The Use of Hybrid Modeling to Examine the Elasticity of Cargo Movement Following a Natural Disaster

Resilient infrastructure is imperative especially following natural or manmade disasters. The ability to move food, water, and relief supplies using multiple modes of transportation to areas recently affected by major disasters is oftentimes very difficult. Following Hurricane Maria’s landfall on Puerto Rico in 2018, 10,000 shipping containers were stranded in just one port of Puerto Rico unable to traverse the island to reach those in need. The lack of resilient infrastructure caused a delay in repair to normalcy for the entire island and delayed supplies that were already late to their destination even longer. The goal of this research is to model the elasticity of cargo arrival and departure under resilient infrastructure. The objective of this research is to determine the responsiveness of a mode of transportation to change under demand and restrictions. This model will be simulated in a hybrid microscopic-mesoscopic model using VISSIM. The individual ports within Puerto Rico will first be modelled microscopically. The cargo from these ports will then move along the major truck routes of the island that will be modelled mesoscopic level. Dwell time and ultimate arrival time from the time it arrives in the port to when it reaches its destination will additionally be examined. Its expected that a delay in final arrival time will still be seen; however, the delay time at the port and the delay time when the cargo reaches its final destination will not be linear. This research will help to bring improve well being to individuals of a society, increase public scientific literacy, improve national security, and enhance infrastructure for research and education

Analysis of crashes involving First Responder Vehicles

First responders face many hazards that put their lives at risk while on duty. Over the period of 1992-97, 887 law enforcement personnel and 259 firefighters were killed in the line of duty (Clarke, 1999). An inherent danger comes with the duties of emergency response. A review of the National Law Enforcement Officers Memorial Fund statistics shows that 553 police officers died in the line-of-duty between 2008 and 2017 as a direct result of a traffic related incidents. That represents more than 36 percent of all on-duty fatalities and is even higher than the number of police officers lost to gun violence over the same period. Sadly, the nation’s first responders are exposed to factors which make them uniquely vulnerable to traffic related injuries and deaths. The goal of this research is to investigate and analyze crashes involving first responder vehicles. This will be achieved by gathering police, fire, and ambulance vehicle crash reports from Signal Four Database from across the state of Florida from 2016 to 2018. Based on the database, approximately 1% of the crashes were first responder vehicles. The data set will be used to look at frequency and severity levels between first responders and the general public. Additionally, an evaluation will be conducted on individual responder vehicle groups; police, fire, and ambulance. This project is expected to help identify factors and situations that can lead to increased risk for first responders on Florida roadways. Potential recommendations discovered through this research should be used to protect the lives of first responders. REFERENCES Clarke, C., & Zak, M. J. (1999). Fatalities to Law Enforcement Officers and Firefighters, 1992-97 [Electronic version]. Compensation and Working Conditions, 3-7

Analysis and Modeling of Manual Traffic Control for Signalized Intersections

Manual traffic control is a common intersection control strategy in which trained personnel, typically police law enforcement officers, allocate intersection right-of-way to approaching vehicles. Manual intersection control is a key part of managing traffic during emergencies and planned special events. It is widely assumed that the flow of traffic through intersections can be greatly improved by the direction given from police officers who can observe and respond to change conditions by allocating green time to the approaches that require it the most. Despite the long history of manual traffic control throughout the world and its assumed effectiveness, there have been no quantitative, systematic studies of when, where, and how it should be used or compared to more traditional traffic control devices. The goal of this research was to quantify the effect of manual traffic control on intersection operations and to develop a quantitative model to describe the decision-making of police officers directing traffic for special events and emergencies. This was accomplished by collecting video data of police officers directing traffic at several special events in Baton Rouge, LA and Miami Gardens, FL. These data were used to develop a discrete choice model (logit model) capable of estimating police officer’s choice probabilities on a second-by-second basis. This model was able to be programmed into a microscopic traffic simulation software system to serve as the signal controller for the study intersections, effectively simulating the primary control decision activities of the police officer directing traffic. The research findings suggested police officers irrespective of their location, tended to direct traffic in a similar fashion; extending green time for high demand directions while attempting to avoid long gaps or waste in the traffic stream. This indicates that when officers are placed in similar situation they are likely to make the same primary control decisions

Epidemiological Models for Transportation Applications: Secondary Crashes

Secondary crashes or crashes that occur in the wake of a preceding or primary crash are among the most critical incidents occurring on highways, due to the exceptional danger they present to the first responders and victims of the primary crash. In this work, we developed a self-exciting temporal point process to analyze crash events data and classify it into primary and secondary crashes. Our model uses a self-exciting function to describe secondary crashes while primary crashes are modeled using a background rate function. We fit the model to crash incidents data from the Florida Department of Transportation, on Interstate-4 (I-4) highway for the years 2015-2017, to determine the model parameters. These are used to estimate the probability that a given crash is secondary crash and to find queue times. To represent the periodically varying traffic levels and crash incidents, we model the background rate, as a stationary function, a sinusoidal non-stationary function, and a piecewise non-stationary function. We show that the sinusoidal non-stationary background rate fits the traffic data better and replicates the daily and weekly peaks in crash events due to traffic rush hours. Secondary crashes are found to account for up to 15.09% of traffic incidents, depending on the city on the I-4 Highway

Multiscale Model for Hurricane Evacuation and Fuel Shortage

Hurricanes are powerful agents of destruction with significant socioeconomic impacts. High-volume mass evacuations, disruptions to the supply chain, and fuel hoarding from non-evacuees have led to localized fuel shortages lasting several days during recent hurricanes. Hurricane Irma in 2017, resulted in the largest evacuation in the nation affecting nearly 6.5 million people and saw widespread fuel shortages throughout the state of Florida. While news reports mention fuel shortages in several past hurricanes, the crowd source 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. Fundamentally, as fueling stations were depleted, the latent demand spread to neighboring stations and propagated throughout the community, similar to an epidemiological outbreak. In this paper, a Susceptible- Infected –Recovered (SIR) epidemic model was developed to study the evolution of fuel shortage during a hurricane evacuation. Within this framework, an optimal control theory was applied to identify an effective intervention strategy. Further, the study found a linear correlation between traffic demand during the evacuation of Hurricane Irma and the resulting fuel shortage data from Gasbuddy. This correlation was used in conjunction with the State-wide Regional Evacuation Study Program (SRESP) surveys to estimate the evacuation traffic and fuel shortages for potential hurricanes affecting south Florida. The epidemiological SIR dynamics and optimal control methodology was applied to analyze the fuel shortage predictions and to develop an effective refueling strategy

Left Recursion in Parsing Expression Grammars

Parsing Expression Grammars (PEGs) are a formalism that can describe all deterministic context-free languages through a set of rules that specify a top-down parser for some language. PEGs are easy to use, and there are efficient implementations of PEG libraries in several programming languages. A frequently missed feature of PEGs is left recursion, which is commonly used in Context-Free Grammars (CFGs) to encode left-associative operations. We present a simple conservative extension to the semantics of PEGs that gives useful meaning to direct and indirect left-recursive rules, and show that our extensions make it easy to express left-recursive idioms from CFGs in PEGs, with similar results. We prove the conservativeness of these extensions, and also prove that they work with any left-recursive PEG. PEGs can also be compiled to programs in a low-level parsing machine. We present an extension to the semantics of the operations of this parsing machine that let it interpret left-recursive PEGs, and prove that this extension is correct with regards to our semantics for left-recursive PEGs.Comment: Extended version of the paper "Left Recursion in Parsing Expression Grammars", that was published on 2012 Brazilian Symposium on Programming Language

The Breathing Human Infrastructure: Integrating Air Quality, Traffic, And Social Media Indicators

Outdoor air pollution is a complex system that is responsible for the deaths of millions of people annually, yet the integration of interdisciplinary data necessary to assess air quality\u27s multiple metrics is still lacking. This case study integrates atmospheric indicators (concentrations of criteria pollutants including particulate matter and gaseous pollutants), traffic indicators (permanent traffic monitoring station data), and social indicators (community responses in Twitter archives) representing the interplay of the three critical pillars of the United Nations\u27 Triple Bottom Line: environment, economy, and society. During the watershed moment of the COVID-19 pandemic lockdowns in Florida, urban centers demonstrated the gaps and opportunities for understanding the relationships, through correlations rather than causations, between urban air quality, traffic emissions, and public perceptions. The relationship between the perception and the traffic variables were strongly correlated, however no correlation was observed between the perception and actual air quality indicators, except for NO2. These observations might consequently infer that traffic serves as people\u27s proxy for air quality, regardless of actual air quality, suggesting that social media messaging around asthma may be a way to monitor traffic patterns in areas where no infrastructure currently exists or is prohibited to build. It also indicates that people are less likely to be reliable sensors to accurately measure air quality due to bias in their observations of traffic volume and/or confirmation biases in broader social discourse. Results presented herein are of significance in demonstrating the capacity for interdisciplinary studies to consider the predictive capacities of social media and air pollution, its use as both lever and indicator of public support for air quality legislation and clean-air transitions, and its ability to overcome limitations of surface monitoring stations

Relevance of the slowly-varying electron gas to atoms, molecules, and solids

Under a certain scaling, the electron densities of finite systems become both large and slowly-varying, so that the gradient expansions of the density functionals for the Kohn-Sham kinetic and exchange energies become asymptotically exact to order $\nabla^2$. Neutral atoms of large $Z$ scale similarly, but a cusp correction at the nucleus requires generalizing the gradient expansion for exchange, producing the wrong gradient coefficient in the slowly-varying limit. Meta-generalized gradient approximations (meta-GGA's) recover both the slowly-varying and large-$Z$ limits. GGA correlation energies of large-Z atoms are found to be accurate.Comment: 5 pages, 4 figures, submitted at PR

Tackling the Tibetan Plateau in a down suit: Insights into thermoregulation by bar-headed geese during migration

This is the final version. Available from Company of Biologists via the DOI in this recordData accessibility: Following the manuscript being accepted data will be uploaded to a public repository such as Dryad.Birds migrating through extreme environments can experience a range of challenges while matching the demands of flight, including highly variable ambient temperatures, humidity and oxygen levels. However, there has been limited research into avian thermoregulation during migration in extreme environments. This study aimed to investigate the effect of flight performance and high-altitude on body temperature (Tb) of free flying bar-headed geese (Anser indicus), a species that completes a high-altitude trans-Himalayan migration through very cold, hypoxic environments. We measured abdominal Tb, along with altitude (via changes in barometric pressure), heart rate and body acceleration of bar-headed geese during their migration across the Tibetan Plateau. Bar-headed geese vary the circadian rhythm of Tb in response to migration, with peak daily Tb during daytime hours outside of migration but early in the morning or overnight during migration, reflecting changes in body acceleration. However, during flights changes in Tb were not consistent with changes in flight performance (as measured by heart rate or rate of ascent) or altitude. Overall, our results suggest that bar-headed geese are able to thermoregulate during high-altitude migration, maintaining Tb within a relatively narrow range despite appreciable variation in flight intensity and environmental conditions.Biotechnology and Biological Sciences Research Council (BBSRC)Natural Sciences and Engineering Research Council of Canada (NSERC)Max Planck Institute for OrnithologyUS Geological SurveyWestern Ecological and Patuxent Wildlife Research Centers, Avian Influenza Programm