2023-08-01--Identifying risky driving behavior a field study using instrumented vehicles.pdf

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Transportation Letters
The International Journal of Transportation Research
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/ytrl20
Identifying risky driving behavior: a field study
using instrumented vehicles
Anna Charly & Tom V. Mathew
To cite this article: Anna Charly & Tom V. Mathew (2023): Identifying risky driving
behavior: a field study using instrumented vehicles, Transportation Letters, DOI:
10.1080/19427867.2023.2233782
To link to this article: https://doi.org/10.1080/19427867.2023.2233782
© 2023 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 28 Jul 2023.
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Identifying risky driving behavior: a field study using instrumented vehicles
Anna Charly a
and Tom V. Mathewb
a
Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, The University of Dublin, Dublin, Ireland; b
Department of Civil
Engineering, Indian Institute of Technology Bombay, Mumbai, India
ABSTRACT
Road crashes continue to be a leading cause of death globally, with most of these crashes reportedly
occurring due to human factors. Traditional road safety assessment utilises geometric and traffic parameters
that consider road design inadequacies and identify traffic conflicts. However, previous studies do not
represent risky driving behavior and its influence on crash occurrence. Incorporating human factors into
safety evaluation is crucial to enhance the prediction and subsequent prevention of unsafe events. This
research establishes a methodology to identify risky driving behavior using driving performance measures.
These measures are computed based on continuous driving profiles collected using instrumented vehicles
from a sample set of drivers on an expressway and are compared with historical crash data. The results
indicate the significance of driving performance measures in evaluating road safety. The performance
measures find application in collision avoidance systems, assessing the road design quality, testing safety
countermeasures and guide for policymakers..
ARTICLE HISTORY
Received 4 August 2022
Accepted 24 June 2023
KEYWORDS
Driver behavior; human
factor; field driving study;
driving performance; risky
driving behavior; safety
evaluation
Introduction
Road crashes occur for several reasons; some are deterministic,
while others are stochastic. Analysis of factors leading to crashes
shows that most of these are due to human factors followed by
vehicle factors, roadway factors, and a combination of these factors
(National Research Council (US) 2010; Abd Rahman, Md Dawal,
and Yusoff 2020; Chin and Quek 1997).
Traditional safety evaluation methods rely on crash prediction
models and safety performance functions, which primarily relate
the safety of roads to geometric characteristics and annual average
daily traffic (Kononov, Bailey, and Allery 2008; Tarko et al. 2009).
However, these methods require historical crash data, which has
several limitations, including a reactive approach, small sample size,
and recording errors (Chauhan, Dhamaniya, and Arkatkar 2022;
Kuang, Qu, and Wang 2015; Vedagiri and Killi 2015).
The proactive safety assessment approach using surrogate mea
sures of safety (SMoS) addresses some of these limitations by
identifying conflicts from the traffic stream based on inter-
vehicular characteristics instead of depending on crash data (Lu
et al. 2021; Shekhar Babu and Vedagiri 2016). Existing SMoS are
based on the proximity of vehicles and are generally measured
between two conflicting vehicles (Charly and Mathew 2019a; Lu,
Grembek, and Hansen 2022; Mullakkal-Babu et al. 2017). Although
these measures consider driver-related factors such as risk percep
tion and exposure to critical conflict events, these measures do not
explicitly consider the contribution of an individual driver’s risky
driving on crash occurrence (Behbahani, Nadimi, and Naseralavi
2015; Guo et al. 2022).
Risky driving behavior is a significant contributor to road
crashes (Albert et al. 2018; Gupta, Choudhary, and Parida 2021;
Luk et al. 2017; Wang et al. 2022). Moreover, risky driving behavior
can further influence the probability of crashes caused by traffic
flow parameters (Guo et al. 2022). This suggests that incorporating
risky driving behavior can improve road safety evaluation leading
to better prediction and prevention of unsafe events (Arbabzadeh
and Jafari 2018).
Driving performance measures have been used to examine the
effect of drivers behavioral characteristics on driving quality based
on the assumption that a variation from typical driving behavior
indicates unsafe situations (Caponecchia and Williamson 2018;
Choudhary and Velaga 2019; Yadav and Velaga 2019; Zhang et al.
2016a). Risky driving behavior is often characterized by abnormal
driving performance, such as a high frequency of critical braking
events or a hard acceleration rate (Alrassy, Smyth, and Jang 2023;
Bagdadi 2013).
This research attempts to improve the detection of risky driving
behavior by using driving performance measures computed based
on the continuous driving profile obtained from field data. The
main contribution of this work is to identify risky driving by
examining the continuous driving profile obtained from an in-
vehicle data collection device. These driving performance measures
are computed over time to accurately represent risky driving beha
vior compared to instantaneous measurements typically used in
such studies (Lu, Grembek, and Hansen 2022; Moreno and García
2013; Polus and Mattar-Habib 2004). The measures are then vali
dated against actual historical crash data.
Literature review
The traditional safety evaluation method mainly focuses on Safety
Performance Functions (SPFs) and accident prediction models that
relate the number of crashes to traffic exposure and risk factors
(Kononov, Bailey, and Allery 2008). The exposure variables con
sidered in most SPFs include road segment length, Annual Average
Daily Traffic (AADT), vehicle miles traveled, weather conditions,
and road geometry (Ahmed et al. 2011). This method provides an
overall understanding of the influence of road geometry and
CONTACT Anna Charly charlya@tcd.ie Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, the University of Dublin, Dublin,
Ireland
TRANSPORTATION LETTERS
https://doi.org/10.1080/19427867.2023.2233782
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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weather on crash frequencies and helps rank study sites based on
crash risk. However, this approach is highly aggregated and
depends on the quality and availability of crash data
(Venkataraman, Ulfarsson, and Shankar 2014). Also, crash data
are not always sufficient due to limited sample size, lack of details
of crash cause and reactive approach (Cafiso et al. 2010; Tarko et al.
2009). In this context, identifying appropriate driving performance
measures can help detect risky driving in real time and help prevent
crashes.
A review of existing driving performance measures is classi
fied into three subsections based on the various aspects of
driving performance measures. First, the existing driving per
formance measures are explored. Second, the data collection
methods used to compute these driving performance measures
are discussed. Further, the applications of the existing driving
performance measures are described. Finally, inferences from
this review and the objectives of the current study are
presented.
Existing driving performance measures
Existing studies use parameters including acceleration (Cafiso and
Cava 2009; Papadakaki et al. 2016), reaction time (Chang et al. 2009;
Zhang et al. 2020) and standard deviation of lane position (Bartrim
et al. 2020; Choudhary and Velaga 2017a) as driving performance
measures. However, most of these driving performance measures
are based on instantaneous measurements, calculated for a specific
location rather than along a road section (Polus and Mattar-Habib
2004). Studies have also used speed derivatives such as the standard
deviation of speed (Bartrim et al. 2020; Knapper, Hagenzieker, and
Brookhuis 2015), and the coefficient of variation in speed to repre
sent driver's speed volatility while making instantaneous driving
decisions and its impact on the likelihood of crashes (Choudhary
et al. 2018; Pawar and Velaga 2021). Further, considering the con
tinuous driving profile over a road section is expected to represent
safety more accurately.
Two accumulated driving performance measures, namely, speed
uniformity and speeding, consider the speed profile over a road
section to evaluate its safety (Moreno and García 2013). Speed
uniformity is used as a surrogate to design consistency and is
defined as the normalized relative area per unit length bounded
between speed profile and average speed line (Polus and Mattar-
Habib 2004). Accumulated speeding is the normalized relative area
bounded between the speed profile values higher than the speed
limit and the speed limit. These driving performance measures
assess driving quality and correlate it with design consistency. The
relationship between driver instability and crash intensity has also
been investigated in literature (Arvin, Kamrani, and Khattak 2019).
Driver instability is measured using parameters representing speed
and deceleration volatilities, which captures the variation in instan
taneous driving behavior. Driving volatility of speed and accelera
tion is expressed using standard deviation, time-varying stochastic
volatility, coefficient of variation, and quartile coefficient of varia
tion. The study concludes that volatility is one of the leading factors
increasing the probability of a severe crash. Speed control is often
considered necessary to prevent vehicle collisions, and the failure to
do so has been correlated with driver distraction (Abd Rahman, Md
Dawal, and Yusoff 2020).
Acceleration and its variants have been widely used to evaluate
driving quality. Longitudinal acceleration, lateral acceleration
(Cafiso and Cava 2009; Charly and Mathew 2019b), and jerk
(Bagdadi and Várhelyi 2011; Pande et al. 2017) are used to identify
critical situations which are further correlated to the safety of the
road segment. Longitudinal jerk is defined as the change in
acceleration between successive observations (Bagdadi and
Várhelyi 2011; Pande et al. 2017). Pande et al. (2017) identified
the instances where the drivers experienced jerks while conducting
a field driving experiment and computed the percentage of jerk
events. The study further compared jerk percentages with historical
crash data based on the hypothesis that road segments, where
drivers have to brake more often, might be ‘unsafe with more
crashes in the long-term’ (Pande et al. 2017).
These studies show that analyzing the driving profile over time
and space could better represent the variation in human driving
behavior and its relationship to road safety compared to instanta
neous measurements.
Data collection methods to compute driving performance
measures
The data required for driver behavior studies are typically collected
using driving simulators, instrumented test vehicles, and naturalis
tic driving studies. Table 1 summarizes the details of data collection
adopted in some studies to compute driving performance measures.
Driving simulators are highly preferred in behavioral studies as it
provides a safe environment for a wide range of experimental
studies (Bartrim et al. 2020; Casutt et al. 2014; Pawar et al. 2020).
The sample size of driving participants in simulator studies varies
from 14 to 140, as seen in Table 1 (Matthews et al. 2012; Pavlou
et al. 2016). These studies explore the effect of mobile phone use,
drowsiness, fatigue, aggressiveness, sleep patterns, and alcohol
impairment on driving behavior (Choudhary and Velaga 2017a;
Li et al. 2016; Matthews et al. 2012). In-vehicle collision warning
systems and communication devices are also evaluated using driv
ing performance measures (Knapper, Hagenzieker, and Brookhuis
2015).
Field data collection with instrumented test vehicles has been
used to evaluate advanced driver assistance systems, automated
driving systems, geometric consistency of roads, and identify near-
crash events (Af Wahlberg 2000; Wu and Jovanis 2012). The sample
size for field data collection varies from 15 to 47 drivers (Abd
Rahman, Md Dawal, and Yusoff 2020; Af Wahlberg 2000; Cafiso
and Cava 2009; Pande et al. 2017). Of the studies conducted in the
field, only a few have compared the results with historical crash data
(Cafiso and Cava 2009; Pande et al. 2017).
Naturalistic driving studies (NDS), which collect rich kinematic
data including GPS, and video for a large sample size over
a considerably long period, have been used to gain a deeper under
standing of driving behavior (Campbell 2012; Paredes et al. 2022;
Ziakopoulos, Kontaxi, and Yannis 2023). Such data provide detailed
information on driving behavior and real crash and near-crash
occurrences and assists in understanding the relationship between
driver, roadway, and environmental characteristics (Hallmark et al.
2015). The relation between driver behavior and pre-crash kine
matics for actual crashes has been examined using naturalistic
driving data (Arbabzadeh and Jafari 2018; Arvin, Kamrani, and
Khattak 2019).
To summarize, previous studies show that driving performance
measures computed based on field data can lead to a deeper under
standing of driving behavior even with a limited sample. However,
out of the existing studies conducted in the field, only a few have
compared the results with actual crash data.
Applications of Driving Performance Measures in Traffic Safety
Driving performance measures have been used in several
domains of traffic safety research. These measures have been
used widely to study driving behavior, such as the effect of mobile
2 A. CHARLY AND T. V. MATHEW

phone use (Choudhari and Maji 2019; Choudhary and Velaga
2017a; Papadakaki et al. 2016; Reimer et al. 2011), drowsiness
(Caponecchia and Williamson 2018), aggressiveness (Precht,
Keinath, and Krems 2017; Zhang et al. 2016b), sleep pattern
(Bartrim et al. 2020), alcohol intake (Zhang et al. 2014), caffeine
intake (Bartrim et al. 2020), and aging (Abd Rahman, Md Dawal,
and Yusoff 2020). This shows the reliability of these measures in
representing driving behavior precisely. In most cases, these per
formance measures assist in identifying situations with variability
in driving or sudden unexpected driving characteristics. These
studies show that a variation from typical driving behavior indi
cates risky situations.
Driving performance measures are used to evaluate in-vehicle
systems such as collision warning systems (Chang et al. 2009),
advanced driver assistance systems (Knapper, Hagenzieker, and
Brookhuis 2015; Lyu et al. 2019; Merat, Lai, and Jamson 2011),
communication and entertainment devices (Crisler et al. 2008), and
automated driving systems (Calvi et al. 2020; Dogan et al. 2019;
Voß, Keck, and Schwalm 2018). Few studies have attempted to
develop real-time traffic safety risk measures to support the
Advanced Driver Assistance System, drivers safety risk profiling,
and roadway segments safety risk scoring (Arbabzadeh and Jafari
2018). Existing studies have shown that driver characteristics are
associated with crash events. However, the relationship between
driver behavioral characteristics and crash risk has not been
explored because of its complex nature and unavailability of data.
Field driving data allow such studies to gain insights into the
relationship between driver behavior and crashes (Wu, Aguero-
Valverde, and Jovanis 2014).
In short, driving performance measures have been used to study
driver characteristics and their effect on driving, thus proving the
reliability of these measures. However, the relationship between
driving performance and crash risk has not been thoroughly inves
tigated because of limited data.
Inferences
Summarizing the inferences from the above literature review, one
can say that investigating driving performance is crucial to under
standing risky driving behavior and reducing crashes caused by
human factors. The best way to explore driving performance is by
using field data and analyzing the same over time and space to gain
a deeper understanding of driving behavior. Comparing the field
findings from driving studies with historical crash data spatially and
temporally could provide further insight into the effect of road
geometry and time of the day on crash occurrence.
The present study aims to establish a methodology for identifying
risky driving behavior using driving performance measures com
puted based on continuous driving profiles from field driving data.
The study considers three driving performance measures: speed
variability, speeding, and percentage of jerk events. These three mea
sures are expected to represent the significant human factors influen
cing crashes. These driving performance measures are computed
from field driving data collected along an expressway. The results
are then compared with historical crash data from the study section.
Methodology
Driving performance measures are estimated based on variation in
the local driving profile over a certain period, and hence, it is expected
to represent risky driving behavior more precisely. The proposed
methodology aims to identify risky driving behavior using perfor
mance measures computed from field driving data by comparing
them with historical crash data. This approach is based on the
Table 1. Details of data collection adopted to compute driving performance in few studies.
Study Type of study Participants Correlated with crash data Study focus
Bartrim et al. (2020) Simulator study 20 No Effect of restricted sleep and caffeine on alertness, reaction & driving
performance
Casutt et al. (2014) Simulator &
field
49 No Driving performance of older drivers
Choudhary and Velaga (2017a) Simulator study 100 No Effect of using mobile phone
Knapper, Hagenzieker, and
Brookhuis (2015)
Simulator study 20 No Effect of in-car devices on driving behaviour
Li et al. (2016) Simulator study 52 No Effect of alcohol impairment
Matthews et al. (2012) Simulator study 14 No Effect of prior wake and time under sleep restriction
Pavlou et al. (2016) Simulator study 140 No Effect of brain pathology
Pawar et al. (2020) Simulator study 85 No Braking behaviour and accident probability under increasing time
pressure
Abd Rahman, Md Dawal, and Yusoff
(2020)
Field study 20 No Driving performance of ageing drivers
Af Wahlberg (2000) Field study 47 5-year historical crash data Assessing the relation between historical crash and driving
behaviour in bus drivers
Cafiso and Cava (2009) Field study 15 5-year historical crash data Relating road alignment consistency with safety
Lyu et al. (2019) Field study 32 No Effect of advanced driver assistance system
Arbabzadeh and Jafari (2018) Naturalistic
driving
1250* Actual crash from
naturalistic data
Predict traffic safety risk
Arvin, Kamrani, and Khattak (2019) Naturalistic
driving
617* Actual crash from
naturalistic data
Influence of pre-crash driving volatility on crash intensity
Hallmark et al. (2015) Naturalistic
driving
202 No Risk of road departure on rural curves
Moreno and García (2013) Naturalistic
driving
1500 No Effect of traffic calming devices
Pande et al. (2017) Naturalistic
driving
33 10-year historical crash
data
Assessing the relation between historical crash and naturalistic
driving
Precht, Keinath, and Krems (2017) Naturalistic
driving
202 No Impact of anger
Wu and Jovanis (2012) Naturalistic
driving
241 No Estimating crashes to crash-surrogates ratio
Note: * Number of actual crash occurrences considered.
TRANSPORTATION LETTERS 3

hypothesis that road stretches where drivers exhibit risky driving
behavior could lead to several crashes. Three performance measures,
namely, speed variability, speeding, and percentage of jerk events, are
computed for every driver along each road segment, using vehicle
trajectory obtained from field driving data. These are then compared
with historical crash data to identify the measures representing the
safety of road sections. The major steps of the methodology are
shown in Figure 1, and each step is explained in detail below.
A study section is selected, and a data collection procedure is
followed to record vehicle trajectory data from the field. The per
formance measures are estimated from field driving data. The
results are compared with historical crash data. A crash frequency
model is developed with historical crash data and performance
measures averaged over each segment. The most significant perfor
mance measures are identified from this analysis. Driving perfor
mance measures considered in the present study include speed
variability, speeding, and percentage of jerk events. Each of these
measures is described in detail below.
Speed variability
Speed variability is the normalized relative area per unit length
bounded between speed profile and average speed line, as illustrated
in Figure 2 (Moreno and García 2013). Speed variability Vi
k (m=s) of
driver i along the road segment k can be computed as:
Vi
k ¼
1
l
ðl
0
ðjvðxÞj vaÞdx (1)
where ò
l
0ðjvðxÞj vaÞdx is the total area (m2
=s) (shaded portion in
Figure 2) bounded between speed profile ðvðxÞÞ and average speed
ðvaÞ of driver i along segment k, and l is the length of the road (m).
Speed variability indicates an individual driver’s speed variation
over a road stretch. Lower speed variability means comfortable
driving and good segment quality. Higher speed variability values
indicate high fluctuations in speed, which corresponds to unsafe
driving and indicates poor design consistency. Hence, the speed
variability can represent risky driving behavior.
Speeding
Speeding Si
k is defined as the normalized relative area bounded
between the speed profile values higher than the speed limit and
the speed limit (Moreno and García 2013) and is illustrated in
Figure 3. Speeding, denoted as Si
k (m=s), of driver i along the road
segment k, can be computed as:
Si
k ¼
1
l
ðl
0
ðvðxÞ v0Þdx "vðxÞ > v0 (2)
where ò
l
0ðvðxÞ v0Þdx "vðxÞ > v0 (m2
=s) is the area (the shaded
portion in Figure 3) bounded between speed profile ðvðxÞÞ and
speed limit ðv0Þ where speed is higher than the speed limit and l
is the length of the road (m).
Speeding measures the variation of speed value above the speed
limit. Lower speeding values indicate safe driving, while higher
speeding indicates risky driving.
Percentage of jerk events
We refer to longitudinal jerk here, which could be braking or
sudden acceleration. However, the present study considers only
hard-braking events. Road segments where drivers have to often
hard-brake may result in more crashes and, hence, can be
considered unsafe. Accordingly, the longitudinal negative jerk
is defined as the rate of deceleration change computed from
successive trajectory observations (Bagdadi and Várhelyi 2011;
Pande et al. 2017). A higher negative jerk value indicates
a sudden application of brakes, which could be an evasive
maneuver because of poor geometric consistency or possible
conflict. This jerk value ðjÞ is further compared with the thresh
old value ðjtÞ to estimate negative jerk events. The percentage of
observations in the driving data of driver i along segment k
with j value higher than the threshold value is the percentage of
jerk events for driver i along segment k, represented as Ji
k.
Percentage of jerk events Ji
k for driver i along segment k can
be computed as:
Ji
k ¼
NðjÞi
k
Ni
k
� 100 "j < jt (3)
j ¼
da
dt
(4)
where NðjÞ is the number of jerk events, N is the total number of
observations, da is the change in acceleration between successive
observations, and dt is the change in the time between successive
observations. A threshold value indicates whether the change in
acceleration between successive observations is severe enough to
be qualified as a jerk event. The threshold value is estimated
generally through sensitivity analysis by comparing it with crash
data. Literature suggests that jerk values below −0.6 m=s3
indicate
jerk events (Pande et al. 2017). It may be noted that the present
study considers only braking behavior for the identification of
jerk events.
Data collection and extraction
Field driving data is used for the study as it provides accurate and
detailed information regarding driver behavior leading to a precise
estimation of driving performance measures. The details of the
selected study section, participants, and experimental procedure
followed for data collection are described below.
Figure 1. Flowchart of the methodology to identifying risky driving behaviour using
driving performance measures.

Study section
A 94-km-long Mumbai-Pune Expressway connecting two cities in
India (Mumbai and Pune) was chosen as the study section because
of its diverse geometric elements (with curves, up and down-
gradients, and plane sections) and long undisturbed length of 188
km in both directions. The study section comprises two toll plazas,
six tunnels, and a few exits. The layout of the study section with the
position of tunnels and tolls is shown in Figure 4.
The traffic along the section is composed of passenger cars
(83%), trucks (14%), and buses (3%), which exhibit considerable
lateral swerving behavior and frequent lane changes (Charly and
Mathew 2019b). The speed limit along the study section is 80 kmph,
except at a few locations where it is 30 kmph due to sharp curves
with a steep gradient.
Crash data on study section
Historic crash data from the study section were available for three
and a half years and consisted of detailed information on the time
and location of the crash, the type of vehicles involved, and a brief
note on the cause of the crash (JP Research 2014). This data was
obtained from JP Research Pvt Ltd, a research organization that
collects detailed crash data along Mumbai–Pune Expressway. The
crash data was segregated into daytime (06:00 h to 22:00 h) and
nighttime (22:00 h to 06:00 h) to see if there was a considerable
difference in the factors that caused the crash based on the time of
the day. The basis for this segregation is from a study conducted by
Bella, Calvi, and D’Amico (2014). Based on the crash information,
the percentage of crashes and cause of the crash were plotted, as
shown in Figure 5.
The cause of the crash was categorized into human factors,
vehicle factors, roadway or environmental factors, and
a combination of these. It was observed that most of the crashes
on the expressway occurred due to human factors followed by
human & roadway factors. Vehicle factors and human, vehicle &
roadway factors were the next leading cause of crashes. It was also
observed that the influence of human factors on crash occurrence is
greater during nighttime. During the daytime, the major influen
cing factor seemed to be human and roadway factors.
A detailed investigation into crash causes revealed that the major
human factors responsible for the crash were driver falling asleep,
overspeeding, lane encroachment, errors in judgment while overtak
ing and abrupt braking or slowing down on the expressway. Tyre-
burst and brake-failure were the primary vehicle-related factors that
led to crashes. It was also observed that vehicles tend to lose control
while navigating curves compared to straight road sections. Existing
literature shows that the effect of these crash influential factors on
driving quality can be successfully measured by driving perfor
mance measures.
Further, the hourly traffic volume along the study section was
examined (Figure 6). This data was obtained from the Rail India
Technical and Economic Service (RITES) Limited. It can be seen
that the traffic volume along the study section was less during
nighttime than during daytime. However, several crashes occurred
Figure 2. Illustration of the speed variability (shaded area corresponds to ò
l
0ðjvðxÞj vaÞdx).
Figure 3. Illustration of the speeding (shaded area corresponds to ò
l
0ðvðxÞ v0Þdx "vðxÞ > v0).

during the night. This shows that factors including driving perfor
mance and time of day influence crash occurrence in addition to the
commonly considered exposure variables, such as traffic volume.
Segmentation of study section
Further, it was necessary to divide the study section into segments for
spatial analysis. A segment length of 1 km was chosen to match the
existing segmentation, thus leading to 188 segments. Historic crash
data were available corresponding to these segments (JP Research
2014). The road segments were then classified based on their gradient
and the radius of turn (Ahmed et al. 2011; Charly and Mathew 2019b;
IRC 2013; Jacob and Anjaneyulu 2013; Misaghi and Hassan 2005).
A reasonable assumption has been made regarding the homogeneity of
segments in each category. If the radius of turn is less than 400 m, the
segment is considered to be Curved. Otherwise, it is considered to be
Gentle-Curve. The gradient is divided into three categories, namely
Downgrade (if the gradient is less than −2%), Flat (if the gradient is
greater than −2% and less than +2%), and Upgrade (if the gradient is
greater than +2%). Thus, six road categories have been defined con
sidering the radius of turn and gradient. These are as follows: Curved-
Downgrade (CD), Curved-Flat (CF), Curved-Upgrade (CU), Gentle-
Curve-Downgrade (GCD), Gentle-Curve-Flat (GCF), and Gentle-
Curve-Upgrade (GCU) (Charly and Mathew 2019b).
Participants and experimental driving environment
The details of participants involved in data collection and the experi
mental driving environment are described in this section. Participants
were randomly selected while ensuring a mix of professional and
nonprofessional drivers. Professional drivers considered in the study
belonged to two major car-rental companies near the study location
and were sent randomly from the company. Nonprofessional drivers
were research scholars and staff from the university.
Figure 4. The 94 km long study section from Mumbai (A) to Pune (B) (Charly and Mathew 2019b).
Figure 5. Crash influencing factors during daytime and nighttime.

Twenty-two male drivers aged 25–57 years (Mean = 39.5,
Standard deviation = 8.6) participated in the study. Drivers
are divided into three categories based on their age: young
(age < 30), mid-age (30 � age < 50) and seniors (age � 50)
(Choudhary and Velaga 2017b). The majority of the partici
pants were mid-age drivers (73%), followed by an equal share
of young (14%) and senior drivers (14%). Of the participants,
68% were professional drivers, and 32% were nonprofessional
drivers. The study was conducted using passenger cars. About
68% of data was collected on Sedans, 14% on hatchbacks, and
18% on SUVs. Data were collected during daytime and night
time to understand the effect of time of day on driving per
formance. Of the total data, 55% was collected during daytime
and 45% during nighttime. The experimental setup and data
extraction are described in the following section.
Experimental setup and data collection
Field driving data, collected using instrumented vehicles, is
utilized for identifying risky driving behavior. Video VBox,
which records kinematic data at an accuracy of 20 Hz, was
used for collecting field driving data (Racelogic Ltd., 2018).
The device was set up in the vehicle, and the drivers were
asked to drive usually. The device was installed 38 km before
the start of the study section to alleviate any bias that may
have happened due to the instruments presence. Figures 7(a,b)
show the movement of surrounding vehicles during data
collection.
Data extraction and preparation
Details of data collected are shown in Figure 8.
Clockwise from the top-left, Figure 8 shows the view of
cameras installed, speed profile, list of data collected, location
of the subject vehicle on the map, and detailed data collected at
20 Hz frequency. Driving data was collected on 22 drivers for
188 km leading to 4136 vehicle-km of data. The driving data
was further extracted, and a few attributes were added to the
driving data, including a driver identification number, time of
data collection and corresponding segment information. Driving
data closer to the tolls and tunnels was omitted from the
analysis. The data extracted from the field driving profile is
further processed according to the methodology discussed in
the previous section to derive the driving performance mea
sures. This was done with the help of an algorithm coded in C+
+ programming language. The driving data collected from the
field, information on the drivers’ personal characteristics and
road segmentation were used as input files to the algorithm.
The processed driving performance measures were then aggre
gated for each segment or each driver as required for further
analysis.
Figure 6. Hourly traffic volume along the study section.
Figure 7. View of study section from the (a) rear and (b) front of the subject vehicle during data collection.

Analysis and results
The computed driving performance measures are compared with
the driver characteristics, historical crash data, road geometry, and
time of day to understand the influence of each of these factors on
driving performance and, subsequently, road safety. A crash fre
quency model is developed to identify the driving performance
measures which best represent risky driving behavior.
Comparison of driving performance measures with driver
characteristics
To understand the influence of driver characteristics on driving
performance, the estimated driving performance measures, aver
aged for each driver on each segment, are compared with the
drivers’ age and profession (Figures 9 and 10).
It can be observed that high speed variability is exhibited among
young drivers, followed by mid-age and senior drivers (Figure 9a).
However, mid-age drivers showed high speeding as compared to
other driver categories (Figure 9b). The percentage of jerk events is
also observed to be higher for drivers in the mid-age group
(Figure 9c). Thus, it can be seen that, though the young drivers
show high variation in speed, they do not overstep the speed limit
and do not experience jerk events as frequently as the mid-aged
drivers. This could be because the younger drivers changed their
speed gradually and hence do not experience as many jerk events.
Further, the driving performance measures for both professional
and nonprofessional drivers are plotted (Figure 10). It is observed
that nonprofessional drivers exhibit high speed variability and high
percentage of jerk events compared to professional drivers
Figures 10(a,c). However, speeding values are found to be higher
for professional drivers (Figure 10b). Hence, one may infer that
professional drivers are in better control of their vehicles than
nonprofessional drivers as they experience less variation in speed
and less frequent jerk events.
It may be noted that a large share of mid-age and senior drivers
were also professional drivers who are experienced, confident, and
often drive long distances. Hence, they could be comfortable navi
gating the vehicles at higher speeds with lower speed variability.
However, this does not indicate they are safer drivers. Their famil
iarity with the study section could be another reason for this
difference in behavior. This also conforms with previous studies,
which revealed that mid-experienced drivers were likelier to take
risks than the less experienced drivers, and professional drivers had
a higher probability of rule violation (Choudhari and Maji 2019;
Wu, Yan, and Radwan 2016).
These results indicate that the driver characteristics influence
the driving performance measures. Hence, these factors could be
incorporated into collision avoidance systems for improved safety.
Comparison of driving performance measures with crash data
A comparison between each driving performance measure and
historical crashes is made to understand the relation between
these measures and crashes. First, speed variability and historical
crashes are compared with each other for every segment
(Figure 11a).
It can be seen that speed variability between around 0.5 m/s
and 1.5 m/s corresponds to more crashes than higher values of
speed variability. Speeding for every segment is compared with
crash data in Figure 11b. Segments with values of speeding
Figure 8. Details of the data collected using Video VBox.

between 2 m/s and 4 m/s correspond to high crashes. The per
centage of jerk events in every segment is compared with crash
data as shown in Figure 11c. The percentage of jerk events is
increasing with the increasing number of crashes. These results
show that all three driving performance measures can poten
tially represent unsafe driving behavior.
Comparison of driving performance measures with geometry
Driving performance measures were further aggregated for
each road category to assess the impact of road geometry on
driving performance measures. Six road categories have been
defined; namely, Curved-Downgrade (CD), Curved-Flat (CF),
Curved-Upgrade (CU), Gentle-Curve-Downgrade (GCD),
Gentle-Curve-Flat (GCF), and Gentle-Curve-Upgrade (GCU) as
discussed previously. The driving performance measures,
aggregated for each road category, and the corresponding
number of crashes per segment are shown in Table 2. It can
be seen that a high value of crashes per segment also corre
sponds to a high percentage of jerk events and high speeding. It
is also observed that road categories, including Curved-
Upgrade (CU), Curved-Flat (CF), and Curved-Downgrade
(CD), correspond to a high percentage of jerk events, high
speeding, and high speed variability. All these road categories
have high curvature.
Higher values of speed variability occur in curved road segments
with maximum on Curved-Upgrade (CU) road category. Higher
speeding values are observed in curved road segments with the
highest value on Curved-Flat (CF) road category. A higher percen
tage of jerk events also occurs in curved road segments with
a maximum along Curved Upgrade (CU) road category. Higher
values of these performance measures correspond to unsafe road
segments, and hence, one may deduce that the driving behavior
followed in these curved sections is highly unsafe. These results
indicate that geometric characteristics influence the driving perfor
mance measures. Hence, these factors must be incorporated into
collision avoidance systems to identify risky driving. Further, the
next section compares the driving performance measures with the
time of day.
Comparison of driving performance measures during daytime
and nighttime
The driving performance measures for each road category are
further segregated based on the time of data collection to study
the influence of time of the day on driving performance, as shown
graphically in Figure 12. It can be seen that the percentage of jerk
events is generally higher during nighttime for all road categories.
However, for all road categories, speeding is more heightened dur
ing the daytime.
Figure 9. Comparison of driving performance measures for drivers of different age groups (error bars denote standard error).

Speed variability during nighttime is higher than during daytime
for road categories with a flat gradient or up-gradient, as seen in
Figure 12a. In road categories with down-gradient, speed variability
during the daytime is marginally higher than at night. Furthermore,
it is observed that speeding values are much higher during the day
than at nighttime (Figure 12b). This implies that drivers travel at
higher speeds during the daytime, especially along road categories
with a high degree of curvature. There is not much difference
between speeding values during daytime and nighttime on roads
with gentle curves. The percentage of jerk events is consistently high
in all road categories during the night (Figure 12c). This could be
because nighttime driving requires more cautious effort on the
drivers part, causing them to apply brakes more often out of the
usual driving style.
To further understand the influence of driving performance
measures on crash occurrence, a statistical model needs to be
developed to estimate crash frequencies based on these measures.
This has been discussed in the following section.
Crash frequency model
The three driving performance measures, namely, speed variabil
ity, speeding, and percentage of jerk events, were considered inde
pendent variables in the study, along with geometric
characteristics of the road. The time of the day is a categorical
independent variable. The dependent variable in the study is the
number of crashes. Since crashes occur for a combination of
reasons, it is essential to scrutinize roadway, environmental,
and human factors. There are 376 data points, each correspond
ing to a road segment during daytime or nighttime. The descrip
tive statistics of the independent variables, such as minimum
value, maximum value, mean, and standard deviation, are
shown in Table 3. It can be observed that speed variability values
have peaked at 3.57 m=s at a particular instance, which indicates
a highly unsafe situation. Similarly, a speeding of 11.61 m=s
means a highly critical event. The variables were further checked
for collinearity, and only non-collinear variables were included in
the study.
A negative binomial model is proposed between crashes and the
identified performance measures as it is used for modeling count
data, and it takes care of over-dispersion (Pande et al. 2017). Those
variables, which were significant at a 90% confidence level, were
retained in the model. The parameter estimates of the final model
are shown in Table 4. The Akaike Information Criterion (AIC)
value, which measures the model fit, has been reported. Also, the
dispersion parameter, which indicates the suitability of the chosen
model, has been reported. Of the several independent variables,
only speeding, percentage of jerk events, mean gradient, and the
categorical variable time of the day are significant in estimating
crashes. The results show that the higher the speeding, the more
crashes. Results indicate that road stretches where drivers tend to
speed beyond the speed limit lead to increased crashes.
Figure 10. Comparison of driving performance measures for professional and nonprofessional drivers (error bars denote standard error).

Crashes also seem to increase with the percentage of jerk events.
The percentage of jerk events indicates all instances when the vehicle
had to apply sudden brakes forcefully. Using sudden brakes shows
an evasive maneuver either because the driver encountered
a conflict or because the road alignment is unsafe and requires the
driver to apply sudden braking. The model results also show that
a decrease in gradient corresponds to increased crashes. Thus, one
may infer that road segments with down-gradient are more prone
to crashes than other road segments. The results from the study
indicate that the driving performance measures, namely, speeding
and percentage of jerk events, significantly influence the occurrence
of a crash. Hence, these measures could represent variation in
driving behavior which could further lead to the successful predic
tion of crashes caused due to human factors.
Discussion
The results show that younger and nonprofessional drivers exhibit
high-speed variability compared to other driver categories.
However, professional drivers were found to travel at speeds higher
than the speed limits. These results indicate that the difference in
driver characteristics, including age and experience, influences
driving performance. One may infer that experienced drivers are
in better control of their vehicles, even at higher speeds, since their
speed variability is less. This finding aligns with past research
suggesting that professional drivers with high driving experience
had lower crash rates (Choudhari and Maji 2019; Wu, Yan, and
Radwan 2016). However, subsequent crash frequency modeling
results indicate that speed variability was not statistically significant
in estimating crashes.
Speeding was consistently higher during the daytime than at
nighttime, whereas the percentage of jerk events was consistently
higher during the night. This reiterates the difference in driving
behavior depending on the time of the day. Results from the crash
frequency model showed that both speeding and the percentage of
jerk events significantly represented safety. This finding is consis
tent with previous research showing that speeding and hard brak
ing, or sharp decelerations, are critical in representing safety
(Alrassy, Smyth, and Jang 2023; Guo et al. 2022).
Previous studies have mainly used phone interviews and ques
tionnaire methods to understand risky driving behavior and its
relationship to historic crash or near-crash data (Love et al. 2022,
2022; Yu, Qu, and Ge 2022; Lazuras et al. 2022). However, identify
ing risky driving behavior using driving performance measures
provides an opportunity to utilize this knowledge to alert drivers
and avoid crashes. Existing studies using driving performance
Figure 11. Scatterplots of driving performance measures and crashes for each road segment.
Table 2. Driving performance measures and crashes per segment for different road categories.
Road Category Speed variability (m/s) Speeding (m/s) % of jerk events Crashes per segment
Curved-Downgrade (CD) 1.346 3.883 8.718 3
Curved-Flat (CF) 1.643 7.408 9.111 27
Curved-Upgrade (CU) 2.197 2.660 9.208 1
Gentle-Curve-Downgrade (GCD) 1.142 1.913 8.374 9
Gentle-Curve-Flat (GCF) .878 2.332 8.325 3
Gentle-Curve-Upgrade (GCU) 1.222 2.112 8.665 2

measures have mostly been done in simulated driving conditions.
Only a few studies have used actual field-collected data, especially in
developing countries such as India.
This work uses some of the existing driving performance mea
sures and computes them over an accumulated period from an in-
vehicle data collection device to more accurately evaluate the varia
tion in driving behavior. The significant contribution of this study
is to identify risky driving by examining the continuous driving
profile obtained from an in-vehicle data collection device. These
driving performance measures are computed over an accumulated
time to accurately represent risky driving behavior compared to
instantaneous measurements typically used in such studies. These
measures are then validated against actual historical crash data,
which is not done in many studies.
Figure 12. Average driving performance measures for different road categories during the day and night. Note: (CD: Curved-Downgrade, CF: Curved-Flat, CU: Curved-
Upgrade, GCD: Gentle-Curve-Downgrade, GCF: Gentle-Curve-Flat, GCU: Gentle-Curve-Upgrade)
Table 3. Variables considered in crash frequency modeling.
Variable Type Levels Min. Max. Mean SD
Crashes (number) Cont. 0 19 1.50 2.07
Speed Variability (m=s) Cont. 0 3.57 1.01 0.54
Speeding (m=s) Cont. 0 11.61 2.37 1.73
Percentage of jerk events (%) Cont. 0 13.17 8.44 0.86
Mean Gradient (%) Cont. −4.98 5.07 1.5 2.07
Mean Radius of Turn (m) Cont. 73 952 737.03 179.40
Time Cat. Day
Night
Note: Cont.: Continuous, Cat.: Categorical, SD: Standard deviation.
Table 4. Crash frequency model results.
Parameter Estimate Std. Error Wald Chi-Square p-value
Intercept −1.541 0.685 5.065 .024
Time=Day 0.356 0.123 8.365 .004
Time=Night - - - -
Speeding 0.070 0.033 4.529 .033
Percentage of jerk events 0.178 0.079 5.17 .023
Mean Gradient −0.192 0.033 33.722 .001
Dispersion 0.626 0.107
Akaikes Information Criterion 1221.42

The driving performance measures discussed in this paper find
application in collision avoidance systems where the driving data is
collected continuously in real-time to assess a driver’s performance
and prevent crashes in real-time (Cai et al. 2020; Guo et al. 2022, 23;
Toledo and Lotan 2006). Such factors also find application in
identifying road segments highly prone to unsafe driving behavior,
testing the effectiveness of implemented safety countermeasures
used before and after studies, and guiding policymakers and enfor
cement personnel.
Conclusions
This study presents a methodology for identifying risky driving beha
vior using performance measures. This approach aims to incorporate
human factors into road safety evaluation. The proposed method is
based on the hypothesis that risky driving behavior can be estimated by
analyzing the variation in driving profile. Field driving data is used in
the study to calculate the performance measures precisely. Driving
performance measures, including speed variability, speeding, and per
centage of jerk events, are computed using driving data collected from
a sample set of drivers along an expressway using instrumented vehi
cles. These measures are calculated by analyzing the driving profile for
a certain period and thus represent the variation in driving behavior.
These driving performance measures were further compared with
historical crash data to assess the relationship between variation in
driving behavior and historical crashes on a road segment.
Results indicate that driver characteristics and road geometry
influence the driving performance measures. Results show that
speeding and the percentage of jerk events positively correlate with
crash data. Speed variability does not seem to correlate with crashes
directly, but it is observed that the variation in speed is low at high
speeding. Hence, instances where both speeding and speed variabil
ity are high are unsafe and could lead to a crash. The results of the
crash frequency modeling show that speeding, percentage of jerk
events, and mean gradient are highly significant in estimating
crashes along a road segment. Thus, the study establishes driving
performance measures, namely, speeding and percentage of jerk
events in identifying risky driving behavior.
The driving performance measures considered in this study could
be used in collision avoidance systems to warn drivers of an upcoming
event. In a connected vehicle environment, these performance mea
sures can identify risky behavior and alert the other road users and the
infrastructure to avoid crashes. These warning systems could also
consider the driver characteristics and road geometry since these influ
ence the driving performance measures. The methodology used in the
study could be adopted to assess the design quality of existing roads and
identify the sections which need improvement. The effectiveness of any
safety measure may be tested by following a similar approach of
traversing the road stretch with an instrumented vehicle before and
after adopting the countermeasures and analyzing the driving perfor
mance measures. This methodology could also guide policymakers to
adjust the speed limits along any particular road stretch. These mea
sures indicate the safety of an individual vehicle-driver unit and can
represent single-vehicle crashes due to abnormal driving behavior. It
can also represent possible two-vehicle crashes where the crash might
be triggered by risky driving behavior exhibited by the subject vehicle.
This study uses field driving data from passenger cars alone. It
does not include the difference in vehicle characteristics based on
the passenger car type, which is a study limitation. Field study can
be extended to other vehicle types with different dynamic charac
teristics, likely influencing the driving performance measures. Also,
historical crash data for 3 years is used for comparison under the
assumption that the traffic conditions remain the same throughout.
Further, the study uses three selected measures based on speed and
acceleration to represent driving performance. This was done based
on the consideration that these can be computed by economical
devices offering a more straightforward field implementation.
However, considering advanced performance measures could lead
to further improvement in the safety assessment.
Acknowledgments
The authors thank JP Research India Pvt. Ltd. for providing the necessary crash
data for the study. The authors also thank Mr Sam Santhosh for assisting in field
data collection. The authors thank the Department of Civil Engineering at the
Indian Institute of Technology Bombay for providing the funding necessary for
field data collection.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Anna Charly http://orcid.org/0000-0002-4444-002X
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