Browsing by Author "Murray-Tuite, Pamela"

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    Emergency Vehicle-to-Vehicle Communication
    Murray-Tuite, Pamela; Phoowarawutthipanich, Aphisit; Islam, Rauful; Hdieb, Naser (Connected Vehicle/Infrastructure University Transportation Center, 2016-08-15)
    Emergency response vehicles (ERVs) frequently navigate congested traffic conditions to reach their destinations as quickly as possible. In this report, several efforts performed by the research group are described, including micro-simulation, field-testing, and optimization, to determine mechanisms for facilitating safe and efficient ERV travel. Micro-simulation of a network based on the Northern Virginia Connected Vehicle Test Bed examined the effect of a variety of factors on ERV travel time, including the presence of vehicle-to-vehicle (V2V) communication, traffic volumes, cycle length, ERV speed distributions, non-ERV speed distributions, and traffic signal preemption. The results indicated that V2V communication could reduce travel time for an ERV in congested traffic conditions. The research group developed a V2V communication prototype to alert non-ERVs of an approaching ERV by triggering a flash of the infotainment system, followed by audible instructions to move to the left, move to the right, or stay put. Twelve drivers, aged 25 to 50, tested the V2V prototype on the Northern Virginia Connected Vehicle Test Bed during off-peak periods. Data from this field test and associated questionnaires were used to investigate reaction time to the instructions. The estimated reaction times using the developed model varied from 1.4 to 5.8 seconds. A mixed-integer nonlinear program (MINLP) optimization model was formulated to maximize the forward progress of ERVs by sending information to ERVs and non-ERVs within a given road segment. A single set of instructions was sent to each non-ERV, assigning them to a location out of the ERVs path. Numerical case analysis for a small, uniform section of roadway with a limited number of non-ERVs revealed the model is capable of optimizing the behavior of non-ERVs to maximize the speed of the ERV.
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    Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States
    Singh, Meghendra; Sarkhel, Prasenjit; Kang, Gloria J.; Marathe, Achla; Boyle, Kevin J.; Murray-Tuite, Pamela; Abbas, Kaja M.; Swarup, Samarth (2019-03-04)
    Background Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. Methods We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia. Results We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions. Conclusions By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza.
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    Modeling Evacuees' Intended Responses to a Phased Hurricane Evacuation Order
    Bian, Ruijie; Murray-Tuite, Pamela; Trainor, Joseph; Edara, Praveen; Triantis, Konstantinos (MDPI, 2023-04-21)
    Phased evacuation is an under-studied strategy, and relatively little is known about compliance with the phased process. This study modelled households' responses to a phased evacuation order based on a household behavioral intention survey. About 66% of the evacuees reported that they would comply with a phased evacuation order. A latent class logit model sorted evacuees into two classes ("evacuation reluctant" and "evacuation keen") by their stakeholder perceptions (i.e., whether government agencies have responsibility for the safety of individuals) and evacuation perceptions (i.e., whether evacuation is an effective protective action), while risk perception becomes non-significant in interpreting their compliance behavior to a phased evacuation order. Those that evacuate to the home of friends/relatives and/or bring more vehicles during evacuation are less likely to follow phased evacuation orders. "Evacuation reluctant" individuals with a longer housing tenure are more likely to follow phased evacuation orders. "Evacuation keen" individuals with a longer travel delay expectation are more likely to comply with phased evacuation orders. This study not only unveiled the impacts of incorporating three psychological perceptions (i.e., risk, stakeholder, and evacuation perceptions) in modeling compliance behavior (e.g., parameter sign/significance shift) but also provides insights of evacuees' compliance behavior to phased evacuation orders.
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    Modeling the impact of traffic management strategies on households' stated evacuation decisions
    Bian, Ruijie; Murray-Tuite, Pamela; Edara, Praveen; Triantis, Konstantinos (Elsevier, 2022-10)
    Evacuation traffic management has been implemented in large-scale disaster evacuations (such as hurricanes) to facilitate traffic flow and reduce travel delay. The outcomes of these strategies were quantified via traffic simulation but were assumed to have no/limited impacts on households' evacuation-related decisions. This study analyzed and modeled the impact of these strategies on five evacuation related household choices (evacuate/stay, departure time, route, vehicle, and destination) separately based on 415 responses collected from a stated preference survey. The survey was conducted in 2017 in coastal areas near Hampton Roads. Traffic management is likely to motivate some (32%) households to evacuate instead of sheltering in place. In addition, not all households take the interstates with traffic management even though route choice is the most likely to be affected by traffic management. Households need more information for their departure time and destination choices in response to traffic management since they are more likely to feel uncertain of the impact of the strategies on these decisions. Such uncertainty in departure time and destination choice is especially true for those who evacuate late and for those who choose accommodations other than the home of friends/relatives. Emergency management agencies should also be aware that some households may intentionally depart before traffic management starts. Among the five choices, vehicle use is the choice that is least likely to be affected. All the above-mentioned findings potentially affect parameter specifications in evacuation traffic simulation studies. This study then used multinomial logit models to estimate the impacts of traffic management on each of the five evacuation related choices. The model estimation results can help improve evacuation demand predictions and guide evacuation information dissemination.
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    Simulation-Based Analysis of Wake Turbulence Encounters in Current Flight Operations
    Swol, Christopher Douglas (Virginia Tech, 2009-08-24)
    One way to address the need for increased airspace system capacity is to reduce the separation requirements between aircraft in-flight. A key limiting factor to any reduction in separation is wake turbulence. The potential for aircraft to encounter wake turbulence poses a threat to both safety as well as increased efficiency. This research effort seeks to develop a model that can be used to evaluate the potential for wake encounters in today's flight operations, as well as serve as a tool for evaluating future reduced separation scenarios. The wake encounter model (WEM) achieves this goal by integrating results from NASA's TDAWP wake turbulence prediction model with a flight operations model based on radar flight track data. Unique in this model's design, is the ability to evaluate the potential for wake encounters throughout the terminal area versus previous research which has largely been restricted to areas near the runway. Expanding the model's reach provides not only for a more thorough analysis of potential wake encounters, but also creates an effective tool for evaluating future reduced separation scenarios. The WEM model was used to evaluate operations at three metropolitan airspaces in the United States: Atlanta, Los Angeles and New York. The results from these model runs indicated that potential wake encounters in today's operations were few. More importantly, the results from the WEM create a baseline for wake turbulence exposure in today's system, by which future scenarios can be compared against as part of any comprehensive reduced separation safety analysis.