Scholarly Works, Virginia Tech Transportation Institute

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Cracking performance evaluation of BMD surface mixtures with conventional and high RAP contents: insights from accelerated pavement testing program
Tong, Bilin; Habbouche, Jhony; Urbaez Perez, Ernesto; Flintsch, Gerardo W.; Diefenderfer, Stacey D.; Diefenderfer, Brian K.; Amarh, Eugene; Katicha, Samer Wehbe (Taylor & Francis, 2026-12-31)
The Balanced Mix Design (BMD) has emerged as a promising approach for mitigating cracking in high reclaimed asphalt pavement (HRAP) mixtures. This study evaluated the cracking performance of a control asphalt mixture and five BMD-optimized asphalt surface mixtures. The mixtures featured various RAP contents, two binder performance grades, a recycling agent, and a warm mix asphalt additive. The analysis integrated continuous longitudinal strain monitoring from Accelerated Pavement Testing (APT), cracking surveys, and laboratory tests. To quantify APT-measured cracking performance, three primary response phases were identified from the continuous strain monitoring. Residual strain was used to determine the initiation of cracking, and deformation uniformity was employed as a data quality indicator. The findings from strain analysis matched APT cracking surveys. Laboratory tests on field cores confirmed no structural damage for the evaluated mixtures, except for a 60% RAP section. All other BMD mixtures demonstrated better cracking resistance over the control mixture, with HRAP BMD mixtures (>30% RAP) outperforming conventional RAP mixtures (≤30% RAP). Correlation analysis between APT and BMD tests examined and supported the corresponding laboratory test thresholds. This study enhanced insights into pavement performance monitoring and highlighted the efficacy of the BMD concept in optimizing the design of HRAP mixtures.
Integrating Pavement Friction and Macrotexture into a Speed-Dependent Pavement Safety Metric for Safety Performance Modeling
Bazmara, Behrokh; Izeppi, Edgar de León; Katicha, Samer W.; McCarthy, Ross; Flintsch, Gerardo W. (MDPI, 2025-12-20)
The paper proposes a pavement safety index, the estimated available friction at the expected travel speed, FRS(v), to model the composed effect of low-slip speed friction and macrotexture on roadway crashes. This index seems to capture the relative contributions of microtexture and macrotexture across different operating speeds. Speed-dependent available friction at 40, 55, and 70 mph was estimated using the speed-correction procedure in ASTM E1960-07 and integrated into Safety Performance Function (SPF) development. Comparison of the resulting SPF models suggests that FRS values corresponding to typical operating speeds can capture the combined influence of SFN (40) and macrotexture on expected crashes for freeways and rural two-lane, two-way highways. For freeways, the estimated available friction at 70 mph (FRS113) produced the most appropriate SPF, evidenced by the lowest AIC. For rural two-lane, two-way highways, the estimated available friction at 40 mph (FRS65) resulted in the lowest AIC value, consistent with the typical operating speeds on these facilities. In contrast, none of the speed-specific friction estimates produced satisfactory model performance for urban and suburban arterials, likely due to the wide variation in traveling speeds and geometric characteristics on these facilities. The applicability of the proposed metric was demonstrated through the development of illustrative investigatory friction levels based on observed crash data, and the identification of candidate roadway segments for friction improvement interventions, and the estimation of the corresponding return on investment for these interventions.
STABLE trial of spectacle provision and driving safety among myopic motorcycle users in Vietnam: study protocol for a stepped-wedge, cluster randomised trial
Le, Vinh Chi; To, Kien Gia; Le, Van Dat; Nguyen, Le; Mackenzie, Graeme; Sigwadhi, Lovemore Nyasha; Piyasena, Prabhath; Tran, Mai; Chan, Ving Fai; Khanna, Rohit C.; Clarke, Mike; Lohfeld, Lynne; Dickey, Heather; Azuara-Blanco, Augusto; Mettla, Asha Latha; Rayasam, Sridevi; Doan, Han Thi Ngoc; Van Do, Dung; Le, Phuoc Hong; Klauer, Charlie; Hanowski, Richard; Bowden, Zeb; Murphy, Lynn; Thompson, Joanne; Mcmullan, Susan; Mcdowell, Cliona; Narayanan, Raja; Little, Julie-Anne; Ha, Huong Thu; Yoon, Sangchul; Goel, Rahul; Luong, Lan; Nguyen, Xuan; Congdon, Nathan (BMC, 2024-12-18)
Background: Traffic crashes are the leading cause of death globally for people aged 5–29 years, with 90% of mortality occurring in low- and middle-income countries (LMICs). The STABLE (Slashing Two-wheeled Accidents by Leveraging Eyecare) trial was designed to determine whether providing spectacles could reduce risk among young myopic motorcycle users in Vietnam. Methods: This investigator-masked, stepped-wedge, cluster randomised naturalistic driving trial will recruit 625 students aged 18–23 years, driving ≥ 50 km/week, with ≥ 1-year driving experience and using motorcycles as their primary means of transport, in 25 clusters of 25 students in Ho Chi Minh City, Vietnam. Motorcycles of consenting students who have failed self-testing on the WHOeyes app will be fitted with Data Acquisition Systems (DAS) with video cameras and accelerometers. Video clips (± 30 s) of events flagged by the accelerometer will be reviewed for crash and near-crash events per 1000 km driven (main outcome). Five clusters of 25 students will be randomly selected every 12 weeks to undergo ocular examination and an estimated 40% of these will have bilateral spherical equivalent < − 0.5 D, and better-eye presenting distance visual acuity < 6/12, correctable bilaterally to ≥ 6/7.5. They will be given free distance spectacles and their driving data before receiving spectacles will be analysed as the control condition and subsequent data as the intervention condition. Secondary outcomes include visual function, cost-effectiveness and self-reported crash events. Discussion: STABLE will be the first randomised trial of vision interventions and driving safety in a LMIC. Trial registration: ClinicalTrials.gov, NCT05466955. Initial registration: 20 July 2022, most recent update: 9 July 2024.
The Rebound Effect of Autonomous Vehicles on Vehicle Miles Traveled: A Synthesis of Drivers, Impacts, and Policy Implications
Ahn, Kyoungho; Rakha, Hesham A.; Wang, Jinghui (MDPI, 2025-11-12)
Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals.
Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare
Naseem, Usman; Thapa, Surendrabikram; Zhang, Qi; Wang, Shoujin; Rashid, Junaid; Hu, Liang; Hussain, Amir (Elsevier, 2024-08-01)
The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extracting meaningful insights. This paper addresses the need for automated analysis of EHRs by proposing a novel graph learning model with label attention (GLLA) for temporal event prediction. GLLA utilizes graph neural networks to capture intricate relationships between medical codes and patients, incorporating hierarchical structures and shared risk factors. Furthermore, it introduces the Label Attention and Attention -based Transformer (LAAT) algorithm to analyze unstructured clinical notes as a multi -label classification problem. Evaluation on the widely -used MIMIC III dataset demonstrates the efficacy of GLLA in enhancing diagnostic prediction performance. The contributions of this research include a comprehensive analysis of existing models, the identification of limitations, and the development of innovative approaches to improve the accuracy and effectiveness of EHR analysis. Ultimately, GLLA aims to advance healthcare decision -making, disease management strategies, and patient outcomes.
A deep dive into enhancing sharing of naturalistic driving data through face deidentification
Thapa, Surendrabikram; Sarkar, Abhijit (Springer, 2025-03-01)
Human factors research in transportation relies on naturalistic driving studies (NDS) which collect real-world data from drivers on actual roads. NDS data offer valuable insights into driving behavior, styles, habits, and safety-critical events. However, these data often contain personally identifiable information (PII), such as driver face videos, which cannot be publicly shared due to privacy concerns. To address this, our paper introduces a comprehensive framework for deidentifying drivers' face videos, that can facilitate the wide sharing of driver face videos while protecting PII. Leveraging recent advancements in generative adversarial networks (GANs), we explore the efficacy of different face swapping algorithms in preserving essential human factors attributes while anonymizing participants' identities. Most face swapping algorithms are tested in restricted lighting conditions and indoor settings, there is no known study that tested them in adverse and natural situations. We conducted extensive experiments using large-scale outdoor NDS data, evaluating the quantification of errors associated with head, mouth, and eye movements, along with other attributes important for human factors research. Additionally, we performed qualitative assessments of these methods through human evaluators providing valuable insights into the quality and fidelity of the deidentified videos. We propose the utilization of synthetic faces as substitutes for real faces to enhance generalization. Additionally, we created practical guidelines for video deidentification, emphasizing error threshold creation, spot-checking for abrupt metric changes, and mitigation strategies for reidentification risks. Our findings underscore nuanced challenges in balancing data utility and privacy, offering valuable insights into enhancing face video deidentification techniques in NDS scenarios.
Pavement Friction Prediction Based Upon Multi-View Fractal and the XGBoost Framework
Peng, Yi; Kai, Jialiang; Yu, Xinyi; Zhang, Zhengqi; Li, Qiang Joshua; Yang, Guangwei; Kong, Lingyun (MDPI, 2025-09-02)
The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies.
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding
Zeng, Tong; Wu, Longfeng; Shi, Liang; Zhou, Dawei; Guo, Feng (ACM, 2025-08-03)
Vision Large Language Models (VLLMs) have demonstrated impressive capabilities in general visual tasks such as image captioning and visual question answering. However, their effectiveness in specialized, safety-critical domains like autonomous driving remains largely unexplored. Autonomous driving systems require sophisticated scene understanding in complex environments, yet existing multimodal benchmarks primarily focus on normal driving conditions, failing to adequately assess VLLMs’ performance in safety-critical scenarios. To address this, we introduce DVBench—a pioneering benchmark designed to evaluate the performance of VLLMs in understanding safety-critical driving videos. Built around a hierarchical ability taxonomy that aligns with widely adopted frameworks for describing driving scenarios used in assessing highly automated driving systems, DVBench features 10,000 multiple-choice questions with human-annotated ground-truth answers , enabling a comprehensive evaluation of VLLMs’ capabilities in perception and reasoning. Experiments on 14 state-of-the-art VLLMs, ranging from 0.5B to 72B parameters, reveal significant performance gaps, with no model achieving over 40% accuracy, highlighting critical limitations in understanding complex driving scenarios. To probe adaptability, we fine-tuned selected models using domain-specific data from DVBench, achieving accuracy gains ranging from 5.24 to 10.94 percentage points, with relative improvements of up to 43.59%. This improvement underscores the necessity of targeted adaptation to bridge the gap between generalpurpose vision-language models and mission-critical driving applications. DVBench establishes an essential evaluation framework and research roadmap for developing VLLMs that meet the safety and robustness requirements for real-world autonomous systems. We released the benchmark toolbox and the fine-tuned model at: https://github.com/tong-zeng/DVBench.git.
Psychology or Physiology? Choosing the Right Color for Interior Spaces to Support Occupants’ Healthy Circadian Rhythm at Night
Jalali, Mansoureh Sadat; Gibbons, Ronald B.; Jones, James R. (MDPI, 2025-07-28)
The human circadian rhythm is connected to the body’s endogenous clock and can influence people’s natural sleeping habits as well as a variety of other biological functions. According to research, various electric light sources in interior locations can disrupt the human circadian rhythm. Many psychological studies, on the other hand, reveal that different colors can have varied connections with and a variety of effects on people’s emotions. In this study, the effects of light source attributes and interior space paint color on human circadian rhythm were studied using 24 distinct computer simulations. Simulations were performed using the ALFA plugin for Rhinoceros 6 on an unfurnished bedroom 3D model at night. Results suggest that cooler hues, such as blue, appear to have an unfavorable effect on human circadian rhythm at night, especially when utilized in spaces that are used in the evening, which contradicts what psychologists and interior designers advocate in terms of the soothing mood and nature of the color. Furthermore, the effects of Correlated Color Temperature (CCT) and the intensity of a light source might be significant in minimizing melanopic lux to prevent melatonin suppression at night. These insights are significant for interior designers, architects, and lighting professionals aiming to create healthier living environments by carefully selecting lighting and color schemes that support circadian health. Incorporating these considerations into design practices can help mitigate adverse effects on sleep and overall well-being, ultimately contributing to improved occupant comfort and health.
Developing a Fatigue Detection Model for Hospital Nurses Using HRV Measures and Machine Learning
Hafiz, Wynona Salsabila; Puspasari, Maya Arlini; Fitriani, Dewi Yunia; Hanowski, Richard J.; Syaifullah, Danu Hadi; Arista, Salsabila Annisa (MDPI, 2025-05-22)
Fatigue among hospital nurses, resulting from demanding workloads and irregular shift schedules, presents significant risks to both healthcare workers and patient safety. This study developed a fatigue detection model using heart-rate variability (HRV) and investigated its relationship with the Swedish Occupational Fatigue Inventory (SOFI) among nurses. Sixty nurses from a hospital in Depok, Indonesia, participated with HRV data collected via Polar H10 monitors before and after shifts alongside SOFI questionnaires. A mixed ANOVA revealed no significant between-subjects differences in HRV across morning, afternoon, and night shifts. However, within-subjects analyses showed pronounced parasympathetic rebound (elevated Mean RR) and sympathetic withdrawal (reduced Mean HR) post-shift, particularly after afternoon and night shifts, contrasting with stable profiles in morning shifts. Correlation analysis showed significant associations between SOFI dimensions, specifically lack of motivation and sleepiness, with HRV measures, indicating autonomic dysfunction and elevated stress levels. Several machine-learning classifiers were used to develop a fatigue detection model and compare their accuracy. The Fine Gaussian Support Vector Machine (SVM) model achieved the highest performance with 81.48% accuracy and an 81% F1 score, outperforming other models. These findings suggest that HRV-based fatigue detection integrated with machine learning provides a promising approach for continuous nurse fatigue monitoring.
On-Road Evaluation of an Unobtrusive In-Vehicle Pressure-Based Driver Respiration Monitoring System
Jain, Sparsh; Perez, Miguel A. (MDPI, 2025-04-26)
In-vehicle physiological sensing is emerging as a vital approach to enhancing driver monitoring and overall automotive safety. This pilot study explores the feasibility of a pressure-based system, repurposing commonplace occupant classification electronics to capture respiration signals during real-world driving. Data were collected from a driver-seat-embedded, fluid-filled pressure bladder sensor during normal on-road driving. The sensor output was processed using simple filtering techniques to isolate low-amplitude respiratory signals from substantial background noise and motion artifacts. The experimental results indicate that the system reliably detects the respiration rate despite the dynamic environment, achieving a mean absolute error of 1.5 breaths per minute with a standard deviation of 1.87 breaths per minute (9.2% of the mean true respiration rate), thereby bridging the gap between controlled laboratory tests and real-world automotive deployment. These findings support the potential integration of unobtrusive physiological monitoring into driver state monitoring systems, which can aid in the early detection of fatigue and impairment, enhance post-crash triage through timely vital sign transmission, and extend to monitoring other vehicle occupants. This study contributes to the development of robust and cost-effective in-cabin sensor systems that have the potential to improve road safety and health monitoring in automotive settings.
Machine Learning–Based Prediction and Optimization of Balanced Mixture Design Performance Indices
Tong, Bilin; Huang, Wenjiang; Habbouche, Jhony; Boz, Ilker; Guo, Qing; Diefenderfer, Stacey D.; Flintsch, Gerardo W. (SAGE Publications, 2025-04-26)
The balanced mix design (BMD) concept is an emerging methodology that facilitates the design of engineered asphalt mixtures. This approach is particularly beneficial for mixtures containing conventional and high reclaimed asphalt pavement, for which the traditional volumetric design methods may fail to effectively address the performance characteristics. However, given production variability, these engineered mixtures can still fail to meet the required thresholds. Additionally, identifying the cause of this imbalance is challenging. To maximize the benefits of BMD implementation, this study introduces machine learning (ML) algorithms including linear regression (LR), random forest (RF), extreme gradient boosting (XGB), and support vector regression (SVR) as strategic tools to predict mixtures’ BMD performance indices. 648 specimens fabricated for quality acceptance as part of the 2020 Virginia Accelerated Pavement Testing Program is used for the modeling and analysis. The durability, cracking, and rutting susceptibility of the specimens were evaluated using the Cantabro test, the indirect tensile cracking test (IDT-CT), and the asphalt pavement analyzer (APA) rut test. Key outcomes include: a) ML models, including RF, XGB, and SVR, demonstrated superior performance compared with LR; b) feature importance analysis from ML models identified dominant factors for each BMD test, also highlighting the reheating process; and c) a pseudo in situ deployment was simulated to optimize BMD implementation. The dimensionality reduction analysis—uniform manifold approximation and projection—highlighted the practical challenges associated with concurrently improving multiple performance metrics. Ultimately, the pivotal role of ML in advancing both the design and production phases was emphasized.
ShipNetSim: An Open-Source Simulator for Real-Time Energy Consumption and Emission Analysis in Large-Scale Maritime Networks
Aredah, Ahmed; Rakha, Hesham A. (MDPI, 2025-03-08)
The imperative of decarbonization in maritime shipping is underscored by the sector’s sizeable contribution to worldwide greenhouse gas emissions. ShipNetSim, an open-source multi-ship simulator created in this study, combines state-of-the-art hydrodynamic modeling, dynamic ship-following techniques, real-time environmental data, and cybersecurity threat simulation to quantify and evaluate marine fuel consumption and CO₂ emissions. ShipNetSim uses well-validated approaches, such as the Holtrop resistance and B-Series propeller analysis with a ship-following model inspired by traffic flow theory, augmented with a novel module simulating cyber threats (e.g., GPS spoofing) to evaluate operational efficiency and resilience. In a case study simulation of the journey of an S175 container vessel from Savannah to Algeciras, the simulator estimated the total fuel consumption to be 478 tons of heavy fuel oil and approximately 1495 tons of CO₂ emissions for a trip of 7 days and 15 h within 13.1% of reported operational estimates. A twelve-month sensitivity analysis revealed a marginal 1.5% range of fuel consumption variation, demonstrating limiting variability for different environmental conditions. ShipNetSim not only yields realistic predictions of energy consumption and emissions but is also demonstrated to be a credible framework for the evaluation of operational scenarios—including speed adjustment, optimized routing, and alternative fuel strategies—that directly contribute to reducing the marine carbon footprint. This capability supports industry stakeholders and policymakers in achieving compliance with global decarbonization targets, such as those established by the International Maritime Organization (IMO).
Motor vehicle traffic fatalities by race and ethnicity (2010 – 2021)
Chavez Orellana, Jacqueline; Witcher, Christina; Perez, Miguel A. (Elsevier, 2024-07-08)
Motor vehicle traffic fatalities (MVTFs) are a public health issue that substantially affects the growing Black or African American, Indigenous, and People of Color (BIPOC) population. To further understand the racial discrepancies that exist in MVTFs, data from the Fatality Analysis Reporting System (FARS) and U.S. Census were utilized to explore factors such as rurality, urbanicity, restraint use, and alcohol-impairment. Calculations considered yearly driver and occupant fatality rates per 100,000 population for each race and ethnicity from 2010 through 2021. A Poisson regression model was used to quantify the relationship between the MVTF rates and the factors of interest. Results demonstrated that the American Indian or Alaska Native population was statistically the most overrepresented group in fatality rates across all factors explored. Additionally, the American Indian or Alaska Native population and Black or African American populations were the only groups to have statistically significant increases in fatality rates in recent years when accounting for factors such as unrestrained vehicle driver/occupants and alcohol-impaired fatality rate. In contrast, the Native Hawaiian or Pacific Islander population has consistently experienced one of the largest statistically significant reductions in driver and occupant fatality rates over time. Further analysis is necessary to develop and implement countermeasures that may reduce the increasing fatality rates of the most vulnerable populations while continuing to decrease rates for others as well. Despite limitations of FARS and population data, these results provide a pathway to reducing MVTFs and associated racial inequities that exist in the nation, particularly as the BIPOC population continues to grow.
Simple Energy Model for Hydrogen Fuel Cell Vehicles: Model Development and Testing
Ahn, Kyoungho; Rakha, Hesham A. (MDPI, 2024-12-18)
Hydrogen fuel cell vehicles (HFCVs) are a promising technology for reducing vehicle emissions and improving energy efficiency. Due to the ongoing evolution of this technology, there is limited comprehensive research and documentation regarding the energy modeling of HFCVs. To address this gap, the paper develops a simple HFCV energy consumption model using new fuel cell efficiency estimation methods. Our HFCV energy model leverages real-time vehicle speed, acceleration, and roadway grade data to determine instantaneous power exertion for the computation of hydrogen fuel consumption, battery energy usage, and overall energy consumption. The results suggest that the model’s forecasts align well with real-world data, demonstrating average error rates of 0.0% and −0.1% for fuel cell energy and total energy consumption across all four cycles. However, it is observed that the error rate for the UDDS drive cycle can be as high as 13.1%. Moreover, the study confirms the reliability of the proposed model through validation with independent data. The findings indicate that the model precisely predicts energy consumption, with an error rate of 6.7% for fuel cell estimation and 0.2% for total energy estimation compared to empirical data. Furthermore, the model is compared to FASTSim, which was developed by the National Renewable Energy Laboratory (NREL), and the difference between the two models is found to be around 2.5%. Additionally, instantaneous battery state of charge (SOC) predictions from the model closely match observed instantaneous SOC measurements, highlighting the model’s effectiveness in estimating real-time changes in the battery SOC. The study investigates the energy impact of various intersection controls to assess the applicability of the proposed energy model. The proposed HFCV energy model offers a practical, versatile alternative, leveraging simplicity without compromising accuracy. Its simplified structure reduces computational requirements, making it ideal for real-time applications, smartphone apps, in-vehicle systems, and transportation simulation tools, while maintaining accuracy and addressing limitations of more complex models.
Assessment of the Production Variability and Composite Performance Index for Conventional and High Reclaimed Asphalt Pavement Balanced Mix Design Mixtures
Tong, Bilin; Habbouche, Jhony; Diefenderfer, Stacey D.; Flintsch, Gerardo W.; Boz, Ilker (Sage, 2024-11-08)
The balanced mix design (BMD) concept enables the design of engineered mixtures containing conventional and high reclaimed asphalt pavement (HRAP) contents, moving beyond the constraints of traditional volumetric design methodologies. During production, the designed mixture undergoes verification and potential modifications at the plant to accommodate actual production and field circumstances, regardless of the mix design method. This study assessed the impact of production and associated performance variability on a volumetrically designed control mixture and five mixtures designed with the BMD concept. This investigation showed relatively precise gradation control, but exceedances of volumetric property tolerances were observed in BMD-optimized mixtures during production. Performance, including durability, cracking, and rutting susceptibility, was evaluated using the Cantabro test, indirect tensile cracking test (IDT-CT), and asphalt pavement analyzer (APA) test, respectively. Test results uncovered that produced mixtures may become unbalanced. Observations from the Cantabro test and IDT-CT highlighted the necessity and effectiveness of employing the BMD for HRAP mixtures. The potential aging effect introduced during the reheating process may compromise durability and cracking resistance. In addition, a three-dimensional plot with a revised composite performance index (CPIR) was used to optimize the process of evaluating the mixture “balance” status among multiple primary performances. It revealed that almost all produced HRAP mixtures demonstrated a well-balanced status. Finally, agencies can use the CPIR as part of their acceptance program for BMD mixtures to determine a pay factor for possible bonuses or penalties.
Multi-level performance evaluation of BMD surface mixtures with conventional and high RAP contents: a case study in Virginia
Tong, Bilin; Habbouche, Jhony; Diefenderfer, Stacey D.; Flintsch, Gerardo W. (Taylor & Francis, 2024-03-07)
This study investigated one control and five Balanced Mix Design (BMD) optimised asphalt surface mixtures, four of which had high reclaimed asphalt pavement (RAP) contents (HRAP mixtures), using laboratory performance tests characterised with different levels of complexity. The performance of the evaluated mixtures was assessed based on durability, rutting resistance, and cracking resistance as emphasized by BMD. The study explored the ranking of a single index and correlations among various indices. Assisted by 3-Dimensional and ternary plots, this study also proposed a novel composite performance index [CPI] that combines major indices (durability, cracking, and rutting) to evaluate the performance of BMD optimised mixtures. The results revealed discrepancies between basic/intermediate performance test results and advanced performance test results. The comparisons conducted also underscored the beneficial impacts derived from using softer binders and/or recycling agents in HRAP mixtures. Furthermore, the findings indicated that the BMD approach can serve as an effective framework for designing asphalt mixtures that simultaneously enhance both fatigue and rutting performance. Moreover, the study revealed HRAP BMD surface mixtures can exhibit superior overall performance when compared to conventionally designed control mixtures.
Electric versus Gasoline Vehicle Particulate Matter and Greenhouse Gas Emissions: Large-scale Analysis
Rakha, Hesham A.; Farag, Mohamed; Foroutan, Hosein (2024-07-31)
This study addresses the contentious issue of non-exhaust particulate matter (PM) emissions from battery electric vehicles (BEVs) compared to internal combustion engine vehicles (ICEVs) by developing models to quantify tire and brake PM emissions and incorporate them in a microscopic traffic simulation environment. Furthermore, exhaust greenhouse gas (GHG) emissions are quantified to develop a comprehensive picture of vehicle network emissions. The key findings are: 1) BEVs emit more tire and less brake PM emissions, thus necessitating a comprehensive analysis to avoid erroneous conclusions. 2) If at least 15% of travel is city driving, BEVs produce less non-exhaust PM emissions. 3) For the freeway section analyzed, a volume-to-capacity ratio of at least 0.25 is required for BEVs to produce less non-exhaust PM emissions. By incorporating these detailed models into traffic simulations, the study provides a tool for policymakers to better understand and manage vehicle emissions at a city level.
Field Study of Asphalt Pavement Texture and Skid Resistance under Traffic Polishing Using 0.01 mm 3D Images
Yang, Guangwei; Chen, Kuan-Ting; Wang, Kelvin; Li, Joshua; Zou, Yiwen (MDPI, 2024-07-17)
Pavement texture and skid resistance are pivotal surface features of roadway to traffic safety, especially under wet weather. Engineering interventions should be scheduled periodically to restore these features as they deteriorate over time under traffic polishing. While many studies have investigated the effects of traffic polishing on pavement texture and skid resistance through laboratory experiments, the absence of real-world traffic and environmental factors in these studies may limit the generalization of their findings. This study addresses this research gap by conducting a comprehensive field study of pavement texture and skid resistance under traffic polishing in the real world. A total of thirty pairs of pavement texture and friction data were systematically collected from three distinct locations with different levels of traffic polishing (middle, right wheel path, and edge) along an asphalt pavement in Oklahoma, USA. Data acquisition utilized a laser imaging device to reconstruct 0.01 mm 3D images to characterize pavement texture and a Dynamic Friction Tester to evaluate pavement friction at different speeds. Twenty 3D areal parameters were calculated on whole images, macrotexture images, and microtexture images to investigate the effects of traffic polishing on pavement texture from different perspectives. Then, texture parameters and testing speeds were combined to develop friction prediction models via linear and nonlinear methodologies. The results indicate that Random Forest models with identified inputs achieved excellent performance for non-contact friction evaluation. Last, the friction decrease rate was discussed to estimate the timing of future maintenance to restore skid resistance. This study provides more insights into how engineers should plan maintenance to restore pavement texture and friction considering real-world traffic polishing.
Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles
Yang, Guangwei; Ridgeway, Christie; Miller, Andrew M.; Sarkar, Abhijit (MDPI, 2024-04-12)
Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

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