Extracting Transportation Information and Traffic Problems
from Tweets during a Disaster
Where do you evacuate to?
Aya Ishino

Shuhei Odawara

Hidetsugu Nanba

Toshiyuki Takezawa

Graduate School of Information Sciences, Hiroshima City University
Hiroshima, Japan{ishino, odawara, nanba, takezawa}@ls.info.hiroshima-cu.ac.jp
Abstract—In a disaster, one of the most important issues forvictims is how to find evacuation routes to safety fromhazardous areas. To offer such routes, we propose methods
automatically extracting transportation information andtraffic problems from tweets written in Japanese and posted
during a disaster. To investigate the effectiveness of our
methods, we conducted some experiments using tweets posted
during the Great Eastern Japan Earthquake in March 2011.
From the experimental results, we obtained precision of 78.2%
and recall of 53.4% in automatic extraction of transportation
information. For extracting traffic problems, we identified
tweets containing relevant information (we call them traffic
problem tweets), and extracted traffic problem from them. In
identifying traffic problem tweets, we obtained precision of
77.7% and recall of 70.7%. In extracting traffic problems, we
obtained precision of 87.0% and recall of 57.1%. Thus, wehave constructed a system for providing transportation
information and traffic problems in a disaster.
Keywords-disaster;
extraction;

evacuationroutes;informationI.
INTRODUCTION
Disasters occur frequently throughout the world. For
instance, there were the large earthquakes in Haiti in January2010 and in Sumatra, Indonesia in December 2004. In March
2011, a massive earthquake of magnitude 9.0 struck off thecoast of eastern Japan. This earthquake is called the Great
Eastern Japan Earthquake. It caused tsunamis and an
accident at a nuclear power plant, and forced large numbersof people to evacuate from their homes and towns. In suchdisasters, one of the most important issues for victims is how
to find evacuation routes to safety from hazardous areas. To
offer such evacuation routes, there is a need to collecttransportation information and traffic problems from othervictims. Because they are so widely used, we focused on
extracting information from tweets.
After the Great Eastern Japan Earthquake, 18 million
tweets were posted on Twitter in a day, which is 1.8 times asmuch as normal. Some tweets contained useful information
about transportation and traffic problems. In this paper, we
propose methods for extracting transportation informationand traffic problems automatically from tweets posted during
disasters. In addition, we construct a system for presenting
the extracted information. We believe that the system canoffer safe evacuation routes for disaster victims andtransportation routes for relief materiel.

The remainder of this paper is organized as follows.
Section II describes the system behavior using snapshots.
Section III describes related work. Section IV explains our
methods. To investigate the effectiveness of our methods, we
conducted some experiments, and Section V reports on theseand the results. We present some conclusions in Section VI.
II.

SYSTEM BEHAVIOR

In this section, we describe our prototype system, which(1) provides transportation information, and (2) identifies
traffic problems. Fig. 1 shows transportation informationfrom disaster victims. Arrows with icons indicate
transportation information. The arrow extends from adeparture place (shown as ① in Fig. 1) to a destination (②).
Each icon depicts a transportation method. If the user clicks
the icon ( ③), the system shows details of transportation
information (④). The user can discover that a disaster victim
evacuated from Ishinomaki City to Ichinoseki City by car.
Fig. 2 shows traffic problems. An arrow with an icon
indicates a traffic problem. A traffic problem is indicated byan arrow with an icon. The arrow shows that a traffic
problem has occurred between one end of the arrow(shown as ① in Fig. 2) and the other end the arrow (②). If
the user clicks the icon (③), the system shows details of thetraffic problem (④). The user can discover that a traffic
problem has occurred between Sendai City and Yamagata
City on Route 48. In this paper, we describe the methods
used by our system for extracting transportation informationand traffic problems.
III. RELATED WORKIn this section, we describe some related studies on
information mining in disasters and extracting transportation
information.
A. Information Mining in Disasters
In time of disaster, vast amounts of data are generated via
computer-mediated communication; however, it is difficult
to extract useful information from them for users. There havebeen some studies of information mining in several disasters.
Verma et al. [1] collected tweets from four different
disasters, and automatically detected tweets that could
contribute to situational awareness automatically. Theyobtained over 80% accuracy.
Sakaki et al. [2] considered each Twitter user as a sensor,
and detected disaster events based on sensory observation.

Transportation Information:
Ishinomaki City ⇒ Ichinoseki City
Transportation device: Car

④
②
③

①

Figure 1. The system that provides transportation information.

Traffic Problem Section:
Sendai City ⇒ Yamagata City
Route: Route 48

They targeted disaster events such as earthquakes andtyphoons. As an application, they constructed an earthquake
reporting system.
There are some studies about evacuation in disasters.
Iwanaga et al. [3] build an earthquake evacuation ontology
from twitter and provided the most suitable evacuation center.
Troung et al. [4] presented a novel framework that manages
and provides various types of context information required
for adapting processes in emergency management systems.
Soon after the Great Eastern Japan Earthquake, many
Natural Language Processing (NLP) researchers, engineers,and students from all over Japan created a working group,
called “ANPI_NLP.” “ANPI” means “safety” in Japanese.
ANPI_NLP tried to collect tweets with hash tags, such as“#anpi (safety information)” or “#hinan (evacuation)”, andextracted information about the safety of people. [5] In thispaper, we use the tweet corpus provided by ANPI_NLP, andextract transportation information and traffic problems fromit.
B. Extracting Transportation Information
There have been a number of studies of extracting
transportation information. Davidov [6] presented analgorithm framework that enabled automated acquisition of
map-link information from the Web based on surface
patterns such as “from X to Y.” Given a set of locations as
initial seeds, they retrieved an extended set of locations fromthe Web and produced a map-link network that connected
these locations using transport-type edges. In this paper, we
propose a method for extraction of transportation
information via machine-learning techniques.
Ishino et al. [7] extracted traveler’s transportation
information automatically from travel blog entries written in
Japanese using machine-learning techniques. They used cues
related to travel, such as “観光” (sightseeing tour) or “旅行”(travel) for machine learning. In this paper, we aim to extract
transportation information from disaster victims. Therefore,
we collect cues related to disasters for machine learning.
IV.

EXTRACTING TRANSPORTATION INFORMATION AND
TRAFFIC PROBLEMSIn this paper, we propose methods for extracting
transportation information and traffic problems from tweets
written in Japanese and posted during the Great EasternJapan Earthquake. We explain our methods for extracting
transportation information in Section IV-A, and for traffic
problems in Sections IV-B and IV-C.

④

③

②

①

Figure 2. The system that provides traffic problem.

A. Extracting Transportation Information
In this section, we describe our method for extracting
transportation information from tweets. We use machinelearning to extract information, such as “a departure place”,“a destination”, or “a transportation method”, from tweets.
First, we define the tags used in our examination. Fig. 3 is atagged example.
 FROM tag includes a departure place.
 TO tag includes a destination.
 METHOD tag includes a transportation method.

[Original](Tweet 1)
私の祖母は<FROM>山田岡</FROM>在住です。津波被害はなくガラスが数枚割れたと聞きました。避難勧告を受けて、楢葉の<METHOD>バス</METHOD>で<TO>いわき市の草野中学校</TO>に避難しています。
(Tweet 2)
義弟の安否確認が取れました。<FROM>石巻</FROM>から<METHOD>徒歩</METHOD>で<TO>仙台市内</TO>の家まで帰って来たそうです。
[Translation](Tweet 1)
My grandmother lives in <FROM>Yamadaoka</FROM>. The
tsunami caused little damage there. She was urged to evacuate,
and went to <TO>Kusano junior high school in Iwaki City</TO>
by <METHOD>bus</METHOD>.
(Tweet 2)
I found out my brother-in-law is safe. He came back home in<TO>Sendai
City</TO>
from<FROM>Ishinomaki
City</FROM> on <METHOD>foot</METHOD>.
Figure 3. Examples of tagged tweets.

We formulate the identification of the class of each word
in a given sentence and solve it using machine learning. For
the machine-learning method, we opted for the Conditional
Random Fields (CRF) method [8]; its empirical success has
been reported recently in natural language processing. The
CRF-based method identifies the class of each entry.
Features and tags are used in the CRF method as follows: (1)k tags occur before a target entry; (2) k features occur before
TABLE I.

Tag
FROM

FROM
TO

TO

METHODa target entry; and (3) k features follow a target entry. We
used the value k = 6, which was determined via a pilot study.
We used the following 13 features for machine learning. A
sequence of nouns (a noun phrase) was treated as a noun. We
used MeCab (http://mecab.sourceforge.net/) as a Japanesemorphological analysis tool to identify the part of speech.
A word.
Its part of speech.
Whether the word is a quotation mark.
Whether the word is a cue phrase, as shown in
Table I.

B. Identifying Traffic Problem Tweets
We proposed a method for extracting traffic problems
from tweets posted during the Great Eastern Japan
Earthquake. In a pilot study, we investigated the number oftweets containing traffic problem information (we call themtraffic problem tweets) and found some examples. Therefore,
this task is divided into two steps: (1) identifying traffic
problem tweets from a tweet corpus; and (2) extracting
traffic problems from the traffic problem tweets. We explain
Step 1 in this section and Step 2 in Section IV-C.
In this section, we explain our method for identificationof traffic problem tweets automatically. Fig. 4 shows
examples of traffic problem tweets. They contain words
related to traffic problems, such as “通行止め” (closed totraffic) or “停止” (shut down), and the name of the relevantroad. We employed Support Vector Machine (SVM) as amachine-learning technique to identify traffic problem tweets.
We use the following features for machine learning.

CUE PHASES FOR EXTRACTION OF TRANSPORTATION INFORMATION

Cue phase
Whether the word is a cue that often appears immediately after the FROM tag, such as“から” (from) or “を出発” (left).
Whether the word is frequently used in the name of a shelter, such as “学校” (school)or “公民館” (community center).
Whether the word is a cue that the FROM tag and the TO tag do not contain, such as“方向” (directions) or “沿い” (along).
Whether the word is the name of a station, provided by ANPI_NLP.
Whether the word is the spot name in eastern Japan, provided by ANPI_NLP.
Whether the word is the name of a school in eastern Japan, provided by ANPI_NLP.
Whether the word is the name of a train line, provided by ANPI_NLP.
Whether the word is a cue that often appears immediately after the TO tag, such as “まで” (to) or “へ避難” (evacuate).
Whether the word is a cue that often appears immediately after the METHOD tag,such as “で行く” (by).
Whether the word is the name of a transportation device, such as “飛行機” (airplane)or “自動車” (car).

The numberof cues
5
23
7
8619
1755
806
569
30
19
37





The word relates to traffic problem, such as “通行止め” (closed to traffic) or “停止” (shut down) (19).
The word relates to a road, such as “自動車道”(Expressway) or “ イ ン タ ー チ ェ ン ジ ”(Interchange) (13).
The word relates to transportation devices, such as“新幹線” (Shinkansen bullet train) or “地下鉄”(subway) (9).

C. Extracting Traffic Problems
In this section, we explain our method for extracting
traffic problems from traffic problem tweets identified in
Section IV-B. We use machine learning to extract
information such as “a road” or “a train line”, or “traffic
problem section”, from tweets. First, we define the tags usedin our examination. Fig.5 is a tagged example.
LINE tag includes a road or a train line.
LOC tag includes a traffic problem section.

We use CRF for the machine learning. Features and tags
are used in the CRF method as follows: (1) k tags occur
before a target entry, (2) k features occur before a target
entry, and (3) k features follow a target entry. We used thevalue k = 6, which was determined via a pilot study. We use
the following 14 features for machine learning. A sequenceof nouns (a noun phrase) was treated as a noun. We used
MeCab as a Japanese morphological analysis tool.
 A word.
 Its part of speech.
 Whether the word is a quotation mark.
 Whether the word is a mark, such as “~”, or “→”.
 Whether the word is a cue phrase, as shown in
Table II.

TABLE II.

Tag

LINE

LINE
LOC

LOC

[Original](Tweet 1)
地震で中央自動車道も上野原―勝沼インターチェンジ間などが通行止め。
(Tweet 2)
新幹線 浜松～品川停止中。
[Translation](Tweet 1)
After a large earthquake, the Chuo Expressway is closed totraffic between Uenohara city and the Katsunuma Interchange.
(Tweet 2)
Shinkansen (Bullet Train) are shout down. Hamamatsu –ShinagawaFigure 4. Example of traffic problem tweets.

[Original](Tweet 1)
地 震 で <LINE> 中 央 自 動 車 道 </LINE> も <LOC> 上 野 原</LOC>―<LOC>勝沼インターチェンジ</LOC>間などが通行止め。
(Tweet 2)<LINE>新幹線</LINE> <LOC>浜松</LOC>～<LOC>品川</LOC>停止中。
[Translation](Tweet 1)
After
a
large
earthquake,<LINE>the
Chuo
Expressway</LINE> is closed to traffic between<LOC>Uenohara city</LOC> and <LOC>the Katsunuma
Interchange</LOC>.
(Tweet 2)<LINE>Shinkansen (Bullet Train)</LINE> are shout down.
<LOC>Hamamatsu</LOC> – <LOC>Shinagawa</LOC>
Figure 5. Example of tagged traffic problem tweets.

CUE PHASE FRO EXTRACTION OF TRAFFIC PROBLEMS

Cue phase
Whether the word is frequently used in the name of a road and a train line, such as “道路” (road) or “号線” (line).
Whether the word is the name of a train line, provided by ANPI_NLP.
Whether the word is the name of a bypass, collected from Wikipedia.
Whether the word is the name of an express way.
Whether the word is the name of a toll road.
Whether the word frequently used in traffic problems, such as “通行止め” (closed totraffic) or “停止” (shout down) .
Whether the word frequently used in traffic problems, such as “通行可能” (available
for traffic) or “復旧” (restoration) .
Whether the word is the name of a station, provided by ANPI_NLP.
Whether the word is the spot name in eastern Japan, provided by ANPI_NLP.
Whether the word is a cue that often appears in the LOC tag, such as “駅” (station) or“インターチェンジ” (interchange).

The
number of
cues
23
569
1301
60
181
51
19
8619
1755
10

V.

EXPERIMENTS

A. Ectracting Transportation Information
Data Sets and Experimental Settings
We randomly selected 1303 tweets written in Japanesefrom the tweet corpus provided by ANPI_NLP, and tagged
them manually, as described in Section IV-A. The numbersof manually assigned tags are shown in Table III. We used
CRF++
(http://www.chasen.org/~taku/software/CRF++)software as the machine-learning package. As a base line
method, we used only a word as a feature for machinelearning. We used precision and recall as evaluation
measures, calculated as follows.
(1)(2)
TABLE III.
NUMBERS OF MANUALLY ASSIGNED TAGS IN THE
EXTRACTED TRANSPORTATION INFORMATION

Tag
FROM
TO
METHOD
Total

Training
237
425
61
723

Test
71
120
17
208

Results and Discussion
The evaluation results are shown in Table IV. Our
method obtained higher precision and recall than the baseline
method. We discuss the experimental results as follows.
TABLE IV.

Tag
FROM
TO
METHOD
Total

EVALUATION RESULTS FOR EXTRACTING
TRANSPORTATION INFORMATION

Our method
Precision
Recall
(%)
(%)
78.4
40.9
76.3
59.2
91.7
64.7
78.2
53.4

Baseline method
Precision
Recall
(%)
(%)
72.4
29.6
73.2
43.3
80.0
23.5
73.3
37.0

[Original](Correct) 助川小学校に避難された方がいらっしゃいましたら、現在どのような状況か情報頂きたいです！
(Analysis result) <TO>助川小学校</TO>に避難された方がいらっしゃいましたら、現在どのような状況か情報頂きたいです！
[Translation](Correct) If victims evacuate to Sukegawa Elementary School,please let me know what is going on!
(Analysis result) If victims evacuate to <TO>Sukegawa
Elementary School</TO>, please let me know what is going on!
Figure 6. Example of a failure in extracting transportation information.

First, we discuss a typical error causing low precision.
Fig. 6 shows an example of a failure in extracting
transportation information. The TO tag was mistakenly
assigned to “ 助 川 小 学 校 ” (Sukegawa Elementary School),which might not be an actual evacuation site. This was
because the “ TO ” cue “ 避 難 ” (evacuate) appears
immediately before it. To improve the performance of
extracting transportation information, we should consider
language structure.
Next, we discuss a typical error causing low recall. A
typical error is the lack of cues. In particular, we could notcollect the names of some facilities or places cyclopedically.
When preparing for a disaster, we must collect the names offacilities and places all over the world.
B. Identifying Traffic Problem Tweets
Data Sets and Experimental Settings
For our examination, we identified traffic problem tweets
among 1750 tweets written in Japanese provided by
ANPI_NLP. The number of manually identified traffic
problem tweets is shown in Table V. We performed a fourfold cross validation test. We used a standard SVM package,
TinySVM (http://chasen.org/~taku/software/TinySVM/). We
used precision and recall as evaluation measures.
TABLE V.

NUMBER OF MANUALLY IDENTIFIED TRAFFIC PROBLEMTWEETS

Traffic Problem Tweets
166

Others
1584

Total
1750

Results and Discussion
Table VI shows the experimental results. Our method
obtained a higher recall than baseline method. We now
discuss the low recall of our method. A typical reason for
low recall is the lack of cues. For machine learning, we used
manually selected cues, as described in Section IV-B. To
improve the coverage of cues, a statistical approach, such as
applying n-gram statistics to a larger tweet corpus, will be
required.
TABLE VI.

EVALUATION RESULTS FOR IDENTIFYING TRAFFIC
PROBLEM TWEETS

Our method
Baseline method

Precision (%)
77.7
80.4

Recall (%)
70.7
61.9

C. Extracting Traffic Problems
Data Sets and Experimental Settings
We manually assigned tags to traffic problem tweets, as
described in Section IV-C, and used them for our
examination. Table VII shows the numbers of manuallyassigned tags. We used CRF++ software as the machinelearning package. As a baseline method, we used only aword as a feature for machine learning. We used recall andprecision as evaluation measures, calculated as equations (1)and (2).

TABLE VII.

Tag
LINE
LOC
Total

NUMBERS OF MANUALLY ASSIGNED TAGS FOR
EXTRACTING TRAFFIC PROBLEMS

Training
126
176
166

Test
39
67
243

Results and Discussion
The evaluation results are shown in Table VIII. Our
method obtained higher recall than the baseline method.
We now discuss the low recall of our method. Errors in
our method have been found in tweets that contain bothproblem information and safety information about traffic
states. Fig. 7 shows an example of failure in the extracting
traffic problems. In the example, the “LINE” tag should be
assigned to “県内高速道路” (expressway in the prefecture),
but our method did not assign any tags to this word. This
tweet contains the cue “通行止め” (closed to traffic), andthe cue “通行可能” (can pass). In this case, we should
consider language modification relationships of cues.
TABLE VIII.

Tag
LINE
LOC
Average

EVALUATION RESULTS FOR EXTRACTING TRAFFIC
PROBLEMS

Our method
Precision
Recall
(%)
(%)
89.7
68.4
85.0
50.8
87.0
57.1

Baseline method
Precision
Recall
(%)
(%)
80.0
31.6
92.0
34.3
87.5
33.3

results, we obtained precision of 78.2% and recall of 53.4%
in automatic extraction of transportation information. For
extracting traffic problems, we identified tweets containing
information about traffic problems (we called them traffic
problem tweets), and extracted traffic problems from them.
In identifying traffic problem tweets, we obtained precisionof 77.7% and recall of 70.7%. In extracting traffic problems,
we obtained precision of 87.0% and recall of 57.1%. Thus,
we have constructed a system for providing transportation
information and identifying traffic problems in disasters. We
consider that the system can offer evacuation routes fordisaster victims and transportation routes for relief materiel.
In this paper, we used tweets that posted during the Great
Eastern Japan Earthquake. Therefore, we used the names offacilities or places in eastern Japan as cues for machinelearning. When preparing for disasters anywhere, we mustcollect the names of a facilities or places all over the world.
In this paper, we focused on tweets written in Japanese.
In our future work, we will translate cue phrases fromJapanese into other languages, and apply our method totweets written in various languages.
REFERENCES
[1]

[2]

[3]

[Original](Correct) 能代南ＩＣ－二ツ井ＩＣ間は通行可能。そのほかの<LINE>県内高速道路</LINE>通行止め。
(Analysis result) 能代南ＩＣ－二ツ井ＩＣ間は通行可能。そのほかの<line>県内高速道路</line>通行止め。
[Translation](Correct) You can pass between the Noshiro Minami Interchange
and the Futatsui Interchange. Another <LINE>expressway in theprefecture</LINE> is closed to traffic.
(Analysis result) You can pass between the Noshiro MinamiInterchange and the Futatsui Interchange. Another expressway inthe prefecture is closed to traffic.
Figure 7. Example of a failure in extracting traffic problems.

VI. CONCLUSION
To offer evacuation routes to safety for disaster victims,
we have proposed methods for extracting transportation
information and traffic problems from tweets posted during
disasters. To investigate the effectiveness of our methods, we
conducted some experiments using tweets posted during theGreat Eastern Japan Earthquake. From the experimental

[4]

[5]

[6]

[7]

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