scholarly journals New Traffic Conflict Measure Based on a Potential Outcome Model

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Kentaro Yamada ◽  
Manabu Kuroki
Keyword(s):  
Traffic Accidents ◽  
Causal Analysis ◽  
Counterfactual Analysis ◽  
Traffic Situation ◽  
Potential Outcome ◽  
Effectiveness Measure ◽  
Traffic Conflict ◽  
Naturalistic Driving Study ◽  
Full Power ◽  
Naturalistic Driving

AbstractA key issue in the analysis of traffic accidents is to quantify the effectiveness of a given evasive action taken by a driver to avoid crashing. Since 1977, the widely accepted definition for this effectiveness measure, which is called traffic conflict, has been “the risk of a collision if the driver movement remains unchanged.” Although the definition is expressed counterfactually, the full power of counterfactual analysis was not utilized. In this paper, we propose a counterfactual measure of traffic conflict called Counterfactual Based Conflict (CBC). The CBC is interpreted as the probability that a driver avoided a crash actually by taking an evasive action in the counterfactual situation in which the crash would have occurred if he/she had not taken an evasive action and the crash would not have occurred if he/she had taken an evasive action. The CBC captures realistic aspects of the traffic situation, and lends itself to modern causal analysis. In addition, we provide some of identification conditions for the CBC. Furthermore, we formulate bounds on the CBC when the proposed identification conditions are violated. Finally, through an application of the CBC to the 100-Car Naturalistic Driving Study, we discuss the usefulness and limitations of the proposed measure.

An extreme gradient boosting method for identifying the factors contributing to crash/near-crash events: a naturalistic driving study

2019 ◽  
Vol 46 (8) ◽  
pp. 712-721 ◽  
Author(s):  
Saleh R. Mousa ◽  
Peter R. Bakhit ◽  
Sherif Ishak
Keyword(s):  
Traffic Accidents ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Detection Accuracy ◽  
Contributing Factors ◽  
Vehicle Accidents ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Boosting Method ◽  
Naturalistic Driving

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 “intersection influence” as the most contributing factors to CNC detection.

Investigating Effect of Driver-, Vehicle-, and Road-Related Factors on Location-Specific Crashes with Naturalistic Driving Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 46-56 ◽  
Author(s):  
Grace Ashley ◽  
Osama A. Osman ◽  
Sherif Ishak ◽  
Julius Codjoe
Keyword(s):  
United States ◽  
Machine Learning ◽  
Traffic Accidents ◽  
The United States ◽  
Crash Risk ◽  
Related Factors ◽  
Data Set ◽  
Intersection Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

According to NHTSA, traffic accidents cost the United States billions of U.S. dollars each year. Intersection accidents alone accounted for 23% of the 32,675 motor crash deaths in 2014. With the advent of the largest naturalistic driving data set in the United States collected by the SHRP2 Naturalistic Driving Study project, this study performs a crash-only analysis to identify driver-, vehicle-, and roadway-related factors that affect the driving risk at different location types using a machine learning tool. The study then analyzes the most important factors obtained from the machine learning analysis to identify how they affect crash risk. The results, in order of importance of variables, were driver behavior, locality, lane occupied, alignment, and through travel lanes. Also, drivers who violated traffic signals were four times more likely to be involved in a crash than drivers who did not. Those who violated stop signs were two times more likely to be involved in crashes than those who did not. Drivers performing visual-manual (VM) tasks at uncontrolled intersections were 2.7 times more likely to be involved in crashes than those who did not engage in these tasks. At nonintersections, drivers who performed VM tasks were 3.4 times more likely to be involved in crashes than drivers who did not. These findings add to the evidence that the establishment of safety awareness programs geared toward intersection safety is imperative.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110035
Author(s):  
Anik Das ◽  
Mohamed M. Ahmed
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lane Change ◽  
Adaptive Boosting ◽  
Extreme Gradient Boosting ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Change Prediction

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110071
Author(s):  
Yingfeng (Eric) Li ◽  
Haiyan Hao ◽  
Ronald B. Gibbons ◽  
Alejandra Medina
Keyword(s):  
Road Traffic ◽  
Wait Time ◽  
Study Data ◽  
Gap Acceptance ◽  
Factors Affecting ◽  
Unsignalized Intersections ◽  
Research Activities ◽  
Stop Sign ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

Studying Driving Behavior on Horizontal Curves using Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (17) ◽  
pp. 83-95 ◽  
Author(s):  
Bashar Dhahir ◽  
Yasser Hassan
Keyword(s):  
Data Collection ◽  
Weather Conditions ◽  
Driving Behavior ◽  
Study Data ◽  
Output Analysis ◽  
Horizontal Curves ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Design Consistency ◽  
Favorable Weather

Many studies have been conducted to develop models to predict speed and driver comfort thresholds on horizontal curves, and to evaluate design consistency. The approaches used to develop these models differ from one another in data collection, data processing, assumptions, and analysis. However, some issues might be associated with the data collection that can affect the reliability of collected data and developed models. In addition, analysis of speed behavior on the assumption that vehicles traverse horizontal curves at a constant speed is far from actual driving behavior. Using the Naturalistic Driving Study (NDS) database can help overcome problems associated with data collection. This paper aimed at using NDS data to investigate driving behavior on horizontal curves in terms of speed, longitudinal acceleration, and comfort threshold. The NDS data were valuable in providing clear insight on drivers’ behavior during daytime and favorable weather conditions. A methodology was developed to evaluate driver behavior and was coded in Matlab. Sensitivity analysis was performed to recommend values for the parameters that can affect the output. Analysis of the drivers’ speed behavior and comfort threshold highlighted several issues that describe how drivers traverse horizontal curves that need to be considered in horizontal curve design and consistency evaluation.

Analysis of near crashes among teen, young adult, and experienced adult drivers using the SHRP2 naturalistic driving study

2018 ◽  
Vol 19 (sup1) ◽  
pp. S89-S96 ◽  
Author(s):  
Thomas Seacrist ◽  
Ethan C. Douglas ◽  
Elaine Huang ◽  
James Megariotis ◽  
Abhiti Prabahar ◽  
...  
Keyword(s):  
Young Adult ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
Experienced Adult
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