Harshad Khadilkar

Network Congestion Control of Airport Surface Operations

This paper presents a novel approach to managing the aircraft taxi-out process at airports, by posing the problem in a network congestion control framework. A network model of the airport surface with stochastic link travel times is first developed. The model parameters for Boston Logan International Airport are estimated using empirical data from a surface surveillance system. Using the entry times of aircraft into the network as the control variables, a control algorithm that aims to reduce taxi-out times is proposed. This algorithm regulates network congestion while limiting the adverse effect on throughput performance. Performance characteristics of the proposed control algorithm are derived using simplified theoretical analysis, and are shown to agree with simulations of traffic on a network model of Boston Logan International Airport.

Estimation of Aircraft Taxi-out Fuel Burn using Flight Data Recorder Archives

The taxi-out phase of a flight accounts for a significant fraction of total fuel burn for aircraft. In addition, surface fuel burn is also a major contributor to CO2 emissions in the vicinity of airports. It is therefore desirable to have accurate estimates of fuel consumption on the ground. This paper builds a model for estimation of on-ground fuel consumption of an aircraft, given its surface trajectory. Flight Data Recorder archives are used for this purpose. The taxi-out fuel burn is modeled as a linear function of several factors including the taxi-out time, number of stops, number of turns, and number of acceleration events. The statistical significance of each potential factor is investigated. The parameters of the model are estimated using least-squares regression. Since these parameters are estimated using data from operational aircraft, they provide more accurate estimates of fuel burn than methods that use idealized physical models of fuel consumption based on aircraft velocity profiles, or the baseline fuel consumption estimates provided by the International Civil Aviation Organization. Our analysis shows that in addition to the total taxi time, the number of acceleration events is a significant factor in determining taxi fuel consumption.

High Confidence Networked Control for Next Generation Air Transportation Systems

This paper addresses the design of a secure and fault-tolerant air transportation system in the presence of attempts to disrupt the system through the satellite-based navigation system. Adversarial aircraft are assumed to transmit incorrect position and intent information, potentially leading to violations of separation requirements among aircraft. We propose a framework for the identification of adversaries and malicious aircraft, and then for air traffic control in the presence of such deliberately erroneous data. The framework consists of three mechanisms that allow each aircraft to detect attacks and to resolve conflicts: fault detection and defense techniques to improve Global Positioning System (GPS)/inertial navigation, detection and defense techniques using the Doppler/received signal strength, and a fault-tolerant control algorithm. A Kalman filter is used to fuse high frequency inertial sensor information with low frequency GPS data. To verify aircraft position through GPS/inertial navigation, we propose a technique for aircraft localization utilizing the Doppler effect and received signal strength from neighboring aircraft. The control algorithm is designed to minimize flight times while meeting safety constraints. Additional separation is introduced to compensate for the uncertainty of surveillance information in the presence of adversaries. We evaluate the effect of air traffic surveillance attacks on system performance through simulations. The results show that the proposed mechanism robustly detects and corrects faults generated by the injection of malicious data. Moreover, the proposed control algorithm continuously adapts operations in order to mitigate the effects these faults. The ability of the proposed approaches to defend against attacks enables reliable air traffic operations even in highly adversarial surveillance conditions.

Assessing the impacts of the JFK Ground Management Program

The Ground Management Program at John F. Kennedy International Airport (JFK) aims to leverage the availability of comprehensive airport surface surveillance data and airline schedule information to better manage the taxi-out process, reduce taxi times, and improve efficiency. During periods when departure demand exceeds capacity, departing aircraft are held at the gate or another holding location, and released to the runway in time to join a short departure queue before taking off. As a result, aircraft absorb delay with engines off, and decrease their fuel burn, emissions, and engine maintenance costs. This paper evaluates data from before and after departure metering was initiated at JFK, to assess its impacts. The results show that airport performance has improved, and that the departure metering is responsible for a significant portion of the improvements. The paper also finds that the new, more automated, Ground Management Program that was implemented in April 2012 has continued to yield significant benefits. The average taxi-out time savings at JFK due to departure metering in the summer of 2012 is estimated to be about 1.5-2.7 minutes per flight.

Analysis of Airport Performance using Surface Surveillance Data: A Case Study of BOS

Detailed surface surveillance datasets from sources such as the Airport Surface Detection Equipment, Model-X (ASDE-X) have the potential to be used for analysis of airport operations, in addition to their primary purpose of enhancing safety. In this paper, we describe how airport performance characteristics such as departure queue dynamics and throughput can be analyzed using surface surveillance data. We also propose and evaluate several metrics to measure the daily operational performance of an airport, and present them for the specific case of Boston Logan International Airport.

Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information

Anomaly detection is an important problem in building energy management in order to identify energy theft and inefficiencies. However, it is hard to differentiate actual anomalies from the genuine changes in energy consumption due to seasonal variations and changes in personal settings such as holidays. One of the important drawbacks of existing anomaly detection algorithms is that various unknown context variables, such as seasonal variations, can affect the energy consumption of users in ways that appear anomalous to existing time series based anomaly detection algorithms. In this paper, we present a system for monitoring the energy consumption of multiple users within a neighborhood and a novel algorithm for detecting anomalies by combining data from multiple users. For each user, the neighborhood is defined as the set of all other users that have similar characteristics (function, location or demography), and are therefore likely to react and consume energy in the similar way in response to the external conditions. The neighborhood can be predefined based on prior customer information, or can be identified through an analysis of historical energy consumption. The proposed algorithm works as a two-step process. In the first step, the algorithm periodically computes an anomaly score for each user by just considering their own energy consumption and variations in the consumption of the past. In the second step, the anomaly score for a user is adjusted by analyzing the energy consumption data in the neighborhood. The collation of data within the neighborhood allows the proposed algorithm to differentiate between these genuine effects and real anomalous behavior of users. Unlike multivariate time series anomaly detection algorithms, the proposed algorithm can identify specific users that are exhibiting anomalous behavior. The capabilities of the algorithm are demonstrated using several year-long real-world data sets, for commercial as well as residential consumers.

Individual and Aggregate Electrical Load Forecasting: One for All and All for One

Electrical load forecasting is an important task for utility companies, in order to plan future production and to increase the efficiency of the distribution network. Although load forecasting at the aggregate level has been extensively studied in existing literature, forecasts for individual consumers have been shown to be prone to errors. This paper deals with the problem of electrical load forecasting at multiple scales, from individual consumers to the network as a whole. We use smart meter data from carefully selected sets of consumers for this purpose. First, we consider the problem of forecasting the load for individual consumers at the outermost nodes of the distribution network. We propose an algorithm which considers external available information like calendar or weather contexts along with the energy consumption profiles of different consumers for accurate mid-term and short-term load forecasting. Multiple aggregation approaches are considered for utility level forecasting, in order to characterize their error properties. We show that careful clustering of consumers for aggregation can result in smaller errors. We experiment with two public data sets for demonstrating the advantages of the proposed method over the state-of-the-art approaches.

UrJar: A Lighting Solution using Discarded Laptop Batteries

Forty percent of the worlds population, including a significant portion of the rural and urban poor sections of the population in India, does not have access to reliable electricity supply. Concurrently, there is rapid penetration of battery-operated portable computing devices such as laptops, both in the developing and developed world. This generates a significant amount of electronic waste (e-waste), especially in the form of discarded Lithium Ion batteries which power such devices. In this paper, we describe UrJar, a device which uses re-usable Lithium Ion cells from discarded laptop battery packs to power low energy DC devices. To understand the usability of UrJar in a real world scenario, we deployed it at five street-side shops in India, which did not have access to grid electricity. The participants appreciated the long duration of backup power provided by the device to meet their lighting requirements. To conclude, we present an ecosystem which consists of a community-level energy shed and UrJar devices individually owned by households, as a mechanism for DC electrification of rural areas in developing countries. We show that UrJar has the potential to channel e-waste towards the alleviation of energy poverty, thus simultaneously providing a sustainable solution for both problems.

Control of Aircraft Pushbacks at an Airport using a Dynamic Programming Formulation

This paper describes a dynamic programming formulation of the airport surface tra c management problem. Movement of aircraft is modeled as the ow of tra c on a network, with stochastic link travel times. This is followed by an algorithm for controlling entry of aircraft into the taxiway system at an airport. Finally, two realistic variations of the formulation are presented variation of parameters and nite bu er capacity. Optimal control policies for all cases are calculated using policy iteration, with delay mitigation and aircraft fuel burn reduction as explicit objectives.

Integrated Control of Airport and Terminal Airspace Operations

Airports are the most resource-constrained components of the air transportation system. This paper addresses the problems of increased flight delays and aircraft fuel consumption through the integrated control of airport arrival and departure operations. Departure operations are modeled using a network abstraction of the airport surface. Published arrival routes to airports are synthesized to form a realistic model of arrival airspace. The proposed control framework calculates the optimal times of departure of aircraft from the gates, as a function of the arrival and departure traffic as well as airport characteristics such as taxiway layout and gate capacity. The integrated control formulation is solved using dynamic programming, which allows calculation of policies for real-time implementation. The advantages of the proposed methodology are illustrated using simulations of Bostons Logan International Airport.