Reference literature / data sources
1) Statistics Bureau, Ministry of Internal Affairs and Communications: 2010 Population Census
2) National Land Information Division, National and Regional Policy Bureau, Ministry of Land,
Infrastructure, Transport and Tourism: National Land Numerical Information
3) ESRI Japan Corporation: Arc GIS Data Collection 20144) Japan Digital Road Map Association: Digital Road Maps (DRM)
5) Takuya Kubo: An Introduction to Statistical Modeling for Data Analysis
6) Iwanami Data Science Publishing Committee: Iwanami Data Science Vol. 17) Harutoshi Yamada: Research and Development of an Integrated System for Accident
Analysis Using Information on Accident Locations8) Google Earth Street View

1

「第５３回日本交通科学学会総会・学術講演会」開催のお知らせ（協賛

公益財団法人交通事故総合分析センター）

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第53回日本交通科学学会総会・学術講演会運営事務局【株式会社ドーモ：水垣】 [TEL] 03 - 5510 - 7923 [mail] info@jcts53.jp

ピアザ淡海 滋賀県立県民交流センター(滋賀県大津市）
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公益財団法人 交通事故総合分析センター●ウェブサイト http://www.itarda.or.jp/ ●Eメール koho@itarda.or.jp●フェイスブック https://www.facebook.com/itarda.or
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ITARDA INFORMATION no.120

つくば 交通事故調査事務所

住 友 水 道 橋 ビル８ 階

当センターは、平成4年（1992年）に国家公安委員会、運輸省（当時）、建設省（当時）から設立許可を受けて、公益法人として設立されました。その後平成24年（2012年）4月に公益財団法人に移行しました。我が国で唯一
道路交通法の定める「交通事故調査分析センター」の指定を国家公安委員会から受けた調査研究機関であり、交通事故の防止と被害の軽減のための交通事故の調査分析を行っています。

lCorrelation analysis between landSpeciafeature use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores,
particularly in rural areas! ~The probability ofa pedestrian accident
occurring increases
in sections withconvenience stores

2.6 - 2.7-fold increase
with 3 storese29

一般社団法人日本交通科学学会から

交通事故分析レポート No.120

Convenienc
store

公益財団法人 交通事故総合分析センター〒101 0064 東 京 都 千 代 田 区 猿 楽 町 ２ ７ ８For details regarding the contents herein, please refer to the materials from the 19th ITARDA
Research Presentation Session (on the ITARDA homepage (address below)).

No.120ITARDA INFORMATION

3

Impact on the occurrenceof pedestrian accidents(Tomoko Kitano)

ITARDA INFORMATIONThis analysis revealed that there is a strong connection between the number of convenience stores,
area of commercial districts, and building site area with the number of pedestrian accidents that
occur. Drivers, particularly those in rural areas, must make an effort to drive carefully in sections withconvenience stores. In addition, they must drive cautiously while keeping in mind that there is ahigher probability of colliding with pedestrians in sections where commercial districts and buildings aretightly packed.
Even in regions with little pedestrian traffic, there is the possibility that they will be delayed inconfirming the presence of pedestrians, especially when turning right. Therefore, drivers must must
reduce their vehicle’s speed and thoroughly check to make sure there are no crossing pedestrians.

交通事故分析レポート

6 Conclusion

イタルダ インフォメーション

Saga

Impact = exp (Estimated factor

Shigaby prefecture x No. of stores)

2

1.4-fold increase
with 1 store

Tochigi
Fukuoka
KanagawaTokyo

1
1 store2 stores3 stores

Estimated results of the impact that the number of convenience
stores has on the occurrence of pedestrian accidents

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Introducing sections where the actual number of accidents
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Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~40%

Percentages of accidents

As Fig. 1 indicates, 1,534 of the 4,117 fatalities from traffic accidents in 2015 were from accidents that occurred
while the person was walking, with this accounting for roughly 40% of the total. Fig. 2 shows the percentages ofcasualty accidents that occurred while walking, while riding a bicycle, or by other type. Accidents while walking
constitute a high percentage of fatal and serious injury accidents compared with the other types, with this being
roughly double those of accidents while riding a bicycle. This shows the extent to which accidents while walking
tend to lead to serious accidents. It will be important to reduce accidents while walking (hereafter referred to as“pedestrian accidents”) by as much as possible in order to reduce the number of traffic accident fatalities resulting
from them.
Fatal
Serious injury
Light injury
Therefore, this issue focuses on
the characteristics of regions thatare strongly correlated with theoccurrence of pedestrian
accidents in order to calculate
the impact on accident
While
Total walking
occurrence as a statistical
Other
probability.
4117 1534
15.8％
2011
In addition, based on this impact,
we performed an analysis on thepeople people people
degree of risk within sections (in2.7％ 8.9％
5.1％
units of 500m2) by estimating the
While riding
0.6％
0.5％
a bicycle
number of pedestrian accidents
※1
※2
While
While riding
Otherthat occurred within each572
walkinga bicycle
section.
people※1：Excluding accidents involving trains

2-foldThe first thing we focused on to derive the characteristics of regions that are strongly correlated with theoccurrence of pedestrian accidents was population. Fig. 4 shows the correlation between the number of pedestrian
accidents and population by area classification. The graph divided up by 10km2 units in (1) reveals that the numberof accidents is very highly correlated with population, with more pedestrian accidents occurring the more people
there are in a section. However, as (2) and (3) show, the smaller these area classifications are, the weaker thiscorrelation becomes and the more the variance increases. This indicates that the correlation with the number ofpedestrian accidents in the 500m2 units of (3) cannot be explained solely by population.
The population data used here is data on the resident population, and therefore the amount of pedestrian traffic
cannot be expressed solely through this data. For example, one would expect to see a great deal of pedestrian
traffic even in sections with a small population, so long as there are a large number of shops located there.

The dotted line
passing throughthe center showswhere thecorrelation isstrongest basedon where themost points areconcentrated

Fig. 2. Percentage of fatal andserious injury accidents (2015)

Characteristics of regions that are strongly correlated

2 with the occurrence of pedestrian accidents

Fig. 3 shows the relationship between the number of pedestrian accidents per 100,000 people versus the populationof each of the 47 prefectures throughout Japan. Tokyo, Kanagawa Prefecture, and Fukuoka Prefecture were selectedfrom the group with populations that are higher than the national average, and Tochigi Prefecture, Shiga Prefecture,and Saga Prefecture were selected from the group with populations lower than the national average as subjects foranalysis. They were selected so as to produce variance in terms of the number of accidents per 100,000 people.

FukuokaPopulation: Current Population Estimates (As of October 1, 2014), Statistics Bureau,
Ministry of Internal Aﬀairs and Communications
Accidents: Number of pedestrian accidents per 100,000 people
between 2013 - 2015 (only accidents on roads)

0.65

0.62

0.62

0.60

0.58

0.57

0.57

0.5

0.56

0.53

0.40

0.36

0.30

0.13

0.0
-0.5
-1.0

-0.04 -0.44(4) Other area↑
National average forthe number of accidents

0.67

The closer to 1 the stronger
the correlation is(15) Distancefrom station

Osaka

Fig. 3. Comparison chart of the population per prefecture
and the number of pedestrian accidents per 100,000 people
ITARDA INFORMATION no.120

11000(3) Area of industrial
districts

Population:
1.98 million people
Accidents: 630(11) Gas stations

Tochigi0(12) Schools

Shiga

35000

(1) Residential
district area

Kyoto
Yamagata
Hiroshima
Yamanashi
Kagoshima
Yamaguchi
Oita
Kumamoto Miyagi
Aomori Ishikawa
Ehime
Niigata
Kochi
Population:
Toyama Fukushima Ibaraki
Tokushima
1.42 million people
Mie
Accidents: 95
Fukui Wakayama Gifu
Iwate
Hokkaido
Shimane
Akita
Tottori

Lower

Population:
13.39 million people
Accidents: 129

Chiba
Aichi

Tokyo

0.75

(8) Hospitals

Okayama
NaganoSaitama

1.0(5) Building site area

KagawaGunma
Miyazaki

Hyogo

Population (people)(13) Road length

Nara

0

(7) Department stores/ supermarkets

Population:
9.1 million people
Accidents: 163

Nagasaki0

Kanagawa(9) Fast foodrestaurants

Shizuoka(14) Numberof intersections

Okinawa

2000000(16) Net population

Population:
840,000 people
Accidents: 1420(10) Banks

Saga

Population:
5.09 million people
Accidents: 193

0(7) Population that
commutes by walking

Population
national average→(6) Convenience stores

HigherNo. of pedestrian accidents
per 100,000 people

HigherPopulation(3) 500m2 units

80

Therefore, we felt that we needed items other than population that would express the amount of pedestrian traffic. So
for our analysis of 500m2 units we added information on the following 17 items in order to perform a detailed analysis.
We performed a number of analyses in order to deduce which of these were particularly strongly correlated with theoccurrence of pedestrian accidents. Of these, here we will introduce the results of a correlation analysis and adecision tree analysis.
● Area by type of land usage [(1) Area of residential districts, (2) Area of commercial districts, (3) Area of industrial
districts, (4) Other]● (5) Building site area (area of places where buildings are tightly packed in residential areas and urban areas)● The number of stores and facilities [(6) Convenience stores, (7) Department stores and supermarkets, (8)Hospitals, (9) Fast food restaurants, (10) Banks, (11) Gas stations, (12) Schools]● (13) Road length (14) Number of intersections (15) Distance from station● Population [(16) Net population, (17) Population whose only means of transport is walking to work / school]
The results of the correlation analysis are shown in Fig. 5. A correlation analysis is a technique for expressing thestrength of the correlation between two items. The closer the correlation coefficient, which expresses the depth of theconnection between the two items, is to 1 (when one item increases the other one also increases), the stronger thecorrelation. This was performed first in order to verify the extent to which it contributed to the occurrence of accidents.
According to this analysis, 11 of the 17 items mentioned above had a correlation coefficient of 0.5 or greater.

Correlation coeﬃcientLower(2) 1km2 units

200

Fig. 4. Correlation between the number of pedestrian accidents
and population by area classification※2：Excluding pedestrian-bicycle accidents& accidents involving trains

Fig. 1. Number of fatalities
by type (2015)(1) 10km2 units

3000

No. of pedestrian
accidents (accidents)

1 Introduction

ITARDA INFORMATION no.120(2) Area of commercial
districtslSpeciafeatureFig. 5. Correlation coefficient between the number of pedestrian accidents
and the related items for 500m2 units
ITARDA INFORMATION no.120

Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~40%

Percentages of accidents

As Fig. 1 indicates, 1,534 of the 4,117 fatalities from traffic accidents in 2015 were from accidents that occurred
while the person was walking, with this accounting for roughly 40% of the total. Fig. 2 shows the percentages ofcasualty accidents that occurred while walking, while riding a bicycle, or by other type. Accidents while walking
constitute a high percentage of fatal and serious injury accidents compared with the other types, with this being
roughly double those of accidents while riding a bicycle. This shows the extent to which accidents while walking
tend to lead to serious accidents. It will be important to reduce accidents while walking (hereafter referred to as“pedestrian accidents”) by as much as possible in order to reduce the number of traffic accident fatalities resulting
from them.
Fatal
Serious injury
Light injury
Therefore, this issue focuses on
the characteristics of regions thatare strongly correlated with theoccurrence of pedestrian
accidents in order to calculate
the impact on accident
While
Total walking
occurrence as a statistical
Other
probability.
4117 1534
15.8％
2011
In addition, based on this impact,
we performed an analysis on thepeople people people
degree of risk within sections (in2.7％ 8.9％
5.1％
units of 500m2) by estimating the
While riding
0.6％
0.5％
a bicycle
number of pedestrian accidents
※1
※2
While
While riding
Otherthat occurred within each572
walkinga bicycle
section.
people※1：Excluding accidents involving trains

2-foldThe first thing we focused on to derive the characteristics of regions that are strongly correlated with theoccurrence of pedestrian accidents was population. Fig. 4 shows the correlation between the number of pedestrian
accidents and population by area classification. The graph divided up by 10km2 units in (1) reveals that the numberof accidents is very highly correlated with population, with more pedestrian accidents occurring the more people
there are in a section. However, as (2) and (3) show, the smaller these area classifications are, the weaker thiscorrelation becomes and the more the variance increases. This indicates that the correlation with the number ofpedestrian accidents in the 500m2 units of (3) cannot be explained solely by population.
The population data used here is data on the resident population, and therefore the amount of pedestrian traffic
cannot be expressed solely through this data. For example, one would expect to see a great deal of pedestrian
traffic even in sections with a small population, so long as there are a large number of shops located there.

The dotted line
passing throughthe center showswhere thecorrelation isstrongest basedon where themost points areconcentrated

Fig. 2. Percentage of fatal andserious injury accidents (2015)

Characteristics of regions that are strongly correlated

2 with the occurrence of pedestrian accidents

Fig. 3 shows the relationship between the number of pedestrian accidents per 100,000 people versus the populationof each of the 47 prefectures throughout Japan. Tokyo, Kanagawa Prefecture, and Fukuoka Prefecture were selectedfrom the group with populations that are higher than the national average, and Tochigi Prefecture, Shiga Prefecture,and Saga Prefecture were selected from the group with populations lower than the national average as subjects foranalysis. They were selected so as to produce variance in terms of the number of accidents per 100,000 people.

FukuokaPopulation: Current Population Estimates (As of October 1, 2014), Statistics Bureau,
Ministry of Internal Aﬀairs and Communications
Accidents: Number of pedestrian accidents per 100,000 people
between 2013 - 2015 (only accidents on roads)

0.65

0.62

0.62

0.60

0.58

0.57

0.57

0.5

0.56

0.53

0.40

0.36

0.30

0.13

0.0
-0.5
-1.0

-0.04 -0.44(4) Other area↑
National average forthe number of accidents

0.67

The closer to 1 the stronger
the correlation is(15) Distancefrom station

Osaka

Fig. 3. Comparison chart of the population per prefecture
and the number of pedestrian accidents per 100,000 people
ITARDA INFORMATION no.120

11000(3) Area of industrial
districts

Population:
1.98 million people
Accidents: 630(11) Gas stations

Tochigi0(12) Schools

Shiga

35000

(1) Residential
district area

Kyoto
Yamagata
Hiroshima
Yamanashi
Kagoshima
Yamaguchi
Oita
Kumamoto Miyagi
Aomori Ishikawa
Ehime
Niigata
Kochi
Population:
Toyama Fukushima Ibaraki
Tokushima
1.42 million people
Mie
Accidents: 95
Fukui Wakayama Gifu
Iwate
Hokkaido
Shimane
Akita
Tottori

Lower

Population:
13.39 million people
Accidents: 129

Chiba
Aichi

Tokyo

0.75

(8) Hospitals

Okayama
NaganoSaitama

1.0(5) Building site area

KagawaGunma
Miyazaki

Hyogo

Population (people)(13) Road length

Nara

0

(7) Department stores/ supermarkets

Population:
9.1 million people
Accidents: 163

Nagasaki0

Kanagawa(9) Fast foodrestaurants

Shizuoka(14) Numberof intersections

Okinawa

2000000(16) Net population

Population:
840,000 people
Accidents: 1420(10) Banks

Saga

Population:
5.09 million people
Accidents: 193

0(7) Population that
commutes by walking

Population
national average→(6) Convenience stores

HigherNo. of pedestrian accidents
per 100,000 people

HigherPopulation(3) 500m2 units

80

Therefore, we felt that we needed items other than population that would express the amount of pedestrian traffic. So
for our analysis of 500m2 units we added information on the following 17 items in order to perform a detailed analysis.
We performed a number of analyses in order to deduce which of these were particularly strongly correlated with theoccurrence of pedestrian accidents. Of these, here we will introduce the results of a correlation analysis and adecision tree analysis.
● Area by type of land usage [(1) Area of residential districts, (2) Area of commercial districts, (3) Area of industrial
districts, (4) Other]● (5) Building site area (area of places where buildings are tightly packed in residential areas and urban areas)● The number of stores and facilities [(6) Convenience stores, (7) Department stores and supermarkets, (8)Hospitals, (9) Fast food restaurants, (10) Banks, (11) Gas stations, (12) Schools]● (13) Road length (14) Number of intersections (15) Distance from station● Population [(16) Net population, (17) Population whose only means of transport is walking to work / school]
The results of the correlation analysis are shown in Fig. 5. A correlation analysis is a technique for expressing thestrength of the correlation between two items. The closer the correlation coefficient, which expresses the depth of theconnection between the two items, is to 1 (when one item increases the other one also increases), the stronger thecorrelation. This was performed first in order to verify the extent to which it contributed to the occurrence of accidents.
According to this analysis, 11 of the 17 items mentioned above had a correlation coefficient of 0.5 or greater.

Correlation coeﬃcientLower(2) 1km2 units

200

Fig. 4. Correlation between the number of pedestrian accidents
and population by area classification※2：Excluding pedestrian-bicycle accidents& accidents involving trains

Fig. 1. Number of fatalities
by type (2015)(1) 10km2 units

3000

No. of pedestrian
accidents (accidents)

1 Introduction

ITARDA INFORMATION no.120(2) Area of commercial
districtslSpeciafeatureFig. 5. Correlation coefficient between the number of pedestrian accidents
and the related items for 500m2 units
ITARDA INFORMATION no.120

Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~There are 9 sections in Shiga Prefecturewith building site areas of 0.211km2 orlarger, commercial district areas of
0.073km2 or larger, and 3 or moreconvenience stores. In such sections, there
are as many as 15 pedestrian accidents, oras few as 4 such accidents.

Building sites
0.211

0.211

Commercial districts
0.073

0.073

15

（n=15410）15（n=283）15（n=57）

2.5

10

10

10

5

5

5

5

Group withfew accidents

0

0

0

（n=9）

15

10

0

Group withmany accidents

Fig. 6. Results of the decision tree analysis based on the number of convenience stores,
area of commercial districts, and building site area in Shiga Prefecture

3 Impact on the occurrence of pedestrian accidents
We used a statistical model to calculate the impact that the three land use characteristics derived in the previous
section have on the occurrence of pedestrian accidents.
■ Impact that the number of convenience stores has on the occurrence of pedestrian accidents

Impact on the occurrenceof pedestrian accidents

Fig. 7 shows the calculation results for the impact that the number of convenience stores has on the occurrence ofpedestrian accidents. In Tokyo, the number of convenient stores does not have that great of an impact on thenumber of accidents, regardless of whether there is one store or three stores. Yet conversely, the results for Saga
Prefecture and Shiga Prefecture show that sections with one convenience store saw a 1.4-fold increase in
accidents, while sections with three convenience stores saw a 2.6 - 2.7-fold increase.
It is conjectured that since convenience stores are built in areas with a great deal of pedestrian traffic, pedestrian
accidents tend to occur in the areas around them. But from just looking at the results from these six prefectures, we
see that the impact this has on the occurrence of traffic accidents is greater in rural areas. In urban areas where
there are numerous convenience stores the people are dispersed, whereas in rural areas with a few convenience
stores the percentage of people that gather together at each store increases. Therefore, this increases theprobability that a vehicle will collide with a pedestrian.
3

2

Fig. 8 shows the calculation results for the area ofc ommerc ial distr icts. The estimated results
obtained were largely identical everywhere, except
for Tochigi Prefecture. Since the area of a single
section is about 0.26km 2, sections that primarilyc o n s i s t o f c o m m e r c i a l d i s t r i c t s i n t h e f i ve
prefectures are roughly three-times more prone toaccidents than sections that do not contain these.

■ Impact that the building site area has on
Fig. 9 shows the calculation results for the buildingsite area. Apparently, the greater the building sitearea is in a section the more prone to accidents thesection will be, which is true everywhere except forin Tokyo. This tendency appears to be particularly
strong in Saga and Tochigi Prefectures.
In regions where buildings are closely packed, notonly are there numerous pedestrians, there arealso numerous blind spots due to the buildings. As
a result, as the area of these regions grows larger it
is believed that accidents become more likely tooccur. The impact from this overall tendency does
not appear to be all that strong in Tokyo.

Saga
Shiga

Tochigi

2 stores3 storesFig. 7. Estimated results of the impact that the number of convenience storeshas on the occurrence of pedestrian accidents
ITARDA INFORMATION no.120

Kanagawa
Shiga
Fukuoka
Saga
Tokyo

Impact =exp (Estimated factor by
prefecture x Area)
2

Tochigi

10.1km²0.2km²

10
9
8
7
6
5

For a 0.2km2 building sitearea this increases 9.5-fold

SagaTochigi

Impact =exp (Estimated factor by
prefecture x Area)

FukuokaShiga

4
3

Kanagawa

2
1

Tokyo
0.1km²0.2km²

Fig. 9. Estimated results of the impact thatthe building site area has on the occurrenceof pedestrian accidents

Based on the impact on the occurrence of pedestrian accidents that was calculated, we calculated estimatedfigures for the number of accidents in each section (500m2 units). Fig. 10 shows an image of four sections in Shiga
Prefecture that were selected as examples, while Fig. 11 shows the estimated results for these same sections. For
example, in the section in the top right, 14 pedestrian accidents occurred over a three-year period. This section has
three convenience stores, a building site area of 0.25km2, and an area of commercial districts of 0.24km2. Since asingle section has an area of roughly 0.26km 2 , more than 90% of this section consists of building sites andcommercial districts.
The estimated results for the number of accidents calculated by plugging the actual measurement results for thesethree land use characteristics (number of stores, area), and each impact on the occurrence of pedestrian
accidents, into a model equation resulted in a median value (most highly probable number of accidents) of 16
accidents. This result is most likely within the range of 10 - 24 accidents, with a probability of 95%.
In this manner, we calculated the estimated values for the number of accidents in every section. The results weobtained showed that the actual number of accidents was within ±2 accidents of the median values for theestimated number of accidents in more than 90% of the sections.

Tokyo
1 storeFor a 0.2km2 area of commercial
districts this increases
2.5 to ≈3-fold

4 Estimates of the number of pedestrian accidents

Fukuoka
Kanagawa

1

3

Fig. 8. Estimated results of the impact thatthe area of commercial districts has on
the occurrence of pedestrian accidents

2.6 - 2.7-fold increase
with 3 stores

Impact = exp (Estimated factor
by prefecture x No. of stores)
1.4-fold increase
with 1 store

districts has on the occurrence ofpedestrian accidents

the occurrence of pedestrian accidents

Convenience stores
2.5■ Impact that the area of commercial

Impact on the occurrenceof pedestrian accidents

Next, we performed a decision tree analysis on the 11 items. A decision tree analysis is a technique for effectively
dividing data up into groups. By analyzing which items are optimal to use in dividing up those sections with lots ofpedestrian accidents and those sections with few accidents into groups, we were able to select the items that arestrongly tied to the occurrence of pedestrian accidents.
Fig. 6 shows the results of this for Shiga Prefecture as an example. From this we learned that a combination of thenumber of convenience stores, the area of commercial districts, and the building site area allowed us to sort thismost neatly. In other words, sections where the values for these three types of land use characteristics are larger,
are more prone to the occurrence of pedestrian accidents. Similar results were found for the other five prefectures
besides Shiga Prefecture.

ITARDA INFORMATION no.120

Impact on the occurrenceof pedestrian accidentslSpeciafeatureAccident points

Fig. 10. Image of the four sectionsin Shiga Prefecture

No. of accidents: 1
No. of convenience stores: 0
Building site area: 0.25km2
Area of commercial districts: 0.02km2

No. of accidents: 14
No. of convenience stores: 3
Building site area: 0.25km2
Area of commercial districts: 0.24km2

Estimated results for the No. of accidents
Median value: 1 accident
95% conﬁdence interval: 0 - 2 accidents

Estimated results for the No. of accidents
Median value: 16 accidents
95% conﬁdence interval: 10 - 24 accidents

No. of accidents: 4
No. of convenience stores: 0
Building site area: 0.25km2
Area of commercial districts: 0.02km2

No. of accidents: 5
No. of convenience stores: 0
Building site area: 0.23km2
Area of commercial districts: 0.14km2

Estimated results for the No. of accidents
Median value: 3 accidents
95% conﬁdence interval: 1 - 5 accidents

Estimated results for the No. of accidents
Median value: 3 accidents
95% conﬁdence interval: 2 - 7 accidents

Image of theestimated results

Medianvalue
95% interval

Fig. 11. Estimated results for the number of pedestrian
accidents in the same sections from Fig. 10
ITARDA INFORMATION no.120

Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~There are 9 sections in Shiga Prefecturewith building site areas of 0.211km2 orlarger, commercial district areas of
0.073km2 or larger, and 3 or moreconvenience stores. In such sections, there
are as many as 15 pedestrian accidents, oras few as 4 such accidents.

Building sites
0.211

0.211

Commercial districts
0.073

0.073

15

（n=15410）15（n=283）15（n=57）

2.5

10

10

10

5

5

5

5

Group withfew accidents

0

0

0

（n=9）

15

10

0

Group withmany accidents

Fig. 6. Results of the decision tree analysis based on the number of convenience stores,
area of commercial districts, and building site area in Shiga Prefecture

3 Impact on the occurrence of pedestrian accidents
We used a statistical model to calculate the impact that the three land use characteristics derived in the previous
section have on the occurrence of pedestrian accidents.
■ Impact that the number of convenience stores has on the occurrence of pedestrian accidents

Impact on the occurrenceof pedestrian accidents

Fig. 7 shows the calculation results for the impact that the number of convenience stores has on the occurrence ofpedestrian accidents. In Tokyo, the number of convenient stores does not have that great of an impact on thenumber of accidents, regardless of whether there is one store or three stores. Yet conversely, the results for Saga
Prefecture and Shiga Prefecture show that sections with one convenience store saw a 1.4-fold increase in
accidents, while sections with three convenience stores saw a 2.6 - 2.7-fold increase.
It is conjectured that since convenience stores are built in areas with a great deal of pedestrian traffic, pedestrian
accidents tend to occur in the areas around them. But from just looking at the results from these six prefectures, we
see that the impact this has on the occurrence of traffic accidents is greater in rural areas. In urban areas where
there are numerous convenience stores the people are dispersed, whereas in rural areas with a few convenience
stores the percentage of people that gather together at each store increases. Therefore, this increases theprobability that a vehicle will collide with a pedestrian.
3

2

Fig. 8 shows the calculation results for the area ofc ommerc ial distr icts. The estimated results
obtained were largely identical everywhere, except
for Tochigi Prefecture. Since the area of a single
section is about 0.26km 2, sections that primarilyc o n s i s t o f c o m m e r c i a l d i s t r i c t s i n t h e f i ve
prefectures are roughly three-times more prone toaccidents than sections that do not contain these.

■ Impact that the building site area has on
Fig. 9 shows the calculation results for the buildingsite area. Apparently, the greater the building sitearea is in a section the more prone to accidents thesection will be, which is true everywhere except forin Tokyo. This tendency appears to be particularly
strong in Saga and Tochigi Prefectures.
In regions where buildings are closely packed, notonly are there numerous pedestrians, there arealso numerous blind spots due to the buildings. As
a result, as the area of these regions grows larger it
is believed that accidents become more likely tooccur. The impact from this overall tendency does
not appear to be all that strong in Tokyo.

Saga
Shiga

Tochigi

2 stores3 storesFig. 7. Estimated results of the impact that the number of convenience storeshas on the occurrence of pedestrian accidents
ITARDA INFORMATION no.120

Kanagawa
Shiga
Fukuoka
Saga
Tokyo

Impact =exp (Estimated factor by
prefecture x Area)
2

Tochigi

10.1km²0.2km²

10
9
8
7
6
5

For a 0.2km2 building sitearea this increases 9.5-fold

SagaTochigi

Impact =exp (Estimated factor by
prefecture x Area)

FukuokaShiga

4
3

Kanagawa

2
1

Tokyo
0.1km²0.2km²

Fig. 9. Estimated results of the impact thatthe building site area has on the occurrenceof pedestrian accidents

Based on the impact on the occurrence of pedestrian accidents that was calculated, we calculated estimatedfigures for the number of accidents in each section (500m2 units). Fig. 10 shows an image of four sections in Shiga
Prefecture that were selected as examples, while Fig. 11 shows the estimated results for these same sections. For
example, in the section in the top right, 14 pedestrian accidents occurred over a three-year period. This section has
three convenience stores, a building site area of 0.25km2, and an area of commercial districts of 0.24km2. Since asingle section has an area of roughly 0.26km 2 , more than 90% of this section consists of building sites andcommercial districts.
The estimated results for the number of accidents calculated by plugging the actual measurement results for thesethree land use characteristics (number of stores, area), and each impact on the occurrence of pedestrian
accidents, into a model equation resulted in a median value (most highly probable number of accidents) of 16
accidents. This result is most likely within the range of 10 - 24 accidents, with a probability of 95%.
In this manner, we calculated the estimated values for the number of accidents in every section. The results weobtained showed that the actual number of accidents was within ±2 accidents of the median values for theestimated number of accidents in more than 90% of the sections.

Tokyo
1 storeFor a 0.2km2 area of commercial
districts this increases
2.5 to ≈3-fold

4 Estimates of the number of pedestrian accidents

Fukuoka
Kanagawa

1

3

Fig. 8. Estimated results of the impact thatthe area of commercial districts has on
the occurrence of pedestrian accidents

2.6 - 2.7-fold increase
with 3 stores

Impact = exp (Estimated factor
by prefecture x No. of stores)
1.4-fold increase
with 1 store

districts has on the occurrence ofpedestrian accidents

the occurrence of pedestrian accidents

Convenience stores
2.5■ Impact that the area of commercial

Impact on the occurrenceof pedestrian accidents

Next, we performed a decision tree analysis on the 11 items. A decision tree analysis is a technique for effectively
dividing data up into groups. By analyzing which items are optimal to use in dividing up those sections with lots ofpedestrian accidents and those sections with few accidents into groups, we were able to select the items that arestrongly tied to the occurrence of pedestrian accidents.
Fig. 6 shows the results of this for Shiga Prefecture as an example. From this we learned that a combination of thenumber of convenience stores, the area of commercial districts, and the building site area allowed us to sort thismost neatly. In other words, sections where the values for these three types of land use characteristics are larger,
are more prone to the occurrence of pedestrian accidents. Similar results were found for the other five prefectures
besides Shiga Prefecture.

ITARDA INFORMATION no.120

Impact on the occurrenceof pedestrian accidentslSpeciafeatureAccident points

Fig. 10. Image of the four sectionsin Shiga Prefecture

No. of accidents: 1
No. of convenience stores: 0
Building site area: 0.25km2
Area of commercial districts: 0.02km2

No. of accidents: 14
No. of convenience stores: 3
Building site area: 0.25km2
Area of commercial districts: 0.24km2

Estimated results for the No. of accidents
Median value: 1 accident
95% conﬁdence interval: 0 - 2 accidents

Estimated results for the No. of accidents
Median value: 16 accidents
95% conﬁdence interval: 10 - 24 accidents

No. of accidents: 4
No. of convenience stores: 0
Building site area: 0.25km2
Area of commercial districts: 0.02km2

No. of accidents: 5
No. of convenience stores: 0
Building site area: 0.23km2
Area of commercial districts: 0.14km2

Estimated results for the No. of accidents
Median value: 3 accidents
95% conﬁdence interval: 1 - 5 accidents

Estimated results for the No. of accidents
Median value: 3 accidents
95% conﬁdence interval: 2 - 7 accidents

Image of theestimated results

Medianvalue
95% interval

Fig. 11. Estimated results for the number of pedestrian
accidents in the same sections from Fig. 10
ITARDA INFORMATION no.120

Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~Introducing sections where the actual number of accidents

5 vastly exceeds the estimated number of accidents

For the comparison, we will start by introducing a section where there is no difference between the actual numberof accidents and the estimated number of accidents. Fig. 12 shows a section located in Ota Ward, Tokyo as an
example. It has four convenience stores, a building site area of 0.25km2, and a commercial district area of 0.09km2.
Five pedestrian accidents occurred over a three-year period, with the median value for the estimated number ofaccidents at five. The majority of sections with five pedestrian accidents in Tokyo are these sorts of comparatively
bustling areas with a large number of buildings.
No. of convenience stores: 4
Building site area: 0.25km2
Area of commercial districts: 0.09km2

No. of accidents:

5

Median value
for the estimatedNo. of accidents:

595% conﬁdence interval:

3 - 10accidents

ITARDA INFORMATION no.120
There are a number of these sections where the actual number of accidents vastly exceeds the estimated numberof accidents, including those in the other prefectures as well. With respect to the causal factors behind this, since
the circumstances differ in each respective section, we feel that a detailed analysis would be needed for eachsection. But a tendency for there to be a high percentage of accidents involving vehicles making a right turn stoodout as an overall characteristic. Fig. 14 shows the percentage of the number of pedestrian accidents involving avehicle making a right turn in sections where the actual number of accidents vastly exceeds the estimated numberof accidents, versus those in sections where this was not the case. From this we learned that in every prefecture, in
those sections where the actual number of accidents vastly exceeded the estimated number there was a higher
percentage of accidents involving vehicles making right turns.

41%

Percentage of pedestrian
accidents involving
vehicles making a right turnlSpeciafeature

35%

38%

31%

24%

Tokyo

24%

Kanagawa

Tochigi

40%
33%
28%

Shiga

Sections where the actual number of accidents vastly
exceeds the estimated number of accidents

27%

Fukuoka

34%
26%

Saga

Other sections

Fig. 14. Percentage of pedestrian accidents involving vehicles making a right turnFig 12. Sample section with no difference between the actual number of accidents
and the estimated number of accidents (Ota Ward, Tokyo)
Next we will introduce a section where the actual number of accidents vastly exceeds the estimated number ofaccidents.
Fig. 13 shows a section located in Ome City, Tokyo. It has zero convenience stores, a building site area of 0.1km2,
and a commercial district area of 0km2. Given the odds, it would be reasonable to assume that there would be one
accident there, but in actuality five pedestrian accidents have occurred there. Four of the five accidents occurred atintersections surrounded by corners. Based on a reading of the accident data, it is believed that three of these
accidents were between pedestrians and vehicles making right turns from north to west. The photograph on thebottom right-hand side shows the central intersection as seen from north to south.
No. of convenience stores: 0
Building site area: 0.10km2
Area of commercial districts: 0.00km2

Three of the four pedestrian
accidents that occurred
here involved vehicles
making a right turnNo. of accidents:

5

Median value
for the estimatedNo. of accidents:

1

95% conﬁdence interval:

0-2
accidents

There is one other hypothesis to explain this. In those sections where the actual number of accidents vastly
exceeds the estimated number of accidents, accidents tend to occur when the actual measured values for the threeland use characteristics, such as the number of convenience stores, is lower. Therefore, while they tend to haveless pedestrian traffic, many sections with less pedestrian traffic have a comparatively lower overall traffic volume.
Regarding the intersection in Tokyo introduced in Fig. 13, to the extent that one checks the photographic image
there does not appear to be much traffic volume. This intersection features a blind spot created by a sign on theright-hand side and other obstructions.
When this is expressed in the schematic diagram in Fig. 15, at the point in time in (1) it is thought that the driverentered the intersection without being aware of the pedestrian, who was behind a blind spot. If there had been alarge volume of traffic, they would have come to a temporary stop at the point of time in (2). But in this situation
they would not stop unless there was a car coming at them from head-on, and would have turned the wheel while
paying attention to what was up ahead. It is possible that they were delayed in confirming the presence of thepedestrian due to the difficulty in looking backwards and to the right while turning the wheel.

(1)
Green light(2)
Green light

Obstruction

Green light

Turns the wheel
while checking
to make surethere are nocars comingfrom up ahead(3)
Green light

Obstruction

Green light

Green light

Obstruction

Does not realize
the pedestrianis thereFig. 13. Sample section where the actual number of accidents vastly
exceeds the estimated number of accidents (Ome City, Tokyo)
ITARDA INFORMATION no.120

Fig. 15. Schematic diagram showing the (hypothetical) conditions at the intersection in Fig. 13
ITARDA INFORMATION no.120

Correlation analysis between land use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores, particularly in rural areas! ~Introducing sections where the actual number of accidents

5 vastly exceeds the estimated number of accidents

For the comparison, we will start by introducing a section where there is no difference between the actual numberof accidents and the estimated number of accidents. Fig. 12 shows a section located in Ota Ward, Tokyo as an
example. It has four convenience stores, a building site area of 0.25km2, and a commercial district area of 0.09km2.
Five pedestrian accidents occurred over a three-year period, with the median value for the estimated number ofaccidents at five. The majority of sections with five pedestrian accidents in Tokyo are these sorts of comparatively
bustling areas with a large number of buildings.
No. of convenience stores: 4
Building site area: 0.25km2
Area of commercial districts: 0.09km2

No. of accidents:

5

Median value
for the estimatedNo. of accidents:

595% conﬁdence interval:

3 - 10accidents

ITARDA INFORMATION no.120
There are a number of these sections where the actual number of accidents vastly exceeds the estimated numberof accidents, including those in the other prefectures as well. With respect to the causal factors behind this, since
the circumstances differ in each respective section, we feel that a detailed analysis would be needed for eachsection. But a tendency for there to be a high percentage of accidents involving vehicles making a right turn stoodout as an overall characteristic. Fig. 14 shows the percentage of the number of pedestrian accidents involving avehicle making a right turn in sections where the actual number of accidents vastly exceeds the estimated numberof accidents, versus those in sections where this was not the case. From this we learned that in every prefecture, in
those sections where the actual number of accidents vastly exceeded the estimated number there was a higher
percentage of accidents involving vehicles making right turns.

41%

Percentage of pedestrian
accidents involving
vehicles making a right turnlSpeciafeature

35%

38%

31%

24%

Tokyo

24%

Kanagawa

Tochigi

40%
33%
28%

Shiga

Sections where the actual number of accidents vastly
exceeds the estimated number of accidents

27%

Fukuoka

34%
26%

Saga

Other sections

Fig. 14. Percentage of pedestrian accidents involving vehicles making a right turnFig 12. Sample section with no difference between the actual number of accidents
and the estimated number of accidents (Ota Ward, Tokyo)
Next we will introduce a section where the actual number of accidents vastly exceeds the estimated number ofaccidents.
Fig. 13 shows a section located in Ome City, Tokyo. It has zero convenience stores, a building site area of 0.1km2,
and a commercial district area of 0km2. Given the odds, it would be reasonable to assume that there would be one
accident there, but in actuality five pedestrian accidents have occurred there. Four of the five accidents occurred atintersections surrounded by corners. Based on a reading of the accident data, it is believed that three of these
accidents were between pedestrians and vehicles making right turns from north to west. The photograph on thebottom right-hand side shows the central intersection as seen from north to south.
No. of convenience stores: 0
Building site area: 0.10km2
Area of commercial districts: 0.00km2

Three of the four pedestrian
accidents that occurred
here involved vehicles
making a right turnNo. of accidents:

5

Median value
for the estimatedNo. of accidents:

1

95% conﬁdence interval:

0-2
accidents

There is one other hypothesis to explain this. In those sections where the actual number of accidents vastly
exceeds the estimated number of accidents, accidents tend to occur when the actual measured values for the threeland use characteristics, such as the number of convenience stores, is lower. Therefore, while they tend to haveless pedestrian traffic, many sections with less pedestrian traffic have a comparatively lower overall traffic volume.
Regarding the intersection in Tokyo introduced in Fig. 13, to the extent that one checks the photographic image
there does not appear to be much traffic volume. This intersection features a blind spot created by a sign on theright-hand side and other obstructions.
When this is expressed in the schematic diagram in Fig. 15, at the point in time in (1) it is thought that the driverentered the intersection without being aware of the pedestrian, who was behind a blind spot. If there had been alarge volume of traffic, they would have come to a temporary stop at the point of time in (2). But in this situation
they would not stop unless there was a car coming at them from head-on, and would have turned the wheel while
paying attention to what was up ahead. It is possible that they were delayed in confirming the presence of thepedestrian due to the difficulty in looking backwards and to the right while turning the wheel.

(1)
Green light(2)
Green light

Obstruction

Green light

Turns the wheel
while checking
to make surethere are nocars comingfrom up ahead(3)
Green light

Obstruction

Green light

Green light

Obstruction

Does not realize
the pedestrianis thereFig. 13. Sample section where the actual number of accidents vastly
exceeds the estimated number of accidents (Ome City, Tokyo)
ITARDA INFORMATION no.120

Fig. 15. Schematic diagram showing the (hypothetical) conditions at the intersection in Fig. 13
ITARDA INFORMATION no.120

Reference literature / data sources
1) Statistics Bureau, Ministry of Internal Affairs and Communications: 2010 Population Census
2) National Land Information Division, National and Regional Policy Bureau, Ministry of Land,
Infrastructure, Transport and Tourism: National Land Numerical Information
3) ESRI Japan Corporation: Arc GIS Data Collection 20144) Japan Digital Road Map Association: Digital Road Maps (DRM)
5) Takuya Kubo: An Introduction to Statistical Modeling for Data Analysis
6) Iwanami Data Science Publishing Committee: Iwanami Data Science Vol. 17) Harutoshi Yamada: Research and Development of an Integrated System for Accident
Analysis Using Information on Accident Locations8) Google Earth Street View

1

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ITARDA INFORMATION no.120

つくば 交通事故調査事務所

住 友 水 道 橋 ビル８ 階

当センターは、平成4年（1992年）に国家公安委員会、運輸省（当時）、建設省（当時）から設立許可を受けて、公益法人として設立されました。その後平成24年（2012年）4月に公益財団法人に移行しました。我が国で唯一
道路交通法の定める「交通事故調査分析センター」の指定を国家公安委員会から受けた調査研究機関であり、交通事故の防止と被害の軽減のための交通事故の調査分析を行っています。

lCorrelation analysis between landSpeciafeature use characteristics and pedestrian accidents~ Caution is needed in sections with convenience stores,
particularly in rural areas! ~The probability ofa pedestrian accident
occurring increases
in sections withconvenience stores

2.6 - 2.7-fold increase
with 3 storese29

一般社団法人日本交通科学学会から

交通事故分析レポート No.120

Convenienc
store

公益財団法人 交通事故総合分析センター〒101 0064 東 京 都 千 代 田 区 猿 楽 町 ２ ７ ８For details regarding the contents herein, please refer to the materials from the 19th ITARDA
Research Presentation Session (on the ITARDA homepage (address below)).

No.120ITARDA INFORMATION

3

Impact on the occurrenceof pedestrian accidents(Tomoko Kitano)

ITARDA INFORMATIONThis analysis revealed that there is a strong connection between the number of convenience stores,
area of commercial districts, and building site area with the number of pedestrian accidents that
occur. Drivers, particularly those in rural areas, must make an effort to drive carefully in sections withconvenience stores. In addition, they must drive cautiously while keeping in mind that there is ahigher probability of colliding with pedestrians in sections where commercial districts and buildings aretightly packed.
Even in regions with little pedestrian traffic, there is the possibility that they will be delayed inconfirming the presence of pedestrians, especially when turning right. Therefore, drivers must must
reduce their vehicle’s speed and thoroughly check to make sure there are no crossing pedestrians.

交通事故分析レポート

6 Conclusion

イタルダ インフォメーション

Saga

Impact = exp (Estimated factor

Shigaby prefecture x No. of stores)

2

1.4-fold increase
with 1 store

Tochigi
Fukuoka
KanagawaTokyo

1
1 store2 stores3 stores

Estimated results of the impact that the number of convenience
stores has on the occurrence of pedestrian accidents

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