A Perspective on NASA Ames Air Traffic Management Research

This paper describes past and present air-traffic-management research at NASA Ames Research Center. The descriptions emerge from the perspective of a technical manager who supervised the majority of this research for the last four years. Past research contributions built a foundation for calculating accurate flight trajectories to enable efficient airspace management in time. That foundation led to two predominant research activities that continue to this day - one in automatically separating aircraft and the other in optimizing traffic flows. Today s national airspace uses many of the applications resulting from research at Ames. These applications include the nationwide deployment of the Traffic Management Advisor, new procedures enabling continuous descent arrivals, cooperation with industry to permit more direct flights to downstream way-points, a surface management system in use by two cargo carriers, and software to evaluate how well flights conform to national traffic management initiatives. The paper concludes with suggestions for prioritized research in the upcoming years. These priorities include: enabling more first-look operational evaluations, improving conflict detection and resolution for climbing or descending aircraft, and focusing additional attention on the underpinning safety critical items such as a reliable datalink

Analysis and operational challenges of dynamic ride sharing demand responsive transportation models

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version

DEMAND CAPACITY BALANCING IN MULTI-MODAL TRANSPORTATION THROUGH OPTIMIZATION AND SIMULATION

International audienceThe current Air traffic System in Europe relies on airspace and airport capacity estimates computed by the Air National Service Providers (ANSPs) using demand forecast and Air traffic Controllers operations schedules. The Demand Capacity Balancing (DCB) aims at reducing the Air Traffic Management resources held in reserve to cope with demand peaks by providing the system with demand smoothing means. A recent study on the subject suggests introducing a congestion-based route fee that encourages users to avoid crowded slots for a given departure and arrival airport [1]. An optimal equilibrium point can then be reached through a clever choice of penalties incurred by flying at departure times adversely impacting congestion. Alternative routes may also be considered in the planning, as for a whole category of customers price tag is more important than travel time. However, taking into account that for short haul flights alternative means of transportation may be a viable option, DCB can be addressed in a wider scope by considering surface vehicles along aircraft. A side effect of this holistic approach is the ability to cope with disruptive events. The present work describes a simulation and optimization model tailored to this particular problem

Algorithms for Control of Arrival and Departure Traffic in Terminal Airspace

This paper presents a design approach and basic algorithms for a future system that can perform aircraft conflict resolution, arrival scheduling and convective weather avoidance with a high level of autonomy in terminal area airspace. Such a system, located on the ground, is intended to solve autonomously the major problems currently handled manually by human controllers. It has the potential to accommodate higher traffic levels and a mix of conventional and unmanned aerial vehicles with reduced dependency on controllers. The main objective of this paper is to describe the fundamental trajectory and scheduling algorithms that provide the foundation for an autonomous system of the future. These algorithms generate trajectories that are free of conflicts with other traffic, avoid convective weather if present, and provide scheduled times for landing with specified in-trail spacings. The maneuvers the algorithms generate to resolve separation and spacing conflicts include speed, horizontal path, and altitude changes. Furthermore, a method for reassigning arrival aircraft to alternate runways in order to reduce delays is also included. The algorithms generate conflict free trajectories for terminal area traffic, comprised primarily of arrivals and departures to and from multiple airports. Examples of problems solved and performance statistics from a fast-time simulation using simulated traffic of arrivals and departures at the Dallas/Fort Worth International Airport and Dallas Love Field are described

Optimized Route Capability (ORC) Intelligent Offloading of Congested Arrival Routes

The Optimized Route Capability (ORC) concept is designed to enable intelligent offloading of congested arrival routes. When ORC predicts arrival route congestion as projected excess arrival meter fix delay, automation offers decision support to traffic managers by identifying candidate flights to strategically reroute to alternate meter fixes and alleviate the congestion. This concept was applied to a model of arrival operations into Houston International Airport. An arrival rush from the Northeast was simulated in fast-time to analyze ORC algorithm behavior. The results demonstrate how strategically rerouting a few flights to alternate meter fixes not only has the potential to manage meter fix delay (and possibly the need for traffic management initiatives applied upstream), but may also increase airport capacity utilization and reduce total flight delay

An agent-based simulation model for autonomous trailer docking

This paper presents a simulation model of a generic automated planning and control system for the pick-up and docking of semi-trailers by means of autonomous Yard Tractors (YTs) in a collision- and conflict free environment. To support the planning and control of the YTs, we propose a Multi-Agent System (MAS). We illustrate our approach using a case study at a Dutch logistics service provider. To evaluate the proposed system, we design an agent-based simulation model, which is set up in a similar way as the MAS. We conclude with the verification and validation of the simulation model