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

Load Transfer Issues in the Tensile and Compressive Behavior of Multiwall Carbon Nanotubes

Carbon nanotubes (CNT) are considered to be ultra strong and stiff reinforcements for structural composite applications. The load transfer between the inner and outer nanotubes in multiwall carbon nanotubes (MWCNT) has to be clearly understood to realize their potential in not only composites, but also other applications such as nano-springs and nano-bearings. In this paper, we study the load transfer between the walls of multiwall nanotubes both in tension and compression using molecular dynamics simulations. It is found that very minimal load is transferred to the inner nanotube during tension. The load transfer in compression of capped nanotubes is much greater than that in tension. In the case of uncapped nanotubes, the inner nanotube is deformed in bending, only after the outer nanotube is extensively deformed by buckling. It is found that the presence of a few interstitial atoms between the walls of multiwall nanotube can improve the stiffness and enhance the load transfer to the inner nanotubes both in tension and compression

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

Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

In this paper, we present a formulation for a multiscale model combining a social force based pedestrian movement including collision avoidance and a stochastic infection dynamics framework to evaluate the spread of multiple infectious diseases during air travel. We apply the multiscale model to evaluate pedestrian movement strategies that can reduce infection spread during air travel. The results are presented for airport lounge and airplane boarding and deplaning. Use of parallel computing to evaluate the vast parameter space created due to stochasticity and discretionary pedestrian behavior is addressed

Effects of Exit Doors and Number of Passengers on Airport Evacuation Effeciency Using Agent Based Simulation

Many factors determine the efficiency of evacuation at an airport during emergencies. These factors are very complicated and many times, unpredictable. The Federal Aviation Administration provides numerous advisory circulars and regulations for managing airport evacuation. However, a thorough literature review suggests that research on airport evacuation is still very limited. A study was designed to simulate an airport evacuation to address this problem. This study selected a local certificated airport in the United States for this purpose. We developed and validated a situation model using AnyLogic to investigate evacuation time at this airport. Using different variables, such as the number of passengers and the number of exits, we calculated the total evacuation time. As a result, this study provided statistical data to show how the reduced number of exits and the increased amount of passenger traffic increased the total duration of the evacuation

Multi-scale Models for Transportation Systems Under Emergency Conditions

The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

Absorbing Boundary Conditions for Molecular Dynamics and Multiscale Modeling

We present an application of differential equation based local absorbing boundary conditions to molecular dynamics. The absorbing boundary conditions result in the absorbtion of the majority of waves incident perpendicular to the bounding surface. We demonstrate that boundary conditions developed for the wave equation can be applied to molecular dynamics. Comparisons with damping material boundary conditions are discussed. The concept is extended to the formulation of an atomistic-continuum multiscale scheme with handshaking between the regions based on absorbing boundary conditions. The multiscale model is effective in minimizing spurious reflections at the interface

Epidemiological Models for Transportation Applications: Secondary Crashes

69A3551747125Secondary 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 I4 Highway