This thesis analyzes road traffic accident hot spots (black spots) in Budapest using kernel density estimation. The author identifies 62 black spots with a kernel density value higher than 14 based on 14,362 traffic accidents from 2013-2016. The most severe location is analyzed, with causes including an unsignalized intersection and insufficient sight distance. Recommendations include obtaining more recent accident data and incorporating traffic flow rates into the analysis.

1. MASTER’S THESIS
Black spot analysis with kernel density
in Budapest
Dr. Tibor Sipos (Supervisor)
Danish Menghwar (Graduate Student)
January 2019

2.  Introduction
 Understanding the Hot spots & Black spots
 Spatial & Statistical Technique to Identify
the Black spots & Result
 Investigation of severe hot spots in
Budapest
 Conclusion, Limitation & Recommendations
2
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu

3. 3
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 To identify the road traffic accident hot spots (Black spots) in Budapest (BKK
Road Network).
 To analyse the most severe places and find out the causes of the occurrences
 Examination and treatment of road accident hot-spots is broadly viewed as
one of the most effective methodology to deal with road accident
counteractive action.
 According to the WHO reports the road injuries was the eighth cause of
death worldwide in 2016.
 The number of road accidents and their effects on our overall human capital
justify the importance of analyzing the black spots & their causes. BUDAPEST, HUNGARY

4. 4
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 UK (28), Denmark (30) and
Ireland (31) are with the
fewest no road accidents per
million in EU.
 Hungary's road safety
performance is below the EU
average.
 In 2018, 64 people per million
residents died on Hungarian
roads network, showing a 1%
rise compared to the previous
year.
Reference: EU Road Safety
Statistics 2018

5. 5
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 an area that has a greater than the average number of criminal or disorder events,
OR
 an area where people have a higher than average risk of victimization.
 The places on the road network where the risk of traffic accidents is higher than the average
number.
OR
 The places where the accidents occur frequently are known as the black spots.

6. 6
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 Data has been collected form the BKK (Budapest Transport Centre)
 The data set contains the 14362 no of road traffic accidents from
the period of Jan 2013 to Dec 2016.

7. 7
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 Kernel density estimation is the non-parametric way of estimation
to get probability density function.
 Basically, it’s used for the data smoothing.
 The formula of the KDE method is given as below:
 The QGIS software uses the kernel density technique to create the
density maps (heat map).
 It calculates the density of each spatial feature (traffic accident).
 The results of this method are raster images.
𝑓𝑛 𝑥 =
1
𝑛ℎ
𝑖=1
𝑛
𝐾
𝑑𝑖
ℎ

8. 8
BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
 The quartic kernel density method is the most suitable
for visualizing the point data sets as continuous
surface.
 This method makes a smooth surface of the variations
in the density of point events over a territory.
 100 m radius (band width) has been selected because of
urban area.
 For better visualization of the heat map the number of
70000 Rows selected (higher the no of pixels better
will be the image quality).
 Contour map of the calculated kernel density of the
road accident hot spots.

9. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 9
 QGIS Vectorized heat map has been imported to
SPSS Software.
 Used the feature of “Descriptive Statistics” for
Statistical Analysis of the Kernel Density.
 Which actually gives us the dispersion of the data
set.
 Box Plot is the best way to represent the dispersion
of the data.
 𝑂𝑢𝑡𝑙𝑖𝑒𝑟𝑠 > 𝑄3 + [1.5 × 𝐼𝑄𝑅]

10. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 10
 𝑂𝑢𝑡𝑙𝑖𝑒𝑟𝑠 > 𝑄3 + 1.5 × 𝐼𝑄𝑅
 𝐹𝑖𝑟𝑠𝑡 𝑄𝑢𝑎𝑟𝑡𝑖𝑙𝑒 𝑄11 𝑜𝑟 25𝑡ℎ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 = 1
 𝑀𝑒𝑑𝑖𝑎𝑛 𝑄2 𝑜𝑟 50𝑡ℎ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 = 3.00
 𝑇ℎ𝑖𝑟𝑑 𝑄𝑢𝑎𝑟𝑡𝑖𝑙𝑒 𝑄3 𝑜𝑟 75𝑡ℎ 𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 = 6
 𝐼𝑛𝑡𝑒𝑟𝑞𝑢𝑎𝑟𝑡𝑖𝑙𝑒 𝑅𝑎𝑛𝑔𝑒 𝐼𝑄𝑅 = 𝑄3 − 𝑄1
= 6 − 1 = 5
 6 + [1.5 × 5] = 6 + 7.5 = 𝟏𝟑. 𝟓~𝟏𝟒 >
𝑂𝑢𝑡𝑙𝑖𝑒𝑟𝑠 (𝑻𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅 𝑳𝒆𝒗𝒆𝒍)
 hot spots with the kernel density value higher
than 14 are knows as Black spots.

11. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 11
 Using the “Filter” feature over the
vectorized heat map layer in QGIS
Softwares.
 From the “query generation” option
wrote the query as below to select
the hot spots.
“𝐷𝑁” > 14
 For counting all the Feature (black
spots), used the query as below:
“𝐷𝑁” = 15
 62 number of black spots!

12. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 12
Maglodi ut - Ujhegyi ut Intersection (47.47071, 19.17433)
 Kernel Density of 37.
 The third most severe place in Budapest.
There are different causes of the accidents:
 un-signalized intersection
 insufficient sight distance
 driver were unable to perceive the stopping
sign-board.
 un-following the priority rules (refusing to
give priority).

13. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 13
 The 62 number of Black spots have been identified over BKK Road Network in
Budapest.
 https://drive.google.com/open?id=1nYzFRYaYyhaOzrzSbyz4_7YU9ir7riae&usp=sharing
 Causes of the accidents
 human error (faults of the drivers) such as:
 violation of overtaking rules,
 violation of priority rules,
 wrong turning, failing to stop,
 speeding, fault of the pedestrians,
 fault of the passengers,
 fault of the vehicles,
 fault of the traffic lights,
 missing of the warning signals of danger,
 geometry of the road and weather conditions.
 The majority of the road accidents involved the fault of the drivers and the pedestrians.

14. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu 14
 Un-availability of the recent data set for analysis (i.e for the years 2017 & 2018).
 Further studies in this subject could be related with the traffic flow over the road
network.
 Suppose two places:
 a particular number of accidents occurs, with a certain traffic flow.
 same number of road accidents occur, but with different traffic flow.
 The place with same number of accidents, but higher traffic flow rate will be less
more sensitive.

15. BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS
Department of Transport Technology and Economics
kukg.bme.hu
Danish Menghwar
danish_parmar@outlook.co
m
THANK YOU!