An application of frequent pattern mining
to traffic accident data
交通事故データへの頻出パターンマイニングの適用
Yuta Takahashi1), Masaru Kiyota1), Yukuo Hayashida1),
Yuichiro Sakamoto1)
1) Saga University

Background
• Traffic accidents per hundred thousands in Saga are
most often in Japan while 4 years
2
0
200
400
600
800
1000
1200
2012 2013 2014 2015
NumberofTrafficAccidents
(perhundredthousands)
year
Saga
Average
We haven’t identified the cause
clearly.
Due to complication
Over
2 times!!

PDCA cycle for preventing accidents
3
Plan
Do
Check
Action
• Analysis a cause of accidents
• Propose a prevention
method
• Take measures
• Verify the effectiveness of
action
• Consider the lack of
measures
• Schedule next measures
Do
Plan
Check
Action
PCDA cycle

PDCA cycle for preventing accidents
4
Plan
Do
Check
Action
PCDA cycle
 Procedure of “Plan”
Select feature
points
Analysis of
accident causes
Planning of
measures
Research entities
Position of
this study

Problem of previous analysis
• Ordinary we make many tables and graphs
⇒ It needs much cost to make and check
5
• We attend to introduce a data mining approach

Data mining
• A method of gaining knowledge from large data
• It is different from statistical analysis
• Performance takes priority over strictness
6
 SOM
 Clustering
 K-means
 Association rule analysis
 Neural Network
 Decision Tree
 SVM
 Bayesian network
① Learning ② Extract patterns

Related works
 Machine Learning Approach
7
Dataset
Fatal injury
Classifier
Incapacitating injury
No injury
…
Model Patterns
Chong, Miao, etc at al. "Traffic accident data mining using machine learning paradigms."
Fourth International Conference on Intelligent Systems Design and Applications (ISDA'04)
Method
 Decision Tree
 Neural Network
 DT & NN

Related works
8
 Clustering Approach
Dataset Clustering
C1
C2
C3
C7
…
Pattern1
Pattern2
Pattern3
Pattern7
…
Depaire, Benoît, Geert Wets, and Koen Vanhoof. "Traffic accident segmentation by means
of latent class clustering." Accident Analysis & Prevention 40.4 (2008): 1257-1266.

Frequent pattern mining
9
• A method of finding frequent items from the dataset
• It can count item combination efficiently
support 𝑋 =
count 𝑋
𝑀
X : Patterns
M : Amount of transactions
 specify minsup parameter
support(X )
Patterns which are under
minsup will be eliminated
We expect this could easy to extract patterns!!

Frequent pattern mining vs statistical analysis
Frequent
pattern mining
Statistical
analysis
strictness △ ○
Analysis cost ○ △
Finding
knowledge ○ ×
10

Purpose of this study
Adapt frequent pattern mining to traffic accident data
- Is it efficient to analyze?
- Can it get knowledge?
11
Data & Analysis
- Traffic Accident
Data
45,653 datas
- Algorithm
Apriori
- Programming
Python

3 type analysis with pattern mining
② Vehicle type
12
① Intersection ③ Age
① ー ② ー ③
⇒ Time rules ⇒ Emerging
Patterns, Similarity
⇒ Correlation

① Time rules in each intersection
13① ー ② ー ③
 Intersection analysis
Name of intersection is
labeled by police
• Occur time prediction
• Causes due to time
 Association rule
support 𝑋 ⇒ 𝑌 =
count(𝑋 ∪ 𝑌)
𝑀
rule ∶ 𝑋 ⇒ Y Sunny ⇒ Road is drying

Flow of the analysis
14① ー ② ー ③
Dataset
Interse
ction 1
Interse
ction 2
Interse
ction N
Patter
ns 1
Patter
ns N
All rules
Patter
ns 2
Rules
1
Rules
2
Rules
N
…
…
…
 Time span
1. Long span
(Season, Weekday or
Holiday, time per 6
hours)
2. Short span
(Season, Days of the
week, time per 3 hours)

Result of rules (Long span)
rule intersection support
{Weekday} ⇒ {12:00-17:00} 下村 0.588
{Autumn} ⇒ {Weekday} 水上交差点 0.583
{Weekday} ⇒ {12:00-17:00} 県庁前交差点 0.571
{Summer} ⇒ {Weekday} 愛敬町 0.545
{Winter} ⇒ {Weekday} 満穴 0.529
{Summer} ⇒ {Weekday} 唐房入口交差点 0.500
{Summer} ⇒ {Weekday} 伊勢町 0.500
{Weekday} ⇒ {12:00-17:00} ハローワーク唐
津入口
0.500
{Summer} ⇒ {Weekday} 栄町北 0.474
{Autumn} ⇒ {Weekday} 村徳永 0.462
15① ー ② ー ③

Result of rules (Long span)
rule intersection support
{Winter} ⇒ {Holiday} 幡崎東 0.455
{Holiday} ⇒ {12:00-17:00} 枯木の塔 0.364
{Holiday} ⇒ {12:00-17:00} 長瀬交差点（信号） 0.364
{Autumn} ⇒ {Holiday} 神埼市役所前 0.357
{Holiday} ⇒ {6:00-11:00} 五条（北） 0.333
16① ー ② ー ③

Result of rules
rule intersection support
{Spring} ⇒ {Friday} 千布北交差点（信号） 0.364
{Saturday} ⇒ {12:00-14:00} 鏡山入口交差点 0.364
{Winter} ⇒ {Saturday} 幡崎東 0.364
{Winter} ⇒ {Monday} 龍谷短大入口 0.350
{Winter} ⇒ {Sunday} 脇田 0.333
{Summer} ⇒ {12:00-14:00} 浜玉中学校前交差点 0.313
{Spring} ⇒ {18:00-21:00} 中副交差点（Ｒ３８５） 0.308
{Monday} ⇒ {12:00-14:00} 東多久駅前 0.286
{Winter} ⇒ {15:00-17:00} 県庁前交差点 0.286
{Summer} ⇒ {Saturday} 材木町浦島通り 0.273
17① ー ② ー ③

② Vehicle type analysis
18① ー ② ー ③
 Road structure
Extract road structure
patterns by comparing
vehicle type
Determine degree of
similarity with
comparing patterns
Car Walker Bicycle Car Auto bicycle
perpetrator victim

Flow of the analysis
19① ー ② ー ③
Dataset
Walker Bicycle
Auto
bicycle
Patter
ns 1
Patter
ns 4
All patterns
Patter
ns 2
Car
Patter
ns 3
Similarity
Emerging
patterns

How to determine emerging patterns
20① ー ② ー ③
Walker
Bicycle
Car
Auto bicycle
 Growth rate
GRG 𝑒 =
supportG 𝑒
1
𝑁 𝑖=0
𝑁
supportGi
𝑒
At pattern e
GRG 𝑒 ≥ 1.0
⇩
Emerging pattern

Growth rate of each vehicle type
21① ー ② ー ③

Elements of the emerging pattern
22
Walker
Bicycle
Car
Auto bicycle
 No traffic light
 Median divider - paint
 Inside of intersection
 Single road・1 lane road
 Near intersection
 Exist pedestrian-vehicle separation
 Median divider - paint
 General traffic location
 No pedestrian-vehicle separation
 No traffic light  Intersection
 No median divider
① ー ② ー ③

Degree of similarity between vehicle type
Walker Bicycle Car
Auto
bicycle
Walker - 0.129 0.129 0.112
Bicycle 0.135 - 0.137 0.056
Car 0.129 0.137 - 0.087
Auto
bicycle
0.112 0.056 0.087 -
23① ー ② ー ③
Si G1, G2 =
1
𝑁
𝑖=0
𝑁
𝑠𝑢𝑝𝑝𝑜𝑟𝑡 𝐺1
𝑒𝑖 − 𝑠𝑢𝑝𝑝𝑜𝑟𝑡 𝐺2
𝑒𝑖
Si → 0.0 : Similar
Si → 1.0 : not Similar

③ Correlation between age and support
24① ー ② ー ③
 Correlation patterns
Many patterns
↓
Some patterns have a
correlation with age?
0
200
400
600
800
1000
1200
1400
1600
0 10 20 30 40 50 60 70 80 90
Amountofperpetrators
Age

Flow of the analysis
25① ー ② ー ③
Dataset
Age 18 Age 19 Age 80
Patter
ns 1
Patter
ns 62
All patterns
Patter
ns 2
Correlation
patterns
…
…

Positive correlation patterns
26① ー ② ー ③ Before noon : 9:00-11:00

Negative correlation patterns
27① ー ② ー ③ Beginning of night : 18:00-20:00

Conclusions
We tried to adapt frequent pattern mining to traffic
accident data
We did 3 type analysis
• Time rules in intersections
→ Some rules that look related to time
• Vehicle type and road structure
→ Emerging patterns and determine similarity
• Frequent patterns which have correlation with age
→ Extract some patterns with the correlation
28

Future works
• Validation of knowledge has not done
• Traffic accident data with location can add more
metadata
• We’d like to introduce this method to real case
29
Dataset
Frequent
pattern
mining
Knowledge ?