29 research outputs found
Developing a Composite Index for Assessing Safety Performance of Drivers Based on Data Envelopment Analysis
No full textTransportation safety issues are still severe, despite many efforts that have been
made to alleviate the negative impacts they pose to society. In most cases, human
factors play a crucial role in the occurrence of traffic crashes. When splitting the
number of road fatalities by road user type, drivers represent the largest share.
As a result, to improve road safety in the future, it is essential to better understand
the behavior of different drivers. Objective driving performance measurements
are valuable to identify the safety status of individual car drivers. However, a
uniform methodological framework has not yet been successfully established to
address these issues. One of the focus areas could be drivers over the age of 65,
as the proportion of elderly drivers is increasing considerably. Diminished sensory,
motor, and cognitive abilities and increased fragility pose a significant challenge
to the traffic safety performance of this user group. Maintaining the balance
between mobility and safety of this cohort has been of recent discussion.
Nonetheless, there is still no universal method to identify elderlies with diminished
driving abilities in the early stages before jeopardizing their own and other
people's life.
Besides, speeding, driving under the influence of alcohol, and the non-use of
protective systems are the main contributing factors to most traffic-related
casualties. Thus, this dissertation aims to evaluate drivers' performance, including
middle-aged and elderly drivers, and road user behavior in different European
countries with scientifically sound methodologies and data-driven approaches.
The thesis objectives are outlined as follows:
1. To objectively evaluate drivers' performance, at the individual level, based on
the driven speed, acceleration, and lateral position.
2. To assess the driving ability (i.e., fitness-to-drive) of elderly drivers based on
the results of a set of functional ability tests.
3. To benchmark countries in terms of the safety performance of road users
concerning drink-driving, speeding, and nonuse of protective systems.
In order to achieve the abovementioned objectives, several research studies were
conducted and provided in different chapters of this dissertation. Chapter 1 is
initiated by presenting the background and theoretical framework of the research
topics. After a general introduction to the road safety issue, the method of
aggregating individual indicators into an index is discussed, and the DEA approach
is introduced. In particular, the DEA is explained with a simple numerical example
in which the concept of the efficient frontier, production possibility set, efficient
and inefficient DMU, and projection points are illustrated utilizing insightful
graphs. Then the mathematical form of DEA and some of its extensions, which
have been developed and applied in this dissertation, are described.
The following five core chapters, as the primary studies in this dissertation, are
categorized into three different parts based on their context:
Part 1. Driving Performance Index
Part 2. Older Driver Ability Index
Part 3. Road User Behavior Index
The first part deals with evaluating driving performance in a curve-taking scenario.
This is performed by executing two case studies (i.e., Chapters 2 & 3). A new
framework for constructing a driving performance index is proposed in Chapter
2 by applying the DEA method to a driving simulator data set. The Driving
Performance Index evaluates the relative performance of individual drivers based
on the three most common parameters used to describe and analyze a driver's
behavior, i.e., speed, acceleration, and lateral position. Then in Chapter 3, the
proposed framework is integrated with the Window Analysis approach. It is a
method for examining the variations in DMUs’ (i.e., the driver in our case)
performance over time. In each period, the driver is treated as if they are a
different driver. Consequently, a driver's performance in each period is evaluated
concerning its performance at other points in time, along with the performance of
other drivers. The aim is to consider the multidimensionality of driving
performance and its changes over time. Apart from identifying the bestperforming and underperforming drivers, all drivers are ranked based on their
calculated index scores. Their relative performance is achieved for speed,
acceleration, and lateral position. The driving performance index can select
candidates for driving jobs, identify high-risk drivers, improve the rating process,
and reward low-risk drivers. It can also be a good tool for insurance companies to
use different pricing schemes for other drivers based on their performance.
The second part focuses on the fitness-to-drive assessment of older drivers (i.e.,
Chapters 4 & 5). In Chapter 4, the driving ability of elderly drivers is assessed
based on specific measures of functional and driving abilities using DEA and MultiCriteria Decision Analysis (MCDA) methods. In this experiment, participants aged
70 years and above completed tests of an assessment battery of psychological
and physical aspects and knowledge of road signs. Additionally, they took a driving
simulator test in which specific driving situations known to cause difficulties for
elderly drivers were included. The overall ability of each elderly driver is evaluated
based on all the above information combined in an index using the Multi-Layer
DEA-based cross-index (MLDEA-based cross-index) approach. The model output
distinguishes the best-performers from those underperforming drivers. It helps
guide the development of training interventions tailored to each individual by
explicitly targeting those (mainly) impaired functions. Next, to assess the
robustness of the results obtained with the DEA model, the analysis was also
performed with one of the MCDA methods, Preference Ranking Organization
Method for Enrichment Evaluations (PROMETHEE II). It is done to enhance the
study of the problem by using the stability intervals (i.e., outputs of the sensitivity
analysis) of PROMETHEE II as a weight restriction in the DEA model. Furthermore,
to give the elderly drivers more insight into characterizing their driving abilities,
the possibilities and challenges of considering the complimentary use of the
MLDEA cross-index model and PROMETHEE II as an analyzing tool in evaluating
the overall performance of older drivers were investigated. Spearman's correlation
coefficient confirmed the replicability of the results with the PROMETHEE II model
and the robustness of the final solutions. Finally, we were able to study the
participants' profiles in more detail using the GAIA plane and spider web
representations. From a practical point of view, it enables road safety stakeholders
and fitness-to-drive evaluators to track elderly’s driving behavior and identify
weak areas of their performance, and then develop strategies and remedial
actions to eliminate some of those weaknesses.
Chapter 5 presents the results of a study in which elderly participants were
evaluated on their visual, physical, and cognitive abilities. Furthermore, the same
participants completed an on-road driving assessment similar to the Belgian
fitness-to-drive evaluation procedure. The dataset contains 19 variables that
describe the functional ability of participants and one variable to indicate whether
they are fit to drive or not based on their performance in the on-road test. Given
the dataset, i.e., 19 independent variables as predictors and the latter categorical
variable as the label, the aim was to develop a cost-effective and easy-toadminister prediction model to predict the fitness-to-drive of elderly drivers based
only upon the result of a set of functional ability tests. The idea was to use
advanced data mining and machine learning techniques rather than traditional
statistical approaches. Various supervised binary classification models were
trained and tested using different classes of machine learning algorithms. Of all
classifiers, the one fitted through the gradient boosting model (GBM) appeared to
be the best model due to its highest predictive performance and the largest area
under the “receiver operating characteristic” curve of 0.92 and accuracy of 90%.
This classifier correctly specified 95% of the fit-to-drive participants as fit (i.e.,
true positive rate or sensitivity=0.95) and 80% of the unfit-to-drive participants
as unfit (i.e., true negative rate or specificity=0.80). This study revealed that the
following factors contributed most to the classification of older drivers in the fit
vs. unfit classes: helpful field of view measuring the processing speed, functional
reach test, audio verbal learning, knowledge of road signs, and visual acuity. In
light of this, all these elements must be tested during license renewal, not just
the eyesight as it is usually done.
The third part (Chapter 6) evaluates road user behavior in a set of selected
European countries. The aim is to assess the safety performance of road users
regarding the following driving safety-related issues: drink-driving, speeding, and
nonuse of protective systems (i.e., seat belts) in a country and draw a meaningful
comparison of the road safety situation among other countries. It helps to learn
from the best-in-class and speed up safety improvements. In this regard, a new
DEA index methodology is proposed in which optimal common weights are
determined. At the same time, the model considers the layered hierarchy of the
indicators. The suggested approach provides a fair and identical basis for
evaluating and comparing road user behavior and reduces the required
computational burden for solving the formulated model.
The final chapter, Chapter 7, summarizes the main findings of the five core
chapters of this dissertation, points out its limitations, and proposes suggestions
for future studies in the field of driving evaluation
Developing a Composite Index for Assessing Safety Performance of Drivers Based on Data Envelopment Analysis
No full textTransportation safety issues are still severe, despite many efforts that have been
made to alleviate the negative impacts they pose to society. In most cases, human
factors play a crucial role in the occurrence of traffic crashes. When splitting the
number of road fatalities by road user type, drivers represent the largest share.
As a result, to improve road safety in the future, it is essential to better understand
the behavior of different drivers. Objective driving performance measurements
are valuable to identify the safety status of individual car drivers. However, a
uniform methodological framework has not yet been successfully established to
address these issues. One of the focus areas could be drivers over the age of 65,
as the proportion of elderly drivers is increasing considerably. Diminished sensory,
motor, and cognitive abilities and increased fragility pose a significant challenge
to the traffic safety performance of this user group. Maintaining the balance
between mobility and safety of this cohort has been of recent discussion.
Nonetheless, there is still no universal method to identify elderlies with diminished
driving abilities in the early stages before jeopardizing their own and other
people's life.
Besides, speeding, driving under the influence of alcohol, and the non-use of
protective systems are the main contributing factors to most traffic-related
casualties. Thus, this dissertation aims to evaluate drivers' performance, including
middle-aged and elderly drivers, and road user behavior in different European
countries with scientifically sound methodologies and data-driven approaches.
The thesis objectives are outlined as follows:
1. To objectively evaluate drivers' performance, at the individual level, based on
the driven speed, acceleration, and lateral position.
2. To assess the driving ability (i.e., fitness-to-drive) of elderly drivers based on
the results of a set of functional ability tests.
3. To benchmark countries in terms of the safety performance of road users
concerning drink-driving, speeding, and nonuse of protective systems.
In order to achieve the abovementioned objectives, several research studies were
conducted and provided in different chapters of this dissertation. Chapter 1 is
initiated by presenting the background and theoretical framework of the research
topics. After a general introduction to the road safety issue, the method of
aggregating individual indicators into an index is discussed, and the DEA approach
is introduced. In particular, the DEA is explained with a simple numerical example
in which the concept of the efficient frontier, production possibility set, efficient
and inefficient DMU, and projection points are illustrated utilizing insightful
graphs. Then the mathematical form of DEA and some of its extensions, which
have been developed and applied in this dissertation, are described.
The following five core chapters, as the primary studies in this dissertation, are
categorized into three different parts based on their context:
Part 1. Driving Performance Index
Part 2. Older Driver Ability Index
Part 3. Road User Behavior Index
The first part deals with evaluating driving performance in a curve-taking scenario.
This is performed by executing two case studies (i.e., Chapters 2 & 3). A new
framework for constructing a driving performance index is proposed in Chapter
2 by applying the DEA method to a driving simulator data set. The Driving
Performance Index evaluates the relative performance of individual drivers based
on the three most common parameters used to describe and analyze a driver's
behavior, i.e., speed, acceleration, and lateral position. Then in Chapter 3, the
proposed framework is integrated with the Window Analysis approach. It is a
method for examining the variations in DMUs’ (i.e., the driver in our case)
performance over time. In each period, the driver is treated as if they are a
different driver. Consequently, a driver's performance in each period is evaluated
concerning its performance at other points in time, along with the performance of
other drivers. The aim is to consider the multidimensionality of driving
performance and its changes over time. Apart from identifying the bestperforming and underperforming drivers, all drivers are ranked based on their
calculated index scores. Their relative performance is achieved for speed,
acceleration, and lateral position. The driving performance index can select
candidates for driving jobs, identify high-risk drivers, improve the rating process,
and reward low-risk drivers. It can also be a good tool for insurance companies to
use different pricing schemes for other drivers based on their performance.
The second part focuses on the fitness-to-drive assessment of older drivers (i.e.,
Chapters 4 & 5). In Chapter 4, the driving ability of elderly drivers is assessed
based on specific measures of functional and driving abilities using DEA and MultiCriteria Decision Analysis (MCDA) methods. In this experiment, participants aged
70 years and above completed tests of an assessment battery of psychological
and physical aspects and knowledge of road signs. Additionally, they took a driving
simulator test in which specific driving situations known to cause difficulties for
elderly drivers were included. The overall ability of each elderly driver is evaluated
based on all the above information combined in an index using the Multi-Layer
DEA-based cross-index (MLDEA-based cross-index) approach. The model output
distinguishes the best-performers from those underperforming drivers. It helps
guide the development of training interventions tailored to each individual by
explicitly targeting those (mainly) impaired functions. Next, to assess the
robustness of the results obtained with the DEA model, the analysis was also
performed with one of the MCDA methods, Preference Ranking Organization
Method for Enrichment Evaluations (PROMETHEE II). It is done to enhance the
study of the problem by using the stability intervals (i.e., outputs of the sensitivity
analysis) of PROMETHEE II as a weight restriction in the DEA model. Furthermore,
to give the elderly drivers more insight into characterizing their driving abilities,
the possibilities and challenges of considering the complimentary use of the
MLDEA cross-index model and PROMETHEE II as an analyzing tool in evaluating
the overall performance of older drivers were investigated. Spearman's correlation
coefficient confirmed the replicability of the results with the PROMETHEE II model
and the robustness of the final solutions. Finally, we were able to study the
participants' profiles in more detail using the GAIA plane and spider web
representations. From a practical point of view, it enables road safety stakeholders
and fitness-to-drive evaluators to track elderly’s driving behavior and identify
weak areas of their performance, and then develop strategies and remedial
actions to eliminate some of those weaknesses.
Chapter 5 presents the results of a study in which elderly participants were
evaluated on their visual, physical, and cognitive abilities. Furthermore, the same
participants completed an on-road driving assessment similar to the Belgian
fitness-to-drive evaluation procedure. The dataset contains 19 variables that
describe the functional ability of participants and one variable to indicate whether
they are fit to drive or not based on their performance in the on-road test. Given
the dataset, i.e., 19 independent variables as predictors and the latter categorical
variable as the label, the aim was to develop a cost-effective and easy-toadminister prediction model to predict the fitness-to-drive of elderly drivers based
only upon the result of a set of functional ability tests. The idea was to use
advanced data mining and machine learning techniques rather than traditional
statistical approaches. Various supervised binary classification models were
trained and tested using different classes of machine learning algorithms. Of all
classifiers, the one fitted through the gradient boosting model (GBM) appeared to
be the best model due to its highest predictive performance and the largest area
under the “receiver operating characteristic” curve of 0.92 and accuracy of 90%.
This classifier correctly specified 95% of the fit-to-drive participants as fit (i.e.,
true positive rate or sensitivity=0.95) and 80% of the unfit-to-drive participants
as unfit (i.e., true negative rate or specificity=0.80). This study revealed that the
following factors contributed most to the classification of older drivers in the fit
vs. unfit classes: helpful field of view measuring the processing speed, functional
reach test, audio verbal learning, knowledge of road signs, and visual acuity. In
light of this, all these elements must be tested during license renewal, not just
the eyesight as it is usually done.
The third part (Chapter 6) evaluates road user behavior in a set of selected
European countries. The aim is to assess the safety performance of road users
regarding the following driving safety-related issues: drink-driving, speeding, and
nonuse of protective systems (i.e., seat belts) in a country and draw a meaningful
comparison of the road safety situation among other countries. It helps to learn
from the best-in-class and speed up safety improvements. In this regard, a new
DEA index methodology is proposed in which optimal common weights are
determined. At the same time, the model considers the layered hierarchy of the
indicators. The suggested approach provides a fair and identical basis for
evaluating and comparing road user behavior and reduces the required
computational burden for solving the formulated model.
The final chapter, Chapter 7, summarizes the main findings of the five core
chapters of this dissertation, points out its limitations, and proposes suggestions
for future studies in the field of driving evaluation
Investigating Individual Driver Performance: Applying DEA on Simulator Data
No full textThe main purpose of the present study is to investigate individual driver’s behavior by us-ing the data from a driving simulator, in order to distinguish the best drivers and identify the problematic behavior of ‘underperforming’ drivers. To this end, 129 participants with different age and gender were enrolled to take part in a particular simulator scenario (i.e., curve taking) and their speed, acceleration and lateral position, the three most important driving performance indicators based on literature review, were monitored at various points (before, during and after the curve) while driving a STISIM simulator. As a widely accept-ed tool for performance monitoring, benchmarking and policy analysis, the concept of composite indicators (CIs), i.e., combining single indicators into one index score, was em-ployed, and the technique of data envelopment analysis – an optimization model for meas-uring the relative performance of a set of decision making units, or drivers in this study – was used for the index construction. Based on the results from the model, all drivers were ranked, and valuable insight were gained by comparing the relative performance of each driver. Finally, the sensitivity of the results was examined
Assessing Older Drivers’ Performance: A Multiple Layer DEA Application
No full textThis publication has been elaborated in the Framework of the project
the Czech Science Foundation (GAČR project 14-31593S), European
Social Fund within the project CZ.1.07/2.3.00/20.0296 and through
the SGS project (SP2015/117) of Faculty of Economics, VŠB-Technical
University of Ostrava
A new approach for index construction: The case of the road user behavior index
No full textA R T I C L E I N F O Keywords: Road user behavior Performance evaluation Hierarchical structure Composite indicators Data envelopment analysis Common set of weights A B S T R A C T In recent years, composite indicators have become increasingly recognized as a useful tool for performance evaluation, benchmarking, and decision-making by summarizing complex and multidimensional issues. In this study, we focus on the application of data envelopment analysis (DEA) on index construction in the context of road safety and highlight the shortcomings of using the classical DEA models. The DEA method assigns a weight to each indicator by selecting the best set of weights for the unit under evaluation. The flexibility in selecting the weights in the classical DEA approach may lead to two interrelated problems: compensability and unfairness. These shortcomings are, respectively, overcome traditionally by imposing weight restrictions and applying a common weights approach. However, the problem of evaluating a layered hierarchy of indicators with a common set of weights (CSW) has not been addressed in the literature. To fill this gap, we propose a new approach for index construction to determine an optimal CSW to assess all units simultaneously while reflecting the hierarchical structure of the indicators in the model. The applicability of the suggested common-weight approach is illustrated by a case study on constructing a road user behavior index for a set of European countries. From a theoretical point of view, our approach provides a fair and identical basis for evaluation and comparison of countries in terms of driver's behaviors and, from a practical point of view, it significantly reduces the required computational burden for solving the formulated model. The obtained results clarify the sharper discrimination power of our model compared to the other methods in the literature.This study was supported by the Czech Science Foundation (GAˇCR 19-13946S), Czechia, and the National Natural Science Foundation of China (71701045), Chin
Assessing Older Drivers’ Performance: A Multiple Layer DEA Application
No full textThis publication has been elaborated in the Framework of the project
the Czech Science Foundation (GAČR project 14-31593S), European
Social Fund within the project CZ.1.07/2.3.00/20.0296 and through
the SGS project (SP2015/117) of Faculty of Economics, VŠB-Technical
University of Ostrava
Combining driving performance information in an index score: a simulated curve-taking experiment
No full textThis study used data from a driving simulator to identify the best car drivers in a sample and gain insight about the most problematic behavior of each driver. To this end, 38 participants varying in age and gender were enrolled to take part in a particular simulator scenario, curve taking. Based on a review of the literature, a driver’s speed, acceleration, and lateral position are the three most important driving performance indicators. In the simulations, the three indicators were monitored at points before, during, and after a curve. As a widely accepted tool for performance monitoring, benchmarking, and policy analysis, the concept of composite indicators, which combines single indicators into one index score, was employed. The technique of data envelopment analysis, which is an optimization model for measuring the relative performance of a set of decision-making units, or drivers in this study, was used for the index construction. On the basis of the results, best performers were distinguished from underperforming drivers. Moreover, by analyzing the weights allocated to each indicator from the model, the most problematic parameter (such as lateral position) and point along the curve (such as at curve end) were identified for each driver; this process led to specific driver improvement recommendations (such as training programs)
Safety Evaluation of Older Drivers Based on Psychological, Physical and Driving Performance
No full textSafety Evaluation of Older Drivers Based on Psychological, Physical and Driving Performance
No full text
