Dynamic DEA models with network structure: An application for Iranian airlines

Efficiency estimation of interdependent divisions within a company or assessing the interrelated processes in a production system provides insights for improving the operational performance. Recent developments in network data envelopment analysis (NDEA) models enable decision making units (DMUs) to be informed of inefficient processes within the system. The NDEA model assesses the processes of the system in a specific moment and ignores the dynamic effects within the production processes. Thus, without considering the temporal dimension of production processes, biased efficiency measurement will be obtained that provides misleading information to DMUs. For evaluating the performance of a DMU with interrelated processes during specified multiple periods, this paper proposes a relational dynamic NDEA (DNDEA) model which measures the efficiencies of the system and its internal processes over the time, simultaneously. To illustrate the capability of the proposed model, this study for the first time measures the efficiency of eight Iranian airlines in several periods connected to each other by carry over flows. The actual data is gathered in three periods from 2010 to 2012 and the results are compared with the dynamic DEA and network DEA models in the same time span.     

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.     

Gravity models for dynamic transport planning: Development and implementation in urban networks

This paper describes the development and comparison through a real-life implementation of dynamic extensions of the entropy-based gravity model of trip distribution for dynamic transport planning purposes in urban networks. The single dynamic gravity model (SDGM) accounts only for the inter-period (long-term) evolution of travel demand. The doubly dynamic gravity model (DDGM) takes into account both the inter-period and intra-period (short-term or within-day) evolution of travel demand. The computational analysis and explanation of the differences between the two models are demonstrated in relation to different demand inputs, model parameters and performance measures.     

The benefits of codesharing and antitrust immunity for international passengers,with an application to the Star alliance

This paper summarizes and extends the empirical findings of previous research by the author, which analyzed the effect of airline cooperation on the level of interline fares paid by international passengers. The analysis focuses on three measures of cooperation: alliance membership, codesharing, and antitrust immunity. The results show that, together, these three forms of cooperation lead to a substantial 27 percent reduction in interline fares. The paper then computes the aggregate benefits from antitrust immunity and codesharing for the Star Alliance's interline passengers. The immunity enjoyed by the Star partners generated an aggregate benefit of about $80 million per year for interline passengers in 1999. Codesharing among Star partners yielded a further annual benefit of around $20 million. Moreover, if cooperation within the Star Alliance had expanded through an extension of antitrust immunity to those 1999 partners who did not enjoy it, then $20 million of additional benefits would have been generated.     

Supply-and-demand models for exploring relationships between smaller airports and neighboring hub airports in the U.S.

Airport passenger leakage is the phenomenon of air passengers choosing to travel longer distances to access more extensive air services offered by airlines at an out-of-region hub (or, substitute) airport, instead of using their local airports. Airport leakage can cause further reduction in services offered by airlines at a local airport, thereby causing even further leakage, and so on, which can significantly impact an airport's role in the growth of the local economy. This paper explores the geographic and operational attributes of local-and-substitute airport pairs in the United States, explicitly accounting for the interactive feedback relationship between passenger volumes and air service characteristics that contribute to the onset, persistence, and exacerbation of airport passenger leakage. A two-stage least squares regression model of air passenger demand at small- and medium-sized airports is first presented, where local passengers may travel by vehicle to larger, out-of-region hub airports. The results confirm that airfare and passenger volume relationships exist between the local and substitute airport pairs included in the dataset, and that lower airfares at the substitute airport have a greater impact on airport choices made by larger travel groups. They also suggest the existence of positive feedback in that if an airport attracts increasingly smaller passenger numbers with fewer air services and fewer air services with fewer passengers, without external intervention airport leakage impacts may be irreversible and exacerbate over time. A conceptual market share equilibrium analysis is used to illustrate the mechanisms of a direct two-way feedback relationship between passenger volumes at a local airport and air service characteristics at both the local and substitute airports. With data, this quantitative framework can help guide airport planners in further assessing and verifying suspected passenger leakage issues at their airport. The results suggest that without intervention, airport leakage impacts may be difficult to reverse; further exacerbating the trend are technological advancements that make driving cheaper and easier (connected and autonomous vehicles). However, the results can also guide planners in choosing the types and degrees of infrastructure investments and airline incentives that may be used to expand or retain air services to attract passengers.     

Calculating load factors for the transatlantic airline market using supply and demand data – A note on the identification of gaps in the available airline statistics

This paper takes a critical view of the verification of load factors for the direct transatlantic airline market by combining supply and demand-data. The supply-related data originate from the Official Airline Guide, a well-known data source that contains information on scheduled flights. The demand-related data originate from the Marketing Information Data Transfer database, a data set containing information on actual airline bookings. Combining both data sets enables us to calculate the seat occupancy of direct flights between North-West Europe and the US for 2001. Based on the results, we assess the utility of the joint use of these data sources for aviation economic research, and identify various gaps in the available airline statistics.     

Tourism in ASEAN-5: A novel panel unit-root test for shocks persistence with gradual structural breaks and common factors

This study investigates whether shocks to tourist arrivals in Indonesia, Malaysia, Philippines, Singapore, and Thailand (ASEAN-5) are permanent or transitory, given the frequent and severe external shocks experienced by the tourism industry. Using monthly data from 2000 to 2019, we employ a novel panel unit-root test that controls for cross-correlations, multiple gradual structural breaks, and other nonlinearities present in the data to assess the stationarity properties of tourist arrivals. Additionally, we apply a panel unit-root test that controls for cross-correlations and allows for abrupt structural changes for robustness. The empirical findings reveal that shocks to tourist arrivals in the ASEAN-5 countries exert persistent effects. Our results demonstrate nonstationarity within the series, illuminated by our careful consideration of both gradual and abrupt structural breaks. This finding underscores the necessity for policy interventions to mitigate the impact of adverse shocks on the tourism industry.     

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.     

A quantum-inspired model for human-automation trust in air traffic controllers derived from functional Magnetic Resonance Imaging and correlated with behavioural indicators

Steady growth in air traffic has resulted in a greater prevalence in automation aids as far as the field of Air Traffic Management is concerned. This has ensued in human factors, particularly trust becoming an essential point of consideration in Air Traffic Controller (ATCO)-automation teams. An undertaking to better embody trust behaviours in ATCOs was attempted by coalescing two schools of thought on trust using the principles of superposition and complementarity from quantum mechanics. This model was further refined with behavioural indicators from the experiment. Brain imaging verification of this synchronised coexistence of both philosophies was established with the use of functional Magnetic Resonance Imaging (fMRI) data, where ATCOs were given conflict detection tasks with the aid of ATS-CAP software that was able to generate credible flight plans with visible waypoints and airports. Data from self-reported questionnaires have been useful in building generalised models of trust. However, the robustness of the model that has been proposed in this paper is higher than generalised models because of the utilisation of unbiased data to represent specifically ATCO trusting behaviour under uncertainty. This is an improvement on current models that are also context-dependent and based on subjective data.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.