Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19

This paper estimates the relationship between the strength of economic shocks and temporal recovery in the world air transport industry. Our results show that world recovery of passenger demand to pre-COVID-19 levels is estimated to take 2.4 years (recovery by late-2022), with the most optimistic estimate being 2 years (recovery by mid-2022), and the most pessimistic estimate 6 years (recovery in 2026). Large regional differences are detected, Asia Pacific has the shortest estimated average recovery time 2.2 years, followed by North America 2.5 years and Europe 2.7 years. For air freight the results show a shorter average world recovery time of 2.2 years compared to passenger demand. At the regional level, Europe and Asia Pacific are comparable with average recovery times of 2.2 years while North America is predicted to recover faster in 1.5 years. The results show that the strength of economic shocks of various origins impacts the linear growth of passenger and freight traffic and the temporal recovery of the industry in a predictable transitory way. Hence, the impact of the COVID-19 recession will represent a temporary, although long-lasting, correction to previous growth levels.

Forecasting Temporal World Recovery in Air Transport Markets in the Presence of Large Economic Shocks: The Case of COVID-19

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLCs) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this article considers a UAV-assisted VLC using nonorthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV’s placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV’s position. Since the problem is nonconvex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using the Harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design that can be used in real-time applications and avoid falling into the “local minima” trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

Sum-Rate Maximization for UAV-Assisted Visible Light Communications Using NOMA: Swarm Intelligence Meets Machine Learning

A novel approach that combines visible light communication (VLC) with unmanned aerial vehicles (UAVs) to simultaneously provide flexible communication and illumination is proposed. To minimize the power consumption, the locations of UAVs and the cell associations are optimized under illumination and communication constraints. An efficient sub-optimal solution that divides the original problem into two sub-problems is proposed. The first sub-problem is modeled as a classical smallest enclosing disk problem to obtain the optimal locations of UAVs, given the cell association. Then, assuming fixed UAV locations, the second sub-problem is modeled as a min-size clustering problem to obtain the optimized cell association. In addition, the obtained UAV locations and cell associations are iteratively optimized multiple times to reduce the power consumption. The numerical results show that the proposed approach can reduce the total transmit power consumption by at least 53.8% compared with two baseline algorithms with fixed UAV locations.

Power Efficient Visible Light Communication With Unmanned Aerial Vehicles

Network planning models, which forecast the profitability of airline schedules, support many critical decisions, including equipment purchase decisions. Network planning models include an itinerary choice model that is used to allocate air total demand in a city pair to different itineraries. Multinomial logit (MNL) models are commonly used in practice and capture how individuals make trade-offs among different itinerary attributes; however, none that we are aware of account for price endogeneity. This study formulates an itinerary choice model that is consistent with those used by industry and corrects for price endogeneity using a control function that uses several types of instrumental variables. We estimate our model using a database of more than 3 million tickets provided by the Airlines Reporting Corporation. Results based on Continental U.S. markets for May 2013 departures show that models that fail to account for price endogeneity overestimate customers’ value of time and result in biased price estimates and incorrect pricing recommendations. The size and comprehensiveness of our database allows us to estimate highly refined departure time of day preference curves that account for distance, direction of travel, number of time zones traversed, departure day of week and itinerary type (outbound, inbound or one-way). These time of day preference curves can be used by airlines, researchers, and government organizations in the evaluation of different policies such as congestion pricing.

Accounting for Price Endogeneity in Airline Itinerary Choice Models: An Application to Continental U.S. Markets

Access mode choice to low-cost airports: Evaluation of new direct rail services at Milan-Bergamo airport

The current city lighting system that leaves many areas uncovered induces unsafe perceptions and instigates crimes. The addition of ubiquitous surveillance is an intrusion on privacy and does not take real-time actions. The cold, lifeless light shines in the darkness, trapping people in the solitude of silence. These absences motivated us to create Twinkle - a luminous transformative creature inhabits on light posts. They are curious aerial animals attracted by human activities. During daytime, they rest on urban light posts, expanding their solar panels for charging. At night, they interact with individuals walking on the street in their own way based on their distinct personalities. Twinkles are indirect solutions for improving urban safety without surveillance. We envisage a future that appliance goes beyond machine and becomes a companion with us.

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Twinkle: A Flying Lighting Companion for Urban Safety

This paper proposes an origin-based air travel demand model that assumes saturation at the origin level and explicitly accounts for substitutability between destinations. We simultaneously integrate demand generation and allocation by means of a multilevel aggregate nested logit formulation that covers the choices of whether or not to travel by air, where to travel (destination), and how to travel (itinerary). Two specifications are proposed to reflect systematic differences between lengths of haul and the bootstrap is applied to jointly address endogeneity issues and data missingness. The validity of the proposed approach is tested over the entire network of outbound air trips from Italy in 2018.

Integrated origin-based demand modeling for air transportation

Flight scheduling and fleet assignment are important steps of an airline planning process In light of the reciprocal relationship between air transport supply and demand, a key element of these models is to devise effective methods to both incorporating estimation of total market demand and allocating passengers over the available itineraries in a specific market In this paper, we present a novel mixed integer nonlinear flight scheduling and fleet assignment optimization model wherein air travel demand generation and allocation are simultaneously and consistently endogenized Using a nested logit formulation, we jointly model competition among air travel itineraries and appraise the contribution of specific itinerary attributes to demand generation, therefore yielding a more comprehensive and explicit representation of supply-demand interactions Computational testing based on realistic problem instances reveals that the model can optimize mid-size hub-and-spoke networks within reasonable time Further analyses illustrate the benefits that can be derived from the application of the proposed approach using real-world data for a major European airline Results demonstrate that the proposed approach can significantly enhance operating profits by up to 69% and better reveal opportunities for demand stimulation against a conventional approach using inelastic trip generation

Sebastian Birolini

Papers

Access mode choice to low-cost airports: Evaluation of new direct rail services at Milan-Bergamo airport

Twinkle: A Flying Lighting Companion for Urban Safety

Integrated origin-based demand modeling for air transportation

Integrated flight scheduling and fleet assignment with improved supply-demand interactions

The geography of suppliers and retailers