The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS)project wa... more The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS)project was the largest naturalistic driving study ever conducted. The data obtained from the study was released to the research community in 2014 through the project’s InSight webpage. The objectives of this research were to (a) explore the content of this large dataset and perform statistical analysis to identify useful performance measures to detect distracted driving behavior, and (b) provide an outline for a crash index model that can be used to quantify the crash risk associated with distracted driving behavior. Time series data on driver GPS speed, lateral and longitudinal acceleration, throttle position, and yaw rate were extracted as five appropriate performance measures available from the NDS that could be used for the purpose of this research. Using this data, the objective was to detect whether a driver was engaged in one of three specific secondary tasks or no secondary task at all us...
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
This study presents a driving simulator experiment conducted on 47 drivers to investigate how dif... more This study presents a driving simulator experiment conducted on 47 drivers to investigate how different populations of users respond to automated system failure. On this account, a major takeover scenario of a level 3 automated vehicle malfunctioning at three high-speed critical curves along a freeway was designed. The drivers are notified with an auditory warning that is triggered instantaneously with the malfunctions, thus indicating a demand to takeover. The reaction time, time to regain control, frequency of time to regain control, frequency of unsafe curves, and type of control were used as measures of users’ behavior. The results show that conservative users may be able to learn how to take control of the car safely compared to aggressive users as they experience more malfunctions. However, there is enough evidence that such group of users are more likely to drop their level of trust in automation if they experience unsafe maneuvers or lose control. These findings are promising as they can help auto-makers better design autonomous vehicles and officials better establish educational programs, which can accommodate different groups of users.
.................................................................................................... more ............................................................................................................................III ACKNOWLEDGMENTS ........................................................................................................ V TABLE OF CONTENTS ....................................................................................................... VII LIST OF FIGURES ................................................................................................................ IX INTRODUCTION .....................................................................................................................1 OBJECTIVE ..............................................................................................................................3 SCOPE .......................................................................................................................................5 LITERATURE REVIEW ..........................................................................................................7 100-Car NDS Studies ........................................................................................ 7 SHRP2 NDS and RID Studies .......................................................................... 8 METHODOLOGY ..................................................................................................................11 Data Description ......................................................................................................... 11 NDS Dataset.................................................................................................... 11 RID Dataset ..................................................................................................... 13 Data Extraction ........................................................................................................... 14 The Process of Linking RID and NDS ....................................................................... 14 DISCUSSION OF RESULTS..................................................................................................16 Exploration of RID Database ...................................................................................... 16 Mapping RID in ArcGIS ................................................................................. 21 Applications of Street View and Bird’s Eye View ......................................... 22 NDS and RID Road Alignment .................................................................................. 24 Potential Topics .......................................................................................................... 26 CONCLUSION ........................................................................................................................29 RECOMMENDATIONS .........................................................................................................30 ACRONYMS, ABBREVIATIONS, AND SYMBOLS ..........................................................31 REFERENCES ........................................................................................................................33
This study presents a comprehensive evaluation of various adaptive ramp metering strategies in or... more This study presents a comprehensive evaluation of various adaptive ramp metering strategies in order to identify the optimum algorithm that can help improve traffic conditions on I-12, Baton Rouge, Louisiana. The evaluated ramp metering strategies included the ALINEA local ramp metering control and mixed strategies case which included HERO coordinated and the local ALINEA control. The coordination was performed between three sets of two on-ramps, one on the eastbound and two on the westbound, while the other on-ramps were operating as ALINEA. The different strategies were compared to the current ramp metering strategy that was fixed-time. Geometric and traffic data were collected to build and calibrate a simulation model to be used to test the different ramp metering strategies. Comparative evaluation was then performed on the simulation results of the three strategies using three performance measures: travel time, speed, and vehicle hours traveled (VHT). The three measures were agg...
This study investigates the use of Basic Safety Messages (BSMs) as the only source of vehicular d... more This study investigates the use of Basic Safety Messages (BSMs) as the only source of vehicular data for cycle-by-cycle queue length estimation. The proposed approach is based on shockwave analysis. The study also investigates the factors affecting the estimation accuracy. Three algorithms are developed to measure: (1) residuals from previous cycle (Ra), (2) maximum queue length, and end of cycle residual (Rb). Simulation data collected for three coordinated intersections were used to test the proposed approach. The results confirm that the queue length estimation is a probabilistic process affected by the stochastic nature of the traffic. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Despite the results show no specific trend for the maximum queue length estimation accuracy over the different market penetrations, the estimation errors are between 0 and 33% which is acceptable. ...
Changes in vehicle fuel-consumption and emission rates are associated with changes in vehicle cru... more Changes in vehicle fuel-consumption and emission rates are associated with changes in vehicle cruise speeds and acceleration. Higher levels of speed is believed to be one of the most prevalent factors contributing to fuel consumption. As a result, the relationship between fuel consumption and driving speed behaviour has been the subject of investigation by several research. The main objective of this paper is to investigate the fuel consumption during different acceleration degrees namely: aggressive, normal and mild. The test vehicle was examined on a 2 km section of Cairo - El Ain El Sokhna Road. The three levels of acceleration were determined based on pre-developed drive scenarios. In addition, fuel consumption was estimated based on a Virginia Tech Power Based Fuel Consumption Model (VT-CPFM). This model is a simple and rapid method for investigating fuel consumption rates. The study demonstrated that the fuel consumed to accelerate an initially stationary vehicle was not relat...
This study investigates the factors affecting estimation accuracy of queue length at signalized i... more This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potenti...
Transportation Research Record: Journal of the Transportation Research Board, 2019
This study introduces a machine learning model for near-crash prediction from observed vehicle ki... more This study introduces a machine learning model for near-crash prediction from observed vehicle kinematics data. The main hypothesis is that vehicles tend to experience discernible turbulence in their kinematics shortly before involvement in near-crashes. To test this hypothesis, the SHRP2 NDS vehicle kinematics data (speed, longitudinal acceleration, lateral acceleration, yaw rate, and pedal position) are utilized. Several machine learning algorithms are trained and comparatively analyzed including K nearest neighbor (KNN), random forest, support vector machine (SVM), decision trees, Gaussian naïve Bayes (Gaussian NB), and adaptive boost (AdaBoost). Sensitivity analysis is performed to determine the optimal prediction horizon length (the time period before the occurrence of a near-crash) and the turbulence horizon length (the time period during which near-crash related changes in vehicle kinematics take place). The results indicate that optimal prediction performance can be achieved...
Despite the research efforts for reducing traffic accidents, the number of global annual vehicle ... more Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to crash and near-crash events (CNC). Recently, many studies attempted to identify the associated crash factors using naturalistic driving study (SHRP2-NDS) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the SHRP2-NDS data limit the accuracy and reliability of the developed models. This study develops an extreme gradient boosting (XGB) classifier, robust to multicollinearity, using the SHRP2-NDS dataset for identifying the factors contributing to CNC events. The performance of the XGB classifier is evaluated against three other advanced machine-learning algorithms. Results indicate that the XGB model outperformed the other models with a detection accuracy of 85% and identified the “driver behavior” and “intersecti...
Transportation Research Record: Journal of the Transportation Research Board, 2018
Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the de... more Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving ...
Transportation Research Record: Journal of the Transportation Research Board, 2017
Lane changing is a complex decision-making process that is affected by factors such as vehicle fe... more Lane changing is a complex decision-making process that is affected by factors such as vehicle features, driver characteristics, network attributes, and traffic conditions. Understanding the changes in driver behavior and vehicle trajectory before the lane change initiation process is essential to the design of a safe and reliable crash avoidance system. The recently introduced connected vehicle (CV) technology provides opportunities for real-time, high-resolution data exchange capability between vehicles. This study explored the high-resolution vehicle trajectory data attainable in CV environments for detecting the onset of lane change maneuvers. The observed change in behavior before the initiation of such a maneuver was examined to identify the associated driving pattern. This pattern was used to develop two lane change detection models: an artificial neural network (ANN) model and a multiple logistic regression (MLR) model. The two models were trained and tested with Next Genera...
Transportation Research Record: Journal of the Transportation Research Board, 2018
Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occ... more Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model...
Distracted driving behavior and driving inattention are two leading causes of roadway crashes. Th... more Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events.
Distracted driving behavior is a perennial safety concern that affects not only the vehicle's occ... more Distracted driving behavior is a perennial safety concern that affects not only the vehicle's occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.
The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS)project wa... more The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS)project was the largest naturalistic driving study ever conducted. The data obtained from the study was released to the research community in 2014 through the project’s InSight webpage. The objectives of this research were to (a) explore the content of this large dataset and perform statistical analysis to identify useful performance measures to detect distracted driving behavior, and (b) provide an outline for a crash index model that can be used to quantify the crash risk associated with distracted driving behavior. Time series data on driver GPS speed, lateral and longitudinal acceleration, throttle position, and yaw rate were extracted as five appropriate performance measures available from the NDS that could be used for the purpose of this research. Using this data, the objective was to detect whether a driver was engaged in one of three specific secondary tasks or no secondary task at all us...
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020
This study presents a driving simulator experiment conducted on 47 drivers to investigate how dif... more This study presents a driving simulator experiment conducted on 47 drivers to investigate how different populations of users respond to automated system failure. On this account, a major takeover scenario of a level 3 automated vehicle malfunctioning at three high-speed critical curves along a freeway was designed. The drivers are notified with an auditory warning that is triggered instantaneously with the malfunctions, thus indicating a demand to takeover. The reaction time, time to regain control, frequency of time to regain control, frequency of unsafe curves, and type of control were used as measures of users’ behavior. The results show that conservative users may be able to learn how to take control of the car safely compared to aggressive users as they experience more malfunctions. However, there is enough evidence that such group of users are more likely to drop their level of trust in automation if they experience unsafe maneuvers or lose control. These findings are promising as they can help auto-makers better design autonomous vehicles and officials better establish educational programs, which can accommodate different groups of users.
.................................................................................................... more ............................................................................................................................III ACKNOWLEDGMENTS ........................................................................................................ V TABLE OF CONTENTS ....................................................................................................... VII LIST OF FIGURES ................................................................................................................ IX INTRODUCTION .....................................................................................................................1 OBJECTIVE ..............................................................................................................................3 SCOPE .......................................................................................................................................5 LITERATURE REVIEW ..........................................................................................................7 100-Car NDS Studies ........................................................................................ 7 SHRP2 NDS and RID Studies .......................................................................... 8 METHODOLOGY ..................................................................................................................11 Data Description ......................................................................................................... 11 NDS Dataset.................................................................................................... 11 RID Dataset ..................................................................................................... 13 Data Extraction ........................................................................................................... 14 The Process of Linking RID and NDS ....................................................................... 14 DISCUSSION OF RESULTS..................................................................................................16 Exploration of RID Database ...................................................................................... 16 Mapping RID in ArcGIS ................................................................................. 21 Applications of Street View and Bird’s Eye View ......................................... 22 NDS and RID Road Alignment .................................................................................. 24 Potential Topics .......................................................................................................... 26 CONCLUSION ........................................................................................................................29 RECOMMENDATIONS .........................................................................................................30 ACRONYMS, ABBREVIATIONS, AND SYMBOLS ..........................................................31 REFERENCES ........................................................................................................................33
This study presents a comprehensive evaluation of various adaptive ramp metering strategies in or... more This study presents a comprehensive evaluation of various adaptive ramp metering strategies in order to identify the optimum algorithm that can help improve traffic conditions on I-12, Baton Rouge, Louisiana. The evaluated ramp metering strategies included the ALINEA local ramp metering control and mixed strategies case which included HERO coordinated and the local ALINEA control. The coordination was performed between three sets of two on-ramps, one on the eastbound and two on the westbound, while the other on-ramps were operating as ALINEA. The different strategies were compared to the current ramp metering strategy that was fixed-time. Geometric and traffic data were collected to build and calibrate a simulation model to be used to test the different ramp metering strategies. Comparative evaluation was then performed on the simulation results of the three strategies using three performance measures: travel time, speed, and vehicle hours traveled (VHT). The three measures were agg...
This study investigates the use of Basic Safety Messages (BSMs) as the only source of vehicular d... more This study investigates the use of Basic Safety Messages (BSMs) as the only source of vehicular data for cycle-by-cycle queue length estimation. The proposed approach is based on shockwave analysis. The study also investigates the factors affecting the estimation accuracy. Three algorithms are developed to measure: (1) residuals from previous cycle (Ra), (2) maximum queue length, and end of cycle residual (Rb). Simulation data collected for three coordinated intersections were used to test the proposed approach. The results confirm that the queue length estimation is a probabilistic process affected by the stochastic nature of the traffic. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Despite the results show no specific trend for the maximum queue length estimation accuracy over the different market penetrations, the estimation errors are between 0 and 33% which is acceptable. ...
Changes in vehicle fuel-consumption and emission rates are associated with changes in vehicle cru... more Changes in vehicle fuel-consumption and emission rates are associated with changes in vehicle cruise speeds and acceleration. Higher levels of speed is believed to be one of the most prevalent factors contributing to fuel consumption. As a result, the relationship between fuel consumption and driving speed behaviour has been the subject of investigation by several research. The main objective of this paper is to investigate the fuel consumption during different acceleration degrees namely: aggressive, normal and mild. The test vehicle was examined on a 2 km section of Cairo - El Ain El Sokhna Road. The three levels of acceleration were determined based on pre-developed drive scenarios. In addition, fuel consumption was estimated based on a Virginia Tech Power Based Fuel Consumption Model (VT-CPFM). This model is a simple and rapid method for investigating fuel consumption rates. The study demonstrated that the fuel consumed to accelerate an initially stationary vehicle was not relat...
This study investigates the factors affecting estimation accuracy of queue length at signalized i... more This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potenti...
Transportation Research Record: Journal of the Transportation Research Board, 2019
This study introduces a machine learning model for near-crash prediction from observed vehicle ki... more This study introduces a machine learning model for near-crash prediction from observed vehicle kinematics data. The main hypothesis is that vehicles tend to experience discernible turbulence in their kinematics shortly before involvement in near-crashes. To test this hypothesis, the SHRP2 NDS vehicle kinematics data (speed, longitudinal acceleration, lateral acceleration, yaw rate, and pedal position) are utilized. Several machine learning algorithms are trained and comparatively analyzed including K nearest neighbor (KNN), random forest, support vector machine (SVM), decision trees, Gaussian naïve Bayes (Gaussian NB), and adaptive boost (AdaBoost). Sensitivity analysis is performed to determine the optimal prediction horizon length (the time period before the occurrence of a near-crash) and the turbulence horizon length (the time period during which near-crash related changes in vehicle kinematics take place). The results indicate that optimal prediction performance can be achieved...
Despite the research efforts for reducing traffic accidents, the number of global annual vehicle ... more Despite the research efforts for reducing traffic accidents, the number of global annual vehicle accidents is still on the rise. This continues to motivate researchers to examine the factors contributing to crash and near-crash events (CNC). Recently, many studies attempted to identify the associated crash factors using naturalistic driving study (SHRP2-NDS) data. Despite the many classifiers developed in the literature, the high dimensionality and multicollinearity within the SHRP2-NDS data limit the accuracy and reliability of the developed models. This study develops an extreme gradient boosting (XGB) classifier, robust to multicollinearity, using the SHRP2-NDS dataset for identifying the factors contributing to CNC events. The performance of the XGB classifier is evaluated against three other advanced machine-learning algorithms. Results indicate that the XGB model outperformed the other models with a detection accuracy of 85% and identified the “driver behavior” and “intersecti...
Transportation Research Record: Journal of the Transportation Research Board, 2018
Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the de... more Lane changing is one of the main contributors to car crashes in the U.S. The complexity of the decision-making process associated with lane changing makes such maneuvers prone to driving errors, and hence, increases the possibility of car crashes. Thus, researchers have been investigating ways to model and predict lane changing maneuvers for optimally designed crash avoidance systems. Such systems rely on the accuracy of detecting the onset of lane-change maneuvers, which requires comprehensive vehicle trajectory data. Connected Vehicles (CV) data provide opportunities for accurate modeling of lane changing maneuvers, especially with the variety of advanced tools available nowadays. The review of the literature indicates that most of the implemented modeling tools do not achieve reliable accuracy for such critical safety application of lane-change prediction. Recently, eXtreme Gradient Boosting (XGB) became a well-recognized algorithm among the computer science community in solving ...
Transportation Research Record: Journal of the Transportation Research Board, 2017
Lane changing is a complex decision-making process that is affected by factors such as vehicle fe... more Lane changing is a complex decision-making process that is affected by factors such as vehicle features, driver characteristics, network attributes, and traffic conditions. Understanding the changes in driver behavior and vehicle trajectory before the lane change initiation process is essential to the design of a safe and reliable crash avoidance system. The recently introduced connected vehicle (CV) technology provides opportunities for real-time, high-resolution data exchange capability between vehicles. This study explored the high-resolution vehicle trajectory data attainable in CV environments for detecting the onset of lane change maneuvers. The observed change in behavior before the initiation of such a maneuver was examined to identify the associated driving pattern. This pattern was used to develop two lane change detection models: an artificial neural network (ANN) model and a multiple logistic regression (MLR) model. The two models were trained and tested with Next Genera...
Transportation Research Record: Journal of the Transportation Research Board, 2018
Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occ... more Distracted driving behavior is a perennial safety concern that affects not only the vehicle’s occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model...
Distracted driving behavior and driving inattention are two leading causes of roadway crashes. Th... more Distracted driving behavior and driving inattention are two leading causes of roadway crashes. The state-of-the-art safety research made several attempts to understand and quantify distracted driving and driver inattention. While each attempt had its limitation, there was a consensus on the relevance of eye glance behavior as a promising parameter in understanding distracted driving. In this study, a renewal cycle approach is implemented to provide deeper insights into how drivers allocate their attention while driving. This approach is then applied to the Naturalistic Engagement in Secondary Tasks (NEST) dataset to analyze drivers’ eye glance patterns and determine the relationship between their visual behavior and engagement in different types of secondary tasks (activities performed while driving). The analysis revealed that distracted driving behavior could be well characterized by two new measures: the number of renewal cycles per event (NRC) and a distraction level index (DI). Consequently, mixed-effects modeling is implemented to test the effectiveness of the two measures to differentiate crash/near-crash events from non-crash events. The analysis showed that the two measures increase significantly for crash/near-crash events compared to non-crash driving events with p-values less than 0.0001. The findings of this paper are promising to the quantification of the risk associated with distraction related visual behavior. The finding can also help build reliable algorithms for in-vehicle driving assistance systems to alert drivers before crash/near-crash events.
Distracted driving behavior is a perennial safety concern that affects not only the vehicle's occ... more Distracted driving behavior is a perennial safety concern that affects not only the vehicle's occupants but other road users as well. Distraction is typically caused by engagement in secondary tasks and activities such as manipulating objects and passenger interaction, among many others. This study provides an in-depth analysis of the increased crash/near-crash risk associated with different secondary tasks using the largest real-world naturalistic driving dataset (SHRP2 Naturalistic Driving Study). Several statistical and data-mining techniques were developed to analyze the distracted driving and crash risk. First, a bivariate probit model was constructed to investigate the relationship between engagement in a secondary task and the safety-critical events likelihood. Subsequently, two different techniques were implemented to quantify the increased crash/near-crash risk because of involvement in a particular secondary task. The first technique used the baseline-category logits model to estimate the increased crash risk in terms of conditional odds ratios. The second technique used the a priori association rule mining algorithm to reveal the risk associated with each secondary task in terms of support, confidence, and lift indexes. The results indicate that reaching for objects, manipulating objects, reading, and cell phone texting are the highest crash risk factors among various secondary tasks. Recognizing the effect of different secondary tasks on traffic safety in a real-world environment helps legislators enact laws that reduce crashes resulting from distracted driving, as well as enabling government officials to make informed decisions about the allocation of available resources to reduce roadway crashes and improve traffic safety.
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Papers by Peter Bakhit