Scholarly Works, Virginia Tech Transportation Institute

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https://hdl.handle.net/10919/89575

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Adaptive Control of the Atmospheric Plasma Spray Process for Functionally Graded Thermal Barrier Coatings
Guduri, Balachandar; Batra, Romesh C. (Hindawi, 2022-11-23)
Functionally graded coatings (FGCs) have a material composition continuously varying through the thickness but uniform in the surface parallel to the coated substrate. When used as a thermal barrier on a metallic substrate, the coating composition varies from an almost pure metal near the substrate to a pure ceramic adjacent to the outer surface exposed to a hot environment. Challenging issues in producing high quality FGCs in the presence of external disturbances with an atmospheric plasma spray process (APSP) include controlling the mean temperature, the mean axial velocity, and the positions of the constituent material particles when they arrive at the substrate to be coated. The unavoidable disturbances include fluctuations in the arc voltage and clogging of the powder in the delivery system. For a two-constituent coating, this work proposes using three modified robust model reference adaptive controllers based on the σ-modified laws and low frequency learning. One controller adjusts the current and flow rates of argon and hydrogen into the torch. The other two controllers adjust the distance of the two powder injector ports from the plasma jet axis and the average injection velocity of each powder. It is shown through numerical experiments that the three controllers implemented in an APSP consistently produce high-quality FGCs.
Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control
Elouni, Maha; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2021-01-03)
This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between 21% and 41%, total delay ranging between 40% and 55%, and emission levels/fuel consumption ranging between 12% and 20%. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers.
Applicability of mesopic factors to the driving task
Gibbons, Ronald B.; Terry, Travis N.; Bhagavathula, Rajaram; Meyer, Jason E.; Lewis, A. (SAGE, 2016-02-01)
With the advent of light-emitting diode technology being applied to roadway lighting, the spectral power distribution of the light source is becoming much more important. In this experiment, the detection of pedestrians at five adaptation levels under three light sources, high pressure sodium and light emitting diodes of two colour temperatures was measured in realistic roadway scenarios. The results show that while the light source type was not significant, an increase in adaptation luminance increased the detection distance. As the offset of the object to the roadway increased, some spectral effects became more significant; however, this effect was not consistent across all angles of eccentricity. The conclusions from this work indicate that mesopic factors may not be applicable on high-speed roads.
Application of Balanced Mix Design Methodology to Optimize Surface Mixes with High-RAP Content
Meroni, Fabrizio; Flintsch, Gerardo W.; Diefenderfer, Brian K.; Diefenderfer, Stacey D. (MDPI, 2020-12-10)
The most common use of reclaimed asphalt pavement (RAP) is in the lower layers of a pavement structure, where it has been proven as a valid substitute for virgin materials. The use of RAP in surface mixes is more limited, since a major concern is that the high-RAP mixes may not perform as well as traditional mixes. To reduce risks or compromised performance, the use of RAP has commonly been controlled by specifications that limit the allowed amount of recycled material in the mixes. However, the ability to include greater quantities of RAP in the surface mix while maintaining a satisfying field performance would result in potential cost savings for the agencies and environmental savings for the public. The main purpose of this research was to produce highly recycled surface mixes capable of performing well in the field, verify the performance-based design procedure, and analyze the results. To produce the mixes, a balanced mix design (BMD) methodology was used and a comparison with traditional mixes, prepared in accordance with the requirements of the Virginia Department of Transportation’s volumetric mix design, was performed. Through the BMD procedure, which featured the indirect tensile cracking test for evaluating cracking resistance and the Asphalt Pavement Analyzer (APA) for evaluating rutting resistance, it was possible to obtain a highly recycled mix (45% RAP) capable of achieving a better overall laboratory performance than traditional mixes designed using volumetric constraints while resulting in a reduction in production cost.
AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research
Gabbard, Joseph L.; Smith, Missie; Tanous, Kyle; Kim, Hyungil; Jonas, Bryan (Frontiers, 2019-10-23)
Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.
Battery Electric Vehicle Eco-Cooperative Adaptive Cruise Control in the Vicinity of Signalized Intersections
Chen, Hao; Rakha, Hesham A. (MDPI, 2020-05-12)
This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.
Challenges in Conducting Empirical Epidemiological Research with Truck and Bus Drivers in Diverse Settings in North America
Soccolich, Susan A.; Ridgeway, Christie; Mabry, J. Erin; Camden, Matthew C.; Miller, Andrew M.; Iridiastadi, Hardianto; Hanowski, Richard J. (MDPI, 2022-09-30)
Over 6.5 million commercial vehicle drivers were operating a large truck or bus in the United States in 2020. This career often has high stress and long working hours, with few opportunities for physical activity. Previous research has linked these factors to adverse health conditions. Adverse health conditions affect not only the professional drivers’ wellbeing but potentially also commercial motor vehicle (CMV) operators’ safe driving ability and public safety for others sharing the roadway. The prevalence of health conditions with high impact on roadway safety in North American CMV drivers necessitates empirical epidemiological research to better understand and improve driver health. The paper presents four challenges in conducting epidemiological research with truck and bus drivers in North America and potential resolutions identified in past and current research. These challenges include (1) the correlation between driving performance, driving experience, and driver demographic factors; (2) the impact of medical treatment status on the relationship between health conditions and driver risk; (3) capturing accurate data in self-report data collection methods; and (4) reaching the CMV population for research. These challenges are common and influential in epidemiological research of this population, as drivers face severe health issues, health-related federal regulations, and the impact of vehicle operation on the safety of themselves and others using the roadways.
Checklist for Expert Evaluation of HMIs of Automated Vehicles—Discussions on Its Value and Adaptions of the Method within an Expert Workshop
Schömig, Nadja; Wiedemann, Katharina; Hergeth, Sebastian; Forster, Yannick; Muttart, Jeffrey; Eriksson, Alexander; Mitropoulos-Rundus, David; Grove, Kevin; Krems, Josef; Keinath, Andreas; Neukum, Alexandra; Naujoks, Frederik (MDPI, 2020-04-24)
Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.
Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques
Alrumaidhi, Mubarak; Farag, Mohamed M. G.; Rakha, Hesham A. (MDPI, 2023-06-21)
As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset’s inherent imbalance and enhance the models’ predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models’ performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving.
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.
Concrete Microstructure Characterization and Performance
Druta, Cristian (IntechOpen, 2020)
Microstructural characteristics such as the interfacial transition zone (ITZ) and cracking patterns from compressive strength testing are main features that characterize concrete behavior. Certain materials such as blast furnace slag or fly ash introduced in the concrete mix aid in improving its strength and durability. Others such as nanosilica particles may affect only the microstructure of the paste without making any significant improvement in the strength of the ITZ or pasteaggregate bond. Additionally, in situ investigation of the microstructures of fresh cement paste can greatly enhance knowledge of the development properties of concrete at an early age (e.g., setting and hydration), which can be helpful for improvement of the quality of concrete. Common technologies such as Scanning Electron Microscope (SEM) are currently employed in petrographic analysis of cementitious materials and concrete microstructure.
Countermeasures to Reduce Truck-Mounted Attenuator (TMA) Crashes: A State-of-the-Art Review
Mosunmola Aroke, Olugbemi; Sylvester Onuchukwu, Ikechukwu; Esmaeili, Behzad; Flintsch, Alejandra Medina (MDPI, 2022-05-09)
To support worker and driver safety, this study conducted a comprehensive literature review to identify methods of enhancing TMA visibility, improving work zone configurations, and ensuring worker safety. To increase TMA recognition, this study observed that the use of a 6-to-8-inch wide yellow and black inverted ‘V’ pattern of retroreflective chevron markings, sloped at a 45-degree angle downward in both directions from the upper center of a rear panel is effective in alerting drivers to work zones. This study also recommends applying amber and white warning LEDs, which flash in an asynchronous pattern at a 1 Hz frequency and are mounted against a solid-colored background for a 360-degree view visible at least 1500 feet from the work zone. In addition, a work zone vehicle configuration consisting of a lead, buffer, and advance warning truck with a buffer space between 100 and 150 ft is suggested to reduce the risk of lateral intrusions and TMA roll-ahead. In parallel, workers should wear high-visibility vests noticeable at a minimum distance of 1000 feet and headwear with at least 10 square inches of retroreflective material. Some intelligent transport systems are also suggested to enhance TMA recognition and potentially minimize work zone fatalities. Application of the recommended guidelines will potentially improve current practices and significantly reduce the occurrence of TMA crashes in construction and maintenance work zones.
Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health
Stern, Hal S.; Blower, Daniel; Cohen, Michael L.; Czeisler, Charles A.; Dinges, David F.; Greenhouse, Joel B.; Guo, Feng; Hanowski, Richard J.; Hartenbaum, Natalie P.; Krueger, Gerald P.; Mallis, Melissa M.; Pain, Richard F.; Rizzo, Matthew; Sinha, Esha; Small, Dylan S.; Stuart, Elizabeth A.; Wegman, David H. (Elsevier, 2019-05)
This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.
Decision-adjusted driver risk predictive models using kinematics information
Mao, Huiying; Guo, Feng; Deng, Xinwei; Doerzaph, Zachary R. (Elsevier, 2021-06)
Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.
Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications
Ahn, Kyoungho; Rakha, Hesham A. (MDPI, 2022-01-12)
This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models.
Developing a Neural–Kalman Filtering Approach for Estimating Traffic Stream Density Using Probe Vehicle Data
Aljamal, Mohammad A.; Abdelghaffar, Hossam M.; Rakha, Hesham A. (MDPI, 2019-10-07)
This paper presents a novel model for estimating the number of vehicles along signalized approaches. The proposed estimation algorithm utilizes the adaptive Kalman filter (AKF) to produce reliable traffic vehicle count estimates, considering real-time estimates of the system noise characteristics. The AKF utilizes only real-time probe vehicle data. The AKF is demonstrated to outperform the traditional Kalman filter, reducing the prediction error by up to 29%. In addition, the paper introduces a novel approach that combines the AKF with a neural network (AKFNN) to enhance the vehicle count estimates, where the neural network is employed to estimate the probe vehicles’ market penetration rate. Results indicate that the accuracy of vehicle count estimates is significantly improved using the AKFNN approach (by up to 26%) over the AKF. Moreover, the paper investigates the sensitivity of the proposed AKF model to the initial conditions, such as the initial estimate of vehicle counts, initial mean estimate of the state system, and the initial covariance of the state estimate. The results demonstrate that the AKF is sensitive to the initial conditions. More accurate estimates could be achieved if the initial conditions are appropriately selected. In conclusion, the proposed AKF is more accurate than the traditional Kalman filter. Finally, the AKFNN approach is more accurate than the AKF and the traditional Kalman filter since the AKFNN uses more accurate values of the probe vehicle market penetration rate.
Developing and Field Testing a Green Light Optimal Speed Advisory System for Buses
Chen, Hao; Rakha, Hesham A. (MDPI, 2022-02-17)
In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively.
Development and Evaluation of a Cellular Vehicle-to-Everything Enabled Energy-Efficient Dynamic Routing Application
Farag, Mohamed M. G.; Rakha, Hesham A. (MDPI, 2023-02-19)
Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings).
Does the Interaction between Vehicle Headlamps and Roadway Lighting Affect Visibility? A Study of Pedestrian and Object Contrast
Bhagavathula, Rajaram; Gibbons, Ronald B.; Nussbaum, Maury A. (SAE International, 2020-04-14)
Vehicle headlamps and roadway lighting are the major sources of illumination at night. These sources affect contrast-defined as the luminance difference of an object from its background-which drives visibility at night. However, the combined effect of vehicle headlamps and intersection lighting on object contrast has not been reported previously. In this study, the interactive effects of vehicle headlamps and overhead lighting on object contrast were explored based on earlier work that examined drivers' visibility under three intersection lighting designs (illuminated approach, illuminated box, and illuminated approach + box). The goals of this study were to: 1) quantify object luminance and contrast as a function of a vehicle's headlamps and its distance to an intersection using the three lighting designs; and, 2) to assess whether contrast influences visual performance and perceived visibility in a highly dynamic intersection environment. Both luminance and contrast of roadway visibility targets and a pedestrian were measured with a calibrated photometer at a realistic intersection. Both target and pedestrian contrast and luminance were substantially affected by the intersection lighting configuration, illuminance level, location at the intersection, and vehicle distance from the intersection. Objects also underwent changes in contrast polarity (positive to negative or vice-versa) as the distance between the vehicle and object changed. During these polarity transitions, objects became invisible because the contrast was zero. Negative contrast on targets was associated with higher visual performance. Within a given contrast polarity (positive vs. negative), visual performance depended on the magnitude of contrast, with higher contrast associated with higher visual performance. The relationship between pedestrian contrast and perceived visibility was complex, since pedestrians were often rendered in multiple contrasts. These findings have important implications for the lighting design of intersections and the development of nighttime pedestrian detection systems that rely on computer vision.
An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19
Alrumaidhi, Mubarak; Rakha, Hesham A. (MDPI, 2024-02-01)
This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape.

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