ITARDA 22th Work Shop Report2019
22nd Presentation Session for Traffic Accident Investigations, Analysis, and Research

Analyzing the causal factors behind the occurrence of bicycle accidents through theuse of data on actual accidents:
A comparison between accident databases with a focus on crossing collision accidents
Daisuke Ito
Visiting Researcher, Research Division(Assistant Professor, Graduate School of Engineering, Nagoya University)

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ITARDA 22th Work Shop Report

1.

Purpose of the research

According to traffic accident statistics from the National Police Agency (NPA),(1),(2) of the total number offatalities from traffic accidents in 2018 of 3,532 people, 453 were fatalities from people riding a bicycle,
making it the fourth most common type of accident. The fatality rate (= number of fatalities ÷ number ofcasualties × 100) for cyclists is 0.54, which is the third highest fatality rate following after those that occur
while walking and while riding a motorcycle. Vehicle-bicycle crossing collision accidents accounted forroughly half of both the number of all bicycle accidents and that of fatal bicycle accidents in 2018. Therefore,clarifying the causes of crossing collision accidents presents an important challenge.
Ito et. al.(3) used drive recorder data to show that the distribution for speed and allowance time between
collisions differs for near misses and minor contact accidents with crossing collision accidents. However, thedata used by Ito et. al. was collected with the cooperation of a taxi company, and cases involving only slight
contact accounted for the vast majority of their contents. There were few cases in which cyclists sufferedinjuries, and thus it cannot be said to be representative of accidents on the whole. Since it is expected that fataland serious injury accidents in particular occur at higher speed ranges, it is necessary to analyze the effect ofaccident severity on the accident parameters, such as the location where the accident occurred, car speed, andthe relative position of the cyclist when the cyclist appeared in front of the car. The purpose of this study is toclarify differences in the collision conditions between fatal and serious injury accidents and minor contact
accidents in crossing collision accidents between four-wheel vehicles and bicycles. Traffic accident datacollected by the Institute for Traffic Accident Research and Data Analysis (hereinafter abbreviated as"ITARDA") and the aforementioned drive recorder data were used for the analysis.
2.

Analytical method

This study used macro data and micro data from ITARDA. The subjects of the analysis are indicated below.
● Statistical data on traffic accidents (macro data)
Accident years: 2012 - 2016 (five years)
Contents:
Limited to vehicle-vehicle crossing collision accidents and accidents between four-wheel
vehicles and bicycles, four-wheel vehicles limited to passenger vehicles
Numbers:
675 accidents involving fatalities, 16,052 accidents involving serious injuries, and 191,163
accidents involving slight injuries● In-depth case studies (micro data)
Accident years: 2009 - 2017 (nine years)
Contents:
Limited to vehicle-vehicle crossing collision accidents and accidents between four-wheel
vehicles and bicycles
Numbers:
103 accidents (of which 21 involved fatalities, 63 involved serious injuries, and 19 involved
slight injuries)
The macro data for accidents between four-wheel vehicles and bicycles was used to analyze the distribution of
the locations and speeds at which the accidents occurred. We compared the distribution of fatal, serious injury,and slight injury accidents against the drive recorder database (hereinafter referred to as the “drive recorderDB”). As for the search conditions for the macro data, data in which the primary party and secondary party
were driving a passenger vehicle (LL, large, small, and kei sized vehicles, as well as mini cars) and a bicycle
was selected, from which only the portion consisting of accidents between four-wheel vehicles and bicycles
was used. The road configuration (intersections, near intersections, and non-intersections) and their respective
widths (intersections: small, small to large, or large; near intersections and non-intersections: 3.5 m and under2

ITARDA 22th Work Shop Report

up to 19.5 m and over) were adopted for use as the analytical items, with the number of accidents aggregatedfor each of these. The definitions for intersection sizes used here are those proposed by Hagita et al.(4): “small”
being defined as less than 5.5 m, “medium” as being from 5.5 - 13.0 m, and “large” as being 13.0 m and over.
This was indicated in the order of the width on the four-wheel vehicle side and then the width on the bicycle
side. Just like with the search through the macro data, the analysis of the micro data was restricted to crossing
collision accidents involving collisions between four-wheel vehicles and bicycles. From this, the location theaccident occurred, the danger perception speed, and the distances from the four-wheel vehicle and bicycle tothe point of the collision at the time when they perceived danger (which were recorded on the survey forms)
were selected out.
This study will compare the results of an analysis of the drive recorder DB reported by Ito et. al.(3) against
accident data from ITARDA. The drive recorder DB was collected from a taxi company within Aichi
Prefecture through the efforts of the Aichi Prefecture Automobile Safety Technology Project Team. It included
41 crossing collision accidents involving collisions between four-wheel vehicles and bicycles. In addition tothis data, case examples of crossing collisions were sampled from the near-miss incident database(5),(6)maintained by the Tokyo University of Agriculture and Technology and added to the data. For the analysis,
collision data on a total of 61 accidents was analyzed. The danger perception speeds as found in the drive
recorder DB were defined as the vehicle's speed at the time when the driver took evasive action, such as bysuddenly braking. For cases where the timing could not be clearly defined, this was defined as the speed at thetime when the bicycle first appeared in the drive recorder video.
The kernel density estimation method(7) was used in order to smooth out the cumulative distribution curve. The
kernel density estimation method is expressed as follows:𝑛1
𝑥 − 𝑋𝑖𝑝̂ (𝑥) =
∑𝐾(
)
𝑛ℎ
ℎ

(1)

𝑖=1

where 𝑋1 , … , 𝑋𝑛 is the data, 𝐾 is a smoothing function known as a kernel function, and ℎ is the bandwidth that
controls the intensity of the smoothing. For this study, the Gaussian function expressed via the following
equation was used for the smoothing function.
1𝑥2(2)
𝐾(𝑥) =
exp (− ).
2√2𝜋
R language Ver. 3.4.0 (8) was used to carry out this smoothing processing.
A chi-squared test and a residual analysis were performed to analyze the effect of the intersection conditions
on crossing collisions in intersections against both the micro data and drive recorder DB. When the absolutevalues for the adjusted residuals were larger than 1.96, this was regarded as being p < 0.05.
3.

Results

3.1. Comparison against the macro data
Fig. 1 shows a comparison of the locations where accidents occurred and the danger perception speed between
the macro data (fatalities, serious injuries, and slight injuries) and the drive recorder DB. Comparing thelocations where accidents occurred reveals that fatal accidents often occur at intersections (medium-medium),near intersections (medium), and non-intersections (medium). As opposed to this, smaller intersections (smallsmall, small-medium, small-large) and near narrow intersections (small) accounted for the vast majority of theoccurrences in the drive recorder DB. Moreover, viewing this by speed reveals that fatal accidents often
occurred at intersections in a range from 20 - 60 km/h. Whereas speeds of 41 km/h and over accounted for thevast majority of these, in the drive recorder DB accidents at a speed of 30 km/h or less accounted for more thanhalf of the accidents. Moreover, for accidents near intersections as well, in the drive recorder DB many of these3

ITARDA 22th Work Shop Report

Intersections
Width on the FWV side /bicycle side

Small
Large-large
Large-medium
Large-small
Medium-large
Medium-medium
Medium-small
Small-large
Small-medium
Small-small

Small
Large

Near
intersections Medium

Large

Nonintersections Medium

Small
Large-large
Large-medium
Large-small
Medium-large
Medium-medium
Medium-small
Small-large
Small-medium
Small-small

Large

Nonintersections Medium

Small
Large

Near
intersections Medium

NumberNumberoccurred at a speed of 30 km/h or less, whereas with fatal accidents there were many cases of these occurring
at 31 km/h or faster. In summary, compared with the accidents in the drive recorder DB, which mainly consistof mild collisions, fatal accidents occur in high speed ranges. Conversely, a high proportion of slight injury andserious injury accidents occur at a speed distribution of 30 km/h or slower, demonstrating a trend close to thatfrom the drive recorder DB.
Next, only the danger perception speeds were compared without categorizing the locations where the accidents
occurred. The results of normalizing the number of accidents that occurred in each speed range via a total
number are shown in Fig. 2. The majority of slight injury accidents occurred at speeds of less than 20 km/h,
and that an increase in the severity of injuries is accompanied by a shift of the speed distribution to higher
speeds. There is a high frequency of accidents at 30 km/h and below in the drive recorder DB yet few at low
speeds, demonstrating a trend that differs from that of the macro investigations.

Intersections
Width on the FWV side /bicycle side

Location where accident
occurred

Location where accident
occurred(a) Slight injuries (macro data)(b) Serious injuries (macro data)55≤ 10 kph≤ 20 kph≤ 30 kph≤ 40 kph≤ 50 kph≤ 60 kph≤ 70 kph> 70 kph
Large
大
Non中
intersections Medium
Small
小
Large
大
Near Medium
中intersections
Small
小Large-large
大-大Large-medium
大-中Large-small
大-小Medium-large
中-大Medium-medium
中-中Medium-small
中-小Small-large
小-大Small-medium
小-中Small-small
小-小Intersections

単路

Width on the FWV side /bicycle side

Location where accident
occurred交差点
交差点
Intersections
付近
Width on the FWV side /発生場所
bicycle side
Location where accident
occurred(c) Fatalities (macro data)(d) Drive recorder DB

Fig. 1. Distribution of the locations where accidents occurred and danger perception speeds(comparison against the macro data)

4

11
00

Number
件数

Number
件数

Number

33

22

Small
Large-large
Large-medium
Large-small
Medium-large
Medium-medium
Medium-small
Small-large
Small-medium
Small-small

Nearintersections Medium

Small
Large

Large

Nonintersections Medium

440.30.3

0.5
0.5

0.25

0.25
0.25

0.25
0.25

0.4
0.4

0.2
0.2

0.20.20.20.2

0.3

0.3

0.15

0.15

0.15
0.15

0.15
0.15

0.2
0.2

0.1
0.1

0.10.1

0.10.1

0.1

0.1

0.05
0.05

0.05

0.05

0.05
0.05

00

00

00

00

≤10kph≤20kph10≤≤10kph20≤≤20kph30≤≤30kph40≤≤40kph50≤≤50kph60≤≤60kph70≤≤70kph70>>71kph0.30.30.25

10≤≤10kph20≤≤20kph30≤≤30kph40≤≤40kph50≤≤50kph60≤≤60kph70≤≤70kph70>>71kph

0.3
0.3

≤ 10
≤ 20
≤30kph≤ 30≤40kph≤ 40≤50kph≤ 50≤60kph≤ 60≤70kph≤ 70>71kph
> 70

0.6
0.6

10
≤≤10kph20≤≤20kph30≤≤30kph40≤≤40kph50≤≤50kph60≤≤60kph70≤≤70kph70>>71kphNumber/Total
件数/総数

ITARDA 22th Work Shop Report

危険認知速度(km/h)
Dangerperception speed(km/h)

危険認知速度(km/h)
Dangerperception speed(km/h)

危険認知速度(km/h)
Dangerperception speed(km/h)

危険認知速度(km/h)
Dangerperception speed(km/h)(a) Slight injuries(b) Serious injuries(c) Fatalities(d) Drive recorder(macro data)(macro data)(macro data)
DB
Fig. 2. Distribution of danger perception speeds (comparison against the macro data)

3.2. Comparison against the micro data
The totals for the locations where accidents occurred and the danger perception speeds from the micro data areshown in Fig. 3. The accidents frequently occur at intersections, just like within the macro data and drive
recorder DB. Regarding the speed, they frequently occur near the 30-40 km/h range, and when the data on
slight and serious injuries from the macro data is compared against that from the drive recorder DB, thedistribution is tilted towards the higher speeds. Fig. 4 shows a categorization and comparison of danger
perception speeds by the extent of the injuries suffered. The cumulative probability distribution curves weresmoothed by the aforementioned method. This figure reveals that the speed distribution from the drive recorder
DB is situated on the lower end of the speed range. It also reveals a sizable difference in the speed distribution
in that the 50% total for speeds was at roughly 25 km/h, whereas with slight injury accidents this wasapproximately 40 km/h. From this, it can be seen that there are numerous cases of even slight injuries occurring
at relatively high speeds from the micro data.

Total probabilityof occurrence (%)
累積発生確率(%)

100

10
10

≤ 10 kph≤ 20 kph≤ 30 kph≤ 40 kph≤ 50 kph≤ 60 kph≤ 70 kph> 70 kph
Large
大
Non中
intersections Medium
Small
小
Large
Near 大中intersections Medium
Small
小Large-large
大-大Large-medium
大-中Large-small
大-小Medium-large
中-大Medium-medium
中-中Medium-small
中-小Small-large
小-大Small-medium
小-中Small-small
小-小

55

件数
Number

15
15

00

80

60
40
Drive recorder DB
ドライブレコーダDB
Slight injuries
ITARDAITARDA
軽傷
Serious injuries
ミクロmicro
重傷
Fatalities データdata
死亡

20
0
0

交差点
交差点
単路
Intersections
付近
Width on the FWV side /
発生場所

10

20

30

40

50

60

70

危険認知速度(km/h)
Dangerperception speed(km/h)

bicycle side

Location where accident
occurredFig. 3. Distribution of the locations where accidents
occurred and danger perception speeds(micro data)

Fig. 4. Distribution of danger perception speeds(comparison against the micro data and drive recorderDB)

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ITARDA 22th Work Shop Report

Next, the location where the driver perceived the appearance of the cyclist was compared between the micro
data and the drive recorder DB. Fig. 5 shows the locations where danger was perceived as categorized for each
danger perception speed. Said locations were determined based on the records in written evidence for the microdata, and for the drive recorder DB these were calculated via an analysis of the video. Moreover, for the drive
recorder DB the locations were portrayed as of the point in time when the bicycle first appeared in the video
data and when the driver began suddenly braking, respectively. The points where cyclists appeared in the micro
data are distributed over a relatively narrow angle range from the direction of motion of the four-wheel vehicle
going straight towards a relatively small angle and off into the distance, but with the drive recorder DB the
distribution that was observed was in the vicinity around the four-wheel vehicle and at a wide angle. While themajority from the micro data is collected within the range of lines in a 25° direction to both sides as listed in
the figure, conversely the low speed data in particular is often indicated as falling outside this range with thedrive recorder DB. This suggests that when considering the sensing capabilities of autonomous emergency
braking system for bicycles, not only those off in the distance, but also the appearance of bicycles from within
the vicinity of the vehicle and wide-angle areas should be considered.
25度
45度

Micro data
ミクロデータ
40
Locationwhere danger
ミクロデータ40
危険認知箇所
危険認知箇所wasperceived

40

35

35

30
25
20

35
<=10
<=10
<=20
<=20
<=30
<=30
<=40
<=40
<=50
<=50
>50
>50

30
25
20

30
25
20

40

Drive
recorder
ドラレコ
ドラレコ
Time自転車
when
自転車
bicycle出現時
appears

出現時

35
<=10
<=10
<=20
<=20
<=30
<=30
<=40
<=40
<=50
<=50
>50
>50

30
25
20

40
35
30

25
20

40
35

10 10

10 10

10 10

5

5 5

5 5

0 0
5 10 10 15 -10
15 -10 -5 -5 0 0

5 5

10 10

15 15
-10-10 -5 -5

45度
45°

20

15 15

5

25度
25°

<=10
<=20
<=20
<=30
<=30
<=40
<=40
<=50
<=50
>50
>50

25

15 15

0 0
-10 -10 -5 -5 0 0

速度速度(km/h)
Speed(km/h)(km/h)
<=10

30

15 15

5

Drive recorder
ドラレコ
ドラレコ
Time
when driver
急ブレーキ
急ブレーキ
beginssuddenly
開始時
braking
開始時

0 0
0 0

5 5

1010

1515

Fig. 5. Relative position of the bicycle when danger was perceived

In order to compare the characteristics of crossing collisions at intersections, a categorization by theconfigurations of intersections is shown in Table 1. Here, "Other irregular intersections" refer to Y-shaped
roads, five-road junctions, crisscrossing intersections, and so forth. Table 1 shows that these other irregularintersections account for a relatively high percentage in the micro data (p < 0.05), whereas the percentage oforthogonal, cross-shaped intersections was high in the drive recorder DB (p < 0.05). Although these results
were derived from a limited number of accidents, this is probably because of differences in the roadenvironment and traffic volume of the area where the data was collected.

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ITARDA 22th Work Shop Report

Table 1. Configurations of locations where crossing collision accidents occurred at intersections
Micro data (n=81)
Drive recorder DB (n=57)
n%Adjusted residualsn%Adjusted residuals
Orthogonal, cross-shaped
51
63
-2.49
47
82
2.49
T-shaped
17
21
1.04
8
14
-1.04
Other irregular intersections
13
16
2.33
2
4
-2.33

In order to compare the characteristics of crossing collision accidents at non-intersections, the accidents werecategorized based on the type of accident (Fig. 7). Crossing collision accidents at non-intersections can be
categorized into two types. The first type of accident occurred when either a four-wheel vehicle or bicycle
entered the road suddenly from a lot off to the side of the road (this includes both cases where a four-wheel
vehicle enters the road and cases where a bicycle does so). The second type of accident occurred either before
or after a four-wheel vehicle or bicycle crossed the median strip or median line. This type of accident was
frequently seen in the micro data, but not seen in the drive recorder DB. With the type where one party crosses
the median line, cases caused by a bicycle suddenly changing course were seen, as was a type whereby thebicycle suddenly appeared between or from behind a line of vehicles. Three of the total of four accidents in thedrive recorder DB involved a line of vehicles, and this type had a high occurrence frequency compared withthe micro data. It is estimated that differences in the road environments and traffic volumes of the areas where
the data was collected are important factors, just like with accidents at intersections.
.
Crossing collision accidents
単路での出会い頭事故
at non-intersections
路外の敷地からの飛び出し
Suddenlyappearing from lot off the side of the（ミクロ
5，ドラレコ
DB0）DB: 0)
road (micro:
5, drive recorder

中央分離帯，中央線を横断
Crossing
median strip / median line（ミクロ5，ドラレコ
DB4）(micro: 5, drive
recorder DB:
4)

Bicyclesuddenly changes course
Suddenlyappears from line of vehicles
自転車の急な方向転換
車列からの飛び出し
（ミクロ内
DBDB:
内 1）
(in micro: 2,2，ドラレコ
in drive recorder1) （ミクロ内(in micro: 2,2，ドラレコ
in drive recorder
3)
DBDB:
内 3）
Sidewalk

Sidewalk

Fig. 7. Causes of crossing collision accidents involving bicycles at non-intersections

The advantages and limitations of the drive recorder DB and micro investigations used for this analysis will belaid out in this section. The first advantage of the drive recorder DB is that it makes it possible to obtain the
speed of the bicycle and its relative trajectory just prior to the accident by video analysis of the drive recorderdata. Another advantage is that the video data enables us to analyze the effect of moving obstacles on theoccurrence of accidents. We can obtain the time at which the driver of the four-wheel vehicle became awareof the bicycle from the video data, even when the bicycle suddenly appeared from behind a row of vehicles in
the oncoming lane of traffic that is moving. Conversely, one limitation of the drive recorder DB used for thisanalysis is that the collected data was sampled from a population consisting of highly safety-conscious
professional drivers. In addition, the number of accidents was limited because the data was only provided withthe cooperation of several taxi companies.
One advantage of the micro investigations is the fact that information on factors like the vehicle, the victim,
and the scene of the accident, as well as the deformation of the vehicle and the extent of the injuries that
occurred following the accident (both of which are particularly difficult to analyze from the drive recorder) can
7

ITARDA 22th Work Shop Reportbe collected by investigators. On the other hand, since investigators are only dispatched to a limited number ofcases, there is the possibility that the data is tilted more towards comparatively severe accidents. In addition,
one limitation of both data sets is the fact that their data collection regions were limited. Therefore, the effectof the regional differences must be taken into consideration when performing comparisons between the data
used in this analysis. Moreover, it will be necessary to develop suitable methods for enlarging and interpolatingin order to estimate the extent of accidents nationwide in the future.
4.

Conclusion

This paper focused on crossing collision accidents between four-wheel vehicles and bicycles to compare
existing drive recorder data with ITARDA's macro and micro accident data, thereby clarifying its
characteristics and differences in it. This was done with the objectives of elucidating the causal factors behindthe occurrence of crossing collision accidents involving bicycles and further improving the accuracy of
accident investigations and causal analyses. The main results from this are indicated below.
● Whereas fatal accidents are distributed more on the high speed side, on the other hand from analyzing thedrive recorder DB it was affirmed that there are differences in the trends, such as the appearance ofbicycles over a wide range.
● Analyzing crossing collisions at intersections and non-intersections revealed differences in the trends withthe locations where these occurred and the types of accidents. However, these could potentially be
attributed to differences in the road environment and traffic volume on the sections surveyed.
For the future, ideally a structure should be established based on an understanding of the characteristics of each
respective database so that they can be used in a mutually complementary manner in order to clarify the causesof bicycle accidents and develop accident prevention devices.
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