297,210 research outputs found

    Benefit Assessment of the Integrated Demand Management Concept for Multiple New York Metroplex Airports

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    Benefits of the Integrated Demand Management (IDM) concept were assessed utilizing a newly developed automated simulation capability called Traffic Management Initiative Automated Simulation (TMIAutoSim). The IDM concept focuses on improving traffic flow management (TFM) by coordinating the FAAs strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The IDM concept leverages a new TFMS capability called Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for tactically managing traffic by generating precise arrival schedules. The IDM concept was developed over a multi-year effort, focusing on solving New York metroplex airport arrival problems. TMIAutoSim closely mimics NASAs high-fidelity simulation capabilities while enabling more data to be collected at higher speed. Using this new capability, the IDM concept was evaluated using realistic traffic across various weather scenarios. Six representative weather days were selected after clustering three months of historical data. For those selected six days, Newark Liberty International Airport (EWR) and LaGuardia Airport (LGA) arrival traffic scenarios were developed. For each selected day, the historical data were analyzed to accurately simulate actual operations and the weather impact of the day. The current day operations and the IDM concept operations were simulated for the same weather scenarios and the results were compared. The selected six days were categorized into two groups: clear weather for days without Ground Delay Programs (GDP) and convective weather for days with GDP and significant weather around New York metroplex airports. For the clear weather scenarios, IDM operations reduced last minute, unanticipated departure delays for short-haul flights within TBFM control boundaries with minimal to no impact on throughput and total delay. For the convective weather scenarios, IDM significantly reduced delays and increased throughput to the destination airports

    Management of threats and errors in normal operations of assistant controllers : a thesis presented in partial fulfilment of the requirements for the degree of Master in Aviation at Massey University

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    "To err is indeed human, so to err is normal" Human errors are usually pronounced in accident or incident reports. Seldom does one pay enough attention to these errors during daily normal operations as these either go unnoticed or unreported for whatsoever the reasons may be. Therefore, the causes of these errors and also the system threats prevalent in the daily operations may not be fully contained. On the other hand, problematic situations that are successfully tackled by human skills are quite often treated as less important than they really are. The job of an assistant controller (AC) is one of the important domains in air traffic management (ATM). The AC work together with air traffic controllers as team members and they do have direct and indirect contributions to the safe, orderly and efficient flow of air traffic. In this study, the threats, errors and potential undesired states occurring with AC during normal operations will be recorded by a methodology, which is new to Hong Kong Air Traffic Control (ATC). This methodology, called Normal Operations Safety Observation (NOSO), is built on the Threat and Error Management (TEM) framework. The results will generate a broad outline on what sorts of threats, errors and undesired states an AC can be facing during normal operations. The relative frequencies of occurrence of these conditions will be presented separately in tables and figures. The AC's potential vulnerabilities and capabilities to cope with these threats, errors and undesired states will be discussed together with a suggested ranking. It is envisaged that an analysis of the data collected will aid the development and evaluation of safety defence measures in ATM and further support the applicability of this data collection methodology in other ATM operations and subsequent researches. KEYWORDS:- Normal Operations Safety Observation, Threat and Error Management, Safety Management, Air Traffic Control

    Spatial inference of traffic transition using micro-macro traffic variables

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    This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature

    Heavy Vehicle Performance During Recovery From Forced-Flow Urban Freeway Conditions Due To Incidents, Work Zones and Recurring Congestion

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    Information contained in the Highway Capacity Manual on the influence heavy vehicles have on freeway traffic operations has been based on few field data collection efforts and relied mostly on traffic simulation efforts. In the 2010 Manual heavy vehicle impact is evaluated based on “passenger car equivalent” values for buses, recreational vehicles and trucks. These values were calibrated for relatively uncongested freeway conditions (levels of service A through C) since inadequate field data on heavy vehicle behavior under congested conditions were available. A number of field data collection efforts, that were not included in deriving the passenger car equivalent values used in the Highway Capacity Manual, indicated that heavy vehicle impacts on traffic operations may increase as freeway congestion levels increase and freeways operate under unstable flow conditions. The goal of the present effort was to collect and analyze field data with an emphasis on heavy vehicle behavior under lower speeds and derive passenger car equivalent values under such conditions

    Evaluating the Effects of Different Control Strategies on Traffic Operations at Isolated Merge Bottlenecks

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    AbstractThe primary objective of this study was to develop variable speed limit (VSL) and ramp metering (RM) strategies that particularly focused on preventing the capacity drop and increasing the discharge flow rate at freeway merge bottlenecks, and to evaluate the effects of the proposed control strategies on traffic operations. A cell transmission model was developed to evaluate the effects of the proposed control strategies on traffic operations. The CTM was adjusted and calibrated using real- world traffic data to accurately capture the capacity drop phenomenon. With space-time diagrams and traffic characteristic diagrams, the occurrence mechanism of traffic congestion at the bottleneck under different control strategies was compared. The simulation results showed that total travel time and vehicle delay were reduced under the proposed control strategies. It was found that the VSL control and RM control were effective in improving traffic operations at freeway isolated merge bottlenecks. In addition, the RM control outperformed the VSL control in reducing traffic congestion when the on-ramp flow rate was low

    Traffic flow modeling and forecasting using cellular automata and neural networks : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand

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    In This thesis fine grids are adopted in Cellular Automata (CA) models. The fine-grid models are able to describe traffic flow in detail allowing position, speed, acceleration and deceleration of vehicles simulated in a more realistic way. For urban straight roads, two types of traffic flow, free and car-following flow, have been simulated. A novel five-stage speed-changing CA model is developed to describe free flow. The 1.5-second headway, based on field data, is used to simulate car-following processes, which corrects the headway of 1 second used in all previous CA models. Novel and realistic CA models, based on the Normal Acceptable Space (NAS) method, are proposed to systematically simulate driver behaviour and interactions between drivers to enter single-lane Two-Way Stop-Controlled (TWSC) intersections and roundabouts. The NAS method is based on the two following Gaussian distributions. Distribution of space required for all drivers to enter intersections or roundabouts is assumed to follow a Gaussian distribution, which corresponds to heterogeneity of driver behaviour. While distribution of space required for a single driver to enter an intersection or roundabout is assumed to follow another Gaussian distribution, which corresponds to inconsistency of driver behavior. The effects of passing lanes on single-lane highway traffic are investigated using fine grids CA. Vehicles entering, exiting from and changing lanes on passing lane sections are discussed in detail. In addition, a Genetic Algorithm-based Neural Network (GANN) method is proposed to predict Short-term Traffic Flow (STF) in urban networks, which is expected to be helpful for traffic control. Prediction accuracy and generalization ability of NN are improved by optimizing the number of neurons in the hidden layer and connection weights of NN using genetic operations such as selection, crossover and mutation

    High performance computing simulator for the performance assessment of trajectory based operations

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    High performance computing (HPC), both at hardware and software level, has demonstrated significant improve- ments in processing large datasets in a timely manner. However, HPC in the field of air traffic management (ATM) can be much more than only a time reducing tool. It could also be used to build an ATM simulator in which distributed scenarios where decentralized elements (airspace users) interact through a centralized manager in order to generate a trajectory-optimized conflict-free scenario. In this work, we introduce an early prototype of an ATM simulator, focusing on air traffic flow management at strategic, pre-tactical and tactical levels, which allows the calculation of safety and efficiency indicators for optimized trajectories, both at individual and network level. The software architecture of the simulator, relying on a HPC cluster of computers, has been preliminary tested with a set of flights whose trajectory vertical profiles have been optimized according to two different concepts of operations: conventional cruise operations (i.e. flying at constant altitudes and according to the flight levels scheme rules) and continuous climb cruise operations (i.e., optimizing the trajectories with no vertical constraints). The novel ATM simulator has been tested to show preliminary benchmarking results between these two concepts of operations. The simulator here presented can contribute as a testbed to evaluate the potential benefits of future Trajectory Based Operations and to understand the complex relationships among the different ATM key performance areasPeer ReviewedPostprint (published version
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