http://www.ierek.

com/press

Print ISSN: 2357-0849

International Journal on: Proceedings of Science and Technolgy

Visualization System for Traffic Accident Data

Su Chenlu1, Hong Yating1, Liu Tianyi1, Raja Majid Mehmood1,*
1
School of Electrical and Computer Engineering,

Information & Communication Department, Xiamen University Malaysia

*Corresponding author: Raja Majid Mehmood (rmeex07@gmail.com).

Keywords Abstract
Information visuliztion,
Visualization system, Three- At present, the traffic problem is a problem that the government attaches great
dimensional model,Traffic
accident importance to. Many papers also put forward their own visualization models for
traffic problems. This research focused on the Map-matching and Spatial-temporal
Visualization of Expressway Traffic Accident Information and improves the original
two-dimensional visual model of accident rate into a three-dimensional model. The
goal is to represent more attributes in a visual model and make them easier to
compare, so as to provide users with more intuitive visual information.

1. Introducation
Since the beginning of the 21st century, there is a large amount of traffic accidents over the world because of increasing
number of vehicles and complex urban construction.
Previous data analysis models were more planar and in the form of digital tables, which made it difficult to analyze
all the data information.
Considering the seriousness of this problem, it is necessary to design a traffic accident data visualization system,
which allows the traffic departments to visually analyze accident data so as to better improve the traffic and road
planning.
Based on that, this study will based on the 2D modeling of traffic accident data from Research on the Map-matching
and Spatial-temporal Visualization of Expressway Traffic Accident Information, and improved it into a three-
dimensional model that can represent more attributes in a visual model and make them easier to compare, so as to
provide users with more intuitive visual information.
In this regard, the transportation department can separately analyze the occurrence of accidents at different time points
according to the results of the visualization, and can clearly see the accident trends of working days and non-working
days, so as to formulate more feasible transportation law. In addition, people can also plan the travel arrangements by
looking at the visual results to avoid peak accidents and avoid high-risk roads.
2. Literature review
In Research on the Map-matching and Spatial-temporal Visualization of Expressway Traffic Accident Information,
This paper proposes a method based on accident collection data and GIS roadmap data to realize rapid location, map
matching and verification of highway accidents. Through visual analysis, traffic management departments can be
helped to improve accident prevention capabilities.
Specific implementation: First, collect data using map collection tools (not only can collect street view information of
roads, but also collect road data). Next, it can achieve automatic road data extraction of panoramic images.

pg. 1
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

The visualization of accident time-space data helps to enhance the understanding of time and space factors and
accident data changes, including three aspects:
a. Time series visulization

Figure 1 Time series visualization using the line graph

Figure 2 Time series visualization using the polar graph

b. Spatial distribution visualization

Figure 3 Spatial distribution visualization using map and scatter plot (simulating data)

pg. 2
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 4 Accidents frequently-occurring road segment visualization

Figure 5 Spatial distribution visualization using reginal hierarchical rendering and scatter plot.

c. Space-time related visualization, considering the time and space of traffic accidents Distribution characteristics,
specifically expressing the regularity of accidents in a certain time and space.

pg. 3
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 6 superimposing the time-line chart for different regions on the map. (simulating data)

Among those visualization model, time series visualization using line graph and Time series visualization using polar
graph are compared. (M1 means Method 1: Time series visualization using line graph; M2 means Method2: Time
series visualization using polar graph.)

Table 1. Type of visualization

M1 M2

Type Line chart Polar chart

Musk Points, lines Circle, clock

and color and coloe

Target Audience Transportaion department

Table 2. Displayed information

M1 M2

Number of acccidents in each hour Displayed Displayed

Number of accidents every day Displayed Displayed

Table 3. Scalability

M1 M2

Scalability High Medium

Reason Because the number of Because the size and color

accidents can refer to of circle represent the
different value in y-axis, quality of accident, and it
is not really precise，but
pg. 4
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

if the value is high, still it still can represent large

can be represented. number.

Table 4. Accuracy

M1 M2

Accuracy Medium Low

Reason Although its visualization It is hard to determine the

is based on exact value to accurate value from size
represent the number of and color shade of circle.,
accidents, but it is not because each color refer to
easy to get the precise a range of value.
quantity of accidents in
each timestamp (any
point) on x axis.

Table 5. Reliability

M1 M2

Reliability High High

Reason The source of the data for the three methods is derived
from the accident record and use a double-check model
to verify whether the data is reliable, so the data is
highly authentic. That means the visualization has high
reliability.

Table 6. A degree of discrimination (when the data is ver close)

M1 M2

Degree of discrimination High Low

Reason It is differentiable from When the data is really

exact value in y axis, so it close, it is not easy to
is really clear to see distinguish them between
discrimination. color and size of circle.

3. Proposed Method
Based on the original data visualization model, we improved that visualization system for the accident data in the road.

pg. 5
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 7 Data set for section2 (the value is number of accidents)

3.1 Version 1
A calender map is proposed to visualize the traffic accident data. In this map, days are arranged in a calendar form.
All the days will be showed as a grid surrounded by red edges with 12 smaller grids inside (4 lines and 3 rows). Each
smaller grid represents two hours in a day. They are arranged in chronological order from left to right, from top to
bottom. Colors change from light green to dark green will show the probability of accidents from low to high.

Figure 8 Calendar map-1

Figure 9 Calendar map-2

pg. 6
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

3.2 Version 2
Since the version 1 cannot give direct comparison between different attributes in more perceptible sequence, in this
version, the proposed model is designed to deliver the information of different hours in a day and different days’
information in a month. It is a three-dimensional visualization model that x axis represents hours in a day, y axis
represents days in a week (from Monday to Sunday) and z axis represents the same weekdays in a month (four
Mondays in a month will be assigned to the same z axis).

Table 7. The explanation of information in different axis

Axis Meaning

X-axis Hours in a day (from

0:00-24:00, each bar is 2
hours)

Y-axis Days in a week (from

Monday to Sunday)

Z-axis The number of accidents

in four same weekdays in
one month.

The reason why we used days in a week (Monday to Sunday) instead of specific date (May 1st or May 2nd) is
because weekday and weekend are more comparative, have more important directive to target audience
(Transportation department).For example, it is more meaningful for user to compare different accident rates between
weekdays and weekend at the same time period (such as the evening rush from 18:00 to 20:00).
In this model, readers can get different contrasts by comparing different dimension. In z axis, there are four weeks
(first week, second week, third week, fourth week) in a month. Each of them is a column which filled with different
colors: green, red, yellow and blue respectively.

Figure 10 Three-dimensional method

When the x axis and z axis is displayed, it can show accident data in every 2 hours on specific day in one week.
(Figure 7 four Mondays etc.). Two hours is a group.

pg. 7
SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 11 The explanation of block with different colors

Figure 12 Monday (four weeks)

Figure 13 Tuesday (four weeks)

pg. 8
SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 14 Wednesday (four weeks)

Figure 15 Thursday (four weeks)

Figure 16 Friday (four weeks)

pg. 9
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 17 Saturday (four weeks)

Figure 18 Sunday (four weeks)

When y-axis and z-axis is displayed, it can show accident data at specific 2 hours t (as Figure 41 shown is 0:00-
2:00) on every weekday in one month.

pg. 10
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

Figure 19 y and x axis

Other than this, the model can also be displayed in x axis and y axis. Blue color from light to dark will give
information according different number of accidents. There are four smaller grids in one rectangle. Each of a smaller
grid represents week in sequence (First row of girds represents the information of first week, and so on). All the
information can be showed in the x and y axis.

Figure 20 x and y axis

3.2 Version 3
Although the proposed version 2 has been greatly improved, for example, it can represent more data and make
clearer comparisons. However, the color classification is so few that the visualization of each set of data is not clear
enough. Therefore, in the proposed version 3, we use more color systems to represent the incidence of traffic
accidents.

Figure 21 Proposed method version 3- whole month [x-y axis]

pg. 11
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

4. Evaluation and Result

Here are the comparisons among the original method, proposed version 1, version 2 and version 3.

Table 1. Comparation among methods and proposed versions 1, 2 and 3

Method Proposed Proposed Proposed

version 1 version 2 version 3

Type Polar graph Calendar garph 3D bar chart 3D bar chart

Displayed data size 7-day 1-month 1-month 1-month

Dimensional 2D 2D 3D 3D

Mask Circle, color Grid, color Bar, grid, color Bar, grid,
shade shade shade color, color
shade

Accuracy Low Medium High High

Scalability Low High High High

Perception High Medium Medium High

The first polar graph only displays 7-day data whereas all method we proposed can show one-month data. And the
previous two methods are 2-dimensional and later two are 3-dimensional. Polar graph consists of circle and color
shade to represent the number of accidents, and the calendar graph use different color shade of grid to show accident
condition in calendar form. However, the last two method display the number of accidents by height of bar and the
top different color shade of grid can represent every day data. Besides, the last method includes 3 colors (red green
blue) to indicate different severity.
The polar graph and calendar graph use color shade to represent number of accidents is not really precise, but the later
methods we proposed are use height corresponding to different values, which is really precise.
Apart from that, if the data set is huge, the polar graph cannot accommodate all data, but later 3 methods can be applied
to large sample size. As for perception, the clock form of the polar graph makes it intuitive to see the time changes
within a day, but the changes in different days are not very obvious. Although the calendar map can reflect the accident
situation within one month and one day, it is difficult to compare each other.
3-D bar charts can be compared from different dimensions (different days of one-week, different times of one day,
data within one month), and then different results are obtained. The amount of information is large, but the color shade
is single, resulting in low information acquisition. In the last 3-D bar chart, we used different colors in the y-z axis
view to distinguish the severity, and the viewer can obtain information more intuitively.
The significance of this part can be divided into Four parts:
Firstly, in the way of data representation, the regular grid provides a neat data visualization model from different
angles (x-y, x-z and y-z), and through the form of a 3d bar chart, different dimensions are integrated into one, which
can clearly display and compare data for each dimension by rows or columns.
Secondly, in the data dimension, this model provides more different dimensions of time representations: hour, day
and week (the x-axis is the hour, the y-axis is the week, and the z-axis is the day), so it is easy for user to represent
and compare the data of the accident occurrence from more different aspects, such as the amount of accidents on
different days of the same time period (such as 0:00 am to 2:00 am) and the number of accidents at different time
periods in same day. In the existing data visualization model, the time dimension is only 7 days, so it is impossible to
provide so many contrast dimensions and information volume.
Thirdly, in the term of the noticeable of the data, through improvement, a strongly contrasting color was used to
indicate the number of accidents on different days of a month. Red and green as a pair of contrast colors which were
used to represent data in the top view allow the user to intuitively feel which time period or day of the week has a high

pg. 12
 SU, HONG, LIU & Mehmood / Proceedings of Science and Technolgy

risk of accidents. Also, the colors with different hue are used to indicate different weeks of a month, which there is a
strong discrimination between each week.
Finally, this model also has good scalability. When the amount of data is more, it is easier to find the similarity of data
in the same time period, and it is easy to get road congestion information and dangerous information. This model has
no spatial limitations and the expression dimension limit of other existing models (like the polar model is limited by
the spatial position size and circle size).
Among the users surveyed, all felt that the improved model could provide more information. At the same time, it is
generally believed that the visual model can be clearly understood, and the legends provide enough information.
However, suggestions have been put forward for the representation of similar data. If two numbers are close, it is
difficult for them to distinguish, that is to say, there are not enough categories and grades of colors. Meanwhile, it is
suspected that if the number of data increases, it may be difficult to express it in this model.

5. Conclusion
In this study, we first selected the paper Research on the Map-matching and Spatial-temporal Visualization of
Expressway Traffic Accident Information and improve the visualization model. Regarding the traffic accident rate of
a certain area every two hours, every day and every week, the two-dimensional time-space visualization model of the
original paper was first changed to calendar model to display more data. And then the calendar model was changed to
three-dimensional model so that each attribute can be clearly compared. Finally, the 3d model was further improved,
more colors were used to represent the accident rate and the legend was also improved. In a word, compared with the
models in the paper, the final three-dimensional model has the advantages of displaying more data, easier comparison,
clearer representation information and easier understanding.

6. Reference
Aifen F, Xuan P, Lihu T. Research on the Map-matching and Spatial-temporal Visualization of Expressway Traffic
Accident Information. 3rd IEEE International Conference on ICITE; 2018. p

pg. 13