Arrival Traffic Flow Research Articles

Four-Dimensional Trajectory Optimization for CO2 Emission Benchmarking of Arrival Traffic Flow with Point Merge Topology

Four-Dimensional Trajectory Optimization for CO2 Emission Benchmarking of Arrival Traffic Flow with Point Merge Topology

One-stage estimation of cyclic arrival rates using license plate recognition data

Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.

Enhancing Traffic Sustainability: An Analysis of Isolation Intersection Effectiveness through Fixed Time and Logic Control Design Using VisVAP Algorithm

This paper addresses the limitations of the fixed-time approach in traffic signal control, which can lead to bottlenecks and inefficiencies. Proposing an alternative algorithm based on design logic control, the study integrates data from inductive detectors and non-linear traffic flow rates to optimize signaling plans. Analytical models are developed for both fixed and semi-actuated traffic signal control approaches, with PTV Vissim software (version 8, 64 bit) used for simulation. The design logic control dynamically adjusts signaling plans, determining the duration of the green interval for the secondary road based on arrival traffic flow. In the absence of traffic, it eliminates the green interval, advancing to the next phase, thereby reducing cycle time. This dynamic adjustment follows a conditional “if-then” statement, optimizing traffic signal operation. The design logic control algorithm was tested in a real isolation intersection with four scenarios, using non-linear traffic flow rate data for one peak hour. Results demonstrated that the proposed design logic control, based on the Semi-Actuated Traffic Signal Control (SATSC) approach, outperformed the commonly used Fixed-Time Signal Control (FTSC) with overall reduction of queue lengths by 39.6% and reduction of vehicle delays by 51.3%. The findings suggest its viability as a solution for many cities, contributing to a more sustainable traffic system.

Collaborative optimization model and algorithm for airport capacity and traffic flow allocation.

How to efficiently utilize the existing airport capacity without physical expansion and considerable economic inputs to meet air traffic needs is one of the important tasks of air traffic management. To improve the efficiency of capacity utilization, it is necessary to find the actual airport capacity properly. In this work, taking Shuangliu International Airport as an example, a methodology for capacity estimation is proposed that combines the empirical method with an analytical approach that uses historical performance data from the airport to construct a capacity envelope to approximate the airport's actual capacity to the greatest extent, establishes a collaborative optimization model that reflects the inherent relations between airport capacity and arrival and departure traffic demand, adopts an improved optimization algorithm to solve the model, and generates an optimal flight allocation scheme. Priority ratio is introduced to dynamically adjust management preferences for arrival and departure traffic demand to further reveal the synergy mechanism between departure and arrival traffic flow demand and the airport capacity. The result shows that the Flight On-time Performance rate is lifted by 6% in the case study which proves the feasibility of the proposed method, demonstrating its value for maximizing airport capacity and traffic flow demand without requiring expansions on airport scales.

Airspace situation analysis of terminal area traffic flow prediction based on big data and machine learning methods

Real-time and accurate prediction of terminal area arrival traffic flow is a key issue for terminal area traffic management. In this paper, we study the advantages and disadvantages of traditional dynamics-based prediction methods and time-series based prediction methods in the first step. Taking the advantages of the two type of methods, a terminal area arrival flow prediction framework based on airspace situation is proposed. In our method, the airspace situation is used as the machine learning feature to estimate the number of arrival aircraft. In addition, also based on machine learning approach, a correction stage is added to the algorithm to improve the accuracy of the prediction. ADS-B data collected from the terminal area of Chengdu is used to study the prediction accuracy based on different machine learning algorithms in the proposed framework. Experimental results show that the proposed method can predict the air traffic flow accurately. The average absolute error is only 0.35 aircraft/15 min, the root mean square error is 0.67 aircraft/15 min, and the maximum absolute error is 2 aircraft/15 min. Compared with the AOL method, our proposed method improves the accuracy of prediction by a margin of 90 % and 60 % according to the evaluation metrics of MAE and MAXAE, respectively.

Comparison of performance between PMS and trombone arrival route topologies in terminal maneuvering area

The contradiction between air traffic capacity and demand exerts congestion and delay, resulting in airspace operating at or above its capacity. This paper proposes a performance comparison of two arrival route topologies in Terminal Maneuvering Area (TMA) with a tailored optimization algorithm, including Point Merge System (PMS) and trombone paradigms. We aim at enhancing the capacity of arrival topologies and efficiently landing more aircraft through identifying which topological route is better suitable for current traffic demands. The problem involves integrating and sequencing arrival operations into the TMA. A mathematical model is developed for which entry times, entry speeds and flight routes are regarded as decision variables. The operation constraints of this optimization modelling correspond to detection of potential conflicts between consecutive aircraft at merging waypoints and on the same link segments, based on the safety separation standards. The objective function is set to minimize the delay and speed deviation for all flights in the optimized system. Because of the highly combinatorial nature of this problem and the non-deterministic polynomial hard (NP-hard), a tailored selective simulated annealing algorithm is proposed to optimally make decisions for the arrival traffic flow. Finally, the validation of our approach has been performed for Ataturk Airport in Turkey, by using 11 different operation scenarios of arrival demands. For lower demands, all aircraft can be positively scheduled, while for more than 80% extra scenarios based on the regular traffic demand, there exist some remaining conflicts for the situations. The results illustrate that, compared with the trombone procedure, PMS paradigm could enhance the arrival capacity and has better performance throughout the arrival procedure. Additionally, more flexible sequence position shift is possible in PMS, and it has the ability to accommodate high-density flight operating environment.

An Efficient Aircraft Conflict Detection and Resolution Method Based on an Improved Reinforcement Learning Framework

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research.

A Chaotic Discriminant Algorithm for Arrival Traffic Flow Time Series Based on Improved Alternative Data Method

<p>Chaos discrimination is a prerequisite for the application of chaos theory modeling. Since the average orbital period of an air traffic flow system is long, it is difficult to obtain time series with a small time scale and many data points, so the Small-Data Method is often adopted to quantitatively calculate the chaotic characteristic quantity. However, when using the power spectrum method, it is found that the Small-Data Method is prone to false judgments when the data volume is small. To reduce spurious judgments, we apply a chaos discrimination algorithm based on an Improved Alternative Data Method combined with the Small-Data Method for air traffic flow and analyze it by example. The algorithm was experimentally demonstrated to correct the false judgment results of the Small-Data Method. In particular, when the data volume is only 150, the discrimination accuracy of the improved algorithm is as high as 80%, which is 26% higher than the discrimination accuracy of the Small-Data Method. Moreover, the improved algorithm has better discriminative performance than the Small-Data Method under the same data volume condition, which is suitable for the chaotic discriminative analysis of the arrival traffic flow time series.</p> <p> </p>

Effectiveness of Aircraft Inter-Arrival Control in Upstream Traffic Flow via a Combined Tandem Fluid Queue Model and Integer Programming Approach

Conventionally, several studies indicated that controlling aircraft arrival time in the enroute airspace mitigates arrival aircraft congestion in the terminal airspace. Further research is required to clarify how to leverage this idea to design an air traffic management system, a so-called Extended Arrival MANager (E-AMAN), to reduce the arrival traffic flow while assisting air traffic controllers and boosting their effectiveness quantitatively. Under these circumstances, this research proposed aircraft inter-arrival time control within the en-route airspace and clarified its effectiveness in reducing arrival delay based on mathematical modeling and simulation evaluations. In this paper, we developed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G_t/GI/s_t</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GI</i> tandem fluid model to analyze the time-varying delay time of flights in both en-route and terminal airspace and demonstrated the effect of inter-arrival time control in the upstream arrival traffic flow in the en-route airspace, combining the model with the nonlinear integer programming problem. The calculation results for 3,074 aircraft over 21 days, arriving at Tokyo International Airport between 17:00 and 22:00, show the possibility for the control to reduce the mean and maximum delay time for flights by 18.8% (5.0 s) and 16.5% (37.6 s) on average within the en-route airspace. Moreover, fast-time simulation by AirTOP is conducted to validate the control, revealing the scope to reduce mean and maximum delay times in the terminal airspace by 11.5% (36.5 s) and 19.2% (148.8 s) on average.

Data-Driven Simulation for Evaluating the Impact of Lower Arrival Aircraft Separation on Available Airspace and Runway Capacity at Tokyo International Airport

Although the application of new wake turbulence categories, the so-called “RECAT (wake turbulence category re-categorization)”, will realize lower aircraft separation minima and directly increase runway throughput, the impacts of increasing arrival traffic on the surrounding airspace and arrival traffic flow as a whole have not yet been discussed. This paper proposes a data-driven simulation approach and evaluates the effectiveness of the lower aircraft separation in the arrival traffic at the target airport. The maximum runway capacity was clarified using statistics on aircraft types, stochastic distributions of inter-aircraft time and runway occupancy time, and the levels of the automation systems that supported air traffic controllers’ separation work. Based on the estimated available runway capacity, simulation models were proposed by analyzing actual radar track and flight plan data during the 6 months between September 2019 and February 2020, under actual operational constraints and weather conditions. The simulation results showed that the application of RECAT would reduce vectoring time in the terminal area by 7% to 10% under the current airspace and runway capacity when following a first-come first-served arrival sequence. In addition, increasing airspace capacity by 10% in the terminal area could dramatically reduce en-route and takeoff delay times while keeping vectoring time the same as under the current operation in the terminal area. These findings clarified that applying RECAT would contribute to mitigating air traffic congestion close to the airport, and to reducing delay times in arrival traffic as a whole while increasing runway throughput. The simulation results demonstrated the relevance of the theoretical results given by queue-based approaches in the authors’ past studies.

Collaborative slot allocation for arrival flights in multi-airport terminal area based on the traffic flow pattern: a case study of Shanghai terminal, China

In order to solve the increasingly serious congestions and delays in busy airports, the problem of collaborative arrival slot allocation for the multi-airport terminal area is investigated in this paper. With the premise of ensuring operation safety and the goal of minimizing total delay, the specific pattern of arrival traffic flow in the terminal area is considered in optimization modelling, and an improved genetic algorithm is designed for effective solution, followed which a simulation experiment of Shanghai terminal in China is conducted based on analysing the spatiotemporal characteristics of the arrival traffic flow in it. Results show that the proposed method in this paper is able to reduce the total arrival delay in Shanghai terminal by 18.67%, and the delays on three runways in the terminal are cut down by 14.56%, 19.35% and 25.37% respectively, indicating a significant improvement in operation efficiency of the total system.

An efficient algorithm for solving the system optimisation problem in transportation

ABSTRACTIn this paper, the classical optimal control problem in air transportation system is studied. The optimisation model is based on the principle of ‘first come first serve’ and the arrival traffic flow only occurs in the first queue which is assumed to be infinite. In addition, it is assumed that all processing times and arrival intervals are predefined. This paper aims to propose a new optimal control problem of series air traffic system and an effective solution. The exact algorithm of each sub-segment is given, and the required results are obtained. Numerical experiments show the effectiveness of the algorithm.

Exploring Dynamic Characteristics of Multi-State Air Traffic Flow: A Time Series Approach

Understanding the dynamics of air traffic flow is important to achieving advanced air traffic management. This work explores the dynamic evolution and fluctuation characteristics of multistate air traffic time series from a complex network perspective, which is essential for understanding the nature of an air traffic system. With the application of the fundamental diagram (FD), we discover that the relative velocity, flight distance and trajectory similarity are the three key variables for interpreting the arrival traffic flow states of the Xiamen Gaoqi International Airport. According to these three variables, time series are classified into four traffic states based on the K-means algorithm: free flow (FF), transitional flow (TF), slightly congested flow (SCF) and heavily congested flow (HCF). The extracted time series in different states are converted into complex networks using the visibility graph method. We analyze and compare the statistical features of the networks in the four states in terms of indexes, such as the degree distribution and network structure. The results indicate that the complex network characteristics can be used to distinguish air traffic states from the original traffic flow. Our work may be helpful for scholars and engineers to better understand the intrinsic nature of air traffic and for the development of intelligent assistant decision-making systems for air traffic management.

Evaluation of Aircraft Descent Profile

Point Merge System (PMS) is a new method for merging and sequencing arrival traffic flows. It consists of sequencing legs, merge point. PMS allows Continuous Descent Approaches (CDAs) which reduces environmental impacts according to aircraft noise, fuel burn and emissions during approach. Aircraft could descend continuously from an optimal position in a low drag with minimum engine thrust. In this study, PMS arrival procedure model will be designed and vertical descent profiles of aircraft will be assessed both in terms of vectoring and PMS. The results show that PMS model achieves more standard vertical descent profiles over vectoring.

Analysis of car-following behaviors considering the green signal countdown device

The purpose of this paper is to analyze the impacts of the green signal countdown device on car-following behaviors at signalized intersections and to propose an improved car-following model to explore how the green signal countdown device affects the arrival traffic flow during the critical phase-change period. The synchronous field data of car-following behaviors and the remaining time displayed by the green signal countdown device at a signalized intersection of Jinan in China are collected from obtained traffic videos for data mining analysis. Velocities and displacements of 11 cars and critical cars charging on homogeneous approaches to the signalized intersection at the end of the green signal are investigated in detail. The numerical results indicate that the improved car-following model can qualitatively describe the impacts of the green signal countdown device on car-following behaviors of the arrival traffic flow. It is also shown that taking the green signal countdown device into account in the simulation modeling and adaptive cruise control system design can help drivers make informed stopping/crossing decisions, reduce the number of cars ejecting to cross the signalized intersection and enhance traffic safety.

Strategies for Choosing Descent Flight-Path Angles for Small Jets

A standard descent procedure with a fixed flight-path angle is proposed to improve trajectory predictability for arriving small jets in the transition airspace into congested terminal area. Three candidate strategies for selecting fuel-efficient and flyable descent flight-path angles are proposed. The three strategies vary in operational complexity and fuel-burn merits. To mitigate variation of wind among flights, the two simpler strategies are adapted to airport, directions of arrival, and time. Three major U.S. airports with different degrees of wind variation and disparate arrival traffic flows are analyzed. Results show that, when compared to the simple airport–static adaptation, the finest adaptation of the simpler strategies recover up to 50–75% of the extra fuel burn relative to the minimum-fuel strategy. Wind variation, descent altitude restrictions, arrival directions, and fleet composition all affect the fuel efficiency of the simple strategies. Tradeoffs between fuel burn and planned speed-brake usage in the choice of the flight-path angle are discussed. Fuel efficiency of simple strategies for the entire national airspace in the United States is estimated. Considerations and implications for air navigation service providers are discussed.

An Improved MPS Algorithm for Optimal Scheduling of Aircraft Arrivals at Congested Airports

This paper presents an improved aircraft arrival sequencing and scheduling model, and designs a new efficient algorithm to optimize the arrival traffic flow at the Terminal Radar Approach Control (TRACON) area. It has three steps: Firstly the aircrafts in same route are united into groups, then the positionmatrix is created according to a series of ATC (Air Traffic Control) constraints, finally the solution space tree is built to search the optimal sequence. The algorithm also take into account Max Position Shift (MPS) and wake vortex separations. Finally the algorithm is validated with practical data, the results shows the computing scale is small and the proportion of continuous landing aircrafts in final sequence is more in contrast to other algorithms, it is more convenient for the operation of controllers.

Performance Related Security Modelling and Evaluation of RANETs

An exposition is undertaken on the performance related security modelling and evaluation of a robotic mobile wireless ad hoc networks (RANETs) and associated tradeoffs. It is based on the discrete event simulation analysis of a stable open gated queueing network model with infinite capacity, arbitrary configuration and multiple classes of packets under first-come-first-served and head-of-line rules. In this context, a quantitative case study on the trade-offs between performance and the wired equivalent privacy (WEP) security/selective security protocol is explored, based on generalised exponential transmission times and bursty arrival traffic flows characterised by an Interrupted Compound Poisson Process. Typical numerical experiments are carried out to evaluate the adverse effect of WEP security/selective security on performance and for illustration purposes, assess the adoption of a dual CPU towards performance enhancement of the RANET. A discussion on other vital related aspects such as broadcasting/multicasting and fault localisation in RANETs and their synergy with cognitive radio ad hoc networks (CRAHNs) is included.

Developments Towards Trajectory Based Operations in Arrival Management at Schiphol Airport

Air Traffic Control the Netherlands (LVNL) supports the Single European Sky ATM Research (SESAR) in the implementation of Trajectory Based Operations (TBO). A key TBO-component for LVNL is to achieve more environmentally friendly arrivals by introducing Continuous Descent Approach (CDA) in the Terminal Area (TMA) during daytime along fixed Precision Area Navigation (P-RNAV) routes. Simulations and calculations demonstrated decreased controllers’ workload and reductions in noise nuisance for use of P-RNAV routes and in fuel burn for CDA operations.To realize CDAs flown along P-RNAV routes, more predictable and stable arrival traffic flows are needed. Therefore, more accurate delivery of flights at the Initial Approach Fix (IAF) is required. LVNL is developing a decision support tool (Speed and Route Advisor - SARA) for controllers in the Area Control Centre. Simulations show that the reached accuracy of the target metering time window at the IAF can enable CDA along TMA P-RNAV routes.

Real-time queue length estimation for congested signalized intersections

How to estimate queue length in real-time at signalized intersection is a long-standing problem. The problem gets even more difficult when signal links are congested. The traditional input–output approach for queue length estimation can only handle queues that are shorter than the distance between vehicle detector and intersection stop line, because cumulative vehicle count for arrival traffic is not available once the detector is occupied by the queue. In this paper, instead of counting arrival traffic flow in the current signal cycle, we solve the problem of measuring intersection queue length by exploiting the queue discharge process in the immediate past cycle. Using high-resolution “event-based” traffic signal data, and applying Lighthill–Whitham–Richards (LWR) shockwave theory, we are able to identify traffic state changes that distinguish queue discharge flow from upstream arrival traffic. Therefore, our approach can estimate time-dependent queue length even when the signal links are congested with long queues. Variations of the queue length estimation model are also presented when “event-based” data is not available. Our models are evaluated by comparing the estimated maximum queue length with the ground truth data observed from the field. Evaluation results demonstrate that the proposed models can estimate long queues with satisfactory accuracy. Limitations of the proposed model are also discussed in the paper.