aimsun sumo comp

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COMPARISON OF THE TWO MICRO-

SIMULATION SOFTWARE

AIMSUN& SUMO

FOR HIGHWAY TRAFFIC MODELLING

AJI RONALDO (aliro229)

TAUFIQ ISMAIL (tauis485)

2012

Linkping University

Department of Science and Technology

Intelligent Transport System


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COPYRIGHT

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ABSTRACTIn order to make good decisions in transportation, decision-makers need some references to support

the decision. One source for such reference is to perform a micro-simulation; a model for

representing real-world conditions including the behavior of travelers, vehicles and the

infrastructure. This study will examine and present a comparison between AIMSUN (a commercial

micro-simulation software) and SUMO (a non-commercial micro-simulation software), identifying

advantages and disadvantages of these applications in relation to the study object Sdra lnken, that

is E266 and E75, in south part of Stockholm, Sweden.

A calibration process is conducted in order to find the best value of a set of parameters in each

software. The best set of parameters will be selected based on the lowest value of a Root Mean

Square Error (RMSE) computed based on observed speed data and the model output. The

parameters is then validated using evening peak-hour data.

This research gave result that from the given experiments with the SUMO software, the best set of

parameters was when the value of Driver Imperfectionat 0,3 and Drivers Reaction Timeat 1,7. For

AIMSUN , the best set of parameters was when the value of Maximum Desired Speed at 100 km/hand Speed Acceptanceat 1,1.

In comparison, AIMSUN has advantages in terms of the simplicity for the user in creating network,

setting the parameters and creating animation over SUMO. The complexity of the SUMO software

stimulate the user to carefully build the model.

Key Word : AIMSUN, micro-simulation, software, traffic, modelling


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ACKNOWLEDGEMENT

Firstly we would like to thank to Allah The Almighty God who gives His grace and mercy so that this

Master Thesis can be completed. For our supervisors Prof. Jan Lundgren and Clas Rydergren, Ph.D,

we also would like to say thank you for their precious supports, guidance and advices all this time.

To our beloved family in Indonesia, who never stops giving their spirits, motivations, love and pray

so that we can finishing our thesis.

Credits also given to our friends from Indonesia Catur Yudo Leksono and Tina Andriyana for their

assistances in the making of our thesis as a teacher and also as a discussion partner. Special thanks

for Amalia Defiani which give us extra power to finish this thesis.

The last but not the least, our gratitude is also given to MSTT UGM and Transportation Ministry of

Indonesia for the support and everything so that the education at LinkpingUniversity

accomplished.

Norrkping,June 2012

Aji Ronaldo and Muhammad Taufiq Ismail
http://www.liu.se/http://www.liu.se/http://maps.google.se/maps?sugexp=chrome,mod%3D10&q=Norrkoping&um=1&ie=UTF-8&hq=&hnear=0x4659286a5f50dca7:0x400fef341e48e80,Norrk%C3%B6ping&gl=se&sa=X&ei=IpXjT_SIO4bcsga57f3BBg&ved=0CBQQ8gEwAAhttp://maps.google.se/maps?sugexp=chrome,mod%3D10&q=Norrkoping&um=1&ie=UTF-8&hq=&hnear=0x4659286a5f50dca7:0x400fef341e48e80,Norrk%C3%B6ping&gl=se&sa=X&ei=IpXjT_SIO4bcsga57f3BBg&ved=0CBQQ8gEwAAhttp://www.liu.se/


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TABLE OF CONTENTS

COPYRIGHT .............................................................................................................................................. ii

ABSTRACT ............................................................................................................................................... iv

TABLE OF CONTENTS ............................................................................................................................. vii

LIST OF TABLES ........................................................................................................................................ x

LIST OF FIGURES ..................................................................................................................................... xi

LIST OF ABBREVIATION ........................................................................................................................ xiii

CHAPTER 1. INTRODUCTION .................................................................................................................. 1

1.1. BACKGROUND ......................................................................................................................... 1

1.2. AIM AND PURPOSE ................................................................................................................. 1

1.3. SIGNIFICANCE .......................................................................................................................... 1

1.4. DELIMINATION ........................................................................................................................ 1

1.5. AREA OF RESEARCH................................................................................................................. 1

1.6. STRUCTURE OF THE REPORT ................................................................................................... 3

CHAPTER 2 LITERATURE REVIEW ............................................................................................................ 5

2.1. Traffic Simulation .................................................................................................................... 5

2.2. AIMSUN ................................................................................................................................... 5

2.3. SUMO (Simulation of Urban MObility) ................................................................................... 6

2.4. MODEL CALIBRATION .............................................................................................................. 9

2.5. Previous Research ................................................................................................................. 10

2.2.1. iTetris ............................................................................................................................ 10

2.2.2. VABENE ......................................................................................................................... 10

2.2.3. CityMobil ....................................................................................................................... 11

2.2.4. VTI Comparison ............................................................................................................. 11

CHAPTER 3 INPUT DATA FOR THE SIMULATION ................................................................................... 13

3.1. Detector ................................................................................................................................ 13

3.2. Data ....................................................................................................................................... 15

3.3. Parameters of the models..................................................................................................... 19

3.3.1. Parameter in AIMSUN ................................................................................................... 19

3.3.2. Parameter of the SUMO................................................................................................ 21

CHAPTER 4 MODEL CONSTRUCTION .................................................................................................... 25

4.1. DEVELOPMENT IN AIMSUN & SUMO .................................................................................... 25


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4.1.1. AIMSUN ......................................................................................................................... 25

4.1.2. SUMO ............................................................................................................................ 31

4.2. CALIBRATION AND VALIDATION ........................................................................................... 35

4.2.1. CALIBRATION IN AIMSUN .............................................................................................. 35

4.2.2. Calibration of the SUMO ............................................................................................... 36

4.2.3. VALIDATION IN AIMSUN ............................................................................................... 37

4.2.4. Validation of the SUMO ................................................................................................ 37

Model Output ....................................................................................................................................... 38

CHAPTER 5 RESULT ............................................................................................................................... 39

5.1. AIMSUN ................................................................................................................................. 39

5.2. SUMO .................................................................................................................................... 46

5.3. COMPARISON AIMSUN AND SUMO ...................................................................................... 51

5.3.1. Result on Speed and Flow ............................................................................................. 51

5.3.2. Network ........................................................................................................................ 53

5.3.3. Demand ......................................................................................................................... 53

5.3.4. Control .......................................................................................................................... 53

5.3.5. Output ........................................................................................................................... 54

5.3.6. Guidance ....................................................................................................................... 54

5.3.7. Technical support .......................................................................................................... 54

CHAPTER 6 CONCLUSIONS .................................................................................................................... 56

REFERENCES .......................................................................................................................................... 58

APPENDIXES ............................................................................................................................................ a

A. SUPPLIED DEMAND DATA ........................................................................................................... a

1) Morning Demand (entrance Detectors) ................................................................................. a

2) Morning Demand (Exit Detectors) .......................................................................................... b

3) Matrix of Morning Demand (5 minutes interval).................................................................... cMatrix of Morning Demand (5 minutes interval)............................................................................ d

4) Evening Demand (entrance Detectors) ................................................................................... e

5) Evening Demand (Exit Detectors) ............................................................................................ f

6) Matrix of Evening Demand (5 minutes interval) ..................................................................... g

Matrix of Evening Demand (5 minutes interval) ............................................................................. h

B. AIMSUN CALIBRATION ................................................................................................................. i

1) Calibration Process Entrance Detectors .................................................................................. i


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2) Calibration process in Exit-Detectors ....................................................................................... j

3) Comparison of Model Output and observation Speed in all detectors .................................. k

4) Validation AIMSUN Using Evening Flow Data ......................................................................... o

C. SUMO CALIBRATION ................................................................................................................... s

1) Detector 1,845 ........................................................................................................................ s

2) Detector 23,205 ....................................................................................................................... t

3) Detector 3,270 ........................................................................................................................ u


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LIST OF TABLESTable 1. Detector inventory .................................................................................................................. 14

Table 2. OD-Matrix for morning peak-hour .......................................................................................... 15

Table 3. OD-matrix for evening peak-hour ........................................................................................... 15

Table 4. Flow number and average speed in morning peak-hour ........................................................ 16Table 5. Flow number and average speed in evening peak-hour ......................................................... 17

Table 6. Flow number and average speed in morning peak-hour ........................................................ 18

Table 7. Flow number and average speed in evening peak-hour ......................................................... 19

Table 8. The Car-following models implemented in SUMO (9) ............................................................ 22

Table 9. Demand matrix for the first 5-minutes demand in morning .................................................. 27

Table 10. Error checking item ............................................................................................................... 31

Table 11. Parameters set in SUMO network ......................................................................................... 32

Table 12. Vehicle properties parameter value ..................................................................................... 33

Table 13. The Kraucar-following model parameter value .................................................................. 33

Table 14. The calibration process in Detector 23, 205 ......................................................................... 36Table 15. t-Test as validation process in detector 23.220 .................................................................... 37

Table 16. Validation process on 1.885 detector ................................................................................... 38

Table 17. RMSE matrix against morning speed data ............................................................................ 39

Table 18. Vehicle parameters ............................................................................................................... 40

Table 19. Behavior Parameter .............................................................................................................. 41

Table 20. t-Test Validation on exit detectors ........................................................................................ 42

Table 21. t-Test validation on entrance detectors ................................................................................ 42

Table 22. The Morning Calibration RMSE matrix .................................................................................. 46

Table 23. t-Test Validation against speed data ..................................................................................... 47

Table 24. t-Test Validation against flow data ....................................................................................... 47

Table 25. Comparison of validation on SUMO and AIMSUN by using evening flow data .................... 52

Table 26. Comparison of validation on SUMO and AIMSUN by using evening speed data ................. 53


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LIST OF FIGURESFigure 1. Area of research. (2) ................................................................................................................ 2

Figure 2.Detailed picture of research area (2) ........................................................................................ 2

Figure 3. Detailed picture (3) .................................................................................................................. 3

Figure 4. Building a network (11) ............................................................................................................ 7Figure 5. Building trips from the OD-matrix (11,11) ............................................................................... 8

Figure 6. Detectors location .................................................................................................................. 13

Figure 7. Location of detector ............................................................................................................... 14

Figure 8.Time-series diagram ................................................................................................................ 15

Figure 9. Flow diagram (morning peak-hour) ....................................................................................... 17

Figure 10. Flow diagram (evening peak-hour) ...................................................................................... 18

Figure 11. Link-Node Diagram .............................................................................................................. 25

Figure 12. Number of lanes and the detectors ..................................................................................... 26

Figure 13. Lane Width ........................................................................................................................... 26

Figure 14. Flow fluctuation in 5-minutes interval in the morning for detector 23,205 ........................ 27Figure 15. Speed fluctuation in 5-minutes interval in the morning for detector 23,205 ..................... 27

Figure 17. 24 morning matrixes and 24 evening matrixes.................................................................... 28

Figure 18. Traffic Demand control for Morning Demand data ............................................................. 29

Figure 19. Steps to create database storage in AIMSUN ...................................................................... 29

Figure 20. example of ReplicationsandAveragein each experiment .................................................. 30

Figure 21.Warming-up for flow stream ................................................................................................ 30

Figure 22. Behavior parameter ............................................................................................................. 31

Figure 23. Networks displayed in SUMO-GUI ....................................................................................... 32

Figure 24. Detectors placement in west section .................................................................................. 34

Figure 25. The model run in the SUMO-GUI ......................................................................................... 35

Figure 26. Graphic of RMSE in the AIMSUN .......................................................................................... 40

Figure 27. Morning Speed comparison between observation and the model output data at detector

23,205 in the AIMSUN........................................................................................................................... 41

Figure 28. Morning Speed comparison between observation and the model output data at detector

3.215 in the AIMSUN ............................................................................................................................. 42

Figure 29. Evening Flow Comparison between observation and modeldata at detector 23.220

(South) in the AIMSUN .......................................................................................................................... 43

Figure 30. Evening Flow Comparison between observation and the modeloutput data at detector

1.885 (West) in the AIMSUN ................................................................................................................. 43Figure 31. Evening Flow Comparison between observation and the modeloutput data at detector

3.215 (East) in the AIMSUN .................................................................................................................. 44

Figure 32 Evening Speed Comparison between observation and modeloutput data at detector

23.220 (south) in the AIMSUN .............................................................................................................. 44

Figure 33. Evening Speed Comparison between observation and the modeloutput data at detector

1.885 (West) in the AIMSUN ................................................................................................................. 45


3.215 (East) in the AIMSUN .................................................................................................................. 45

Figure 35. Morning calibration RMSE chart of the SUMO .................................................................... 46

Figure 36. Junction no 1445695315 ...................................................................................................... 48
http://f/Linkoping%20University/thesis/Thesis%20revised%20%236%2030%20June.docx%23_Toc328792535http://f/Linkoping%20University/thesis/Thesis%20revised%20%236%2030%20June.docx%23_Toc328792536http://f/Linkoping%20University/thesis/Thesis%20revised%20%236%2030%20June.docx%23_Toc328792536http://f/Linkoping%20University/thesis/Thesis%20revised%20%236%2030%20June.docx%23_Toc328792535


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1.885 (West) in the SUMO .................................................................................................................... 48


23.220 (South) in the SUMO ................................................................................................................. 49


3.215 (South) in the SUMO ................................................................................................................... 49


1.885 (West) in the SUMO .................................................................................................................... 50

Figure 41. Evening Speed Comparison between observation and the model output data at detector

23.220 (South) in the SUMO ................................................................................................................. 50


3.215 (East) in the SUMO ...................................................................................................................... 51

Figure 43. Comparison between SUMO and AIMSUN in Evening flow data on exit detectors ............ 51

Figure 44. Comparison between SUMO and AIMSUN in Evening speed data on exit detectors ......... 52


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LIST OF ABBREVIATION

AIMSUN Advanced Interactive Micro-Simulation for Urban and Non-urban network

SUMO Simulation of Urban Mobility

PC Personal Computer

MSE Mean Square Error

RMSE Root Mean Square Error


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CHAPTER 1. INTRODUCTION

1.1.BACKGROUNDTransportation decision-making needs references as basis for decision in order to make good

decisions. References which can be used include technical theories, literature studies,professional judgment or traffic simulation. Simulation is an important tool since simulation can

represent a real condition into a visual model almost perfectly, and also can visually represent

the effect of alternatives.

There are several micro-simulation softwares, both commercial or non-commercial, which can

be used by academics or decision-makers to analyze transportation related problems. Since the

softwares are built based on different theories and assumptions related to the modeling of

traffic, each of these softwares have their own advantages and disadvantages and differences in

outputs and results.

AIMSUN is one popular commercial micro-simulation applications which has been used

frequently in the transportation research field. AIMSUN stands out for the exceptionally high

speed of its simulations and for fusing static and dynamic traffic assignment with mesoscopic,

microscopic and hybrid simulation all within a single software application (1). One non-

commercial micro-simulation application is SUMO (Simulation of Urban MObility) which is

developed by theInstitute of Transportation Systems at the German Aerospace Center.

1.2.AIM AND PURPOSEThe aim of this thesis is to build, calibrate, validate two simulation models and perform

experiments on a short stretch of motorway, outside of Stockholm. The purpose of the thesis is

to give insights in how traffic simulation models can be used for analyzing real world traffic

problems. The analysis will be based on a comparison of pros and cons of the two traffic

simulation packages SUMO and AIMSUN for motorway traffic simulation. The study object is the

area around E266 and E75 in the south part of Stockholm, Sweden. Input to the simulation study

is data from the motorway control system (MCS) in the area.

1.3.SIGNIFICANCEThis kind of study is needed in order to find out the advantages and disadvantages of two

softwares related to the area of study object. Therefore in the future time, decision makers shall

be able to decide which software, of SUMO and AIMSUN, that would more suitable in a situation

similar to the one studied in this thesis.

1.4.DELIMINATION

The limitations of the study are as follow:

1. The traffic simulation is developed based on provided data from traffic flow and speed

detectors in the study area. The peak-hour data is used for building the traffic simulation

model.

2.

The calibration process in each software will only adjust two main parameters in order

to find the best model.

1.5.

AREA OF RESEARCHThe area of this research is on Underground Street intersection in Stockholm. The area is the one

of intersection between E266-Street and E75-Street (Sdra lnken). The location is a 3-approach
http://www.dlr.de/fs/en/desktopdefault.aspxhttp://www.dlr.de/fs/en/desktopdefault.aspx


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intersection which has 2-3 lane streets, as shown in figure 1. A detailed picture of the research

area can be seen in figure 2 and figure 3.

Figure 1. Area of research. (2)

Figure 2.Detailed picture of research area (2)


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Figure 3. Detailed picture (3)

1.6.STRUCTURE OF THE REPORTThis report will be conducted in six chapters that represent the all of its contents. The structure

is the following.

Chapter 2 describes about the description about traffic simulation classification and steps

needed to build a simulation in SUMO and AIMSUN software. This chapter also describes thecalibration process as a adjustment tool to find the best model from the experiments. Previous

research, related to this thesis, is also included in this chapter.

Chapter 3 describes the input data used in each software SUMO and AIMSUN. This chapter

presents speed and flow data on morning and evening peak-hour at the detectors. The model

parameters available in AIMSUN and SUMO are presented in this chapter in order to give better

understanding of the softwares.

Chapter 4 describes the model construction in the two softwares SUMO and AIMSUN. The model

construction is described in terms of network, traffic demand and control in each software. This

chapter also gives more details about how the calibration and validation processes in eachsoftware are conducted.

Chapter 5 describes the finalized models for each software SUMO and AIMSUN. Root Mean

Square Error (RMSE) from the calibration process, and the model output speeds and flows are

also described in this chapter. Further, a general comparison of SUMO and AIMSUN in the term

of Network, Demand, Control, Output, Guidance, Technical Supportis presented.

Chapter 6 gives the conclusions of this research including pros and cons of the two softwares

SUMO and AIMSUN.


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CHAPTER 2 LITERATURE REVIEW

SUMO and AIMSUN are tools that help their user to present simulation of alternatives in traffic

engineering or traffic management. Result of the simulation can be good reference in policy-making

of traffic in object area. This section will give better understanding for the reader about traffic

simulation and the two softwares , of SUMO and AIMSUN.

2.1.

Traffic SimulationIn general, simulation is defined as dynamic representation of some part of the real world achieved

by building a computer model and moving it through time (4). The use of computer simulation

started when D.L.Gerlough published his dissertation titled Simulation of freeway traffic on a

general-purpose discrete variable computer at the University of California, Los Angeles, in 1955

(5).

In traffic research, there are four classes of the traffic models, but usually people dividing it into

three, such as:

a.

Macroscopic simulation model

The macroscopic simulation model are based on the deterministic relationship of the flow,speed, and density of the traffic stream. The simulation in a macroscopic model takes place on

a section-by-section basis rather than by tracking individual vehicles (6).

b.

Meso-scopic simulation model

The mesoscopic simulation models combine the properties of both microscopic and

macroscopic simulation models. Traffic flow unit in this type of simulation is individual vehicle

but the movement is using the approach of the macroscopic mode. Mesoscopic simulation

takes place on an aggregate level and does not consider dynamic speed/volume relationship.

(6)

c.

Microscopic simulation modelThe microscopic model simulate the movement of individual vehicles based on car-following

and lane-changing theories. Typically, vehicles enter a transportation network using a

statistical distribution of arrivals (6). The microscopic model incorporate sub-models for

acceleration, speed adaptation, lane-changing, gap acceptance etc., to describe how vehicles

move and interact with each other and with the infrastructure (7).

2.2.

AIMSUNAIMSUN is a widely used commercial transport modeling software, developed and marketed by

TSS- Transport Simulation Systems based in Barcelona, Spain. Microscopic simulator and

Mesoscopic simulator are the components of AIMSUN which allow dynamic simulations. They can

deal with different traffic networks: urban networks, freeways, highways, ring roads, arterials andany combination thereof (8).

The input data required by AIMSUN Dynamic simulators is a simulation scenario, and a set of

simulation parameters that define the experiment. Based on AIMSUN Manual (8), the scenario is

composed of four types of data such as:

a. Network descriptions

The network is a package of links which connected each other by nodes (intersection) and may

have different traffic feature. In order to build the network in AIMSUN, the user needs following

data:

1) Map of the area (preferably in .DXF format)

2) Details of the number lanes for each section, reserved and side lanes.


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3)

Possible turning movement for every junction, including details about the lanes from which

each turning is allowed and solid lines marked on the road surface.

4) Speed limits for every section and turning speed for allowed turns at every intersection

5) Detectors

The network consists of main and side lanes; lane identification; geometry specification;

reserved lanes as part of section in the network. Another elements which might be put on the

network are detectors, metering, Variable Message Sign, and pedestrian crossing. The other

part that also important is node or intersection on lanes in the network.

b. Traffic control plans

There are several type of traffic control taken by AIMSUN which are traffic signals, give-way

signs and ramp metering. Traffic signals and give-way signs are used for junction nodes, while

ramp metering is for sections that end at join node. The input data required to define the traffic

control are (1) :

1)

Signalized junction: location of signals, the signal groups into which turning movement are

grouped, the sequence of phases and, for each one the signal groups that have right of

way, the offset for the junction and duration of each phase.2) Un-signalized junctions : definition of priority rules and location of Yield and/or Stop signs

3)

Ramp metering: location, type of metering, control parameters (green time, flow or delay

time).

c. Traffic demand data

Traffic demand data in AIMSUN can be defined in two different ways, by the traffic flows at the

sections or by an O/D matrix. Several O/D matrices or Traffic States will be grouped into a traffic

demand. Following data must be provided for those different types of traffic demand data

based on AIMSUN Users Manual:

1) Traffic Flows

a)

Vehicle type and the attributesb) Vehicle classes

c) Flows at the input sections for each vehicle type

d)

Turning proportion at all section for each vehicle type

2)

O/D Matrix

a) Centroid definition : traffic source and sinks

b) Vehicle type and attributes

c) Vehicle classes

d)

Number of trips going from every origin centroid to any destination one.

d.

Public transport plans.In order to define Public Transport, user is required to input following data:

1) Public Transport lines : a set of consecutive sections composing the route of a particular

bus

2) Reserved lanes

3)

Bus stops: location, length and type of bus stops in the network.

4)

Allocation of Bus Stops to Public Transport Lines

5) Timetable: departures schedule, type of vehicle and stop times.

2.3.

SUMO (Simulation of Urban MObility)Simulation of Urban MObility is an open source, highly portable, microscopic road traffic

simulation package designed to handle large road networks developed by the Institute of


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Transportation System at the German Aerospace Center (9). SUMO is not only a traffic simulation,

but rather a suite of applications which help to prepare and perform the simulation of traffic (10).

In order to simulate in a proper format, SUMO requires the representation of road networks and

traffic demand, both have to be imported or generated using different sources. SUMO allows to

generate various outputs for each simulations run. The outputs are ranging from simulated

induction loops to single vehicle positions.

In the SUMO, there are 4 steps needed in order to run the simulation such as:

a. Building the network

Network file of the SUMO describes the traffic related part of a map. It contains the network

of roads/ways, junctions/intersections, and traffic lights in a map. To build the network can be

either done by generating an abstract network using NETGEN, setting up an own description

in XML and importing it using NETCONVERT or by importing an existing road network using

NETCONVERT. The imported existing road network can be found from non-SUMO networks

such as: OpenStreetMap, VISUM, Vissim, openDRIVE, MATsim, ArcView (GIS shape-file),

Elmars GDF and Robocup Simulation League. Although the imported network is representing

the real condition of the network, SUMO users still should make some adjustments in order tofind the exact condition as wanted.

Figure 4. Building a network (11)

b.

Building the demand

Once the network has been built, SUMO users can take a look at it using SUMO-GUI, but will

not see any traffic inside it yet. Using existing OD-Matrix to fill the traffic flow in the network


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and then convert it into traffic description is one of the ways. In the SUMO there are some

methods to generate routes into the model:

1) Generating explicit routes

To describes own routes there are three possible ways, such as by hand, by using route and

vehicle type distributions, and the last is by using trip definitions. By hand method is the

simplest way to define own routes, but it only be possible if the number routes is not toohigh. In the SUMO, a vehicle consists of three parts: the vehicle type which describes the

vehicles physical properties, a route the vehicle shall take, and the vehicle itself.

Some things that should be considered when building on routes, the first thing is all the

routes in the network have to be connected; secondly, the routes have to contain at least

two edges; thirdly, the starting edge has to be at least as long as the car starting on it; and

the last is the route file has to be sorted by starting times.

2) Using flow definitions and turning ratios

This method can be used by dynamic user assignment and alternatives route as an

application for building demand by using flow definitions. This method can also be used to

import demand data given by trips and flows.

3)

Importing OD-matrices

To generate routes into the model, the OD-matrices cannot imported independently. So,

the OD-matrix should imported along with the network. The OD-matrix/ matrices can be

converted as amounts of vehicles that drive from one district to another within a certain

time period.

The imported routes only use data saved in VISUM/VISION/VISSIM formats. If the imported

routes are not saved in these formats, there still a way to convert those (OD-

matrix/matrices) into one of the supported formats, write our own reader for OD2TRIPS, or

convert them into flow definitions and then give the OD-matrix/matrices DUAROUTER

(dynamic user assignment and alternatives route application). How to importing OD-matrices so that it can be used to generate routes to the model is shown in figure 5.

Figure 5. Building trips from the OD-matrix (11,11)

4)

Using random routes

It is said that the random routes are the easiest, but also the most inaccurate way to feed

the network with vehicle movements.


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c. Computing the dynamic user assignment (if needed)

In order to know which routes taken by the driver in the simulation, dynamic assignment is

used. There are also some others use of dynamic user assignment, to detect how is the mean

speed of the lane, how much flow over the network, to solve the congestion problem, etc.

d.

Performing the simulationThe network file, the information of routes that will be used in the simulation and the period

of time that will be used in the model are essential to run the model. If one of them is not

applied in the model, the model will not be run properly.

2.4. MODEL CALIBRATIONThe calibration is become most important tool in finding the best model from the two softwares.

Result from the calibration will determine the best value of the model parameters. The model

simulation using those value of parameters will generate output which then will be used in

validation process.

Comparison between the model result and the real data which are taken from the field bycomparison test will use 95% probability. Calculated prediction output from both AIMSUN and

SUMO are used to test observed real word data. This thesis is using Excel to conduct all of statistical

calculation in order to find whether the simulation will be considered as good-representing

simulation or not.

CALIBRATION

This research using Mean Square Error (MSE) analysis to calibrate data between the model output

and observation data to find optimal value of parameters in the model development. Based on

Traffic Analysis Toolbox Volume III (12)the formula for the calibration is :

(1.1)Where:

MSE = Mean Square Error

= 5-minutes interval = Detector= Model estimate of speed at detector and -th time= Observation data of speed at detector and -th time = Number of ime intervalsD = Number of detector

In the AIMSUN and SUMO calibration, speed data is used to find optimal value of parameters. Sincethe research decide to develop the model for 2-hours peak period and also use 5 minutes time

interval to measure the average speed, so 24 5-minutes time-interval for 2 hours observation data

is made. The lowest number MSE will take from several experiments on each set of parameters

value. Based on the equation, since the calibration will use speed data then the result from this

equation will have km2/h

2 as unit (quadratic function). In order to make it back to the single

function, RMSE (Root Mean Square Error) (13)has been used as shown on equation 2 .

(1.2)


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The value of RMSE then will become the main criteria from choose the best set of parameter from

experiments on each set of parameter.

VALIDATION

In order to compare the data of scenario which is defined as (x) with the mean of the existing

validated model data (y), following hypothesis need to be checked

Where:

= average of observed data = average of model output

will be rejectedif Where :

(1.3) (1.4)

When rejected, its mean that the model output cannot representing the observed data.When accepted, its mean that the model output can represent the observed data.2.5.

Previous ResearchThere are several previous research about the two softwares SUMO and AIMSUN. Those researches

will give the reader better understanding and information about another usefulness of those

software to solve the problems.

2.2.1. iTetris

V2X communication is a technology invented to be applied in cars so that the cars can communicate

with the traffic network. These days, the use of V2X communication is increasing in many countries.

However, the price and the risk is too high to implement such system. Therefore a simulation is

needed in order to measure the benefits of a system before it is deployed into the real world. The

aim of iTETRIS project was to develop such framework and to couple the communication simulator

ns3 and SUMO using an open source system called iCS ITETRIS Control System which was

developed within the project (14). The iCS is responsible for starting the named simulators and

additional programs which simulate the V2X applications. It is also responsible for synchronizing the

participating simulators, and for the message exchange. Using this simulation framework it was

possible to investigate the impacts on V2X communication strategies.

Within the project several traffic management strategies were simulated e.g. prioritization of

emergency vehicles at controlled intersections (15)and rerouting vehicles over bus lanes using V2X

communication (16).

2.2.2. VABENEFor every big city, the need of good traffic condition is essential. When a big event or a disaster

happens, traffic jams will occur and it will have a great impact to the transport systems and may

jeopardize the people who live in the city. The institutions responsible for the traffic should take

proper actions to overcome the worst case. The objective of VABENE is to implement a system

which supports the public authority to decide which action should be taken (14).


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This project is focusing on simulating the traffic condition of large cities. In order to simulate Munich

city traffic, the mesoscopic traffic model was implemented into SUMO which is available for internal

proposes only. This system was used during popes visit in Germany in 2005 and during the FIFA

World Cup in 2006.

2.2.3. CityMobilTo evaluate the changes in vehicle or driver behavior of large scale effects, microscopic traffic

simulations can be used. In past time, that condition was examined with the help of SUMO in the EU

project CityMobil where different scenarios of (partly) automated cars or personal rapid transit were

set up on different scales, from a parking area up to whole cities (14).

In this study, the evaluation of the benefits of an autonomous bus system was performed.

Furthermore, scenario being applied in this study is the information of passengers waiting and route

adaption to the demand given to the busses. The investigation regarding the influence of platooning

vehicles on a large scale also implemented, where a middle-sized city with 100.000 inhabitants were

used as the model. Those two simulations were showing good result of the transport automation.

2.2.4.

VTI ComparisonSwedish National Road Administration perform a research that comparing the Car-following models

from traffic simulation softwares which are AIMSUN, MITSIM, VISSIM, and Fritzsche. The diiferences

between the Car-following model used in these versions and the Fritzsche car-following model is

however unknown (17). The Fritzsche and VISSIM have similar approach, but AIMSUN and MITSIM

have different approaches. Results from those models are show similarities even though have

different car-following approaches.


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CHAPTER 3 INPUT DATA FOR THE SIMULATION

Flow as input data is extracted from detectors on study area. Each detector in this area have its own

flow data in 1-minute interval. These detectors also contain the speed average of vehicles in 1-

minute intervals. The location of each detector, the speed and flow data from these detectors will be

described in this chapter.

3.1.

DetectorThere are several detectors placed around the area, figure 6 show the detector spots of the

intersection street E75 and E266. The numbers shown in figure 6 represent the stationary location of

the detector, each stationary location have 1-4 detectors depends on the number of lanes.

E75

E75

Yellow and purple circle in figure 6 represent the detector that be used in this research. Those

detector have different role in the micro-simulation model development depends on the direction of

traffic movement. By considering the detector which been marked by yellow circle as the border, the

total number of detectors is 85. In order to develop the micro-simulation in this research, this

research using data from 29 detectors within this area as input and validation data.

Based on the needs of data input, it was decided to choose several detectors data as input and

validation data for the micro-simulation model that would be created by using AIMSUN and SUMO.

The detector number that will be used in this research are shown in table 1 and to ease for

understanding the situation about research area, a simple picture of detector location/point in

figure 7 is created. The data taken from 00.00 to 18.24 (GMT+1) at 16th of March 2010.

The number inside the box represents stationary point associated with the table 1 below. The yellow

boxes represent the detectors whose data will be used as input for this micro-simulation

development. Figure 7 describe about the position of each detectors according to table 1.

Figure 6. Detectors location


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Table 1. Detector inventory

No Stationary point Quantity Detector Numbering Role

1 1.845 3 49,50,51 Main Input

2 23.205 2 49,50 Main Input

3 3.270 4 49,50,51,52 Main Input4 2.300e 1 49 Proportion Input for 1.845

5 23.495 1 49 Proportion Input for 23.205

6 3.015h 1 49 Proportion Input for 3.270

7 23.49 1 49 Proportion Input for 23.205

8 1.885 3 49,50,51 Validation point

9 3.215 4 49,50,51,52 Validation point

10 23.220 2 49,50 Validation point

11 2.170 4 49,50,51,52 Proportion input for 1.885

12 2.750e 1 49 Proportion input for 23.220

13 2.370h 1 49 Proportion Input for 23.220

14 23.865r 1 49 Proportion Input for 3.215

Those detectors have different role in data extraction. Table 1 shows the different role of those

detectors. Main input means that the flow extracted from related points will become the basic of

the calculation for determine the O/D matrix. Proportion inputmeans that the flow extracted from

related points became the tool to know the flow proportion in other direction. For example, In order

to get the number of flow for West-to-East direction, the flow is taken from the flow in detector

1.845 minus the flow in detector 2.300e. Similar role as main inputis given to the validation point,

the difference is on the purpose of data. The flow extracted from validation pointwould be used for

validation process while main inputwill be used for the calibration process.

23.220

3.215

1.885

3.270

23.205

1.845

23,4902.370h

3.015h

2.300e

2.750e23.495r

23.865

2.170

South

EastWest

Figure 7. Location of detector


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3.2. DataBased on the supplied data, the research chooses 2 peak hour in this micro-simulation as morning

and evening peak hour. For the peak hour in the morning 06.30 - 08.30 was chosen and for the

evening peak hour is between 13.30 - 15.30. This duration was chosen based on the flow profile in

this location as shown on figure 8.

Figure 8.Time-series diagram

Based on this characteristic, data from all selected detectors during the peak hour (06.30 - 08.30 and

13.30 - 15.30) was taken as main data for this micro-simulation development. Data in detector

number 2.305; 1.845 and 3.270 are used as demand input in micro-simulation program. Detector

number 2.300e; 23.495; 23.490 and 3.015h (in figure 7) is used to determine the proportion of flow

in each direction. As a result from this extraction, the OD-matrix is found for both peak hour

morning and evening for each direction in following table 2 and 3. Table 2 and 3 represent the traffic

flow condition on peak-hour in the area for 2 hours duration.

Table 2. OD-Matrix for morning peak-hour

OD East South West

East - 1402 4096

South 1924 - 1813

West 5540 275 -

Table 3. OD-matrix for evening peak-hour

OD East South West

East - 1327 4474

South 1194 - 867

West 5665 545 -

0

1000

2000

3000

4000

5000

6000

7000

9

3

9

161

Numbero

fvehicles(unit)

Set of time series

Flow Diagram (time series)

Flow


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It should be noted that the data in table 2 and table 3 will not used as input in the two softwares

SUMO and AIMSUN. The data in table 2 and 3 will be divided into 5-minutes interval O/D matrix and

will be used as demand input which represents the traffic movement within the intersection.

Further, based on this number of demand and the parameter, the traffic model will be created to

represent the real condition of traffic.

Table 4. Flow number and average speed in morning peak-hour

Time

Detector

23.205 1.845 3.270

Flow Speed Flow Speed Flow Speed

6:30:00 - 6:35:00 130 67.36 320 72.94 314 65.25

6:36:00 - 6:40:00 153 64.4 255 73.53 296 67.5

6:41:00 - 6:45:00 156 64.1 291 71.93 293 56.65

6:46:00 - 6:50:00 172 61 300 73.60 292 37.36:51:00 - 6:55:00 162 64.3 262 73.53 291 36

6:56:00 - 7:00:00 170 63.3 261 72.33 292 36.9

7:01:00 - 6:05:00 175 62.4 267 70.33 309 37.7

7:06:00 - 7:10:00 152 60.4 239 72.13 252 33.05

7:11:00 - 7:15:00 139 61.7 240 71.47 212 27.55

7:16:00 - 7:20:00 174 60.7 254 70.67 198 29.1

7:21:00 - 7:25:00 166 59.6 257 70.40 199 29.65

7:26:00 - 7:30:00 143 60.4 269 67.80 249 28.7

7:31:00 - 7:35:00 139 64.3 272 69.47 212 28.65

7:36:00 - 7:40:00 170 61 262 71.13 213 30.9

7:41:00 - 7:45:00 138 62.9 275 70.67 175 25.35

7:46:00 - 7:50:00 152 61.3 287 67.73 167 28.1

7:51:00 - 7:55:00 142 61.9 242 71.67 261 28.15

7:56:00 - 8:00:00 160 60.6 247 70.13 180 25.65

8:01:00 - 8:05:00 152 60.3 243 71.93 168 29.45

8:06:00 - 8:10:00 152 61.7 222 50.40 165 37.2

8:11:00 - 8:15:00 149 61.5 147 20.60 240 30.7

8:16:00 - 8:20:00 137 63.5 144 20.40 133 23.15

8:21:00 - 8:25:00 141 62.9 135 18.33 214 31.258:26:00 - 8:30:00 119 63.4 124 18.80 173 29.9

Total 3643 5815 5498

The OD-matrix in table 2 is created based on the total number of flow in table 4 and table 6. It is

implied from table 2 that 25.5% flow from East-section go to the south-section and rest of them go

to the west. It also can be implied that 95.3% traffic flow from the west go to the east and 51.5%

traffic flow from the south-section go to the East-section. Similar with the table 2, table 3 is created

based on the flow number in table 5 and table 7. In the evening peak-hour there are no significant

different. Flow percentage of traffic flow from East-to-West section and South-to-East section are

bigger that morning peak-hour, but for the West-to-East section traffic flow is smaller that morning

(91.2%).


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Figure 9. Flow diagram (morning peak-hour)

Table 5. Flow number and average speed in evening peak-hour

TimeDetector

23.205 1.845 3.27

Flow Speed Flow Speed Flow Speed

13:30:00 - 13:35:00 74 64.5 311 68.22 260 67.5

13:36:00 - 13:40:00 69 64 235 70.53 230 69.65

13:41:00 - 13:45:00 71 63.1 259 67.53 243 67.8

13:46:00 - 13:50:00 75 66.2 245 69.40 236 68

13:51:00 - 13:55:00 98 64.3 243 68.47 228 76.7

13:56:00 - 14:00:00 84 65.6 250 69.07 235 76.45

14:01:00 - 14:05:00 78 65.1 228 70.93 229 68.05

14:06:00 - 14:10:00 71 65.7 247 68.07 231 67.6

14:11:00 - 14:15:00 85 65 268 68.80 257 76

14:16:00 - 14:20:00 99 64 249 71.60 253 77.15

14:21:00 - 14:25:00 87 65.1 243 70.40 237 67.55

14:26:00 - 14:30:00 85 65.3 258 69.13 248 68.45

14:31:00 - 14:35:00 74 65.3 260 68.53 241 67.4

14:36:00 - 14:40:00 105 64.3 254 69.20 240 65.95

14:41:00 - 14:45:00 88 62.5 260 67.67 277 67.45

14:46:00 - 14:50:00 110 62 248 68.73 224 77.65

14:51:00 - 14:55:00 88 66 297 66.27 231 69.514:56:00 - 15:00:00 63 84.6 279 62.67 254 67.7

15:01:00 - 15:05:00 96 66.9 313 64.27 306 64.5

15:06:00 - 15:10:00 119 65.2 304 57.87 285 65.8

15:11:00 - 15:15:00 100 62.7 311 42.53 264 67.4

15:16:00 - 15:20:00 90 63.8 292 34.13 286 65.2

15:21:00 - 15:25:00 109 65 291 36.60 306 65.05

15:26:00 - 15:30:00 65 43.33

2018 6210 5801

050100

150200250300350

Flo

w(unit)

flow diagram (morning peak-hour)

Detector 2,305 Flow

Detector 1,845 Flow

Detector 3,270 Flow


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Figure 10. Flow diagram (evening peak-hour)

Table 6. Flow number and average speed in morning peak-hour

Time

Detector

2300e 23.495 3.015h 23.49

Flow Speed Flow Speed Flow Speed Flow Speed

6:30:00 - 6:35:00 11 72.00 76 67.5 51 76.33 65 68.2

6:36:00 - 6:40:00 9 66.00 72 67.8 40 76.6 90 65.8

6:41:00 - 6:45:00 11 70.00 71 66.2 48 73.4 77 66

6:46:00 - 6:50:00 4 73.00 98 63.6 63 73.2 76 66.2

6:51:00 - 6:55:00 14 74.00 93 64 52 71.2 84 64.4

6:56:00 - 7:00:00 25 71.00 98 63.2 57 72.6 68 67.27:01:00 - 6:05:00 23 68.50 93 65.2 56 73.4 81 64.4

7:06:00 - 7:10:00 11 74.00 83 65.4 66 69.2 75 64.2

7:11:00 - 7:15:00 11 70.00 74 62.2 65 63.4 70 61.6

7:16:00 - 7:20:00 8 73.00 88 63 62 68.8 81 63

7:21:00 - 7:25:00 0 0.00 73 64.6 67 67.6 91 60.4

7:26:00 - 7:30:00 0 0.00 91 62.6 67 65.4 75 62.2

7:31:00 - 7:35:00 22 67.33 71 68.4 63 62.2 70 66

7:36:00 - 7:40:00 11 75.00 88 60.2 65 66.2 78 63.8

7:41:00 - 7:45:00 14 69.50 75 65.4 66 62.2 73 63.8

7:46:00 - 7:50:00 5 68.00 90 63 69 63.8 67 64.2

7:51:00 - 7:55:00 4 74.00 69 64.6 63 64.2 77 63

7:56:00 - 8:00:00 15 69.00 64 64.2 60 63 92 61.8

8:01:00 - 8:05:00 7 74.50 95 60 54 66.2 71 62.2

8:06:00 - 8:10:00 12 62.00 81 63 59 61.8 72 63.8

8:11:00 - 8:15:00 10 59.50 81 62.4 58 65.8 66 62.8

8:16:00 - 8:20:00 23 58.25 65 63.4 55 63.4 81 62.4

8:21:00 - 8:25:00 17 63.50 71 63.4 52 67 74 60.2

8:26:00 - 8:30:00 8 62.50 64 64.2 44 66 59 63.4

Total 275 1924 1402 1813

050

100150

200250300350

Flow(unit)

Flow diagram (evening peak-hour)

Detector 2,305 Flow

Detector 1,845 Flow

Detector 3,27 Flow


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Table 7. Flow number and average speed in evening peak-hour

Time

Detector

2300e 23.495 3.015 23.49

Flow Speed Flow Speed Flow Speed Flow Speed

13:30:00 - 13:35:00 25 66.20 47 66 49 71.2 38 71.113:36:00 - 13:40:00 25 69.25 45 65.6 44 72.4 30 67.6

13:41:00 - 13:45:00 28 66.50 44 65.4 66 71.4 39 69.8

13:46:00 - 13:50:00 10 68.50 45 67.8 53 71.8 31 69.6

13:51:00 - 13:55:00 13 70.50 53 65 60 71.4 44 67.4

13:56:00 - 14:00:00 7 66.00 50 66.8 53 73.4 33 70

14:01:00 - 14:05:00 12 66.00 53 66 44 75.8 25 68.2

14:06:00 - 14:10:00 11 68.00 40 65.2 42 76.2 36 68.2

14:11:00 - 14:15:00 23 72.33 61 64.4 63 72 23 67.8

14:16:00 - 14:20:00 18 70.33 51 65 63 74.2 48 65.8

14:21:00 - 14:25:00 28 70.25 53 66 57 72.8 36 69.4

14:26:00 - 14:30:00 30 68.00 61 64.6 65 75 28 68.6

14:31:00 - 14:35:00 12 66.50 36 67 50 73.4 44 69.2

14:36:00 - 14:40:00 20 71.25 56 64.2 62 69.6 46 67.4

14:41:00 - 14:45:00 23 68.00 54 64.2 66 73.6 39 66.6

14:46:00 - 14:50:00 19 69.67 60 63 54 72 47 66,4

14:51:00 - 14:55:00 35 69.25 36 69.8 60 71.8 38 66.8

14:56:00 - 15:00:00 29 70.00 49 64.6 62 70.8 26 69

15:01:00 - 15:05:00 18 69.50 48 67 63 71.4 44 71.2

15:06:00 - 15:10:00 31 68.25 63 63.6 67 74 61 67.615:11:00 - 15:15:00 59 63.50 69 63.8 61 75.6 36 69

15:16:00 - 15:20:00 23 69.00 49 66.6 57 71.2 41 68.6

15:21:00 - 15:25:00 46 63.25 71 63.2 66 72.2 34 67.4

Total 545 1194 1327 867

3.3.

Parameters of the models

The model contains several parameters that will influence the output of the model. This section will

describe about what kind of parameters that will be used in the two softwares SUMO and AIMSUN.

3.3.1.

Parameter in AIMSUNVehicle Parameters

Vehicle characteristic of vehicle are include length, power ratio and it width. Higher power ratio on a

car can be used in the special condition such as overtaking condition, in which car driver tend to give

bigger power ration or acceleration. Based on AIMSUN Manual (1) Vehicle parameters are included:

a)

Width (meters)

This parameter is only for graphical purpose and does not have a direct influence on the modeling of

traffic.


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b)

Maximum desired speed (km/h)

Maximum speed of the type of vehicle can travel at any point in the network.

c)

Maximum acceleration (m/s2)

Maximum acceleration that can achieved by vehicle at any circumstance

d)

Normal deceleration (m/s2)

Maximum deceleration of vehicle under normal circumstances (as used in the Gippscar-following

model).

e)

Maximum deceleration (m/s2)

It can be considered as most severe breaking that can apply under special circumstances.

f)

Speed acceptance

This parameter (>0) canbe interpreted as the level of goodness ofthe drivers or the degree of

acceptance of speed limits. > 1 means that the vehicle will take as maximum speed for a section a

value greater than the speed limit, while < 1 means that the vehicle will use a lower speed limit.

See the next section for details on how maximum desired speed for a vehicle is calculated fordifferent parts of the network (1).

g) Minimum distance between vehicles (meters)

A vehicle will keep this distance between itself and preceding vehicle when stopped.

h)

Maximum give-way time

This parameter applies either the normal gap-acceptance model or the lane-changing model in order

to cross or merges with traffic.

i) Guidance acceptance,

This parameter (0 1) gives the level of compliance of related vehicle type with the guidance

indication.

Driver Parameter

The model also will use driver behavior to determine which the model is fit to the real condition.

Parameters applied for driver parameter are:

a) Driver's reaction time

Driver's reaction time is the time that used by the driver to react to speed change in the preceding

vehicle. Reaction time can be either Fixed (equal to Simulation Step) or Variable (multiple of

Simulation Step). In Fixed type, the values are same for all vehicles. In Variable type, the user can

define a discrete probability function for each vehicle type (18).

b)

Reaction time at stopReaction time at stop is the time taken for a stopped vehicle to react to the vehicle acceleration in

front or to the light traffic changing to green. This parameter has a strong influence in the queue

discharge behavior.

c)

Queuing Up Speed

This is the threshold of vehicles speed value (m/s). If any vehicles speed decrease below this

threshold, then it will be considered stopped and join the queue.


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d)

Queuing Leaving Speed

This is the threshold value of vehicles speed (m/s). If any stopped-vehicle speed in a queue

increases above this threshold value, then it will be considered left the queue and no longer to be at

a standstill.

e) Car-Following model,

The car-following model used in AIMSUN is based on the Gipps model which basically consists of two

components, which are acceleration and deceleration. Acceleration represent the vehicle intention

to reach a certain point of speed (desired speed), and Deceleration represent the limitation imposed

by the preceding vehicle when trying to drive on the desired speed (1).

2-lanes Car-Following parameters used in this model are:

a)

Number of vehicles

Maximum number of vehicles in the 2-lane variation of the Car-Following Model, which is used for

modeling the influence of adjacent lanes in the Car-Following Model.

b)

Maximum Distance,

Maximum distance ahead to be considered in the 2-lane Car Following Model.

c) Maximum Speed difference,

Maximum speed difference between one lane and the adjacent lane in the 2-lane Car Following

Model.

d)

Maximum Speed difference On-Ramp

Maximum speed difference between the main lane and an on-ramp lane in the 2-lane Car following

Model.

Lane Changing Parameters

Percent overtake

This parameter represents the percentage of the speed from which a vehicle decides to overtake.The value has to be greater than zero and less than or equal to one

Other global parameters

Road side of vehicle movement

The effect of this parameter on simulation result varies, depending on the geometry and type of the

network, but it always affects the behavior of the vehicles.

3.3.2. Parameter of the SUMOVehicle Parameters

a) Vehicle Properties

Vehicles used in the model should be defined whether is it a passenger car, truck, or bus. To define

what kind of vehicle used in the SUMO, some vehicle properties have to be set first. The vehicle

properties were set in the flow file and the parameters to be set on that property are:

a. idthe name of the vehicle type;

b.

lengththe vehicles netto-length (m);

c.

miniGapempty space after leader (m);

d.

maxSpeed- the vehicles maximum velocity (m/s);

e. guiShapehow this vehicle is rendered;

f. guiWidththe vehicles width (m).


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b)

Routes

The parameters used in route path and flow proportion are:

a. idthe name of route in certain interval;

b. fromthe name of the edge which is route started;

c. tothe name of the edge which is route ended;

d.

Number- the number of vehicles generated in the model;e.

typethe name of vehicle types;

c) Car-Following model

The car-following model describes that the speed of following car is influenced by theacceleration of the preceding car, which can be described best by the following equation:

: speed of vehicle n

: speed of succeeding vehicle n : drivers reaction time (s)The equation above was first proposed by Pipes as cited in Krauss, 1998 (19).

In the SUMO, the car-following models implemented:

Table 8. The Car-following models implemented in SUMO (9)

Element Short Name Description

1. carFollowing-Krauss

2. carFollowing-KraussOrig1

3. carFollowing-

PWagner2009

4. carFollowing-BKerner

5. carFollowing-IDM

SUMOKrau

SKOrig

PW2009

Kerner

IDM

The Krau-model with some modifications

which is the default model used in SUMO

The original Krau-model

A model by Peter Wagner, using Todosievs

action points.

A model by Boris Kerner

Note: currently under work

The Intelligent Driver Model by Martin Treiber

Note: Problems with lane changing occur

In SUMO, the SUMOKraumodel is used. This model is a modification of the original Krau model.

Basically, the Krau model can be formulate as follow(19):

Where,


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The index vnis the notation of vehicles position where the leading vehicle always has bigger value

than the following. The gap or distance between two vehicles denoted as , desired gap or expresses how long the gap between vehicles can be achieved, vehicle desiredspeed or shows how large the driver can run the vehicle with maximum speed, time step oris assumed has the same value or equal to the drivers reaction time, time scale is defined aswhere it has the same value with . Here the random perturbation has been introduced toallow for deviations from optimal driving. has the same value with a, where is having valuebetween 0 and 1. The maximum acceleration (a), deceleration (b), and jammed spacing (l) are

assumed constant.

The parameters used in the SUMO Kraumodel are:

a. accelacceleration ability [m/s2],

b. decaldeceleration ability [m/s2],

c.

sigmadriver imperfection (real value between 0 and 1 inclusive),

d.

tau drivers reaction time *s+,

e.

minGapgap between preceding and following cars [m].

Vehicles and Routes

a)

Repeated vehicles

It is possible to define repeated vehicle flows, which have the same parameters as the vehicle except

for the departure time. The id of the created vehicles is "flowId.runningNumber" and they are

distributed equally in the given interval. The following additional parameters are known:

a. beginfirst vehicle departure time [s],

b. endend of departure interval [s],

c.

vehsPerHournumber of vehicles per hour (not together with period) [h],

d.

periodrepetition period (not together with vehsPerHour) [s],

e. numbertotal number of vehicles [#].

b) Routes

Each route has its own class and to differentiate one to others, it colored with different color in the

map. As well as in the SUMO, to distinguish one route to another, it gives attributes to the route

such as id (the name of the route), color (the routes color) and edges.

A vehicles depart and arrival parameter

To control how a vehicle is inserted into the network and how it leaves it, SUMO needs to add some

parameters such as:

a.

departLanethe lane on which the vehicle shall be inserted.

b. departPosthe position at which the vehicle shall enter the network [m],

c. departSpeedthe speed with which the vehicle shall enter the network [m/s]

d. arrivalLanethe lane at which the vehicle shall leave the network,

e.

arrivalPosthe position at which the vehicle shall leave the network [m],f.

arrivalSpeedthe speed with which the vehicle shall leave the network [m/s].

Stops

In some case e.g. at signalized intersections, roundabout, etc. drivers have to stop their car in order

to give way to other drivers for a couple of seconds or minutes. As if in the modeling like SUMO,

vehicles may be stop for a defined time span or wait for persons by using the stop element either as

part of a route or a vehicle.

Lane Changing Parameters

In the SUMO version 0.15.1, parameters such as the lane change model, and others, are not directly

accessible to a user in configuration files. Therefore, the parameters mentioned before were not


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considered during the calibration process. For vehicle and driver parameters of the car following

model, special attentions is given to:

a.

accelacceleration ability [m/s2]

b.

decaldeceleration ability [m/s2]

c. sigmadriver imperfection (real value between 0 and 1 inclusive),

d.

lengthvehicle length (increased by a typical gap distance between stopped vehicles) [m],

e. maxspeedvehicle maximum velocity [m/s],


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CHAPTER 4 MODEL CONSTRUCTION

4.1.

DEVELOPMENT IN AIMSUN & SUMOThere are several elements that needed to construct a model in both software SUMO and AIMSUN.

This section will explain about each element included in each software in order to make the model.

4.1.1. AIMSUN

LINK-NODE DIAGRAM

Network development can be done by using GUI (Graphical User Interface) which facilitated by

AIMSUN itself. Generally, there are 6 links, 4 nodes and 3 centroids in this network as shown in

figure 10 below.Centroid

345

Centroid

346

Centroid

347

Figure 11. Link-Node Diagram

GUI in AIMSUN made user can directly build and modified the network as closely as possible to the

real traffic condition. Actually, the network can be considered as 3-approachment intersection but

with different stage of lane. The directions are coming from south, east, and west.

LINK GEOMETRY DATAa)

Number of Lane

There are different numbers of lane within links in this network. For link from west to east (E75), the

number of lane change 3 times. At the beginning (from west) the road has 3 lanes, but since there is

diverging link the number of lane reduced into 2 lanes. About hundred meters before merging lane,

the number of lane of this link increased into 3 lanes and then increased again into 4 lanes after

there is merging lane from south direction. All links which directly connected to East-centroid have 4

lanes road. All links which directly connected to the west-centroid are 3-lanes-roads for and 2-lanes-

road for all links which directly connected to south-centroid. Figure 11 shows about the number of

lane in study area.


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Figure 12. Number of lanes and the detectors

b)

Lane width

Most part of the network is in the tunnel. In main tunnel, lane width is 3.75 m and at the ramps 4.5

m. Specific dimension for the road in the main tunnel is 0.75 m for left shoulder; 3.75 m for main

lane and 1.75 m for the right shoulder. For the ramp part, the specific dimension is 2.5 m for left

shoulder; 4.5 m for main lane and 1 m for right shoulder. The dimension can be seen at figure 12 .

Along the network there are no pedestrian crossing facilities, bus stop, or reserved lane for public

transport. Detail picture of lane width in on ramp and main tunnel can be seen in figure 12below.

2,5 m

4 m

1 m

0,75 m

3,75 m 3,75 m

1,75 m

ON RAMP LANE

MAIN TUNNEL LANE

LEFT SHOULDER LANERIGHT

SHOULDER

LEFT SHOULDER LANE LANE RIGHT SHOULDER

Figure 13. Lane Width

= Main detectors


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TRAFFIC DEMAND DATASince the supplied data do not mention about the type of the car, every vehicle counted on related

detector are considered as private car. 24 demand matrix was used on the morning as traffic

demand input in order to get the real fluctuation as the real condition. Each matrix is a 5-minutes

flow aggregation for all direction. Table 8 shows about demand matrix for the first 5-minutes from 2

hours demand which become input for the model. Graphic in figure 11 shows about the fluctuationof demand in 5-minutes interval which covered by detector 23,205. It can be implied that O/D-

matrix in table 8 give a contribution on the first point in figure 13.

Table 9. Demand matrix for the first 5-minutes demand in morning

O/D West East South

West 0 309 11

East 263 0 51

South 54 76 0

Figure 14. Flow fluctuation in 5-minutes interval in the morning for detector 23.205

The flow data are subtracted from the counting of 14 different detectors. Each detector also count

the average spot speed in 1 minute interval. For analysis purpose, those speeds were broke down

into 5-minutes average speed. Figure 14 shows speed average in 5-minutes interval on detector

23.205.

Figure 15. Speed fluctuation in 5-minutes interval in the morning for detector 23.205

0

20

40

60

80

100

120

140

0 5 10 15 20 25

Flow(veh)

Time series (5 minutes)

detector 23.205 (enterance)

Observation

0

20

40

60

80

100

0 5 10 15 20 25

Speed(km/h)

Time series (5 minutes)

detector 23,205

Observation


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SIMULATION CONTROL DATAa) Scenario

In order to run the simulation, Dynamic Scenario has to be created. Each Dynamic Scenario contains

traffic demand that will be used in every experiment on the same scenario. The user has to

determine what kind of traffic demand will be used. In each Dynamic Scenario also determine where

the result will be saved as database, and how long the interval of the result will be generated. ADynamic Scenario can hold 15-19 experiments. Figure 15 shows the user interface in AIMSUN where

the user shall select the traffic demand.

Figure 16. Scenario control interface in AIMSUM

b)

Traffic Demand Control Data

Traffic demand as mentioned in previous part has important part in the modeling process. The

demand will determine the result of the model in the end. This analysis covered 2 hours demand

with 5-minutes interval of flow, which means 24 matrixes of 5-minutes interval flow as seen in table

8 for morning demand control and the other 24 for evening demand control (figure 16).

Figure 17. 24 morning matrixes and 24 evening matrixes

In AIMSUN, all 24 matrixes of morning demand will combine each other in Traffic Demandscontrol

as seen in figure 17.


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Figure 18. Traffic Demand control for Morning Demand data

c) Database

Each scenario has to be connected to one database file as data storage. Scenario and database are

connected by QODBC driver. Every QODBC driver has several connections and each connection have

its own database. In every scenario, the user has to determine the QODBC connection to keep the

result of each replication in each experiment. Figure 18 shows steps to create database storage

connection in AIMSUN.

Figure 19. Steps to create database storage in AIMSUN


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d)

Experiment

Each experiment has different set of parameter value. 10 replications were created on each

experiments and 1 average that will be calculates the average of those 10 replications. In order to

make it easier to find the data and to remind experiment that have been done, each experiment was

renamed based on the number or value of parameters that changed in each experiment. Right part

of figure 19 shows an example about experiment in AIMSUN.

Figure 20. example of ReplicationsandAveragein each experiment

The number of average replicationhas to recorded for each experiment to ease for finding the

data in database. In each experiment, value of behavior parameters were set as default (figure 21)

and put 15 minutes as warming-up of flow stream (figure 20).

Figure 21.Warming-up for flow stream


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Figure 22. Behavior parameter

Options such as applying the 2-lane car following model, warm-up time, and reaction time, lane-

changing (figure 21) have to be performed in each experiment .

Table 10. Error checking item

No Item Access

1

2

3

4

5

6

Network

Demand Data

Vehicle Parameter

Behavior parameter

Database Connesction

Number of Replication

GUI

Demand option

Vehicle option

Experiment option

Scenario option

Experiment Option

When the entire item above checked by user, a proper simulation or experiment can be performed

using parameters which decided to be taken at the beginning of each simulation.

4.1.2. SUMO

NetworkA network is very essential in building a traffic simulation, because it is the core or the foundation of

the model itself. Without the existence of a network, the traffic simulation model cannot be run. In

the SUMO, there are two ways to build a network such as:

a)

Writing our own network on XML-descriptions; and

b) Importing from non-SUMO networks.

In this thesis, non-SUMO networks was used to create the networks. The non-SUMO networks being

used as the base of the networks is coming from OpenStreetMap file. Due to the true nature of the

studied location are arterial roads, set some parameters were applied when editing the network in

Java OpenStreetMap editor, such as:


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Table 11. Parameters set in SUMO network

Parameter Value

1. Maximum Speed

2.

Lane width (Sdra lnken Tunnel)

3.

Lane width (on ramp & off ramp)

70 km/h

3.75 m

4.5 m

After the networks editing is completed, then it saved in .osmfile format. The output from in Java

OpenStreetMap editor cannot be directly used in the SUMO, because SUMO cannot read any .osm

files. SUMO can only identify networks in .xml format files, therefore the .osm file should be

converted into .xml format files by using an application called NETCONVERT run in windows

command prompt. Now the networks can be seen in the SUMO-GUI as seen in figure 22.

Figure 23. Networks displayed in SUMO-GUI

Demand ModelingThe demand modeling is classified into three section, they are vehicle properties, the Krau car-

following model and flow. All parameters of vehicle properties, the Krau car-following model and

flow are set altogether in one file called flow file.

a)

Vehicle propertiesParameters measured as vehicle parameters of the SUMO are including dimension, classes,

visualization, speeds, gap, and vehicle emission classes. However, in this thesis, there are some

parameters that not included or consider as one type such as vehicle emission classes, vehicle

classes, and visualization. For vehicle classes and visualization, due to the existence of the data

regarding those parameters are not available, those parameters are considered as one type such

as sedan with yellow color.


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Parameters to be set in vehicle properties:

Table 12. Vehicle properties parameter value

Parameter Value

1. id

2.

length

3. miniGap

4.

maxSpeed

5.

guiShape

6. guiWidth

Car

4.5 m

1 m

100 km/h (27.78 m/s)

Passenger/sedan

2 m

b) Kraucar-following model

As mentioned in chapter 3 above, the Kraumodel is used as the car-following model. This model

which has some modifications is the default model used in the SUMO. The parameters to be set

in the SUMO Kraucar-following model can be seen in the following table.

Table 13. The Kraucar-following model parameter value

Parameter Value

1. accel

2. decel

3. sigma

4. tau

5.5 m/s2

5 m/s2

0 - 1

From 1 - seconds

c)

flow

After vehicles properties and the car-following model have been set, route path and flow

proportion of the vehicles should be made to be inserted to the model. The route built shouldensure the vehicles travelled from the right origin nodes to the destination nodes as well. In this

model, the 5 minutes period aggregated vehicle flow is made for two hours duration.

Those three inputs are written in the format of .xml. After those inputs are saved, the next step is

combining the network with the demand modeling by using a SUMO application called

duarouter.exe via command prompt windows. The extensions of the combined file should be

.rou.xml.

Output

To generate an output to analyze, an additional file should be made first. This additional file is saved

in .xml format and it contains two sections. The first section is containing detectors data to give

mean speed output to validate. In the second section, an edge dump was set in order to have the

wanted output. The outputs will be kept in .xml format as well. The parameters being used in

additional file are:

Detector

a. idthe name of the detectors;

b.

typethe type of the detectors (e1, e2 or e3);

c.

lanethe id of the lane where the detectors shall be laid on;

d.

pos - the position of the detector in the lane being used (m);

e. lengththe length of the detectors (m);

f.

freqthe aggregation of time period (second);


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g.

filethe output file name (.xmlformat);

h. measurescontain the list of measures that will be computed (allto compute all measures;

i. timeThreshold time that describe how much time that assumed as vehicle has stopped

(second);

j. speedThresholdspeed that describe how speed that assumed this vehicle count as suffered to

the jump (m);k.

jamThreshold minimum distance to the next vehicle that assumed this vehicle count as

suffered to the jump (m);

l. keep_forinformation how long the memory of the detector has to

aimsun sumo comp

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