to connect the road links without the complex simulation at the

intersection.

While each vehicle running through the two-dimensional road, the driving

14

RoadNode

RoadLink

RunningVehicles

WaitingVehicles

RoadLink

RunningVehicles

WaitingVehicles

Figure 5: Vehicles on the road link

behavior layer decides next driving behavior from the condition of its vehicle, the

conditions of vehicles around it, and the current road. The driving behavior

layer operates the accelerator pedal, the brake pedal, and the steering wheel

based on the decided driving behavior. The driving behavior layer record the

departure/arrival time for each road link, and calculate the road link travel

time, and send it to the routing layer.

In this platform, microscopic model is integrated into macroscopic model as

above by designing each agent to have both a routing layer and a driving layer

and designing each road link to have a two-dimensional road and a queue for

waiting vehicles.

Sequence diagram of traffic flow simulation

Figure 6 is a sequence diagram which shows messages among major objects

during the traffic flow simulation. There are six major objects as follows:

• TraficFlowSimulator (Main body of traffic flow simulator)

• SimulationStep (A simulation run every unit of time)

• Node (A road node in a road network)

• Link (A road link in a road network)

• Vehicle (A vehicle moving on the road network)

• Driver (A driver which drives the vehicle)

In a traffic flow simulation, there is a loop executed every unit of time. The

loop includes a loop for each node and a loop for each link. The loop for each

node has a loop for vehicles entered to the node. The loop for each link has a

loop for vehicles entered to the link. The process of traffic flow simulation is

proportional to the number of road nodes, road links, and vehicles on the road.

15

Here is the details about messages among major objects:

TrafficFlow

Simulator

Simulation

StepNode Link Vehicle Driver

loop: [SimulationStep : SimulationSteps]

loop: [Node : Nodes]

loop: [Vehicle : Vehicles]

loop: [Link : Links]

loop: [Link : Links]

1. run()

2. moveNode()

4. moveVehicle()

3. getWaitingVehicles()

Vehicles

7. getDriver()

Driver

6. moveLink()

5. getNextLink()

Link

loop: [Vehicle : Vehicles]

8. driveVehicle()

9. getEnvoronment()

10. doOperation()

11. update()

Environment

Figure 6: Sequence diagram of a traffic flow simulation

1. TrafficFlowSimulator executes SimulationStep．SimulationStep is executed

once per unit of time in the simulation. The unit of time in the platform

16

is 0.5 seconds by default.

2. SimulationStep starts the process of Node. The process of Node is repeated

for each Node in the road network.

3. Node gets Vehicles ready for leaving a Link whose end point is this Node.

4. Node moves ready Vehicles to the next Link. If the Link is full of Vehicles,

Node does not move Vehicles.

5. Vehicle gets the next Link from Driver.

6. SimulationStep starts the process of Link. The process of Link is repeated

for each Link in the road network.

7. Link gets Driver who drives the Vehicle from each Vehicle running on the

Link.

8. Link makes Driver drive Vehicle.

9. Driver gets Environment from Link. This process is the recognition of the

environment which is the input to the driving behavior model.

10. Driver drives Vehicle. The operation is calculated by driving behavior

model.

11. Vehicle tells Link its condition. The condition is like the position and the

velocity.

Road on the two-dimensional surface

Each road link has a road on the two-dimensional surface and a queue of vehicles

ready for leaving the link. In the microscopic traffic simulator, each road link has

two queues of running vehicles and waiting vehicles. This platform replaced the

queue of running vehicles with the road on the two-dimensional surface in order

to calculate the vehicle behavior on the raod link based on driving behavior

model.

Figure 7 is an example of road on the two-dimensional surface. A black

circle is a road node and an arrow is a road link. Rectangles are placed on the

left of the road links to express the road shape. If a road link is bidirectional,

the link is like Link-1 and Link-2 with rectangles on both sides. If a road link

has only one lane, the link is like Link-2. If a road link has two lanes, the link

is like Link-1 and Link-3. Since Link-3 is one-way, it has only a rectangle on

the left. A road node, which is an intersection, does not have a road on the

17

two-dimensional surface.

Link-1

Link-2

Link-3

Figure 7: Example roads on the two-dimensional surface

When a agent enters a road link, the agent is put on the two-dimensional

surface. The agent moves on the surface based on its driving behavior model.

When the agent comes to the end of the rectangle road on the surface, it is

moved to the queue of vehicles waiting for the next road link. The process of a

road node is to move a vehicle from the waiting queue to the two-dimensional

surface on the next road link. The process of a road link is to move a vehicle

on the two-dimensional surface and move a vehicle at the end of the rectangle

road to the waiting queue.

3.3 Process and data flow of the platform

Figure 8 shows the main process and the data flow of the platform. The platform

iterates three processes: traffic flow simulation, scoring, replanning. The driving

behavior layer of the agent is executed during the traffic flow simulation. The

routing layer of the agent is executed during the scoring and the replanning.

An iteration corresponds to one day of the simulation time.

Input to the platform are a road network and initial plans. The initial plan

18

3. Replanning

2. Scoring1. Traffic FlowSimulation

Snapshots

InitialPlans

RoadNetwork

to Viewer

Iteration

Data Flow Data Process

Events

Plans ScoredPlans

Figure 8: Process and data flow of the platform

of an agent is the least cost path calculated on the road network to satisfy the

OD of the agent. The cost is defined as the length of road link divided by the

free speed. The free speed is the speed of a vehicle which freely runs through

the road without any disturbance, which is defined for each road link.

While the platform iterates traffic simulation, scoring, and replanning, each

agent keeps learning by searching for a much better plan which satisfies its OD.

The score of the plan depends on how long it takes to execute the plan; shorter

plan is better. The actual travel time depends on the density of roads caused

by the route selection of other agents. Thus the plan which is thought to be

the best is not necessarily the best plan.

Strictly speaking, each agent’s learning never ends, so users of the platform

must decide when to stop the iterations. When some routes are found by agents

and the number of agents for each route becomes stable, it is the time to stop

the simulation.

Output from the platform are the snapshots and the agents’ learned plans.

Both snapshots and the plans are dumped every iteration. The snapshots are for

the viewer software as described later in this section. The number of iterations

19

required for enough learning of agents depends on the size of the simulation.

Factors of the size are the number of agents, road links, and simulation time.

Three processes of one iteration is described below. Data used by those

processes are described in the next section in detail.

1. Traffic Flow Simulation

Input data are a road network and agents’ plans. Output data are events

and snapshots. Traffic flow simulation is conducted based on the driving

behavior model. The driving behavior layer works for the simulation based

on driving behavior model. An event is a record of an agent’s action with

the time and the place (road link). A Snapshot is made up of records of

agents’ positions and conditions at each simulation time step. Snapshots

are playable by the viewer software in the platform.

2. Scoring

Input data are events. Each agent has to score its executed plan. The

routing layer works for the scoring process. The score is calculated based

on the actual travel time in the previous traffic flow simulation. The travel

time is computable from the departure time and the arrival time recorded

in events.

3. Replanning

Input data are scored plans and events. Each agent has to reconsider its

plan. The routing layer works for the replanning process. Existing plans are

scored based on the results of the past traffic flow simulation. For searching

a new plan, previous road link times are available since the routing layer can

communicate with other agents and the driving behavior model of itself.

The road travel time is computable from the leaving/entering time for each

road link recorded in events.

The input to the platform are as follows.

Road Network

A road network is a set of roads (road links) and intersections (road nodes) in

the area to simulate. The road network is used by both traffic flow simulation

and replanning.

20

Initial Plans

The initial plan of an agent is the least cost path calculated on the road network

to satisfy the OD of the agent. OD of the agent is the combination of the start

and the goal. The start and the goal is specified by the road link ID.

The output from the platform are as follows.

Snapshots

A Snapshot is made up of records of agents’ positions and conditions at each

simulation time step. A record in the snapshot consists of an agent’s position,

velocity, and direction as shown in table 1.

Table 1: Record in Snapshot

Type1) Attribute Notes

long Simulation time Elapsed time in ms from 0h0m0s0ms

int Agent ID

double Latitude North latitude, Tokyo Datum, in second

double Longitude East longitude, Tokyo Datum, in second

float Velocity in meters/second

float Direction East is 0, counterclockwise rotation, in rad

The snapshot is generated every 0.5 seconds which is the length of one step

of the simulation in this platform. The viewer software plays snapshots filling

the gap between the steps of the simulation.

Learned Plans

Learned plans are the plans exported by replanning process at the last iteration.

Plan is described in the next section.

3.4 Data used inside the platform

In this section, data inside the platform are described. Figure 9 is the class

diagram which shows relations among data during the traffic flow simulation.

A TrafficFlowSimulator has a RoadNetwork. A RoadNetwork has Nodes and

1) Type notation of Java

21

Links. A Link starts at a node and ends at a Node. There are Vehicles on a

Link. A Vehicle is driven by a Driver. A Driver has Events and Plans. A Plan

has a Score.

RoadNetwork

Node Link

Vehicle Driver

Plan

Score

Event

TrafficFlowSimulator

1

*

1

*

1

*

1

*

1

1..*

1

1 1

1

1

1

has

has has

has

drives

starts at

ends at

has

has

has

Figure 9: Class diagram of relations among data during traffic flow simulation

Road network

A Road network consists of road nodes and road links. Table 2 shows the

major attributes of a road node. Each road node has ID and two-dimensional

coordinate. Table 3 shows the major attributes of a road link. Each road link

has ID, IDs of nodes on both ends, length, width, the number of lanes, capacity,

curvature, and slope. A road link is directed and has ordered pairs of nodes:

source node and target node. A Two-way street is defined as two road links;

each link is 180 degrees from another link.

22

Table 2: Major attributes of road node

Attribute Meaning

id ID of the node

x, y two-dimensional coordinate, in meters

The abstract architecture of a road network connecting road links is used by

agents’ routing models. The attributes like length, width, curvature, and slope

are used by agents’ driving behavior models.

Table 3: Major attributes of road link

Attribute Meaning

id ID of this road link

from ID of the source node

to ID of the target node

length Length in meters

width Width in meters

permlanes The number of lanes

freespeed Free speed of vehicles in meters/second

capacity The limit number of vehicles per hour

curvature Curvature of the road

slope Slope of the road

Plan

A Plan is a route which satisfies the OD of an agent. A route is ordered IDs of

road nodes. An agent creates at most one Plan per iteration. At each iteration,

each agent selects a plan in the replanning process and executes the plan in the

traffic flow simulation. Plans executed in the traffic flow simulation is scored

based on actual travel times of the plans. At the end of the simulation, agents

have more than one plan.

23

Event

Event is a record of agent’s action with time and place, which is defined as a

set of simulation time, ID of road link, and the type the action. There are five

types of actions:

1. The agent arrived at the goal link at the time.

2. The agent left at the start link at the time.

3. The agent was waiting at the road link because the capacity of the next

link is not enough at the time.

4. The agent entered the road link at the time.

5. The agent left the road link at the time.

Users of the platform can analyze the Events to know:

• Traffic volume of each road link

Traffic volume of each road link is the number of vehicles passing through

the road link. The number of passing vehicles is countable by selecting

the leave events of the agents which occurred at the target road link. The

number of passing vehicles for each period of time is countable by selecting

the agents’ leave events which occurred at the target road link at the period

of time.

• Driving distance of each agent

Driving distance of each agent is the sum of the lengths of the road links

which the agent passing through. Road links which the agent passing

through can be identified by selecting the leave events of the agents which

is an action of the target agent.

• Travel time of each agent

Travel time of each agent is the time from the departure to the arrival

at the goal. The departure/arrival time of each agent can be identified

by selecting the agents’ departure/arrival event which is an action of the

target agent.

• Average driving speed of each agent

Average driving speed of each agent is driving distance of each agent divided

by travel time of each agent.

• Route selection of each agent

24

The route selected by an agent can be identified by selecting the agent’s

leave events, each of which includes a road link.

• Average driving distance of agents having the same OD

Agents having the same OD should be specified first, and the average driv-

ing distance of them can be calculated.

• Average travel time of agents having the same OD

Agents having the same OD should be specified first, and the average travel

time of them can be calculated.

• Ratio of each route selected by agents having the same OD

Agents having the same OD should be specified first, and the route selection

of them can be countable.

Score

Agents remember the score of each plan when the plan is executed. Score of a

plan represents how good the plans is to the agent. The plan which requires

shorter travel time is better plan. Score is calculated based on the travel time

in the previous traffic simulation so it is not sure that the highest scored plan is

the best in the next traffic simulation. The travel time of the plan is the time

from the departure of the agent to the arrival of the agent.

25

Chapter 4 Simulations for platform verification

In this chapter, simulations using the platform are shown. These simulations

aim at the verification of the platform.

4.1 Introduced driving behavior model

The driving behavior model introduced to simulations in this chapter is Optimal

Velocity Model[10] which is one of the car following models. Rules for lane

change in the case of slow car in front are also defined.

Optimal velocity model

The acceleration of a vehicle is the difference between the optimal velocity and

the current speed.

Let n denote the order of a vehicle, and xn denote the position of the vehicle

n, the acceleration of the vehicle n:

d2xn

dt2= a

(U(xn−1 − xn) − dxn

dt

)(1)

Note that a is an index of sensitivity of the driver agent, and U is the optimal

velocity as a function of the distance h between two vehicles:

U(h) =u

2

{tanh

(h

8− 2

)+ tanh(2)

}(2)

Note that u is the desired speed of the driver agent. For example, let u = 50,

then the graph of the optimal velocity function U is like figure 10. If the gap

between vehicles is 0, then the optimal velocity is 0; as the gap becomes bigger,

the optimal velocity becomes bigger; the desired speed is the limit of the optimal

velocity function.

Rules for lane change

Thanks to the optimal velocity model, agents can avoid crashing into the car

in front and follow the car in front. However, there is another way to avoid

crashing besides deceleration by braking. It is lane changing. There are two

statements which must be true for the lane change:

1. “An agent has to decelerate to follow the car in front.”

The acceleration of the agent is calculated by the optimal velocity model.

26

10

20

30

40

50

2010O 30 40 50h[m]

U(h)[km/h]

Figure 10: Optimal velocity function when u = 50

So if the equation (1) is negative, then the statement is true; otherwise,

the statement is false.

2. “The lane on the left or right has enough space.”

“The lane on the left has enough space” is defined as “The gap between

the vehicle diagonally forward left and the agent is more than 15 [m].” and

“The gap between the vehicle diagonally backward left and the agent is

more than 15 [m].” “The lane on the right has enough space” is defined as

the same.

When these two statements are true, the agent change the lane with a fixed

probability. Let us call the probability “lane change probability”. Lane change

probability is a factor of characteristics of the agent.

4.2 Two routes selection simulation without driving be-

havior model

In this simulation, agents have to choose one route from two routes. The con-

figuration is simple so that the results of the simulation can be predicted. This

simulation is not based on driving behavior model in order to compare with

the simulation based on driving behavior model. Each agent has no model for

vehicle behavior. Each road link has a macro model for vehicle behavior.

27

Configuration

Figure 11 shows the road network which has two routes from the extreme left

node to the extreme right node. The number next to each link is the number

of lanes. There are three lanes on the road link from the extreme left node to

the branch point. Let us call the upper route “Route-1” and the under route

“Route-2”. Route-1 has one lane and Route-2 has two lanes. There are three

lanes from the meeting point to the extreme right node.

Origin

Route-1

Route-2

3 3

1 1

2 2Destination

Figure 11: OD of agents and the number of lanes on the two routes network

There are 100 agents leaving the node on the left extreme at intervals. The

limit number of vehicles for each lane per hour is 3,000. The limit number of

vehicles, 9,000 vehicles per hour, leave the node on the left extreme. Roads

having one lane are capable of 3,000 vehicles per hour, and roads having two

lanes are capable of 6,000 vehicles per hour. So if the number of agents for each

route is imbalanced, the route with too many vehicles will be jammed and it

takes longer time to go through the route than another route. 1)

ODs of all agents are the same: from the node on the left extreme to the

right extreme. The number of iterations are 100, from the iteration 0 to the

iteration 99. In the replanning process for each iteration, 10% of the agents use

ReRoute model and 90% of the agents use BestScore model.

Three configurations of initial plans for each agent are examined.

1) When the capacity of the road link is large enough compared to the number of vehiclesentering the road link, the travel time has nothing to do with the route selection of eachagent.

28

1. Route-1: 100 agents, Route-2: 0 agents



Expectation

The results of the simulation are predictable since the road network is simple

and ODs and departures of agents are completely controlled. The expectations

are as follows:

If the number of agents for each route is imbalanced, the route with too

many vehicles will be jammed and it takes longer time to go through the route

than another route. The route selected by too many vehicles will not be selected

by ReRoute model or BestScore model during the next replanning, and agents

changed the route to another route. Eventually, the number of agents for each

route will stable at the ratio of the number of lanes, 1:2.

Review of the results

Figure 12 is the graphs showing the number of agents for each route. Each

graph shows the result of three different configurations from the top to the

bottom: Route-1: 100 agents, Route-2: 0 agents; Route-1: 50 agents, Route-2:

50 agents; Route-1: 0 agents, Route-2: 100 agents.

The graph legends show the color of Route-1 and Route-2. The horizontal

axis is the iteration, and the vertical axis is the number of agents for each routes

at the iteration.


From iteration 0 to around iteration 10, the number of agents which choose

the Route-1 decreased and the number of agents which choose the Route-

2 increased. From around iteration 20, the number of agents for each

route oscillated. The average number of agents which choose Route-1 from

iteration 19 to iteration 99 was 31; the average number of agents which

choose Route-2 was 69. The ratio of 31:69 is about 1:2, thus the result is

said to be correct.




29

0 20 40 60 80Iteration

Numberof Agents

1. Route-1: 100 agents, Route-2: 0 agent (Initial Plans)

2. Route-1: 50 agents, Route-2: 50 agents (Initial Plans)


0

20

40

60

80

100

0

20

40

60

80

100

0

20

40

60

80

100

Route 2

Route 1


Route 2

Route 1


Route 2

Route 1

Figure 12: The number of agents for each route (Without driving behavior

model)





30

said to be correct.



the Route-2 decreased and the number of agents which choose the Route-1

increased. From around iteration 5, the number of agents for each route

oscillated. The average number of agents which choose Route-1 from iter-

ation 19 to iteration 99 was 30; the average number of agents which choose

Route-2 was 70. The ratio of 30:70 is about 1:2, thus the result is said to

be correct.

It was confirmed that the platform works correctly in the case of simulations

without driving behavior model. The simulations in this section showed that

regardless of initial plans given to agents, the number of agents converge at the

ratio of the number of lanes of each route.

4.3 Two routes selection simulation based on driving be-

havior model

In this section, the driving behavior model is introduced to the simulation of

two routes selection already shown in section 4.2 Each agent has the driving

behavior model which determines the vehicle behavior.

Configuration

The road network used in this simulation is the same as section 4.2. The limit

number of vehicle for each lane per hour is 3,000. Types of agent are defined as

the combination of the desired speed and the lane change probability described

in section 4.1.

The desired speed is 30 [km/h] or 60 [km/h] and the lane change probability

is 0.1 or 0.4. There are four types of agents. The number of agents is 100

and the number of each type is 25. To avoid the same type of agents clinging

together, four types of agents leave the start point one after the other. Four

types of agents are called as shown in table 4. ODs of all agents are the same

from the node on the left extreme to the node on the right extreme. The number

of iterations is 100 from the iteration 0 to the iteration 99.

Two different configurations of initial plans are examined.

31

Table 4: Agent Types

Type of Agent Desired Speed[km/h] Lane Change Probability

Slow Cautious 30 0.1

Slow Aggressive 30 0.4

Fast Cautious 60 0.1

Fast Aggressive 60 0.4



Expectation

This simulation is not as simple as the simulation in section 4.2 because agents

have four different types of driving behavior model. So it is a little more difficult

to predict the result of the simulation.

If a fast agent runs behind a slow agent, the fast agent has to run at the

speed of the slow agent. Thus fast agents choose Route-2 more often because it

is possible for them to overtake slow agents and the travel time is shorter than

Route-1. Among fast agents, aggressive agents may choose the Route-2 more

often than cautious agents. Slow agents choose both routes because there is

no difference between Route-1 and Route-2 for these agents in terms of travel

time.


Figure 13 includes two graphs each of which shows the number of agents for each

route. The horizontal axis is the iteration, and the vertical axis is the number

of agents for each routes at the iteration. Each graph shows the result of two

different configurations: Route-1: 100 agents, Route-2: 0 agents; Route-1: 0

agents, Route-2: 100 agents.






32



said to be correct.








said to be correct.


Number

of Agents

0

20

40

60

80

100

0 20 40 60 80Iteration0

20

40

60

80

100

Route 2

Route 1

Route 2

Route 1


1. Route-1: 100 agents, Route-2: 0 agent (Initial Plans)

Figure 13: The number of agents for each route (With driving behavior model)

33

0

25

0

25

0

25

0

25


Number of Agents

Route 2

Route 1

Slow Aggressive

Slow Cautious

Fast Aggressive

Fast Cautious

Figure 14: The number of agents for each iteration by agent types (Route-1:

100 agents, Route-2: 0 agents)

0

25

0

25

0

25

0

25


Number of Agents

Route 2

Route 1

Slow Aggressive

Slow Cautious

Fast Aggressive

Fast Cautious

Figure 15: The number of agents for each iteration by agent types (Route-1: 0

agents, Route-2: 100 agents)

Figure 14 and figure 15 shows the number of agents for each iteration by

agent types. Figure 14 shows the results of configuration “Route-1: 100 agents,

Route-2: 0agents”. Figure 15 shows the results of configuration “Route-1: 0

agents, Route-2: 100 agents”. The number of agents for each route by agent

types became stable around iteration 35.


34


the Route-1 decreased and the number of agents which choose the route-

2 increased. From around iteration 10, the number of slow agents which

choose Route-1 stopped decreasing while the number of fast agents which

choose Route-1 kept decreasing. By iteration 35 almost all the fast agents

moved from Route-1 to Route-2. The average number of agents for each

route from iteration 34 to 99 is shown in table 5.

Choosing Route-2, Fast agents can overtake slow agents and the travel time

of Route-2 is shorter than Route-1. Thus fast agents chose Route-2. Slow

agents began to avoid Route-2 after iteration 10 when more and more fast

agents began to choose Route-2. The result is said to be correct.



the Route-1 increased and the number of agents which choose the route-2

decreased. From around iteration 10, the number of slow agents which

choose Route-1 kept increasing while the number of fast agents which

choose Route-1 began decreasing. By iteration 20 almost all the fast agents

moved away from Route-1. The average number of agents for each route-

from iteration 34 to 99 is shown in table 6.

Choosing Route-2, Fast agents can overtake slow agents and the travel time

of Route-2 is shorter than Route-1. Thus fast agents chose Route-2. Slow

agents began to avoid Route-2 after iteration 10 when more and more fast

agents began to choose Route-2. The result is said to be correct.

It is not clear if the lane change probability cause the route selection of each

agent. In table 5 and table 6, Fast aggressive agents tends to choose Route-2

more often than fast cautious agents.

Let us see more details of the result of the configuration “Route-1: 0 agents,

Route-2: 100 agents”. Figure 16 includes graphs of the average speed of agents

at iteration 69 and iteration 99 by agent types.

1. Iteration 69

Slow agents run at about 20 [km/h] in both routes. Slow agents running

in Rouute-1 is slightly faster than agents running in Route-2. Whichever

35

Table 5: The average number of agents for each route by types of agents (Route-

1: 100 agents, Route-2: 0 agents)

Type of Agent Route-1 Route-2

Slow Cautious 14.5 10.5

Slow Aggressive 16.9 8.1

Fast Cautious 2.0 23.0

Fast Aggressive 1.6 23.4

Table 6: The average number of agents for each route by types of agents (Route-

1: 0 agents, Route-2: 100 agents)

Type of Agent Route-1 Route-2

Slow Cautious 16.2 8.8

Slow Aggressive 15.7 9.3

Fast Cautious 2.6 22.4

Fast Aggressive 0.3 24.7

route a slow agent take, it takes as much time as another route. So the

number of agents for each route is never imbalanced.

Fast agents run at about 27 [km/h] in Route-2, 20 [km/h] in Route-1 since

there are slow agents blocking them. So the number of fast agents of Route-

1 is less than Route-2 since it takes more time than Route-1.

2. Iteration 99

Slow agents run at about 20 [km/h] in both routes. Slow agents running

in Rouute-1 is slightly faster than agents running in Route-2. Whichever

route a slow agent take, it takes as much time as another route. So the

number of agents for each route is never imbalanced.

Fast agents run at about 27 [km/h] in Route-2, 20 [km/h] in Route-1 if

there are slow agents blocking them. There is only one fast agent which

runs in Route-1 and there are no slow agents in front. So the speed of fast

aggressive agent in Route-1 is 56 [km/h] which is nearly the desired speed

60 [km/h].

36

0

30

60Route-1

SlowCautious

Iteration 69

Iteration 99

SlowAggressive

FastCautious

FastAggressive

Route-2D

riv

ing

Sp

eed

[m

/s]

0

30

60Route-1

SlowCautious

SlowAggressive

FastCautious

FastAggressive

Route-2

Dri

vin

g S

pee

d [

m/s

]

Figure 16: The average spped of agents at iteration 69 and iteration 99 by agent

types

Table 7 shows the average number of agents by types which chose the dif-

ferent route than in the previous iteration from the iteration 34 to the iteration

99. Slow agents tend to change their routes more often than fast agents.

Table 7: The average number of route changers by types

Type of Agent Route Changer

Slow Cautious 4.5

Slow Aggressive 3.2

Fast Cautious 1.2

Fast Aggressive 1.3

Summary

Simulations of two routes selection with/without driving behavior model are

examined. The ratio of the agents for each route is the same in both simulations.

37

It was confirmed that the platform can conduct a simulation based on driving

behavior model which is also correct from the macroscopic point of view.

4.4 Simulation of traffic in a real road network

By simulations in the simple two routes road network, it was confirmed that

the platform can conduct a simulation based on driving behavior model which

is not contradicting the results of a simulation without driving behavior model.

The simulation aiming at testing the scalability of the platoform is shown

in this section. The simulation uses the actual road network of Kyoto City.

The amount of calculation depends on the number of agents, the size of the

simulation area, and the simulation time.

Configuration

The road network of Kyoto City was used for this simulation. Since the data

of the road network is commercially produced, every road link is included and

attributes like the number of lanes and one-way flag are accurate. Road links

which are closed for vehicles in normal time are removed from the road network.

As seen above, the road network is real, but ODs of agents are not real. The

number of iterations is 50 from the iteration 0 to the iteration 49.

Figure 17 shows ODs of agents running though the central part of Kyoto

City. There are also names of major places and streets on the map. The

simulation area is from Oike Street to Gojo Street (north to south) and from

Kawaramachi Street to Karasuma Street (east to west). The area is a historical

center of the city which includes amusement centers, business districts and

tourist sites with heavy road traffic. Kyoto City is promoting “Strategy for

pedestrians friendly streets”1) which encourages people to walk and take public

transportation instead of auto mobiles.

The number of OD types is 42. ODs are the combinations of two different

intersections from red circles on the map. Some ODs are named for analysis

later in this section using the intersection number shown in figure 17:

• OD-1: from intersection 1 to intersection 2

1) http://www.city.kyoto.jp/tokei/trafficpolicy/machinaka/

38

Oike St.Oike St.

Ka

rasu

ma

St.

Ka

wa

ram

ac

hi

St.

Shijo St.Shijo St.

Gojo St.Gojo St.

Street

Origin/Destination

Simulated Area

1

3

6 7

4 5

2

Kyoto StationKyoto Station

Yasaka ShrineYasaka Shrine

Heian Jingu ShrineHeian Jingu Shrine

Kiyomizu TempleKiyomizu Temple

Figure 17: ODs of agents running though the central part of Kyoto City






The desired speed is 30 [km/h] or 60 [km/h] and the lane change probability

is 0.05 or 0.4. There are four types of agents as the combinations of the desired

speed and the lane change probability. The number of agents for each OD is 40

and the number of agents is 1,680. To avoid the same type of agents clinging

39

together, four types of agents leave the start point one after the other. The

duration of the simulation is one hour.

Table 8: Types of Agents

Type of Agent Desired Speed[km/h] Lane Change Probability

Slow Cautious 30 0.05

Slow Aggressive 30 0.4

Fast Cautious 60 0.05

Fast Aggressive 60 0.4


Figure 18 shows the screenshot of the simulation snapshots reproduced by the

viewer software. Agents found more than one way for each OD. At iteration

49, the average number of routes executed for each OD by agents is 12.9. The

viewer can zoom in the parts of simulation area, and users can see the details

of driving behavior of each agent.

The simulation was conducted on a PC with 32-bit Windows Vista Oper-

ating System on Intel Core2 2.40GHz Processor and 1016.125MB memory was

allocated. The average of actual time required for the simulation was about

6 minutes. The entire time of simulation from iteration 0 to iteration 49 was

about 5 hours. The amount of calculation depends on the number of agents,

the size of the simulation area, and the simulation time. So the actual time

required for the simulation depends on these factors.

Figure 19 shows the average travel distance by agent types for each OD.

The average travel distance is not quite different among different agent types

compared to the average speed. The route to avoid congestion is longer than

the original route. The ratio of four types of agents is not imbalanced. Thus

the average travel distance are the same among different types of agents.

Figure 20 shows the average travel time by agent types for each OD. The

average travel time is a little bit different among different agent types. The

travel times of slow agents are a little bit shorter than that of fast agents having

40

Figure 18: Screenshot of the simulation snapshots reproduced by the viewer

software

0

3000

Tra

vel

Dis

tan

ce[m

]

OD-1 OD-2 OD-3 OD-4 OD-5 OD-6

Slow, Cautious

Slow, Aggressive

Fast, Cautious

Fast, Aggressive

Figure 19: Comparison of Travel Distance

the same OD.

Figure 21 shows the average driving speed by types of agents for each OD.

The average driving speed is the distance from the start to the goal divided by

the travel time. The travel speeds of fast agents are a little bit faster than that

of slow agents having the same OD.

The desired speed is 30 [km/h] or 60 [km/h], but the actual driving speed is

slower than the desired speed. The difference of driving speed between fast and

slow agents is the biggest in OD-1. Fast cautious agents drive at 21.6 [km/h]

41

0

800T

rav

el T

ime

[s]


Slow, Cautious

Slow, Aggressive

Fast, Cautious

Fast, Aggressive

Figure 20: Comparison of Travel Time

0

10

Dri

vin

g S

pee

d [

m/s

]


Slow, Cautious

Slow, Aggressive

Fast, Cautious

Fast, Aggressive

Figure 21: Comparison of Driving Speed

slow aggressive agents drive at 15.5 [km/h]. The difference of driving speed

between fast and slow agents is the smallest in OD-4. Fast aggressive agents

drive at 5.0 [km/h] and slow aggressive agent drives at 4.3 [km/h].

The driving speed of an agent does not depend on the desired speed or

the lane change probability of the agent but depends on its OD. The average

driving speed of agents having OD-1 is 18.4 [km/h], and the speed of agents

having OD-4 is 4.7 [km/h]. The speed of OD-1 is as fast as the speed of agents in

the simulation of two routes selection based on driving behavior model, which

implies that agents experienced no traffic congestion. However the speed of

OD-4 is as slow as pedestrians, which implies there were too many cars in the

routes and agents experienced heavy traffic jams.

Table 9: Four routes for OD-6

length[m] Slow Cautious Slow Aggressive Fast Cautious Fast Aggressive

2341 1 2 1 2

2346 1 0 2 0

2374 0 1 2 2

2378 1 3 1 2

42

The average driving speed of agents having ODs other than OD-1 and OD-4

is close enough to each other. 14 routes were executed at iteration 49 for OD-6.

Four in 14 routes were executed by more than one agent. Table 9 shows the

number of agents for each route. The average driving speed of agents for each

route was 18 [km/h], 18.4 [km/h], 19.1 [km/h], 14.8[km/h].

There were no clear difference among different types of agents. That is be-

cause there were no difference between any two routes. Fast agents want to

overtake slow agents to go faster, but there is no space for overtaking. Even if

there are two lanes on the road, agents cannot change lanes. So the lane change

probabilities do not have effects on the vehicle behaviors. Such traffic conges-

tions are caused by multiple ODs crossing each other which is also common in

the real urban traffic.

43

Chapter 5 Conclusion

There are two major approaches to a traffic simulation: a microscopic approach

and a macroscopic approach. “Driving behavior model” and “Large-scale traffic

simulation” are the key words of this study. Driving behavior model has been

studied in the area of traffic simulation based on micro model Large-scale traffic

simulation has been studied in the area of traffic simulation based on macro

model.

Models and purposes of existing simulators were introduced. The design of

a driver agent and the design of a road link required for introducing driving be-

havior model into large-scale multi-agent traffic simulation are proposed. The

process and input/output of the platform is also described. The platform is im-

plemented using MATSim-T. MATSim-T can run large-scale traffic simulation

efficiently. Simulations using the platform are shown. Those simulations aim

at the verification of the platform.

The contributions of this study are as follows.

1. Design of an agent architecture and a road link architecture to

integrate micro model into macro model

First contributions is to design an agent architecture and a road link ar-

chitecture. The agent consists of two layers: a layer of routing and driving

behavior. The routing layer determine the route on the abstract road net-

work and the driving behavior layer drives the vehicle based on driving

behavior model on a road link. Each road link has a two-dimensional sur-

face in which vehicles run through. The platform is implemented using

a toolkit named MATSim-T which can effectively run a large-scale agent-

based traffic simulation.

2. Verification of introducing driving behavior model and execution

time of simulation

The platform was applied to two examples for verification. First example

was simple enough to predict the results of the simulation and confirm

that the platform worked properly. Second example was complex enough

to show the capability of running a large-scale simulation based on driving

44

behavior models.

This study proposed how to integrate a driving behavior model into a large-

scale traffic simulation and implemented the platform to realize the proposed

design. In some cases, agents with different driving behavior models choose

different routes during the simulation. The platform is able to run a large-scale

simulation at the micro level.

45

Acknowledgments

I would like to express my gratitude to Professor Toru Ishida for his exact

directions and helpful discussions. Assistant Professor Hiromitsu Hattori and

Researcher Yuu Nakajima and PhD Student Shohei Yamane have always been

tolerant and supportive.

I appreciate the appropriate advice from Professor Hajime Kita at Kyoto

University and Associate Professor Satoshi Oyama at Hokkaido University. I

have learned a lot from Satoshi Yokota and Masahiro Okada at Advanced Sci-

entific Technology & Management Research Institute of Kyoto while developing

the simulation platform.

Finally, I would like to thank all members of Ishida and Matsubara Labora-

tory for creating a world-class environment for research and development.

46

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