Near-crash Events Research Articles

Modeling Pedestrian Near-Crash Events at a Rectangular Rapid Flashing Beacon-Controlled Intersection Using Video Analytics and Long Short-Term Memory Neural Network

Pedestrian safety is a long-standing issue in urban areas, where pedestrian near-crash events are more frequent than in suburban or rural areas. To address the pedestrian safety problem, a proactive approach was explored to assess and predict the severity of these events, which are valuable indicators of potential crashes. Object detection and tracking techniques were used to establish the temporal relationship of pedestrian near-crash events involving vehicles at an intersection controlled with rectangular rapid flashing beacons. A long short-term memory (LSTM) neural network model is proposed to warn a driver 2 s before the vehicle reaches the conflict zone. However, this scenario can be considered optimistic, as the 2 s interval represents an ideal driver’s reaction time, which is more likely to happen in a connected and automated vehicle environment where vehicles receive real-time information about their surroundings and perform some basic tasks such as braking without waiting for the driver reaction. The results demonstrate the effectiveness of the proposed LSTM neural network model, with an area under the curve value of 78.5% on the training data and an overall recall of 71.1% on the test data. The practical significance of this model is its potential to provide timely information about near-crash events, thereby enhancing pedestrian safety at critical points such as intersections.

The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash Events.

Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone.

“Did you fall asleep?” – Younger and older drivers’ recollection of prior sleepiness while driving

Determining causality and driver culpability in fall asleep crashes requires extensive forensic examination, with post-crash interviews forming a critical part of the process. To date, there is no evidence regarding whether drivers’ recollection of sleepiness and associated symptoms accurately reflects driving impairment during an earlier drive. To address this, our study examined whether established questionnaires assessing sleepiness (Karolinska Sleepiness Scale) and sleepiness symptoms (Sleepiness Symptoms Questionnaire) taken post-drive accurately reflected earlier driving impairment as characterised by near-crash events and lane deviations during a closed loop track drive. Sixteen younger (21–33 years) and seventeen older (50–65 years) drivers completed two 2 h afternoon track drives under supervision of a licensed driving instructor under two conditions: well rested (8 h sleep opportunity) and sleep deprived (0 h sleep). For younger drivers, all sleepiness symptoms (except mind wandering) significantly predicted prior severe (near-crashes AUC 0.78–1.00) and moderate (lane deviations AUC 0.78–0.94) driving impairment, with good to excellent accuracy. For older drivers, only severe impairment was accurately reflected in post-drive ratings (near crashes AUC 0.86–0.94), and for fewer symptoms (8/9 vs. 6/9). Optimal thresholds for the frequency of these symptoms, in addition to thresholds for minimum acceptable false positives are presented. Our data suggest that post-crash interviews should utilise questions around subjective sleepiness and associated symptoms, providing evidence regarding the presence and awareness of drowsiness while driving, taking into account the reliability of self-report evidence. This may aid in the investigation of fall-asleep crashes where drowsiness is suspected.

Risky Driver Identification Using Beta Regression Based on Naturalistic Driving Data

Naturalistic driving data are widely used to investigate factors related to road safety. Crashes and near-crashes can be regarded as the critical events on the road. The existing studies typically modeled crash and near-crash events at the trip level. However, individual drivers may have different risk levels, and other factors such as distraction can also play a role. This study uses variables automatically derived from naturalistic driving data. Driver distraction is detected from videos using facial landmarks. Based on the collected variables, a beta regression model is developed to identify the significant variables affecting drivers’ risk levels. It is found that the average acceleration rate, number of hard accelerations, driver distraction, and age are significant variables. The findings from this study can be used to identify risky drivers and improve the design of automated vehicles by eliminating human errors and risky driving patterns. Moreover, advanced driver assistance systems (ADAS) can be promoted to alert drivers to risky driving behaviors. The proposed model is also easy to implement in real driving conditions as most of the variables can be extracted automatically. Relevant agencies can also use the model to identify risky drivers and provide proactive customized education programs.

Transit Safety System Evaluation and Hotspot Identification Empowered by Edge Computing Transit Event Logging System

This paper discusses the importance of near-crash events and associated metadata as valuable sources for smart transit applications, such as surrogate safety measures for transit safety research. The STAR Lab at the University of Washington, sponsored by the Federal Transit Administration, has developed an edge computing system that processes onboard videos for near-crash detection. This paper builds on previous work by addressing two research questions: first, how to leverage the near-crash detection system to synthesize rich data sources on transit vehicles, and second, how to use the smart data hub to support transit operation and safety studies. The proposed procedures for event-based transit data collection, evaluation of commercial collision avoidance warning systems (CAWS) technologies, and transit safety hotspot identification are detailed. CAWS’ performance was benchmarked on four transit buses that were operated for almost a year in Pierce County, WA, U.S. Furthermore, the meta-information of near-crash events enables hotspot analysis and the identification of several exemplar clusters that can be explained by driver behavior and roadway geometries. The results of the experiments demonstrate the system’s promising performance and its applicability to addressing various transit operation questions.

Lightweight Edge Intelligence Empowered Near-Crash Detection Towards Real-Time Vehicle Event Logging

A major role of automated vehicles is that vehicles serve as mobile sensors for event detection and data collection, which support tactical automation in autonomous driving and post-analysis for traffic safety. However, most data collected during regular operations of vehicles are not of interest, while it costs a large amount of computation, communication, and storage resources on the cloud servers. Vehicular edge computing has emerged as a promising paradigm to balance these high costs in traditional cloud computing. But edge computers often have limited resources to support the high efficiency and intelligence of advanced vehicular functions. Motivated by the existing challenges and new concepts, this paper proposes and tests a lightweight edge intelligence framework for vehicle event detection and logging that runs in an event-based and real-time manner. Specifically, this paper takes vehicle-vehicle and vehicle-pedestrian near-crashes as the events of interest. The lightweight algorithm design of modeling the bounding boxes in object detection/tracking enables real-time edge intelligence onboard a vehicle; The event-based data logging mechanism eliminates redundant data onboard and integrates multi-source information for individual near-crash events. Comprehensive open-road tests on four transit vehicles have been conducted.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle’s (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

This study used a pedestrian-involved near-crash database and adopted an interpretable machine learning framework using SHapley Additive exPlanations (SHAP) to understand the factors associated with critical pedestrian-involved near-crash events. The results indicate that pedestrians with a relatively higher walking speed are more likely to be involved in critical near-crash events. Furthermore, critical pedestrian-involved near-crash events are highly associated with vehicles with driving speeds of less than 10 mph. A higher pedestrian volume is highly associated with critical near-crash events with left-turn vehicles. It is possible that a higher pedestrian volume increases the occurrence of jaywalking behavior or encourages more pedestrians to step into the crosswalk when they should not. By contrast, a higher pedestrian volume is highly associated with non-critical near-crash events with right-turn vehicles. Right-turn vehicles often expect that there will be pedestrians crossing, and a higher volume of pedestrian traffic increases a driver’s awareness and caution while turning. The study also found that a longer signal cycle is highly associated with critical near-crash events when the pedestrian volume is low, while a relatively short signal cycle length is highly associated with critical near-crash events when the pedestrian volume is high. During non-peak hours, pedestrians have less tolerance for a relatively longer signal cycle. Moreover, a relatively shorter signal cycle length at peak hours will limit the number of pedestrians that can cross during a cycle and encourage the possibility of pedestrians jaywalking or stepping onto the crosswalk when they should not.

How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data

Distracted driving is among the leading causes of roadway crashes worldwide. However, due to limitations of police-reported crash data, it is often challenging to understand the nature and magnitude of this problem. Distraction has also been shown to affect driver speed selection, which is important as both mean speed and speed variance have substantive impacts on crash risk. This study utilizes naturalistic driving data to investigate the relationship between the engagement in various secondary (non-driving) tasks and driver speed selection under different driving contexts. Separate analyses were conducted for low-speed and high-speed driving environments. Two-way random effects linear regression models were estimated for both speed regimes, while controlling for driver, roadway, and traffic characteristics. The differences were assessed based upon ten types of secondary tasks. In general, engagement in all tasks was found to decrease speeds in high-speed environments while the effects were mixed in low-speed settings. The changes in speeds were much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, an average episode of a driver talking on a handheld cellphone was associated with a 6-mph speed reduction in high-speed environments, but a 3.5-mph increase in low-speed settings. In addition to examining impacts on speed selection, the risk of involvement in crash and near-crash events was also evaluated in consideration of the type and duration of distraction. Interestingly, distractions tended to show similar relationships, in both direction and magnitude, with the risk of involvement in both crash and near-crash events. From a policy standpoint, this study provides further motivation for legislation and other programs aimed at curbing distracted driving.

Crash/Near-Crash Analysis of Naturalistic Driving Data Using Association Rule Mining

This study explores the associations between crash/near-crash (C/NC) events and roadway, driver-related, and environmental factors in naturalistic driving studies (NDS). We used the Naturalistic Engagement in Secondary Tasks (NEST) dataset, which is massive and detailed and contains 50 million miles of naturalistic driving data resulting from the Strategic Highway Research Program 2 (SHRP2). Association rule mining (ARM) is applied to extract the rules for frequently occurring events. The generated association rules are filtered by four metrics (support, confidence, lift, and conviction) and validated by the lift increase criterion. A three-step analysis is performed to obtain a comprehensive understanding of the rules of C/NC events. The 20 most frequent items are first selected to investigate their relationship with the C/NC events. Subsequently, the association rules are used to identify the factors contributing to C/NC events. Finally, correlations between contributing factors and different severities of crashes (I—most severe, II—police-reportable, III—minor crash, and IV—low-risk tire strike) are analyzed by ARM. The results demonstrate that C/NC events occur most frequently on straight and level road segments with no controlled intersections or traffic control devices when drivers are performing secondary tasks. Thus, the reasons for these crashes are carelessness and overconfidence. In addition, a median strip or barrier and a wider road can significantly reduce the frequency and severity of crash events. Moreover, gender, age, average annual mileage, and secondary tasks are highly correlated with the frequency and severity of C/NC events. Drivers with visual-spatial disabilities or crash records are more likely to be involved in the most severe crash events. Near-crash events occur more frequently at higher traffic density and on roads with traffic control devices and controlled intersections. These conditions may keep drivers alert, preventing crashes.

In-Depth Understanding of Near-Crash Events Through Pattern Recognition

Studying near-crashes can help safety researchers understand the nature of crashes from different perspectives. Conventional crash data sets lack information about what occurred directly before the crash event. This study used a near-crash data set extracted from a naturalistic driving study that includes features describing the vehicles, drivers, and information on other vehicles involved before and during the near-crash incidents. This data set provides us with a unique perspective to understand the patterns of near-crashes. This study applied the cluster correspondence analysis (cluster CA) algorithm to explore the patterns and the magnitude of each feature’s dominance within and between the clusters through dimension reduction. The analysis identifies six clusters with four types of near-crashes: near-crash with adjacent vehicles; near-crash with the following or leading vehicles; near-crash with turning vehicles; and near-crash with objects on the roadway. The results also show that the first two types of near-crash are the most common. The patterns for these two most common types of near-crash are different with or without the engagement of secondary tasks. The findings of this study provide a fresh perspective to understand different types of crash and associated patterns. Furthermore, these findings could help transportation agencies or vehicle designers develop a more effective countermeasure to mitigate the risk of collision.

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes.

Testing Connected Vehicle-Based Accident Mitigation for Red-Light Violation Using Simulation Strategies

Simulation is often suggested as a way to analyze the safety improvements of geometric changes and operational strategies. But the results from simulations are mixed. This paper presents new ideas about how to do such assessments, especially in the context of testing the value of vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to pedestrian (V2P) communications in preventing crashes because of red-light violation at signalized intersections. Algorithms are created that watch for impending collisions through sensing and then issue speed and trajectory changes to avoid accidents. Red-light violation is a primary focus because it increases the likelihood that collisions will occur. VISSIM is used to test these ideas, including new communication and control algorithms that link to vehicles, pedestrians, and signal controllers through the communication interface. The algorithms predict unsafe conditions, determine an appropriate crash remedial decision, and communicate those controls with the appropriate vehicles and pedestrians. The impacts of these algorithms are explored under various demand patterns, connected vehicle market penetration rates, and red-light violation rates in a hypothetical simulated environment. The simulation analysis suggests that the number of near-crash events can be reduced significantly if V2V and V2P communications are implemented. Moreover, adding V2I communication on top of these may further reduce the number of near-crash events. These results suggest that not only could such control strategies have significant impacts, but also those impacts can be assessed through simulation.

On-road driving impairment following sleep deprivation differs according to age

Impaired driving performance due to sleep loss is a major contributor to motor-vehicle crashes, fatalities, and serious injuries. As on-road, fully-instrumented studies of drowsy driving have largely focused on young drivers, we examined the impact of sleep loss on driving performance and physiological drowsiness in both younger and older drivers of working age. Sixteen ‘younger’ adults (M = 24.3 ± 3.1 years [21–33 years], 9 males) and seventeen ‘older’ adults (M = 57.3 ± 5.2, [50–65 years], 9 males) undertook two 2 h drives on a closed-loop track in an instrumented vehicle with a qualified instructor following (i) 8 h sleep opportunity the night prior (well-rested), and (ii) after 29-h of total sleep deprivation (TSD). Following TSD, both age groups displayed increased subjective sleepiness and lane departures (p < 0.05), with younger drivers exhibiting 7.37 × more lane departures, and 11 × greater risk of near crash events following sleep loss. While older drivers exhibited a 3.5 × more lane departures following sleep loss (p = 0.008), they did not have a significant increase in near-crash events (3/34 drives). Compared to older adults, younger adults had 3.1 × more lane departures (p = < 0.001), and more near crash events (79% versus 21%, p = 0.007). Ocular measures of drowsiness, including blink duration, number of long eye closures and PERCLOS increased following sleep loss for younger adults only (p < 0.05). These results suggest that for older working-aged adults, driving impairments observed following sleep loss may not be due to falling asleep. Future work should examine whether this is attributed to other consequences of sleep loss, such as inattention or distraction from the road.

Driver Response Time in Cut-Off Scenarios from the Second Strategic Highway Research Program Naturalistic Database

A cut-in or cut-off scenario involves a vehicle intruding into the path of another vehicle traveling in the same direction. These lane changes can lead to potentially dangerous situations, either a sideswipe or a rear-end crash. In this study, 552 cut-in events were analyzed, including four crash and 548 near-crash events from the Second Strategic Highway Research Program (SHRP-2) data set. Video and onboard-data-recorder data from the responding vehicle were used to analyze various factors associated with drivers’ responses. Driver response times were measured from three different event onsets, and the effects of different factors on the respective response times were measured. These factors included the behavior of the subject driver, the behavior of the intruding vehicle/principal other vehicle (POV), and different environmental and infrastructural factors. The results showed that drivers responded more slowly when the POV took longer to move laterally to the subject driver’s lane edge and faster when this time was short. Similarly, drivers responded faster to merging vehicles that started from a stop. Yet, response times were no different when the POV utilized a directional signal. These results point to a kinematic threshold involving lateral distance and lateral speed that best describes how drivers were triggered to respond. Drivers also responded faster near intersections, and at night. The results can be utilized to design crash mitigation systems in autonomous vehicles, as well as non-automated vehicles, to supplement human responses where their abilities may be lacking.

Differences in Near-Crash Risk by Types of Distraction: A Comparison of Trends between Freeways and Two-Lane Highways using Naturalistic Driving Data

Distracted driving is among the leading causes of motor vehicle crashes in the United States, though the magnitude of this problem is difficult to quantify given limitations of police-reported crash data. This study leveraged data from the second Strategic Highway Research Program Naturalistic Driving Study to gain important insights into the risks posed by driver distraction on both freeways and two-lane highways. More than 50 types of secondary tasks were aggregated into ten distraction type categories and mixed-effects logistic regression models were estimated to discern how the risks of near-crash events varied by distraction type while controlling for the effects of driver, roadway, and traffic characteristics. In general, the types of distractions that created the most pronounced risks were those that introduced a combination of cognitive, visual, and manual distractions. For example, drivers who used cell phones were subject to higher risks and these risks tended to be most pronounced when both visual and manual distractions were involved. Likewise, risks tended to be highest when drivers reached for other objects inside the vehicle, engaged in personal hygiene-related activities, or focused on activities occurring outside of the driving environment. Although the same factors tended to increase near-crash risk on both types of facilities, the impacts of several factors tended to be more pronounced on two-lane highways where interaction with other vehicles occurred more frequently. From a policy standpoint, the results of this study provide further motivation for more aggressive legislation and enforcement of distracted driving.

Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method – the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g ≤ deceleration rate ≤ -4.5 g) and non-trivial near-crash events (≤-7.5 g). From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial near-crash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events. This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.

Mining patterns of near-crash events with and without secondary tasks

The engagement of secondary tasks, like using a phone or talking to passengers while driving, could introduce considerable risks to driving safety. This study utilizes a near-crash dataset extracted from a naturalistic driving study to explore the patterns of near-crash events with or without the involvement of secondary tasks as a surrogate approach to understand the impact of these behaviors on traffic safety. The dataset contains information about driver behaviors, such as secondary tasks, vehicle maneuvers, other conflict vehicles’ maneuvers before and during near-crash events, and the driving environment. The patterns for near-crashes with or without the involvement of secondary tasks are mined by adopting the apriori association rule algorithm. Finally, the mined rules for the near-crash events with or without the involvement of the secondary tasks are analyzed and compared. The results demonstrate that near-crashes with the involvement of secondary tasks often occur with drivers in a relatively stable and presumably predictable environment, such as an interstate highway with a constant speed. This type of near-crash is highly associated with the leading vehicle’s sudden slowing or stopping since there is no expectation of any interruptions for these drivers performing the secondary tasks. The most common evasive maneuver in this kind of emergency is braking. Near-crashes without the involvement of secondary tasks is often associated with lane-changing behavior and sideswipe incidents. With shorter reaction time and awareness of the driving environment, the drivers in this type of near-crash can often make more complex maneuvers, like braking and steering, to avoid a collision. Understanding the patterns of these two types of near-crash incidents could help safety researchers, traffic engineers, and even vehicle designers/engineers develop countermeasures for minimizing potential collisions caused by secondary tasks or improper lane changing behaviors.

Using SHRP2 NDS data to examine infrastructure and other factors contributing to older driver crashes during left turns at signalized intersections

Drivers age 65 and over have higher rates of crashes and crash-related fatalities than other adult drivers and are especially over-represented in crashes during left turns at intersections. This research investigated the use of SHRP2 Naturalistic Driving Study (NDS) data to assess infrastructure and other factors contributing to left turn crashes at signalized intersections, and how to improve older driver safety during such turns. NDS data for trips involving signalized intersections and crash or near-crash events were obtained for two driver age groups: drivers age 65 and over (older drivers) and a sample of drivers age 30−49, along with NDS pre-screening and questionnaire data. Video scoring of all trips was performed to collect additional information on intersection and trip conditions. To identify the most influential factors of crash risk during left turns at signalized intersections, machine learning and regression models were used. The results found that in the obtained NDS dataset, there was a relatively small volume of crashes during left turns at signalized intersections. Further, model results found the statistically significant variables of crash risk for older drivers were associated more with health and cognitive factors rather than the infrastructure or design of the intersections. The results suggest that a study using only SHRP2 NDS data will not lead to definitive findings or recommendations for infrastructure changes to increase safety for older drivers at signalized intersections and during left turns. Moreover, the findings of this study indicates the need to consider other data sources and data collection methods to address this critical literature gap in older driver safety.

Crash Factor Analysis in Intersection-Related Crashes Using SHRP 2 Naturalistic Driving Study Data

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories. The severity of the categories is determined based of the frequency of occurrences of the crashes and near-crashes. Factors contributing to each crash category are then examined based on their frequency of occurrence with the categories. This study reveals the severity of different intersection-related crash patterns, as well as the effects of some crash-contributing factors. The information presented in this paper has the potential to guide investigators interested in developing simulation and test track scenarios to evaluate vehicles equipped with automated driving systems (ADS).&lt;/div&gt;&lt;/div&gt;