2nd International Workshop on Traffic Data Collection & its Standardisation

An Arrangement of Benchmark Dataset for ModelValidation in CO2 Emission Volume Estimation
Shinji TANAKA, The University of Tokyo, stanaka@iis.u-tokyo.ac.jp
Tadashi KOMIYA, i-Transport Lab. Co,. Ltd., komiya@i-transportlab.jp
Hisatomo HANABUSA, i-Transport Lab. Co,. Ltd., hanabusa@i-transportlab.jp
Seiji HAYASHi, Japan Automobile Reserach Insitute, shayashi@jari.or.jp
Ryota HORIGUCHi, i-Transport Lab. Co,. Ltd., r-horiguchi@i-transportlab.jp
Masao KUWAHARA, Tohoku University, kuwahara@iis.u-tokyo.ac.jpAbstract:
As global warming has become one of the major issues in recent years, reliable
methodologies to estimate CO2 emission volume using traffic simulation models andemission models are highly demanded. To meet this demand, we proposed avalidation framework for CO2 estimation methodologies to ensure theirreliability.Then we conducted a comprehensive field data collection in an urban street
in Tokyo, which can be used not only for traffic simulation model validation but also
CO2 emission model validation. It recorded the number of all the passing vehicles,vehicle travel time, signal change timing, individual fuel consumption and so on. The
collected data was applied to a set of models to check the validity of CO2 volumeestimation.
Keywords: benchmark dataset, model validation, CO2 emission

1. Introduction
There is a growing concerns in the global warming issues all over the world. As roadtransportation is one of the major source of CO2 emission, it is highly demanded toestablish reliable methodologies to estimate CO2 emission volume from vehicle
traffic in order to make effective countermeasures. Therefore a lot of efforts havebeen made to develop models for CO2 emission estimation. Here, the scope of theevaluation is usually different depending on the purposes and then it is difficult to use
a common model. It is generally preffered to use customised or self-developed models,and as a result, a lot of models are developed which are not compatible each other. Itis very hard to determine one unified model.
Considering this situation, we are proposing a framework of evaluation which isapplicable to various different models. The basic concept is not to determine aspecific model but to set a common validation scheme. This paper introduces theoutline of this framework and the concept of the validation process. Then, a1

2nd International Workshop on Traffic Data Collection & its Standardisation

comprehensive field data collection to arrange a benchmark dataset which plays animportant role in the framework is explained. And finally, a demonstration of model
validation is shown using this dataset.

2. Proposed Framework
There are already a lot of models to estimate CO2 emission volume by various
developers. Each model has different characteristics such as scale, resolution,input/output variables, parameters, sensitivity, and so on. Model users also choose
models from a lot of candidates to satisfy their requirements. Therefore, it would bealmost impossible to select only one model which can be universally used in any
types of evaluation.
On the other hand, the result of the evaluation sometimes leads to conflicts of intersets
among countries/regions, so it is very important to gurantee the reliability to facilitate
international discussions. If only one unified model is used for all cases of evaluation,
we can easily examine the validity of the model in a obvious way. However, we haveto consider a lot of models in reality, and the internal mechanisms of them are usually
unknown to other users. Then it becomes a challenging issue to show the reliability of
the result from different models.
To cope with this situation, we would like to propose a framework which does not
specify a certain model but set a common validation scheme. Figure 1 shows theconcept of this framework. Every model can be developed by different developers
depending on its scope, but it has to pass the common validation process and disclose
the result of the validation. Here, the common validation scheme should bedetermined to meet the requirements through the international discussion andagreement. Then model users can choose a set of models which is suitable for theirinterests based on the disclosed validation results. Through this process, thetransparency of models can be guranteed to a certain extent, and we can use theresults of the model as a reliability ones.
CO2 emission evaluation
by various models
Model A

Model B・・・

common validation schememodel implementation
which satisfies
international requirements
Model A

Model X

Figure 1: Framework for Model Validation22nd International Workshop on Traffic Data Collection & its Standardisation

3. Data collection and Arrangement
To realize the common validation scheme, it is necessary to prepare benchmark
datasets which will be commonly used to validate various models. For this purpose,
we conducted a field survey and collected data comprehensively in an urban street inTokyo.
3.1 Survey Area and TimeTo set the location and time of the survey, the following things were considered.
- The data should include various stop/start patterns.
- The section should include a bottleneck intersection.
- The survey time period should include the beginning and the end of the traffic
congestion- The complete number of vehicles should be observed and counted including
minor access roads and parkings.
- The data should be recorded on the second time scale.
Considering these conditions, we set the survey area in the South West of Tokyo
downtown, which is densely populated. The survey section (Komazawa-dori) is 1.7
km length and there are 10 intersections along this section. There is a bottleneckintersection near the downstream end of the section. Figure 2 shows the map of thesurvey section. The data collection was done from 6:30 am to 10:30 am on weekday
so that traffic congestion in the morning peak would be observed.

Figure 2: Survey Section

3.2 Data Collection Methods and Items
Data were collected by a couple of differnt measures in order to arrange acomprehensive dataset.
3.2.1 Video observation
Video recording was conducted to collect vehilce volume data at all the intersections
along the target section. Multiple video cameras were placed depending on thenumber of legs of an intersection in order to cover the vehicles exiting from theintersection. Figure 3 shows an example of the alignment.

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2nd International Workshop on Traffic Data Collection & its Standardisation

Passing Point・ ガードパイプの切れ目
No.
1

・ 木の前

出入口

※三脚は伸ばして最大に高くする
有料
パーキン グ・ 植樹帯の切れ目

水廻りのリフォ ーム
駐車場木

ampm

2
木

木

4
電柱木

・ ガードパイプ・ ゴ ミ置き場ネット

・ ガードパイプ切れ目
・ 木 及び電柱

3Figure 3: Example of video camera alignment

After the survey, recorded video images were processed and the following data items
were extracted from them.
-Traffic volume (by direction, by vehicle type) at each intersection
Signal change timing at each intersection

3.2.2 Probe vehicle run
To obtain the traffic condition along the section, 13 probe vehicles were prepared
including a large truck. They ran through the section in a specified route repeatedly sothat they could pass the section every 5 minutes. They were equipped with a logger torecord GPS data, vehicle speed and fuel consumption. After processing the recorded
data, the following data items were obtained.
-Position of each vehicle at every second
Speed, acceleration/deceleration of each vehicle at every second
Average link/section speed
Average section travel time

3.2.3 Manual recoding
In addition to the above, some other types of data were collected by manual recording
as follows.
-Incoming traffic from minor paths (time of enterance)
Outgoing traffic to minor paths (time of exit)
Incoming traffic from roadside facilities (time of enterance)
Outgoing traffic to roadside facilities (time of enterance)On-street parking vehicles (time of beginning/end of parking)4

2nd International Workshop on Traffic Data Collection & its Standardisation-Bus stopping at a bus stop (time of beginning/end of stopping)

3.3 Arrangement as a Benchmark Dataset
The collected data items were processed to eliminate data errors and outliers. Thenthey were arranged into one package as a dataset together with explanatory
documents. As it was supposed to be used as a standard in order to validate newly
developed models, it was uploaded to the International Traffic Database (ITDb) to beshared among other model developers. Registered users of ITDb can browse theuploaded datasets through the data repository, and can download them from it. Figure
4 shows how it is stored in the ITDb.

Figure 4: Stored dataset on ITDb

4. Model validation
Using the arranged benchmark dataset, a model validation trial was conducted inorder to confirm the feasibility of the proposed validation scheme and process. Here,
we examined a set of traffic simulation model and CO2 emission model we developed.
We prepared the same road network as the surveyed area, including 23 zones of origin
and destination. OD demand table was given in the unit of 5 minutes and two vehicle
types (passenger car and truck) were set. The siganl parameters were also set same as
the observed value.
4.1 Traffic Simulation Model
Figure 5 shows a comparison of simulated and measured values of traffic flow in thetarget section. They are plotted more or less around 45 degree line. Figure 6 shows the52nd International Workshop on Traffic Data Collection & its Standardisationtransition of the section travel time. Here, measured travel time is obtained from theprobe data. Both results shows good correspondences, which means the traffic
simulation model can reproduce the traffic condition accurately enough.

measured[veh/5min]
観測値[台/5分]

120

100
y = 1.025x
2
R = 0.9061

806040passenger
car
小型
大型
truck

20

線形 (小型)線形 (大型)y = 0.6278x
2
R = 0.4926

00

20406080
シミュレーション値[台/5分]

100

120

simulated [veh/5min]Figure 5: Comparison of the traffic flow
区間旅行時間比較（上り）1000

traveltime [sec]区間旅行時間[秒]

900
800
700
600
500
400
300
200

シミュレーション値（秒）simulated
観測値（秒）
measured

100
0
6:00

6:30

7:00

7:30

8:00

8:30
時刻9:00

9:30

10:00 10:30

time
Figure 6: Transition of the section travel time

4.2 CO2 Emission Model
Figure 7 shows the estimated CO2 emission volume from individual vehicles using
the results of traffic simulation model. The measured values are obtained from thefuel consumption of the probe vehicles. Note that the number of “measured” plots isless because the vehicle category has to be more finely classified in calculating CO2
emission and then the number of corresponding probe vehicles becomes also limited.
Nevertheless, the simulated and the measured values are in the same range, meaningthat the CO2 emission model provides reasonable results.

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2nd International Workshop on Traffic Data Collection & its Standardisation

2000

simulated

1800

measured

CO2 emission[g-CO2]

1600
1400
1200
1000
800
600
400
200
0
6:00

6:30

7:00

7:30

8:00

8:30

9:00

9:30

10:00 10:30

time

Figure 7: Estimated CO2 emission volume from individual vehicles

5. Conclusion
This paper proposed a common validation framework which can be applied todifferent combinations of traffic simulation models and CO2 emission models that areused to estimate CO2 emission volume. Then a comprehensive data collection activity
to arrange a benchmark dataset and a validation trial using this dataset are shown.
Estimation of CO2 emission is becoming more and more important for policy making,project evaluation and international discussion, therefore we have to establish reliable
methodologies for it. Here, validation framework plays a very important role togurantee mutual trust. And data collection, storage and sharing are essential to support
the framework. We have been working on this activity collaborating with otherinternational partners, and would like to expand the framework over the world.

References:Ryota HORIGUCHI, Hisatomo HANABUSA, Masao KUWAHARA, Shinji TANAKA,Takashi OGUCHI, Hiroyuki ONEYAMA, Hiroshi HIRAI, Seiji HAYASHI : Validation
scheme for traffic simulation to estimate environmental impacts in ‘Energy‐ITS
project’, 17th ITS World Congress, CD‐ROM, 2010.10

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