Vikrant Vaze

Calibration of dynamic traffic assignment models with point-to-point traffic surveillance

Accurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy.

Modeling Passenger Travel and Delays in the National Air Transportation System

Many of the existing methods for evaluating an airlines on-time performance are based on flight-centric measures of delay. However, recent research has demonstrated that passenger delays depend on many factors in addition to flight delays. For instance, significant passenger delays result from flight cancellations and missed connections, which themselves depend on a significant number of factors. Unfortunately, lack of publicly available passenger travel data has made it difficult for researchers to explore the nature of these relationships. In this paper, we develop methodologies to model historical travel and delays for U.S. domestic passengers. We develop a multinomial logit model for estimating historical passenger travel and extend a previously developed greedy reaccommodation heuristic for estimating the resulting passenger delays. We report and analyze the estimated passenger delays for calendar year 2007, developing insights into factors that affect the performance of the National Air Transportat...

Demand and capacity management in air transportation

This paper summarizes research trends and opportunities in the area of managing air transportation demand and capacity. Capacity constraints and resulting congestion and low schedule reliability currently impose large costs on airlines and their passengers. Significant capacity increases that would solve these problems are not expected in the near- or medium-term. This paper outlines first a number of directions for effecting improvement through marginal capacity increases and better management of demand and available capacity. It then describes strategic initiatives that airlines and civil aviation authorities might undertake over time horizons of months to years as well as tactical measures that may be adopted on a daily basis in response to dynamic, “real-time” developments like poor weather or schedule disruptions. Research challenges in these areas are identified and classified in terms of specifying, allocating, and utilizing capacity. The first two categories reflect challenges faced by infrastructure providers, the last category challenges faced by airlines.

An assessment of the impact of demand management strategies for efficient allocation of airport capacity

Airport demand management strategies have the potential to mitigate congestion and delays. However, the extent to which the delays can be reduced using such strategies is not clear. In this paper, we develop a bound on the minimum possible level of delays that can be achieved using these strategies. We solve the aggregated timetable development and fleet assignment problem to minimise the system-wide delays assuming a single monopolistic carrier that satisfies all the passenger demand in the USA and maintains the same level-of-service as achieved with the current revenue management practices of individual carriers. The problem is formulated as a large-scale, integer linear programming model and solved using linear programming relaxation and heuristics. A network delay simulator is used for calculating the delays under different capacity scenarios. The results indicate the large inefficiencies in the usage of airport infrastructure in the domestic US caused by competitive airline scheduling decisions.

Robust Optimization: Lessons Learned from Aircraft Routing

Abstract Building robust airline scheduling models involves constructing schedules and routes with reduced levels of flight delays as well as fewer passenger and crew disruptions. In this paper, we study different classes of models to achieve robust airline scheduling solutions, with a focus on the aircraft routing problem. In particular, we compare one domain-specific approach and two general paradigms of robust models, namely, (i) an extreme-value based or robust optimization-based approach, and (ii) a chance-constrained or stochastic optimization-based approach. Our modeling and solution approach demonstrates the creation of data-driven uncertainty sets for aircraft routing using domain-specific knowledge and develops a completely data-driven simulation-based validation and testing approach. We first demonstrate that additional modeling, capturing domain knowledge, is required to adapt these general robust modeling paradigms to the aircraft routing problem, in order to meaningfully add robustness features specific to aircraft routing. However, we show that these models in their naive forms, still face issues of tractability and solution quality for the large-scale networks which are representative of real-world airline scheduling problems. Therefore, we develop and present advanced models that address these shortcomings. Our advanced models can be applied to aircraft routing in multiple ways, through varied descriptions of the uncertainty sets; and moreover, are generally applicable to linear and binary integer programming problems. Through our detailed computational results, we compare the performance of solutions arising from these different robust modeling paradigms and discuss the underlying reasons for their performance differences from a data-driven perspective.

Price of Airline Frequency Competition

Competition based on service frequency influences capacity decisions in airline markets and has important implications for airline profitability and airport congestion. The market share of a competing airline is a function of its frequency share. This relationship is pivotal for understanding the impacts of frequency competition on airport congestion and on the airline business in general. Additionally, airport congestion is closely related to several aspects of runway, taxiway, and airborne safety. Based on the most popular form of the relationship between market share and frequency share, we propose a game-theoretic model of frequency competition. We characterize the conditions for Nash equilibrium’s existence and uniqueness for the two-player case. We analyze myopic learning dynamics for the non-equilibrium situations and prove their convergence to Nash equilibrium under mild conditions. For the N-player symmetric game, we characterize all the pure strategy equilibria and identify the worst-case equilibrium, i.e., the equilibrium with maximum total cost. We provide a measure of the congestion level, based on the concept of price of anarchy and investigate its dependence on game parameters.

Integrated Airline Scheduling: Considering Competition Effects and the Entry of the High Speed Rail

Airlines and high speed rail are increasingly competing for passengers, especially in Europe and Asia. Competition between them affects the number of captured passengers and, therefore, revenues. We consider competition between airlines (legacy and low-cost) and high speed rail. We develop a new approach that generates airline schedules using an integrated mixed integer, nonlinear optimization model that captures the impacts of airlines’ decisions on passenger demand. We estimate the demand associated with a given schedule using a nested logit model. We report our computational results on realistic problem instances of the Spanish airline IBERIA and show that the actual airline schedules are found to be reasonably close to the schedules generated by our approach. Next, we use this optimization modeling approach under multimodal competition to evaluate multiple scenarios involving entry of high speed rail into new markets. We account for the possibility of demand stimulation as a result of the new services. We validate our approach using data from markets that had an entry by high speed rail in the past. The out-of-sample validation results show a close match between the predicted and observed solutions. Finally, we use our validated model to predict the impacts of future entry by high speed rail in new markets. Our results provide several interesting and useful insights into the schedule changes, fleet composition changes, and fare changes that will help the airline cope effectively with the entry of high speed rail.

Airline Frequency Competition in Airport Congestion Pricing

Airport congestion pricing has often been advocated as a way to control demand for airport operations and achieve efficient resource allocation. Competition between airlines affects the extent to which an airline is willing to pay for airport slots. Accurate modeling of competition is critical to determining the effectiveness of a congestion pricing mechanism. This paper develops an equilibrium model of airline frequency competition in the presence of delay costs and congestion prices. A small hypothetical network is used to evaluate the impacts of congestion prices on the various stakeholders and to investigate the dependence of effectiveness of congestion pricing on the characteristics of frequency competition in individual markets. The effectiveness of congestion pricing depends on three parameters of frequency competition. The results show that variation in the number of passengers per flight plays a vital role in determining the degree of attractiveness of congestion pricing to the airlines. A significant part of the impact of congestion pricing cannot be accounted for by using models in the literature, which are based on the assumptions of constant load factors and constant aircraft sizes. It was found that, compared with flat pricing, marginal cost pricing is more effective in reducing congestion without penalizing the airlines with exceedingly high congestion prices.

Airline-Driven Performance-Based Air Traffic Management: Game Theoretic Models and Multicriteria Evaluation

Defining air traffic management as the tools, procedures, and systems employed to ensure safe and efficient operation of air transportation systems, an important objective of future air traffic management systems is to support airline business objectives, subject to ensuring safety and security. Under the current model for designing air traffic management initiatives, the central authority overseeing and regulating air traffic management in a region makes trade-offs between specified performance criteria. The research presented in this paper aims instead to allow the airline community to set performance goals and thus make trade-offs between different performance criteria directly, before specific air traffic management strategies are determined. We propose several approaches for collecting inputs from airlines in a systematic way and for combining these airline inputs into implementable air traffic management initiatives. These include variants of averaging, voting, and ranking mechanisms. We also propose multiple criteria for evaluating the effectiveness of each approach, including Pareto optimality, airline profitability, system optimality, equity, and truthfulness of airline inputs. We apply a game-theoretic approach to examine the potential for strategic gaming behavior by airlines. We offer a broad evaluation of each approach, first by providing some theoretical insights, and then by simulating each of the approaches for a generic system using Monte Carlo methods, sampling values for input parameters from a wide range. We also provide an indication of how the approaches might perform in a real system by simulating ground delay programs at two airports in the New York City area. We first apply a simplified model that simulates the process of selecting only planned end times of a ground delay program, using Monte Carlo methods. Next, we apply a more detailed model that simulates the process of selecting planned end times and reduced airport arrival rates. Finally, we characterize the effectiveness of each of the considered approaches on the proposed criteria and identify the most desirable approaches. We conclude that voting schemes, which score highly on all criteria including airline profitability, system optimality, and equity, represent the most promising approaches among those considered to elicit airline preferences, thereby allowing the central authority to design air traffic management initiatives that optimize system performance while respecting the objectives of airlines.

Modeling Crew Itineraries and Delays in the National Air Transportation System

We propose, optimize, and validate a methodological framework for estimating the extent of the crew-propagated delays and disruptions (CPDDs). We identify the factors that influence the extent of the CPDDs and incorporate them into a robust crew-scheduling model. We develop a fast heuristic approach for solving the inverse of this robust crew-scheduling problem to generate crew schedules that are similar to real-world crew-scheduling samples. We develop a sequence of exact and heuristic techniques to quickly solve the forward problem within a small optimality gap for network sizes that are among the largest in robust crew-scheduling literature. Computational results using four large real-world airline networks demonstrate that the crew schedules produced by our approach generate propagation patterns similar to those observed in the real world. Extensive out-of-sample validation tests indicate that the parameters calibrated for one network perform reasonably well for other networks. We provide new insights...