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

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

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

T5-A: Improving Student learning by Assigning Functional Engineering Roles and Employing Industry Style Performance Evaluations in The Capstone Design Course

Aircraft detail design capstone course in Aerospace Engineering at Embry-Riddle Aeronautical University employs group projects of four to six students and is designed to provide real world experience in structural design process, by designing and analyzing and documenting an aerospace structure. This paper reports on a few innovations in the course to provide industry like experience to students namely (1) Assigning functional engineering roles and (2) Industry style performance evaluations to inform grading. Survey and observations indicate that students found assigning of engineering roles to be helpful in enhancing their experience, but did not benefit much from performance evaluations as implemented

Software Infrastructure For Analysis of Infection Propagation Through Air Travel

This NSF funded project seeks to develop a novel software that will provide a variety of pedestrian dynamics models, infection spread models, as well as data so that scientists can analyze the effect of different mechanisms on the spread of directly transmitted diseases in crowded areas. The initial focus of this project is on air travel. However, the software can be extended to a broader scope of applications in movement analysis and epidemiology, such as in theme parks and sports venues. Development of the proposed software will involve several innovations. It will include a novel phylogeography model that links fine-scale human movement data with virus genetic information to more accurately model geographic diffusion of viruses. New models for pedestrian movement will enable modeling of complex human movement patterns. A recommendation system for the choice of pedestrian dynamics models and a domain specific language for the input of policies and human behaviors will enhance usability by researchers in diverse fields. Community building initiatives will catalyze inter-disciplinary research to ensures the long-term sustainability of the project through a critical mass of contributors and users