Ground Access Research Articles

Resource and trajectory optimization for UAV-assisted MEC communication systems over unlicensed spectrum band

The new radio unlicensed (NR-U) technology is proposed by 3GPP to extend NR to the unlicensed spectrum because of the shortage of the licensed spectrum. Different from the ground and fixed communication equipment-based unlicensed spectrum access scheme, the unmanned aerial vehicle (UAV) mobile platform-based unlicensed spectrum access scheme is not only related to incumbent users but also its trajectory and resource allocation. Therefore, this paper proposes a hybrid unlicensed spectrum access scheme for the UAV-assisted unlicensed mobile edge computing (MEC) communication (UAUM) system, where each flight time slot of the UAV is divided into two parts: power free (PF) and power controlled (PC) stages. In the PF stage, the transmit power is only restrained by the unlicensed spectrum regulations, and thus the UAV can provide high-rate services for real-time downlink users (RDUs) and uplink computing users (UCUs). In the PC stage, the transmit power of the UAV is mainly restrained by the interference to WiFi devices, and thus UAV can be allowed to provide low-rate services for non-realtime downlink users (NDUs) without affecting WiFi users. Based on the proposed scheme, a multi-variable optimization problem regarding trajectory, bandwidth, transmit power, and duty cycle is formulated to maximize the total offloaded computing bits on the premise of ensuring the quality of experience of RDUs, NDUs, and WiFi users under the maximum energy budget. To solve this problem efficiently, we propose an iterative algorithm based on the block coordinate descent method and successive convex approximation technique to decompose the original problem into four optimization subproblems of trajectory, bandwidth, transmit power and duty cycle, which are then solved alternatively in an iterative manner. A large number of simulation results demonstrate that in terms of spectrum efficiency and total offloaded computing bits, the proposed algorithm outperforms other unlicensed spectrum access schemes and optimization algorithms. The other performances of the proposed algorithm are deeply evaluated to prove its effectiveness and feasibility.

Preferences and welfare impacts of new metro serving airport access

The construction of a new rail infrastructureto connectairportswith cities plays a vital role in enhancing the accessibility of airport ground access and mitigating negative externalities resulting fromautomobiles. To identify air travelers’ preferences for airport ground access by considering a new metro line in Taiwan, we develop nested logit models, which combine revealed and stated preference data. The results reveal significant effects of travel time, travel cost, and individual characteristics; the metro and taxis are highly substituted for airport access. Consumer surplus or accessibility is not equally distributed across air travelers. Travelers who are female, young adults, lower income, and without vehicles in the household obtain considerable consumer benefits from using the new metro. Policy incentives to time and cost have a small impact on the increase in metro share, but consumer surplus surges considerably.

Multi-criteria decision-making for the selection of best airport ground access mode with a new fuzzy rough-entropy based method

In order to cope with the inherent complexities and uncertainties of settings such as transportation problems, the adoption of proper decision procedures becomes a pivotal issue. Particularly, multi-criteria group decision-making (MCGDM) uses multiple evaluations of the options based on several criteria to select the optimal choice and/or rank the alternatives. In this paper we are interested in the Entropy-REGIME method for MCGDM and its ability to handle uncertainty in the presence of fuzzy rough numbers (that allow the practitioner to represent and process imprecise data). To this end we propose a hybrid framework of the fuzzy rough-entropy based REGIME method that involves two main components: fuzzy rough-entropy for the evaluation of the objective weights of the decision criteria, and fuzzy rough-REGIME to rank the alternatives based on the weighted criteria. Its motivation is a case study that concerns the selection of the best transport mode to access Istanbul newly constructed airport. We consider four alternatives for ground access options, namely, underground metro, bus rapid transit, light rail transit, and premium bus services. Our findings show that an underground metro system is the optimal mode, followed by light rail transit, bus rapid transit, and premium bus services. Additionally, to demonstrate the superiority and effectiveness of the proposed technique, comparisons are made with existing methods.

Sum rate maximization in UAV-assisted data harvesting network supported by CF-mMIMO system exploiting statistical CSI

Unmanned Aerial Vehicles (UAVs) have been considered to have great potential in supporting reliable and timely data harvesting for Sensor Nodes (SNs) from an Internet of Things (IoT) perspective. However, due to physical limitations, UAVs are unable to further process the harvested data and have to rely on terrestrial servers, thus extra spectrum resource is needed to convey the harvested data. To avoid the cost of extra servers and spectrum resources, in this paper, we consider a UAV-based data harvesting network supported by a Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) system, where a UAV is used to collect and transmit data from SNs to the central processing unit of CF-mMIMO system for processing. In order to avoid using additional spectrum resources, the entire bandwidth is shared among radio access networks and wireless fronthaul links. Moreover, considering the limited capacity of the fronthaul links, the compress-and-forward scheme is adopted. In this work, in order to maximize the ergodically achievable sum rate of SNs, the power allocation of ground access points, the compression of fronthaul links, and also the bandwidth fraction between radio access networks and wireless fronthaul links are jointly optimized. To avoid the high overhead introduced by computing ergodically achievable rates, we introduce an approximate problem, using the large-dimensional random matrix theory, which relies only on statistical channel state information. We solve the nontrivial problem in three steps and propose an algorithm based on weighted minimum mean square error and Dinkelbach's methods to find solutions. Finally, simulation results show that the proposed algorithm converges quickly and outperforms the baseline algorithms.

Analysis of Traffic Organisation in the Kiss-and-Fly Zone of Kraków Airport: Eye-Tracking Study

When choosing the way to come to airports, quite a large number of passengers prefer when they are dropped off/picked up at airports using kiss-and-fly (K&F) zones. Such a travel option is associated with a special traffic organisation on the airport ground access. As there are no common regulations or standards when creating such a complex infrastructure object, it could be a challenge for drivers when searching and moving through it. Therefore, the main aim of the presented study was to assess and verify the eye-tracking technique as an objective tool, which can allow one to identify and estimate confusion points met by road users when using such an object. The field tests with 23 drivers were conducted in the K&F zone of Kraków Airport, and the data analysis focused on the traffic organisation and road signage as its key and integral parts. The eye-tracking approach allowed us to clearly find confusing situations for drivers as well as explain their reasons confirming its suitability and usefulness for the declared aim. Also, the perception of standardised and unstandardised signage of the K&F zone as well as the influence of route familiarity for drivers were discussed.

Completion Time Optimization in UAV-Relaying-Assisted MEC Networks With Moving Users

As one of the most popular consumer electronics, Unmanned Aerial Vehicle (UAV) has the potential to assist User Equipment (UE) in the Internet of Everything. Mobile edge computing (MEC) is also an emerging technique that can provide sufficient computing resources to IoE users. In this paper, we focused on the UAV-assisted MEC problem with mobile UEs, where the UAV serves as an MEC server to assist UEs in computing and acts as a relay to deliver tasks to a ground access point (AP). The objective is to minimize the average time for completing all tasks in the network while jointly optimizing resource allocation such as communication bandwidth, CPU frequency, task division ratio, and UAV’s three-dimensional location deployment. First, we proposed an online MEC network adjustment scheme. Then, we decomposed the formulated non-convex problem into three low-complexity subproblems. Last, we proposed a successive convex approximation-based joint optimization algorithm to solve them. In addition, we presented heuristic conclusions in task allocation rules, UAV flight trajectory prediction, and UAV hovering altitude selection, which can further reduce task completion time and deepen the understanding of the working mode of mobile edge servers. Simulation results show that the algorithm can significantly reduce the completion time.

Optimal planning of urban air mobility systems accounting for ground access trips

The concept of Urban Air Mobility (UAM), an on-demand aviation service using air vehicles for urban short-distance trips, such as electric vertical takeoff and landing aircraft (eVTOL), has recently drawn public attention as one of possible solutions to traffic congestion in metropolitan areas. We present an optimal planning framework for fleet size and vertiport numbers using generalized cost models of UAM trip chains with two different ground access modes for first and last mile trips, including taxis and robotaxis (i.e. autonomous taxis or shared autonomous vehicles). As it is a relatively new and untested mode of transportation, our research aims to provide a decision-making tool for initial UAM system planning and analysis of its economic feasibility, appropriately considering its unique characteristics, such as high free-flow speed, low circuity, needs for ground access, and specific cost factors, such as vertiport and electric vertical takeoff and landing aircraft costs. Through a parametric study, we quantitatively examine the necessary conditions for UAM, with or without ridesharing, to become more economically viable than other ground-only modes. The study confirms that UAM can have potential economic benefits especially in large cities with longer average trip lengths and severe traffic congestion, where the average ground vehicle speed is low.

Assessing airport ground access interventions: An integrated approach combining mode choice modeling and microscopic traffic simulation

Airport ground accessibility is an important and integral part of airport planning, which significantly impacts airport attractiveness and its environmental sustainability. In this paper, we propose the combined use of road micro-simulation with meso-scope discrete choice modeling to support the evaluation of airport access interventions. The proposed integrated framework allows us to appraise mode choice and infrastructural interventions simultaneously to accurately assess the performance of airport access road networks in accommodating larger traffic. We validate and demonstrate the benefits of the proposed approach by applying it to a real-world case study based on the Milan–Bergamo Airport (BGY), in which (i) we conduct an ex-post counterfactual analysis to assess the impact of the interventions undertaken to mitigate the large growth (54.6%) in the years 2013–2019, and (ii) evaluate future scenarios up to 2030 considering the additional expected growth and introduction of direct rail services. Results indicate that BGY’s ground access interventions have contributed to effectively accommodating traffic growth by significantly mitigating road congestion and associated pollutant emissions. Results also inform on the need and effectiveness of additional interventions to meet the substantial expected future growth.

Unraveling passengers’ preferences: investigating airport access mode choice in Tehran, Iran

Airports are imperative elements in fueling the economic growth of a country by reducing the distances between origins and destinations for the movement of both passengers and goods. Managing ground access modes of travel has become essential as the majority of trips are made by private cars, leading to increased environmental issues and congestion. To alleviate these problems, it is vital to identify the factors that shape air passengers' choice of ground access mode. In this work, we utilized inclusive Revealed Preference data (RP) collected through face-to-face interviews and applied a Multinomial Logit (MNL) model to study passengers' ground access mode choice behavior at Mehrabad International Airport, the busiest airport in Iran. The results showed that different factors such as socioeconomic status, travel characteristics, and distance to the airport affect the mode choice behavior of passengers in reaching the airport.

Use mobile location data to infer airport catchment areas and calibrate Huff gravity model in the New York metropolitan area

Mobile location data have emerged as a pivotal asset for analyzing travelers' spatial behaviors and movement patterns. In the context of air travel, the data empower researchers to gain empirical insights into travelers' choices of airports. This study employs mobile location data to scrutinize the market shares and infer catchment areas of three primary hub airports within the New York Metropolitan Area. Our study, together with Teixeira and Derudder (2021), helps contribute to a better understanding of competitive airport dynamics in the New York Metropolitan Area. In addition, the mobile location data allow us to calibrate the two key components of the Huff Gravity Model, which is frequently used in existing studies focusing on airport competition and catchment areas in Multiple Airport Regions (MARs). Our investigation underscores that the application of the Huff Model should not follow a uniform approach across different scenarios. The dynamics of airport competition and ground access alternatives exhibit unique characteristics within each MAR. Furthermore, our study unveils inherent quality challenges associated with mobile location data. Future studies intending to incorporate mobile location data are advised to conduct preliminary assessments of data quality before embarking on empirical analyses.

Understanding ground access dynamics at Lokpriya Gopinath Bordoloi International Airport, Guwahati: a case study analysis

Understanding ground access dynamics at Lokpriya Gopinath Bordoloi International Airport, Guwahati: a case study analysis

A Routing Strategy Based Genetic Algorithm Assisted by Ground Access Optimization for LEO Satellite Constellations

A Routing Strategy Based Genetic Algorithm Assisted by Ground Access Optimization for LEO Satellite Constellations

The Impacts of Low-Carbon Incentives and Carbon-Reduction Awareness on Airport Ground Access Mode Choice under Travel Time Uncertainty: A Hybrid CPT-MNL Model

Developing strategies to incentivize travelers towards adopting sustainable mobility options is one of the effective approaches to mitigate carbon emissions. Using Xi’an Xianyang International Airport as a case study, this study aims to explore the effects of low-carbon incentives and carbon-reduction awareness on airport ground access mode choices. In addition, to account for the complex road environment, an innovative stated preference choice experiment was designed, integrating the factor of travel time uncertainty. Then, a hybrid cumulative prospect theory–Multinomial Logit (CPT-MNL) model was also developed. The estimated results revealed that travelers increasingly prioritize emissions reduction and consciously prefer sustainable mobility options to reach the airport. Furthermore, the potential of low-carbon incentives to encourage public transport usage over private vehicles has been highlighted. Notably, travel time uncertainty had a significant impact on the choice of private cars. When the travel time to the airport is uncertain, travelers exhibit a greater inclination towards selecting public transport. The findings of this study offer nuanced insights for transportation authorities, aiding them in fostering the adoption of sustainable mobility options and achieving carbon reduction objectives.

Investigating the recovery of For-Hire-Vehicle, Taxi, and AirTrain at two New York City airports during the COVID-19 pandemic

For operators of large-scale infrastructure systems such as airports, predicting landside access demand amid constant exogenous changes that modify travel volumes and preferences is immensely challenging. COVID-19 disrupted travel behavior and mode preferences, making it even more challenging for airport operators to plan infrastructure and design strategies to accommodate landside access. We use time series regression discontinuity to explore to what extent the volumes of airport ground access modes changed during the pandemic, how the recovery differed across modes, and how the pandemic changed the established relationship among access modes. We do so by examining the change in the three main airport access modes at two large New York City airports, John F. Kennedy International Airport and LaGuardia Airport (traditional taxis; for-hire ride-hailing services such as Uber; AirTrain, the public transit system, at JFK only). We find the mismatch between taxi pick-up and drop-off trips that predates the pandemic widened and persisted during the pandemic; the pandemic diminished taxis’ role and exacerbated the replacement of taxis by for-hire-vehicles. For-hire-vehicle trips recovered at both airports, possibly at the expense of taxis. AirTrain continued to function as an important and resilient airport access mode during the pandemic. Our research helps to inform the airport operators’ resource allocation and traffic management strategies while showcasing the usefulness of time series regression discontinuity in examining changes in travel behavior for future planning in the aftermath of a major shock.

Mortality among populations affected by armed conflict in northeast Nigeria, 2016 to 2019

Armed conflict, displacement and food insecurity have affected Adamawa, Borno, and Yobe states of northeast Nigeria (population ≈ 12 million) since 2009. Insecurity escalated in 2013 to 2015, but the humanitarian response was delayed and the crisis' health impact was unquantified due to incomplete death registration and limited ground access. We estimated mortality attributable to this crisis using a small-area estimation approach that circumvented these challenges. We fitted a mixed effects model to household mortality data collected as part of 70 ground surveys implemented by humanitarian actors. Model predictors, drawn from existing data, included livelihood typology, staple cereal price, vaccination geocoverage, and humanitarian actor presence. To project accurate death tolls, we reconstructed population denominators based on forced displacement. We used the model and population estimates to project mortality under observed conditions and varying assumed counterfactual conditions, had there been no crisis, with the difference providing excess mortality. Death rates were highly elevated across most ground surveys, with net negative household migration. Between April 2016 and December 2019, we projected 490,000 excess deaths (230,000 children under 5 y) in the most likely counterfactual scenario, with a range from 90,000 (best-case) to 550,000 (worst-case). Death rates were two to three times higher than counterfactual levels, double the projected national rate, and highest in 2016 to 2017. Despite limited scope (we could not study the situation before 2016 or in neighboring affected countries), our findings suggest a staggering health impact of this crisis. Further studies to document mortality in this and other crises are needed to guide decision-making and memorialize their human toll.

A cooperative MEC framework based on multi-UAV and AP to minimize weighted energy consumption

In this paper, a cooperative MEC system with multi-UAV and a ground access point (AP) is considered, in which UAVs can act as both a computing platform to help Internet of Things devices (IoTDs) deal with their computing tasks and a relay platform to offload some of the task data from IoTDs to the AP with higher computing ability. We aim to minimize the weighted overall energy consumption of UAVs and IoTDs by jointly optimizing connection scheduling, CPU frequency, task offloading bits and the flight trajectory of UAVs. The formulated problem is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is hard to solve. To tackle this problem, we divided it into three sub-problems and resolved them iteratively by the Lagrangian dual method and succession convex approximation (SCA) technique. Finally, an alternately iterative optimization algorithm is proposed. The numerical results show that our proposed algorithm has better performance compared to other benchmark algorithms.

UAV-Enabled Integrated Sensing, Computing, and Communication: A Fundamental Trade-Off

We propose an unmanned aerial vehicle (UAV) enabled integrated sensing, computing, and communication (U-ISCC) framework, where a treble-functional UAV performs local computation and also delivers its computational tasks to ground access points. Meanwhile, this UAV is employed to sense a target by using the unified beam waves. Our goal is to characterize the Pareto boundary between the computation capacity and the sensing beampattern gain, by which the fundamental trade-off of them is unveiled. To solve the challenging problem, we propose a joint optimization algorithm by applying the semidefinite programming method and the concave-convex procedure technique, which ensures the rank-1 constraint with a low complexity. Numerical results finally verify that the proposed algorithm explicitly depicts the performance region, and demonstrate that the UAV boosts the ability of sensing, computing, and communication to strike a balance by fully exploring its functions.

Racial and Ethnic Disparities in Access to Pediatric Trauma Centers in the United States: A Geographic Information Systems Analysis.

Injury is the leading cause of death and disability for children, making access to pediatric trauma centers crucial to pediatric trauma care. Our objective was to describe the pediatric population with timely access to a pediatric trauma center by demographics and geography in the United States. Level 1, 2, and 3 pediatric trauma center locations were provided by the American Trauma Society. Geographic information systems road network and rotor wing analysis determined US Census Block Groups with the ground and/or air access to a pediatric trauma center within a 60-minute transport time. We then described, at the national and state levels, the 2020 pediatric population (< 15 years old) with and without pediatric trauma center access by ground and air, stratified by race, ethnicity, and urbanicity. There were 157 pediatric trauma centers (82 Level 1, 64 Level 2, 11 Level 3). Of the 2020 US pediatric population, 33,352,872 (54.5%) had timely access to Level 1-3 pediatric trauma centers by ground and 45,431,026 (74.1%) by air. The percentage of children with access by race and ethnicity were (by ground, by air): American Indian/Alaskan Native (31.0%, 43.5%), White (48.7%, 71.3%), Native Hawaiian/Pacific Islander (59.3%, 61.0%), Hispanic (60.2%, 76.9%), Black (64.2%, 78.0%), and Asian (76.5%, 89.5%). Only 48.2% of children living in rural block groups had access, compared with 83.6% in urban block groups. Significant disparities in current access to pediatric trauma centers exist by race and ethnicity, and geography, leaving some children at risk for poor trauma outcomes.

Non-Cooperative Target Attitude Estimation Method Based on Deep Learning of Ground and Space Access Scene Radar Images

Determining the attitude of a non-cooperative target in space is an important frontier issue in the aerospace field, and has important application value in the fields of malfunctioning satellite state assessment and non-cooperative target detection in space. This paper proposes a non-cooperative target attitude estimation method based on the deep learning of ground and space access (GSA) scene radar images to solve this problem. In GSA scenes, the observed target satellite can be imaged not only by inverse synthetic-aperture radar (ISAR), but also by space-based optical satellites, with space-based optical images providing more accurate attitude estimates for the target. The spatial orientation of the intersection of the orbital planes of the target and observation satellites can be changed by fine tuning the orbit of the observation satellite. The intersection of the orbital planes is controlled to ensure that it is collinear with the position vector of the target satellite when it is accessible to the radar. Thus, a series of GSA scenes are generated. In these GSA scenes, the high-precision attitude values of the target satellite can be estimated from the space-based optical images obtained by the observation satellite. Thus, the corresponding relationship between a series of ISAR images and the attitude estimation of the target at this moment can be obtained. Because the target attitude can be accurately estimated from the GSA scenes obtained by a space-based optical telescope, these attitude estimation values can be used as training datasets of ISAR images, and deep learning training can be performed on ISAR images of GSA scenes. This paper proposes an instantaneous attitude estimation method based on a deep network, which can achieve robust attitude estimation under different signal-to-noise ratio conditions. First, ISAR observation and imaging models were created, and the theoretical projection relationship from the three-dimensional point cloud to the ISAR imaging plane was constructed based on the radar line of sight. Under the premise that the ISAR imaging plane was fixed, the ISAR imaging results, theoretical projection map, and target attitude were in a one-to-one correspondence, which meant that the mapping relationship could be learned using a deep network. Specifically, in order to suppress noise interference, a UNet++ network with strong feature extraction ability was used to learn the mapping relationship between the ISAR imaging results and the theoretical projection map to achieve ISAR image enhancement. The shifted window (swin) transformer was then used to learn the mapping relationship between the enhanced ISAR images and target attitude to achieve instantaneous attitude estimation. Finally, the effectiveness of the proposed method was verified using electromagnetic simulation data, and it was found that the average attitude estimation error of the proposed method was less than 1°.

Autonomous Lunar L1 Halo Orbit Navigation Using Optical Measurements to a Lunar Landmark

<h3>Abstract</h3> Autonomous cislunar spacecraft navigation is critical to mission success as communication to ground stations and access to global positioning system (GPS) signals could be lost. However, if the satellite has a camera of sufficient quality, geometric line-of-sight (unit vector) measurements can be made to known lunar landmarks (e.g., Tycho Crater) to provide observations that enable autonomous estimation of the position and velocity of the spacecraft. In this study, an improved batch gaussian initial orbit determination (IOD) differential corrector (DC) algorithm, based on the approximated values of the two-body f and g series, is applied to initialize a (non-conic based) circular restricted three body problem (CR3BP) extended Kalman Filter (EKF) navigator. This navigator collects geometric line-of-sight unit vector (angle only) measurements to a known location on the Moon to sequentially estimate the position and velocity of an observer spacecraft flying on an approximate southern L1 Halo orbit. In this study, it was found that the best approach is to initialize the CR3BP EKF (navigator) using the solution from the batch DC filter with at least 10 measurements taken against the perceived centroid of Tycho Crater. Thereafter, it is best to continue the navigator with subsequent measurements taken against the same center coordinates of the Tycho Crater, where these coordinates are now expressed in the CR3BP rotating frame. For successful conic-based batch filter initialization and long-term CR3BP EKF convergence, it was found that the cadence for all optical measurements should be taken at 10 minutes for a simulated measurement noise of 0.1° one sigma uncertainty about the line-of-sight measurement unit vector.