DECCAN COLLEGE OF ENGINEERING & TECHNOLOGY
(A Unit of Deccan Group of Institutions)
A Self-Financed Muslim Minority Institution Established by Dar-us-salam Educational Trust
Approved by AICTE, New Delhi & Affiliated to Osmania University
Dar-us-salam, Aghapura, Hyderabad-500 001 (T.S.)
DEPT. OF COMPUTER SCIENCE AND ENGINEERING
IV-YEAR|VIII-SEM|CSE-A & B(2024-2025)
SUBJECT: ESSENTIALS OF ROAD SAFETY ENGINEERING(OE805CE)
Unit-II
Accident data collection and Analysis
Causes of Road Accidents
Road accident is most unwanted thing to happen to a road user, though they happen quite often. The most
unfortunate thing is that we don't learn from our mistakes on road. Most of the road users are quite well aware
of the general rules and safety measures while using roads but it is only the laxity on part of road users, which
cause accidents and crashes. Main cause of accidents and crashes are due to human errors. We are elaborating
some of the common behaviour of humans which results in accident.
1.Over Speeding
2.Drunken Driving
3.Distractions to Driver
4.Red Light Jumping
5.Avoiding Safety Gears like Seat belts and Helmets
6.Non-adherence to lane driving and overtaking in a wrong manner
Various national and international researches have found these as most common behavior of Road drivers,
which leads to accidents.
Over Speeding:
Most of the fatal accidents occur due to over speeding. It is a natural psyche of humans to excel. If given a
chance man is sure to achieve infinity in speed. But when we are sharing the road with other users we will
always remain behind some or other vehicle. Increase in speed multiplies the risk of accident and severity of
injury during accident. Faster vehicles are more prone to accident than the slower one and the severity of
accident will also be more in case of faster the severity of accident will also be more in case of faster vehicles.
Higher the speed, greater the risk. At high speed the vehicle needs greater distance to stop i.e. braking distance.
A slower vehicle comes to halt immediately while faster one takes long way to stop and also skids a long
distance due to law of notion. A vehicle moving on high speed will have greater impact during the crash and
hence will cause more injuries. The ability to judge the forthcoming events also gets reduced while driving at
faster speed which causes error in judgment and finally a crash.
Drunken Driving:
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Consumption of alcohol to celebrate any occasion is common. But when mixed with driving it turns celebration
into a misfortune. Alcohol reduces concentration. It decreases reaction time of a human body. Limbs take more
to react to the instructions of brain. It hampers vision due to dizziness. Alcohol dampens fear and incite humans
to take risks. All these factors while driving cause accidents and many a times it proves fatal. For every increase
of 0.05 blood alcohol concentration, the risk of accident doubles. Apart from alcohol many drugs, medicines
also affect the skills and concentration necessary for driving. First of all, we recommend not to consume
alcohol. But if you feel your merrymaking is not complete without booze, do not drive under the influence of
alcohol. Ask a teetotaler friend to drop you home.
Distraction to Driver:
Though distraction while driving could be minor but it can cause major accidents.
Distractions could be outside or inside the vehicle. The major distraction now a days is talking on mobile phone
while driving. Act of talking on phone occupies major portion of brain and the smaller part handles the driving
skills. This division of brain hampers reaction time and ability of judgement. This becomes one of the reasons
of crashes. One should not attend to telephone calls while driving. If the call is urgent one should pull out beside
the road and attend the call. Some of the distractions on road are:
1.Adjusting mirrors while driving
2.Stereo/Radio in vehicle
3.Animals on the road
4.Banners and billboards.
The driver should not be distracted due to these things and reduce speed to remain safe during diversions and
other kind of outside distractions.
Red Light jumping:
It is a common sight at road intersections that vehicles cross without caring for the light. The main motive
behind Red light jumping is saving time. The common conception is that stopping at red signal is wastage of
time and fuel. Studies have shown that traffic signals followed properly by all drivers saves time and commuters
reach destination safely and timely. A red light jumper not only jeopardizes his life but also the safety of other
road users. This act by one driver incites other driver to attempt it and finally causes chaos at crossing. This
chaos at intersection is the main cause of traffic jams. Eventually everybody gets late to their destinations. It has
also been seen that the red light jumper crosses the intersection with greater speed to avoid crash and challan
but it hampers his ability to judge the ongoing traffic and quite often crashes.
Avoiding Safety Gears like seat belts and helmets:
Use of seat belt in four-wheeler is now mandatory and not wearing seat belt invites penalty, same in the case of
helmets for two wheeler drivers. Wearing seat belts and helmet has been brought under law after proven studies
that these two things reduce the severity of injury during accidents. Wearing seat belts and helmets doubles the
chances of survival in a serious accident. Safety Gears keep you intact and safe in case of accidents. Two
wheeler deaths have been drastically reduced after use of helmet has been made mandatory. One should use
safety gears of prescribed standard and tie them properly for optimum safety.
Detrimental effects of traffic on environment
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1. Safety
2. Noise
3. Land Consumption
4. Air Pollution
5. Degrading the Aesthetics
How different factors of Roads contribute in Accidents:
Drivers: Over-speeding, rash driving, violation of rules, failure to understand signs, fatigue, alcohol.
Pedestrian: Carelessness, illiteracy, crossing at wrong places moving on carriageway, Jaywalkers.
Passengers: Projecting their body outside vehicle, by talking to drivers, alighting and boarding vehicle from
wrong side travelling on footboards, catching a running bus etc.
Vehicles: Failure of brakes or steering, tyre burst, insufficient headlights, overloading, projecting loads.
Road Conditions: Potholes, damaged road, eroded road merging of rural roads with highways, diversions,
illegal speed breakers.
Weather conditions: Fog, snow, heavy rainfall, wind storms, hail storms.
Preventive measures for accidents:
1. Education and awareness about road safety
2. Strict Enforcement of Law
3. Engineering:
(a) Vehicle design (b) Road infrastructure
Direct Consequences of Accidents:
1 Fatality (Death) 2. Injury 3. Property Damage
Statistical Data Analysis:
➢ Accident Rate/km (R): Accident hazard is expressed as a number of accidents of all types per
Km of each highway and street classification.
R=A/L.
Where,
A= Total no.of accidents in one year.
L= Length of control section in kms.
➢ Accident involvement rate: It is expressed as number of drivers of vehicles with certain
characteristics who are involved in accident per 100million vehicle kilometers of travel.
R= (N/V) X 100000000
Where,
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N = Total no.of drivers of vehicles involved in accidents during the period of
investigation.
V = Vehicle-kms of travel on road section during the period of investigation.
➢ Death rate based on population: Traffic hazard to life in a community is expressed as the
no. of traffic fatalities for 100000 population. This reflects the accident exposure for entire
area.
R= (B/P) X 100000.
Where,
B= Total no.of accident deaths in one year.
P= Population of the area
Methods to Identify and prioritize Hazardous Locations and elements
The three approaches to identification of hazardous road locations:
(1) Traditional reactive accident-based approach, resulting in identification of accident black
spots
(2) State-of-the-art empirical Bayes method using accident prediction model, which identifies
critical locations, i.e. both real and potential black spots
(3) Proactive “preliminary” road safety inspection, identifying the risk factors, which may
potentially increase accident occurrence and severity.
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Crash Analysis
Determine possible causes of crashes
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DIAGNOSIS OF ROAD CRASH PROBLEMS
• Six steps in the diagnosis phase
• Study detailed crash reports
• Data sorting to determine groups of accident types and the locations at which
they occur
• Data amplification by detailed on-site investigation (perhaps including conflict
studies)
• Detailed analysis of all data
• Identification of dominant factors and/or Road features
• Determine nature of the crash problem
Crash Reduction Capabilities and Countermeasures
Black Spots
• At certain sites, the level of risk of road accidents is higher than the general level of risk
in surrounding areas.
• Crashes tend to be concentrated at these relatively high-risk locations. These locations
with an abnormally high number of crashes are generally described as black spots.
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• 120 black spots are identified across our city and suburb of Hyderabad, where 26 were
located in GHMC limits.
• Areas included Yousufguda checkpost, chilkalguda crossroads, RTC cross roads, NTR
Garden Road etc. in the year 2019 for the month of July as per Telangana Today, Times
Now report.
• Rectifying works were made at these identified spots with a cost of 1.50 crore by the
municipal corporation of Hyderabad.
Accident Evaluation and Black Spot Investigation
• The accident data collection involves extensive investigation which involves the
following procedure:
Reporting: It involves basic data collection in form of two methods:
• Motorist accident report - It is filed by the involved motorist involved in all accidents
fatal or injurious.
• Police accident report - It is filed by the attendant police officer for all accidents at which
an officer is present. This generally includes fatal accidents or mostly accidents
involving serious injury required emergency or hospital treatment or which have
incurred heavy property damage.
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At Scene-Investigation: It involves obtaining information at scene such as measurement of skid
marks, examination of damage of vehicles, photograph of final position of vehicles, examination
of condition and functioning of traffic control devices and other road equipment.
Technical Preparation: This data collection step is needed for organization and interpretation
of the study made. In this step measurement of grades, sight distance, preparing drawing of after
accident situation, determination of critical and design speed for curves is done.
Professional Reconstruction: In this step effort is made to determine from whatever data is
available how the accident occurs from the available data. This involves accident reconstruction
which has been discussed under Section No.7 in details. It is professionally referred as
determining “behavioral” or “mediate” causes of accident.
Cause Analysis: It is the effort made to determine why the accident occurred from the data
available and the analysis of accident reconstruction studies
Empirical Bayes Method
• The Empirical Bayes method is used to estimate the expected long-term crash
experience, which is a weighted average of the observed crashes at the site of interest
and the predicted crashes from an SPF
• The empirical Bayes method assumptions are (1) a Poisson distribution for the accidents,
and (2) a Gamma distribution for the distribution of the averages in the population of
systems.
• With these two assumptions, the number of systems with k accidents must obey the
negative binomial distribution. The expected number of accidents, a‟k, in the after period
on a system that had k accidents in the before period is
a'k =(k+1)*N‟(k + 1)/N‟
Accident Reconstruction
The purpose of reconstruction is to identify factors that are critical in a road accident like pre-
impact direction and velocities of colliding vehicles
To answer the questions “how” and “why” an accident happened. There are numerous factors
to be considered, all of which add up to an understanding of the events that took place
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Application of computer analysis of accident data
• TEAAS-Traffic Engineering Accident Analysis System: a Software used to analyze the
accident data ( North Carolina)
• FARS (Fatality Analysis Reporting System) and GES (General Estimates System) UK
• Road Accident Data Management System (RADMS) India
➢ Data collected from accident sites covers several aspects and is subjective to the person
collecting the data.
➢ Identifying the cause of road accidents is the aim behind accident data collection and
reconstruction of the event with the main aim being reduction damages caused by traffic
accidents.
➢ Because of exponential growth in population leading to increased number of vehicles on
the road and consequently increasing accidents, the volume of data from accidents has
reached explosive proportions.
➢ In order to manage this humongous data and analyse it to make sense to policy planners,
data mining technologies are used 'WEKA' is a popular data mining program that can
handle huge sets of data efficiently
➢ The results of data mining will help organizations such as transportation, to explore
the accident data recorded by the police information
system, discover patterns to predict future behaviors and
effective decisions to be taken to reduce accidents.
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➢ Road accidents are predicted through machine learning algorithms and advanced
techniques for analyzing information, such as convolutional neural networks and long
short-term memory networks, among other deep learning architectures.
• Data sources for the road accident forecast is made.
• A classification is proposed according to its origin and characteristics, such as open data,
measurement technologies, onboard equipment and social media data.
• Road accident forecasting and Traffic accident prediction are driven by traffic
engineering, data analysis and machine learning
The main areas of interest of models obtained from computer analysis of accident data are
• detection of problematic areas for circulation
• real time detection of traffic incidents
• road accident forecasting and
• prediction of the severity of the consequences suffered by involved in a road accident
➢ Therefore, the study of road accident prediction is a field of relevant and current
scientific knowledge, open to innovation in the research of algorithms and data
analysis techniques that respond to the challenge of
generating a more secure mobility environment, which considers the pecularities
of each country or region, i.e., traffic composition, weather conditions, roads conditions,
and demography.
➢ Data about accidents can be gathered by installing equipment on vehicles, for
example satellite positional systems (GPS, GLONASS, Galileo), cameras and sensors,
in order to gather data like acceleration, unexpected braking events, sudden lane changes
and information about the driver behavior and status like drowsiness and level of stress
➢ Another emerging data source suitable for proposing models of road accident
prediction is social media
➢ Government data like police bodies, traffic police and road concessionaires can be
characterized as historical, since it contains data spanning several decades, and can be
considered as reliable, because it is supported by the custody process of the entities
responsible for the data.
➢ Open data can be defined, as the data that is produced and funded with public money,
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that is made available and accessible without restriction to the public .
➢ Road traffic information is usually one of the most available data.
➢ Measurement technologies include all kind of equipment that is part of the road
infrastructure, such as radar, cameras, or equipment embedded on the road itself.
➢ By using analytic methods, researchers seek to characterize the information and
variables of the road accident, in order to discover hidden patterns, profile behaviors,
generate rules and inferences.
These patterns are useful to
• profile drivers or drivers‟ behavior on the road
• limit unsafe areas for driving
• generate classification rules related to road accident data
• perform selection of variables to be fetched in real-time model of accidents and
• Select relevant variables to be used to train other methods, such as artificial neural
networks and deep learning algorithms.
➢ Clustering is a method of partitioning and grouping objects into groups (clusters), so that
objects grouped in each cluster share common characteristics, while looking for them
to be clearly different from other objects grouped in other clusters.
➢ Common characteristics can be interpreted as the level of correlation of objects
according to the characteristics on which clustering techniques are applied.
➢ Unlike classification methods, clustering does not require that the data be previously
marked with any particular category in order to distinguish different groups within the
data.
➢ The absence of these previous categories or classes indicates that the objective
of clustering is to find an underlying structure in the information and achieve a more
compact representation of it instead of discriminating future data into categories.
➢ The main advantages of clustering algorithms are that they do not require prior data
processing, work well with large data sets, and their results can be interpreted
graphically.
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➢ On the other hand, clustering algorithms are sensitive to the possibility of finding a local
maximum instead of a global maximum on their optimization functions.
➢ Clustering algorithms use a distance function to calculate the similarity in characteristics
when they work with continuous elements and a measure of similarity for data with
qualitative elements.
➢ Among the techniques based on similarity functions we can include K- nearest neighbor
and K-means clustering
➢ Cluster techniques whose similarity function is based on distribution probabilities, their
operation is based on the premise that each cluster has an
underlying probability of distribution from which the data elements are generated. An
example of this type of algorithm is latent class clustering (LCC)
➢ For data sets with attributes both qualitative and quantitative, clustering techniques such
as two-step clustering
➢ Batch clustering, in combination with fuzzy C-means and real time clustering is used to
study abrupt braking events in real time
➢ Batch clustering results, correlations were obtained that indicate potentially dangerous
places for driving, according to the time of day.
➢ K-means clustering and association rules model in order to determinate the variables
that influence the event of road accidents, obtaining a 6-cluster model, which was used
as an input to a rules association model.
➢ It was found by computer analysis of accident data that accident severity, type of road,
lighting present in the road and the type of surrounding area were important factors
in any accident
➢ Real-traffic data is used in order to predict the number of accidents on any road or
intersection and to identify risk factors using clustering to group roads and finding risk
patterns.
➢ The quantity of clusters was evaluated and selected using the Bayesian information
criterion (BIC)
➢ A decision tree builds classification models in the form of trees or dendrogram, each
node represents one of the input variables, and each node has several branches equal to
the number of possible values of said input variable.
➢ Decision trees are useful tools in pattern classification applications.
➢ Decision tree method of analysis is exploratory and not inferential.
➢ arners and classifiers do not require prior data processing and work well with large data
sets and rule learners and classifiers can be interpreted graphically; however, their results
are not as accurate
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➢ Road Accident Data Management System (RADMS) is a Geographic Information
System (GIS) based software that is funded by world bank used for collecting,
comparing and analyzing road accident data.Currently, it is being used by the
government of Tamil Nadu.
➢ RADMS is a comprehensive traffic-management system which helps to study and
analyse traffic accidents in a scientific manner.
The various components of RADMS are:
• Creation of GIS database
• Web based access and data flow
• Report generation and plotting results on maps
• Analysis and identification of black-spots for police and
transport departments to take-up necessary measures
• RADMS generates the following twelve types of reports for analysis and
suggestion of remedial measures
• Driver report
• Vehicle report
• Road report
• Yearly report
• Enforcement
• Collision type
• Time period report
• Alcohol usage report
• Person report
• Landmark report
• Weather report
• General report
➢ RADaR is a robust road crash database in order to reduce road accidents. RADaR is
Road Accident Data Recorder
➢ RADaR is an end-to-end solution for road accident data recording and reporting as it
helps identify the factors contributing to road accidents
➢ RADaR is designed as a n application for android tablet with connectivity to web-based
database server.
➢ It used GPS/GPRS to record exact accident location in global coordinate system and
transmits data to web-based central server
➢ It also provides a facility to take photographs of the accident scene and upload it to the
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network
➢ It features a pictorial menu-driven recording of road layout of crash site and collision
diagram plotted on layout for scientific investigation
➢ RADaR can draw data for vehicle registration and driver license information from
national databases
➢ The pilot studies for RADaR was carried out in New Delhi (India) and Addis Ababa
(Ethiopia)
➢ AI machine-learning method is used to create decision trees distinguishing the
characteristics of accidents
➢ In order to identify factors causing accidents, Data Mining (DM) techniques such as
Decision Trees (DTs) that are used as they allow certain decision rules to be extracted.
These rules could be used in future road safety campaigns thereby enabling managers to
implement priority actions.
➢ Artificial Neural Network (ANN) models are used for the analysis and prediction of
accidents. In this technique, the number of vehicles, accidents, and population are
selected and used as model parameters. The sigmoid and linear functions are used as
activation functions with the feed forward-back propagation algorithm.
➢ The ANN model has demonstrated to be better than statistical methods in use.
➢ Since the data collected from accident sites is huge, it falls under the domain of 'BIG
DATA'.
➢ Traffic on highways is monitored and lots of data is processed daily to predict probability
of accidents based on highway conditions like road surface, light on highway, turns etc.
➢ Accident prediction is based on different queries and in order to process this big data,
Hadoop has been used.
➢ Execution time is very less on Hadoop as compared to other sequential techniques.
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