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Browsing by Author "Han, Shu"

Now showing 1 - 5 of 5
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    Does Eyeglance Affect Lane Change Safety: Analysis of Eyeglance Pattern Prior to Lane Change
    Guo, Feng; Han, Shu; Xu, Jingbin (National Surface Transportation Safety Center for Excellence, 2022-09-23)
    The driver’s eyeglance patterns prior to lane change can have a major impact on crash risk. This study focuses on the area-of-interest (AOI) in eyeglances related to lane changes, including rearview mirror, left/right window, left/right mirror, windshield, and over-the-shoulder (OTS) checks of corresponding lane change direction. Key AOI characteristics such as type, percentage, duration, timing, and time-varying properties were examined thoroughly. We also evaluated driver attention on the driving task and how it changed over time by event type using the AttenD algorithm to reconstruct eyeglance data into a continuous variable. The AttenD score incorporates the glance history in the profile to reflect how effectively a driver may be allocating attention and storing information about the roadway and other vehicles. A higher AttenD score indicates more attention on primary driving tasks. Baselines had drivers with significantly higher attention scores and lower variance than near-crashes and crashes. This indicates that drivers who conducted a safe lane change tended to look away from the road less often and were more consistent in allocating eyeglances forward and on the surrounding environment.
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    In-depth Analysis of Crash Risk Associated with Eyes-off-road Duration by Road Control Type and Intersection Type
    Han, Shu; Guo, Feng; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-01-27)
    This study quantified the odds ratios (ORs) associated with eyes-off-road (EOR) durations on different road control and intersection types using the Second Strategic Highway Research Program Naturalistic Data Study (SHRP 2 NDS) dataset. The motivation of this project was to provide support for driver state monitoring systems (DMSs) regarding alert timer settings when drivers look away from the road. The main research questions addressed are: (1) Should there be a different DMS alert timer setting on controlled access roads vs. uncontrolled access roads? And (2) Should there be a different alert timer setting on intersections vs. non-intersections? It is not surprising that the longer drivers look away, the higher the resulting ORs. Overall, this study suggests that different DMS alert timer settings are needed for different road geometrical characteristics. For uncontrolled access roads, a timer with a lower threshold is recommended, and a higher threshold is recommended for controlled access roads. For intersections, a zero tolerance for vision interruption is ideal. But practically, a relative lower threshold is recommended at intersections compared with non-intersection related segments. This finding could provide critical information for advanced driver assistance system development and driver behavior education programs.
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    Risk Factors Re-evaluation with Bayesian Network Using SHRP 2 Data
    Han, Shu; Guo, Feng (National Surface Transportation Safety Center for Excellence, 2024-09-11)
    Traffic safety is a complex system influenced by numerous factors, including human behavior, road design, vehicle technology, and environmental conditions. Each of these factors can impact the safety of the transportation system in unique ways, and all factors could interact with each other in complex ways. The goal of this study was to evaluate the joint contribution of multiple risk factors to traffic safety by examining the interactions among different factors. This study considered 24 potential risk factors that reflect different perspectives in the analysis, including driver demographics, driving behavior, environmental conditions, road characteristics, traffic context, vehicle kinematics within a 5-second window of each event, and cell phone ban policies. There were two aspects to this study: first, it explored the relationships between traffic safety risk factors using unsupervised learning models with data from the Second Strategic Highway Research Program Naturalistic Driving Study. Second, with supervised learning models, the study developed a robust data-driven Bayesian network model, evaluated impacting risk factors, quantified their corresponding importance on driving risk, and consequently identified high-risk scenarios.
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    The Shanghai Naturalistic Driving Study
    Guo, Feng; Han, Shu; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2022-09-02)
    The Shanghai Naturalistic Driving Study (SHNDS) was a collaborative research study conducted by the Virginia Tech Transportation Institute (VTTI), the General Motors Company, and Tongji University. The SHNDS was the first large-scale naturalistic driving study conducted in China using the same data collection system employed in the Second Strategic Highway Research Program (SHRP 2). The overall objective of the SHNDS was to evaluate driver behavior, traffic conditions, and traffic safety in Shanghai, China. As the SHNDS driver population was primarily middle-aged, the comparison focused on middle-aged participants from SHRP 2. In addition, as Shanghai is a megacity, and therefore primarily an urban environment, a SHRP 2 data subset from Seattle was also chosen due to the city’s similar urban environment and road network. The comparison was conducted from three perspectives: safety-critical event (SCE) rate, secondary task engagement, and relative risk assessment. The main results show that (1) the rate of SCEs in the SHNDS was much higher than the SCE rate in SHRP 2; (2) the proportion of near-crashes was much higher than the proportion of crashes in the SHNDS; (3) the prevalence of overall distraction was significantly lower in the SHNDS than in the Seattle (middle-aged) and SHRP 2 (middle-aged) datasets; and (4) almost all distractions, including handheld cell use, judgement error, and performance error and impairment, significantly increased driving risk for SHRP 2 (middle-aged) and Seattle drivers, while only judgement error significantly increased driving risk in the SHNDS.
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    What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach
    White, Elizabeth; Guo, Feng; Han, Shu; Mollenhauer, Michael A.; Broaddus, Andrea; Sweeney, Ted; Robinson, Sarah; Novotny, Adam; Buehler, Ralph (Elsevier, 2023-06)
    Introduction: Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors. Method: A fleet of 200 e-scooters was deployed on Virginia Tech’s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios. Results: Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech’s pedestrian-dense campus environment. Conclusions: To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders. Practical Applications: The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.