Symposium Schedule

Analyzing the Role of Air-Transportation in COVID-19 Pandemic Disaster

COVID-19 pandemic has resulted in an over 60% reduction in airtravel worldwide according to some estimates. The high economic and public perception costs of potential superspreading during air-travel necessitates research efforts that model, explain and mitigate disease spread. The long-duration exposure to infected passengers and the limited air circulation in the cabin are considered to be responsible for the infection spread during flight. Consequently, recent public health measures are primarily based on these aspects. However, a survey of recent on-flight outbreaks indicates that some aspects of the COVID-19 spread, such as long-distance superspreading, cannot be explained without also considering the movement of people. Another factor that could be influential but has not gained much attention yet is the unpredictable passenger behavior. Here, we use a novel infection risk model that is linked with pedestrian dynamics to accurately capture these aspects of infection spread. The model is parameterized through spatiotemporal analysis of a recent superspreading event in a restaurant in China. The passenger movement during boarding and deplaning, as well as the in-plane movement, are modeled with social force model and agent-based model respectively. We utilize the model to evaluate what-if scenarios on the relative effectiveness of policies and procedures such as masking, social distancing, as well as synergistic effects by combining different approaches in airplanes and other contexts. We find that in certain instances independent strategies can combine synergistically to reduce infection probability, by more than a sum of individual strategies.

Multiscale Model for Hurricane Evacuation and Fuel Shortage

High-volume evacuations, disruptions to the supply chain, and fuel hoarding from non-evacuees have led to localized fuel shortages lasting several days during recent hurricanes. While news reports mention fuel shortages in past hurricanes, the crowdsource platform Gasbuddy has quantified the fuel shortages in the recent hurricanes. The analysis of this fuel shortage data suggested fuel shortages exhibited characteristics of an epidemic. Here, a Susceptible- Infected-Recovered (SIR) epidemic model is developed to study the evolution of fuel shortage during a hurricane evacuation. Additionally, we apply optimal control theory to identify an effective intervention strategy. The study found a linear correlation between traffic demand during the evacuation of Hurricane Irma and the resulting fuel shortage data. This correlation is used in conjunction with the Statewide Regional Evacuation Study Program (SRESP) surveys to estimate the evacuation traffic and fuel shortages for potential hurricanes affecting south Florida. Results indicate that evacuation of Miami-Dade County in the event of a Category-3 hurricane landfall in the region, could lead to fuel shortages in up to 90% of the local refuelling stations. The model indicates that this reduces to 28% by providing relief to 75% of the gas stations during the first two days of the evacuation.

Epidemiological Models for Transportation Applications: Secondary Crashes

Secondary crashes occur in the aftermath of a primary crash, increasing the likelihood of subsequent crashes, reducing highway capacity, high-density queues, and increasing travel time uncertainty. In this study, we developed a self-exciting temporal point process model to evaluate and categorize the crash event dataset into primary and secondary crashes. The model uses a background rate function to represent primary crashes and a self-exciting function to represent secondary crashes. We applied the model to crash data from the Florida Department of Transportation on Interstate-4 (I-4) highway from 2015 to 2017 to determine the model parameters. Based on the model parameter, the probability of the given crash to secondary crash is calculated, and also the queue time. The model is investigated in six different cities on I-4. Initially, the model is fit using a stationary background rate. However, the result from the stationary background rate model does not sufficiently fit the data since it is based on the premise that crash events are invariant to any external factors. Therefore to fit the periodic variation of traffic and crash incidents for weekly and daily trends, we model a sinusoidal non-stationary function and a piecewise non-stationary function. The goodness-of-fit of the models is assessed using Akaike Information Criterion (AIC) values for each model. When comparing the performance of the stationary and non-stationary background rate model, the AIC values for stationary background rate are greater. This shows that the stationary background rate model has a higher prediction error when compared to other models. We were able to fit the crash data with non-stationary background rate models accurately and generate queue time curves with peaks on Fridays and troughs on Sundays, which matched the crash data. Furthermore, the sinusoidal background rate model outperforms the piecewise function. Using the sinusoidal non-stationary background rate model, we find that secondary crash events account for 3.38 percent to 15.09 percent of the traffic incidents on the I-4. An average queue time of 82.5 minutes is obtained for the non-stationary background rate model using the sinusoidal function. The results of the point process model compare favorably to those of other models for identifying secondary crashes in literature. The proposed model can be used to create policies and countermeasures that aim to reduce the risk of secondary crashes. Based on the probability distribution of secondary crashes and the average queue time, the proposed model's results can be utilized as a reference to inform Traffic Incident Management (TIM) to clear the traffic incident scene. The exposure of secondary crashes is reduced drastically by clearing the crash scene effectively.

Usability of Urban Air Mobility: Quantitative and Qualitative Assessments of Usage in Emergency Situations

The purpose of these studies is to determine the usability of urban air mobility (UAM) vehicles in the emergency response to natural disasters and the ideal locations for their take-off and landing sites to occur, consistent with the Center's Theme 2. UAM involves aerial vehicles, mostly operated autonomously, which can complete short flights around urban areas, although their applications are expanding to rural operations as well. While initially designed to support advanced transportation mobility, these vehicles could offer numerous advantages in the emergency response to natural disasters. Through a series of four studies with over 2,000 total participants, quantitative and qualitative methods will be used to identify UAM vehicles' usability in response to natural disasters. The studies will examine the types of natural disasters and types of missions where UAM could be considered usable, along with the creation of a valid scale to determine vertiport usability. Interviews will also be conducted to provide qualitative insights to complement the quantitative findings.

Dynamic Routing Of Unmanned-aerial and Emergency Team Incident Managements

Unmanned aircraft vehicles (UAVs) have been actively used for crash scene reconstruction and Lancashire fire and rescue. However, there has been a lack of application of UAVs to emergency (ambulatory) response vehicles (ERVs) and a successful usage will lead to a quicker response to the emergency site to save lives, reduce secondary crash occurrences, which are more frequent than disasters, and reduce delays to the vulnerable users. UAVs can be coordinated with ERVs, but without an automated framework, it is challenging to adapt to the revision of FAA rules that already have been announced to accommodate more advanced operations. To maximize the efficiency, this project will make a new generation of research in UAV-guided ERV Routing and with a real-world validation with a small example at the VTTI test site. In a two-step stochastic dynamic program, UAV will be operated to provide useful information on traffic conditions of potential ERV routes. We will provide the framework to apply our model to catastrophic emergency scenarios (e.g., hurricane) when some traffic sensors are not working properly and require more UAV assistance. Markov processing model will be developed to predict evolving traffic conditions and future dependent emergencies in high-performance computing and visualization environments.

Multi-agent Reinforcement Learning-based Pedestrian Dynamics Models for Emergency Evacuation

While there are numerous successful models like the social force model and agent-based models that address high-density crowds, there is a glaring lack of effective modeling techniques targeted at low-to-medium-density pedestrian situations. Furthermore, previous studies have focused on either pedestrians' route planning or pedestrians' physical movements without considering the interactions between these two levels. This project will integrate these two levels to dynamically plan routes and control pedestrian movements during plan execution. The information of the local environment and human behavioral characteristics are formulated into a reward matrix to re-plan pedestrians' paths or adapt to the changes in the environments. This facilitates the modeling of the nonlinear characteristics of the human decision-making processes beyond simple rule-based models. We will develop the computational modeling framework and simulate the emergency evacuation of a midsize airport. We will develop a reinforcement learning model to learn local navigation behaviors and simulate dynamic pedestrian behaviors. This model will be utilized to determine intermediate goals for each pedestrian particle, which is a key input for the time evolution of pedestrian trajectories. The project outcome will lead to a multidisciplinary computational framework for understanding and modeling the human decision-making process and resulting actions in emergency evacuations.

Travelers' Rationality in Online Anticipatory Emergency Response Model

Traditional decisions in response to requests for emergency vehicle resources do not account for traffic flow behavior, nor the rationality (i.e., ability to make wise and sound decisions) of travelers in the transportation network. Existing models have prioritized the fastest response, regardless of the severity of the incidents or potential need for additional emergency resources in a later stage in the incident chain. In this research, an approach from a patent titled “Transportation Infrastructure Location and Redeployment,” issued by the United States Patent and Trademark Office (USSN 16/254,474), has been modified to accommodate online models, consider the availability of emergency vehicle resources in the near future (Figure 1), and minimize the total travel time delay encountered by the users in the transportation network.

Vulnerable Road Users Demand-responsive Transit Optimization with Healthcare Privatization (VRUTOP)

This research focuses on improving the transit service of vulnerable road users while addressing recent trends in Medicaid transformation. One of the most notorious issues in this transit optimization problem is the difficulty of knowing how much added time cushion should be considered for picking-up each user and transit time. This temporal time uncertainty will be uniquely formulated by taking advantage of the real-world data collected before and after the Medicaid transformation, which will make this research a pioneer in demand response transportation systems. The time-uncertainty model will be designed to improve both fixed and flexible transit operations. The VRUTOP project aims to improve the access to health care in underserved areas using public transportation and Mobility as a Service (MaaS), while considering Medicaid shifts towards privatization with two main objectives:

1. model the interactions between the key contributing trip characteristics influencing the uncertainty of time window, and
2. using these unique patterns, provide a new service tool for paratransit.

Alternative operating strategies will be simulated in the optimal policy developed and change the objective function to various performance measures including wait times, transit system volumes, and costs. This initial model will be extended to accommodate real-time operational improvement scenarios (e.g., deviated fixed routes from demand response patterns), create zone-based service structures, and design performance measures, targeted for Medicaid and non-Medicaid populations of trips or individuals to be used in the simulation. Supported by actual data in collaboration with concurrent projects from local state agencies, VRUTOP is expected to generate numerous scholarly activities and provide an easy-to implement tool for state agencies, practitioners and other researchers.

VRU-Personalized, Optimum, and Dynamic (POD) Routing

Transport is a challenge for vulnerable road users, reducing their participation in social and recreational activities. Real-time mixing and matching transport-means from private, public, and on-demand transportation at different pick-up locations is a solution that improves mobility, providing the best multimodal transport options to wheelchair users, who are sensitive to dynamic environmental barriers. A dynamic route planning for wheelchair users is required to account for all constraints changing by time with a cost function. Although transportation agencies have favored a simpler static route planning, static planning is only satisfactory when conditions of intermediate nodes in the transportation networks are consistent and the same fixed routes are valid every day. Recalculating the static version without modeling nonlinear function for cost to the destination may not appropriately reflect vulnerable road users' personal preference and tolerance to time. Especially, congested urban areas have less reliable transit arrival time at a stop which significantly increases the cost of the originally planned route for users with disabilities. Through personalization, optimization, and simulation, the proposed project will incorporate multimodal transportation information into the optimal route search. Artificial intelligence (AI) based algorithms to deal with the complexity of algorithms and efficiently find optimal solutions.

Machine Learning for Dynamic Airspace Configuration towards Optimized Mobility in Emergency Situations

The objective of this project is to develop a prototype for dynamic airspace configuration (DAC) through the use of machine learning (ML) techniques, to achieve optimized mobility in emergency situations. Disasters, such as hurricanes, tornadoes, and thunderstorms, affect many people and cause severe economic losses every year. When disasters occur, air travel is an efficient mode of transportation for emergency evacuation. With large volume of travelling occurring in short period of time, the current structured, static airspace cannot accommodate rapid increases in traffic demand during emergency situations. An adaptive and dynamic scheduling program for the air travel during the crisis is in demand. The purpose of this study is to enable dynamic airspace configuration (DAC) to optimize air mobility in emergency evacuation. We propose to identify, apply, and evaluate machine learning (ML) techniques as they relate to DAC. The anticipated outcome is a prototype that would demonstrate the ML-augmented capability supporting DAC.

Multi-scale and Collaborative Disaster Evacuation Planning Framework

Extreme weather conditions, such as floods, hurricanes, and wildfires, cause large-scale human population movements and evacuations in the world. Taking flights to evacuate the area before a disaster approach is mostly preferred because it is much more efficient than driving or taking trains. However, making effective flight plans serving evacuation before these natural disasters occur is a significant challenge because it relies heavily on many dynamic factors including human resources, environments, and traffic loads.

In this study, we propose an adaptive flight dispatching framework that aims to coordinate available resources such as aircraft and airports to transport people from evacuation areas to safe places. We formulate the evacuation flight dispatch as a weighted graph matching problem where weights between edges (aircraft-airway) indicating the dynamics of the air route network are inferred by machine learning approaches. The problem is solved by a learning and planning manner: (a) in the offline learning phase, we first summarize demand (evacuation airway) and supply (aircraft) patterns into a spatiotemporal quantization, each of which refers to expected value of an aircraft/pilot being in a particular state. Furthermore, in order to tackle uncertainty in the complex air route network for real-time air traffic management, we apply elaborated machine learning models to estimate air mobility indicators accurately in a local view; (b) in the online planning phase, the dispatch is solved using the combinatorial optimization approach, where each aircraft-airway pair is valued considering both immediate rewards and future gains that are attained based on the trained predictive machine learning model and the learned unified evaluation metric on spatiotemporal states.

Our framework is evaluated using data collected during Hurricane Irma, and we also conducted extensive experiments in terms of limited resources, incident response, and uncertainty of external factors, which demonstrate the effectiveness and robustness of the proposed schema in the real-world application. It is believed that our model will be beneficial for real-time evacuation planning and decision-making for a wide range of situations that involve air traveling.

Modeling Future Outbreaks of COVID-19 Using Traffic as Leading Indicator

The movement of people is inherently connected to the spread of viral diseases. Infected individuals expose others as they travel between home, work, school, shopping, and recreation destinations. Understanding the relationship between social/economic activity and the spread of COVID-19 could prove invaluable, as the nation looks to reopen. Unfortunately, some states that reopened first are experiencing spikes in COVID-19 cases. For example, Florida, which entered Phase 1 of the reopening process on May 18, 2020, recorded significant increases in the daily number of COVID-19 cases approximately two weeks later and by June 9th saw the highest single day increases in positive cases. Prior research has demonstrated how drastic changes in human behavior can be measured using highway volume data as a representation of personal activity. As states begin to reopen, it would appear that increases in highway traffic might be a leading indicator of where and when outbreaks of COVID-19 are likely to occur. This research will investigate and model the relationship between roadway traffic and viral outbreaks. The traffic informed SIR model developed by this research will help identify where and when second wave outbreaks are likely to occur and assist in the planning of recovery effort.

Multi-scale Models for Transportation Systems Under Emergency

In recent years natural disasters have caused significant disruptions to transportation systems, which had to cascade negative impacts on humanitarian operations, related infrastructure, and associated industries in the affected areas. How to prepare for and respond to transportation system disruptions is a complex decision incorporating a variety of factors, from system use to system preparation. To address these challenges, the project team has developed optimization models for flight rescheduling and road restoration after a natural disaster and integrated the models as a decision-making tool. The data of North Carolina emergency response activities, air flights, and road closures during Hurricane Matthew were used to test the models and tool. The testing results show that the integrated tool can quickly find optimal sets and sequences for road restoration and flight schedules recovery at an affected airport and 50 counties. The tool can also visualize the damaged connections between counties, airports and resource centers, and the road restoration schedule and flight schedules recovery plan. The optimization models and decision-making tool developed in this project can support deploying effective restoration and recovery of transportation systems during an emergency event, which can improve the mobility of people and disaster relief under emergency.

Real-Time Recommendations for Traffic Control in an Intelligent Transportation System during an Emergency Evacuation - Part 1

Recent hurricanes such as Irma (2017) and Florence (2018) caused mass evacuations and the issues occurring during the evacuations have been brought to the public attention. To address these issues, the project team has investigated significant cues for evacuation planners' decisions using a Linear Lens Model and machine learning algorithms, and has analyzed traffic data during hurricane evacuations in North Carolina to discover spatial-temporal evacuation traffic patterns and create predictive models of hurricane evacuation traffic volumes. Our results show that only one of the seven cues tested (wind speed) contributes to evacuation planners' decisions and the sensor locations at the same county and adjacent counties form the cluster for evacuation traffic prediction. These models and findings can support the deployment of an effective evacuation and improve the mobility of the people and evacuation resources during a hurricane. We have also proposed and tested the quantitative methods to quantify a hurricane disruption to the U.S. airport network and identify feasible airports to reroute disrupted flights. Our results show that the proposed methods can identify the airports to be disrupted by an approaching hurricane and feasible airports for flight rerouting, which can support airlines administrators to divert flights from the affected airports.

Real-Time Recommendations for Traffic Control in an Intelligent Transportation System during an Emergency Evacuation - Part 2

Recent hurricanes caused mass evacuations and brought attention to many issues and challenges during these mass evacuations. Effective and proper traffic control is crucial during an emergency evacuation. Moreover, diversity in human evacuation behavior (e.g., leaving versus staying, and different evacuation times and routes chosen by individuals) should be considered in the planning and implementation of an emergency evacuation. In this project, we developed a Lens model framework to quantify decision behavior of individuals in Air Traffic Conflict Judgment Task Environment using Machine Learning algorithms, which extended a single system design Lens model to a double system design using Monte-Carlo (MC) and Latin hyper cube sampling (LHS). We also developed an approach for multimodal rescheduling of airline passengers integrating network theory to mitigate passenger disruption during a hurricane, and proposed a multi-commodity network flow model. In addition, we developed a hurricane evacuation simulation model using an open-source agent-based transport simulation (MATSim) framework to estimate evacuation traffic volumes and patterns under different scenarios.

Connected electric vehicles: Vehicle-pedestrian communications to enhance vision impaired pedestrian safety

The steady increase of electric vehicles (EVs) on roadways has led to safety concerns for vulnerable populations. The electric motor utilized in EVs produces considerably less noise compared to the internal combustion engine (ICE) in gasoline-powered vehicles, especially when traveling at slow speeds. Although pedestrians across all demographics are at risk, visually impaired pedestrians face significantly greater disadvantages in environments where ambient noise levels are high in relation to EV noise output. A major reason for this is because they depend on auditory cues to discern traffic flow when making life-threatening decisions such as crossing complex intersections or walking through city streets. Considering traffic data provided by the National Highway Traffic Safety Administration (NHTSA), the aim of the proposed research is to improve the lives of vulnerable pedestrians by determining the effects of different vehicle-to-pedestrian (V2P) alert systems on signal-response times. This study seeks to: (1) identify and evaluate modes by which V2P systems can be effectively implemented for use in urban street crossing environments; (2) determine the efficacy of V2P systems in relation to pedestrian reaction time; and (3) obtain evidence to support the need for non-intrusive V2P alert systems as a safety precaution for vulnerable road users.

Acoustic Situation Awareness and Its Effects on Pedestrian Safety within a Virtual Environment

A major component of the U.S. Department of Transportation's (DOT) mission is to focus on pedestrian populations and how to enable safe and efficient mobility for vulnerable road users. However, evidence states that college students have the highest rate of pedestrian accidents. Due to the excessive use of personal listening devices (PLDs), vulnerable road users have begun subjecting themselves to reduced levels of achievable situation awareness resulting in risky street crossings. The ability to be aware of one's environment is critical during task performance; however, the desire to be self-entertained should not interfere or reduce one;s ability to be situationally aware. The current research seeks to investigate the effects of acoustic situation awareness and the use of PLDs on pedestrian safety by allowing pedestrians to make “safe” vs. “unsafe” street crossing within a simulated virtual environment. The outcomes of the current research will (1) provide information about on-campus vehicle and pedestrian behaviors, (2) provide evidence about the effects of reduced acoustic situation awareness due to the use of personal listening devices, and (3) provide evidence for the utilization of vehicle-to-pedestrian alert systems.

Equitable Dynamic Pricing for Express Lanes

Congestion pricing implementations such as express lanes mitigate traffic congestion by internalizing the congestion externality in travelers' costs while generating much-needed revenue for infrastructure projects. Dynamic tolls on express lane facilities raise equity concerns: do these facilities leave the economically-disadvantaged travelers worse off? Real-world case studies reveal that express lane usage is more impacted by factors other than income such as travelers' residential location and urgency of travel purpose. However, the choice of dynamic tolls can significantly skew the distribution of benefits towards travelers' who are already well off. For example, tolls as high as $47 on express lanes in Virginia might be too high for low-income travelers to afford. Similarly, revenue-maximizing tolls exhibit a jam-and-harvest (JAH) phenomenon where the regular lanes are unintentionally jammed in earlier time periods to harvest more revenue later, which can cause inequitable outcomes.

In this research, we analyze the equity concerns posed by express lanes in the design of dynamic tolls. Methodologically, we will contribute to the state-of-the-art equity considerations for express lanes by (a) quantifying the factors that contribute to JAH as an unintended consequence of tolling and (b) identifying variables for a system-level measurement of equity and creating component lane-choice and traffic flow models to measure those variables in our modeling framework. Building on the choices of component models such as lane choice and traffic flow models, toll-optimization methods are used to optimize differential toll prices such that the equity gap is minimized.

Development, Design, and Calibration of the Vulnerable Road User Mobility Assistance Platform (VRU-MAP)

For people with physical disabilities, navigation through complex environments can pose a significant challenge, particularly when the environment is unfamiliar. The goal of the Vulnerable Road User Mobility Assistance Platform (VRU-MAP) project was to develop an application platform that can assist people with disabilities in planning and executing pedestrian or multimodal trips in urban environments. The project resulted in a prototype smartphone/tablet application that can provide route guidance that takes into consideration conditions that may prove inconvenient, frustrating or debilitating to individuals with mobility disabilities, including people in wheelchairs. Through the integration of novel and existing technologies, including mapping, weather, traffic, and accessibility information, combined with personal information about the specific needs of the user, this project provides a robust set of tools that can address the needs of individuals who have limited mobility.

Analysis of the Non-Driving Mobility Needs of People with Disabilities

This study was conducted to enable better understanding of the mobility-related needs and barriers facing people with disabilities (PWD) in the United States. A nationwide survey was conducted, with follow-up interviews, were conducted of travelers with a range of disabilities. Overall, this study provided significant insight into the broad range of challenges facing PWD as they traverse the built environment. These people represent a heterogeneous population with diverse functional impairments living in a wide range of areas, with different access to public transportation and paratransit. The findings of this project underline that technology and transportation advancement must be done with significant input from the disability community, and with consideration of the wide range of voices within this community. Only then can equity be ensured in our future transportation environment.

ROAD TRIP (Rural Older Adult Driver Tailored Research-Integrated Plan)

Older adult drivers face many challenges as they age — physically, perceptually, and cognitively. These may affect their ability to drive, which is crucial, as well as their mobility in general. Those older adults living in rural areas may face even greater impediments to mobility and access to services due to the lack of infrastructure or feasible alternative transportation options. Several tools exist to aid drivers or extend their capabilities to maintain mobility for longer periods of time. To date, these tools have not been applied or integrated in a systematic fashion for rural senior drivers. This effort will utilize available tools to create customized driving plans for seniors. Once developed, the program will be implemented with 30 seniors and evaluated in a naturalistic data collection study. Participants will be assessed along a variety of dimensions, then drive for one month, demonstrating patterns and concerns. The program will then be tailored and delivered for each. Their driving will then be monitored for two additional months to evaluate program efficacy. Results will highlight the importance of customized solutions that focus on specific needs rather than a blanket approach. The toolbox and technologies utilized will be considered a living document, one that can be altered based on emerging new technologies and learnings.

Detecting Early-Stage Dementia Using Naturalistic Driving

Research has demonstrated that seniors exhibiting early-stage Alzheimer Disease (AD) brain pathology made more driving errors than a similar-aged cohort without such biomarkers during a standardized real-road driving test (Roe et al., 2017). However, participants' composite scores on a standard battery of cognitive tests were not related to the observed number of driving errors. In other words, driving behaviors may serve as the virtual canary in the coal mine (i.e., early warning sign) for detecting AD or other forms of mild cognitive impairment (MCI) prior to the point where cognitive tests can detect issues or where biomarkers have been clinically observed (Hendrix, 2012). An in-vehicle driving rehabilitation specialist administered the driving test employed by Roe and her colleagues. On the other hand, a naturalistic driving study (NDS) would provide an opportunity to observe how such individuals perform in the context of their everyday driving routines, without the presence (and possible confounding effects) of an onboard experimenter.

Therefore, the primary objectives of the proposed study are to collect novel NDS data from pre-MCI patients to examine their driving behaviors and performance and to compare these with NDS data collected from a similar-aged cohort of individuals with no indication of such impairment. In this instance, Pre-MCI is operationalized as experiencing a cognitive functional decline without ever having received any formal diagnosis of a related malady. A key question to be examined is whether the driving patterns of older adults so classified begin to demonstrate measurable risk-increasing deficits prior to the point where other more standardized methods of cognitive screening can detect impairment.

Advanced driver assistance systems (ADAS) are rapidly proliferating into the U.S. fleet. ADAS include a broad array of tools, including adaptive cruise control (ACC), blind spot warning (BSW), and lane keep assist (LKA) or lane centering (LC). These features may help to supplement the declining capabilities of seniors experiencing pre-MCI and help them to drive longer and more safely; therefore, another objective is to determine if such individuals driving ADAS-equipped vehicles use these technologies in a manner that enhances safety and mobility.

In all, ten individuals, five who meet the aforementioned definition of pre-MCI and five who do not report experiencing any functional cognitive decline, all of whom drive ADAS-equipped vehicles on a regular basis, will be recruited to participate in the driving portion of the study for 1 month each. Outcome metrics will include safety-critical events as well as mobility-based metrics.

Hendrix, S.B. Measuring clinical progression in MCI and pre-MCI populations: enrichment and optimizing clinical outcomes over time. Alz Res Therapy 4, 24. https://doi.org/10.1186/alzrt127

Roe, C. M., Barco, P. P., Head, D. M., Ghoshal, N., Selsor, N., Babulal, G. M., Fierberg, R., Vernon, E. K., Shulman, N., Johnson, A., Fague, S., Xiong, C., Grant, E. A., Campbell, A., Ott, B. R., Holtzman, D. M., Benzinger, T. L., Fagan, A. M., Carr, D. B., … Morris, J. C. (2017). Amyloid imaging, cerebrospinal fluid biomarkers predict driving performance among cognitively normal individuals. Alzheimer disease and associated disorders, 31(1), 69-72.

Automated Last Mile Connectivity for Vulnerable Road Users

An EasyMile EZ10 Low-Speed Autonomous Vehicle was deployed on a route between the Virginia Tech Transportation Institute (VTTI) campus and a nearby bus transit stop as part of a study focusing on prospective user attitudes and acceptance with regard to trust in technology, system safety, and personal security. The LSAV operated on this route within normal travel lanes and interacted with mixed public traffic that included the full range of transportation users from pedestrians to heavy vehicles.

Mask-Wearing Behaviors in Air Travel During Coronavirus Pandemic: An Extended Theory of Planned Behavior Model

The COVID-19 pandemic has devastated the air transport industry, forcing airlines to take measures to ensure the safety of passengers and crewmembers. Among the many protective measures, mask mandate onboard the airplane is an important one, but travelers' mask-wearing intentions during flight remain uninvestigated especially in the US where mask use is a topic of on-going debate. This study examines the intention of airline passengers to wear a mask when they fly during COVID-19. An extended theory of planned behavior (TPB) model was developed to examine the relationship between nine predicting factors and the mask-wearing intention in the aircraft cabin. A survey instrument was developed to collect data from 1,124 air travelers on Amazon Mechanical Turk, and the data was statistically analyzed using structural equation modeling and logistic regression. Results showed that attitude, descriptive norms, risk avoidance, and information seeking significantly influenced the travelers' intention to wear a mask during flight in COVID-19. Group analysis further indicated that the four factors influenced mask-wearing intentions differently on young, middle-aged, and senior travelers. It was also found that demographic and travel characteristics including age, education, income, and travel frequency can be used to predict if the airline passenger was willing to pay a large amount to switch to airlines that adopted different mask policies during COVID-19. The findings of this study fill the research gap of air travelers' intentions to wear a mask when flying during a global pandemic and provide recommendations for mask-wearing policies to help the air transport industry recover from COVID-19.

Evaluation of Web-Based Driving Feedback for Teens and their Parents

Teen drivers will participate in a six-month driving study that will yield comprehensive data on several driving performance measures. This feedback will be available on a website provided by the research team. Two versions of driving feedback for teens and their parents will be evaluated along several dimensions. The effectiveness of each type of driving feedback will be measured by examining teen driver safety (e.g., kinematic risky driving events, speeding, etc.). The usability and acceptability of this feedback will also be measured by analyzing the number of website logins and duration of time spent on the website. Parent and teen focus groups and/or interviews will also be conducted to obtain qualitative data on user preferences and acceptability.