ABSTRACT
Recent studies have applied the percolation theory to analyze the connectivity of networks in the transportation field. However, research was conducted in a manner that completely removed the function of nodes or links. There was a limit in that applying public transportation was difficult to guarantee the right to move the captive rider. In this study, penalties were imposed on public transportation nodes in the form of wait times to remove the function of node partially. Accordingly, the travel time of a network was calculated by optimal strategy assignment to reflect passenger behavior. When nodes were randomly penalized without transfer distinctions, there was a critical point of travel-time increase between cases with penalties of 50 and 60 nodes, respectively, and percolation was observed indirectly. A large and global effect of increased travel time was observed when the penalties were issued only to transfer stations. The application of a trip frequency weight increases the effect of penalties on medium- or short-timed trips. The results of this study can be used to establish quarantine policies for controlling public transportation networks. Furthermore, it is the first attempt at observing percolation by partially limiting its function in the form of node penalties in a public transportation network.
ABSTRACT
Air traffic, despite the recent dip due to Covid, is expected to grow 30-40% year on year. With the potential inclusion of UAVs (Unmanned Aerial Vehicles) into controlled airspace over the next decade, it is anticipated that the congestion levels in airspace will increase 10 fold. This paper presents an AI-based approach to air traffic control, with the aim of alleviating the load and improving the efficiency of human agents (air traffic controllers). One of the primary goals of air traffic control is to safely navigate an aircraft through controlled airspace using real-time control actions - such as changes to speed, heading (direction of travel) and altitude of an aircraft. The safety critical nature of this environment calls for precise explanations (why take an action) and counterfactual (why not take an action) explanations, real-time responsiveness, the ability to present succinct actions to a human agent, while simultaneously optimizing for air traffic delays, fuel burn rates, and weather conditions. This paper presents algorithms and a system architecture for anticipating separation losses (conflicts in airspace) and a lattice-based search space exploration AI planner to recommend actions to avoid such conflicts. The key contributions of the paper include: (i) fast detection (prediction) of conflicts in a controlled airspace, and (ii) fast lattice space exploration based AI solver to produce a set of feasible resolutions for the detected conflicts. Additionally, this paper discusses how to weight the different resolutions and how future work on optimisation techniques could improve the efficiency of the algorithm and address various known limitations of the current approach from both technical and human-agent perspective. The evaluations are conducted against an air traffic simulator, Narsim, showing the ability to avoid separation losses, while minimizing the number of actions even at 3 x normal capacity. © ATM 2021. All rights reserved.
ABSTRACT
This paper proposes a novel approach for the prediction of the risk of expansion of local epidemics to 3rd regions or countries in the world through the air traffic network. The approach relies on the definition of a new indicator, the Imported Risk, which represents the overall risk of having infected individuals entering an airport from any other airport with connections. We performed a proof-of-concept of the proposed approach by using daily data of the air traffic movements on a global scale and of the evolution of the COVID-19 epidemic at the beginning of 2020. For that purpose, we developed a complex network model based on Tagged Graphs to calculate the Imported Risk indicator, together with other complementary indicators showing the centrality of the air traffic network weighted with the Imported Risk. We implemented our complex network model into an on-line platform which provides the daily risk of expansion of the epidemic to other regions or countries. The platform supports the identification of the components of the network (airports, routes…) that have a major impact on the risk of expansion. The paper also provides findings on how the short-term prediction of diseases' expansion through the Imported Risk indicator allows the identification of effective measures to take control of the virus spread. © ATM 2021. All rights reserved.
ABSTRACT
This paper develops models to quantify the dynamics of the impact of air travel on the spread of the COVID-19 pandemic, using a wide range of datasets covering the period from March to December 2020. With the help of flight operation data, we first develop a novel approach to estimate the county-level daily air passenger traffic, which combines passenger load factor estimates and information about the air traffic distribution. Cross-sectional models using aggregated county-level variables are estimated. While this study focuses on air travel variables, we also control for potential spatial autocorrelation and other relevant covariates, including vehicle miles traveled (VMT), road network connectivity, demographic characteristics, and climate. The model results indicate that air travel has a strong and positive impact on the initial pandemic growth rate for both case-based and fatality-based aggregate models. © ATM 2021. All rights reserved.
ABSTRACT
The amount of air traffic is rapidly recovering from the COVID-19 pandemic and beginning to rise above previous levels. As a result, the VHF band is expected to become saturated in the near future, harmfully affecting air traffic management. As one solution for the increased need for aeronautical connectivity, the terrestrial LDACS data link has been designed and is in the process of ICAO standardization. However, LDACS development has been primarily focused on data communication and digital voice protocols have not been fully defined yet. This paper presents the proposed LDACS digital voice architecture developed in the framework of the Single European Sky ATM Research (SESAR) program. The LDACS digital voice air-ground communication protocol is validated and evaluated in computer simulations. It is shown that the SESAR-specified functional and performance requirements are satisfied. © 2022 IEEE.
ABSTRACT
Significant effort has to be devoted to surviving the businesses relying on fleet vehicles in the year 2020 and ahead as the novel coronavirus (COVID-19) epidemic became pandemic. Executing profitable business while keeping the staff safe and productive is a critical challenge to deal with. To find a solution, we focus on driver management out of major functions in fleet management such as vehicle, driver, and operation management. We were unable to identify a study conducted to capture real-time data on a ride in a fleet. Therefore, to fill that gap we implemented a cost-effective real-time Fleet Management System (FMS) using data analytics with the use of ESP32 SIM800L with reprogrammable capabilities. Fleet can use this system to monitor real-time data on vehicle location, remaining time to the destination, vehicle speed, and distance traveled. Moreover, the system can be personalized as it has reprogrammable features to be enabled or disabled based on the customer's preference. Once the data is captured through the Global Positioning System (GPS) receiver, data will be transmitted via General Packet Radio Service (GPRS) to two remote servers. One server is hosted locally with SQL and where the other is hosted in a cloud environment with a Firebase realtime database. The vehicle location is tracked using GPS. For fast data transfer, 3G Global System for Mobile communications (GSM) with ESP32 800L microprocessor was used. A web-based graphical user interface is developed to analyse and present the transmitted data. Vehicle information can be viewed and located on the web application in form of google maps. Real-time data analytics is used with Firebase's real-time database. Furthermore, Short Message Service (SMS) facility is made available for the driver to communicate with configured mobile numbers © 2021 IEEE.