OPTIMIZATION OF AIRPORT TAXIING PLANNING DURING CONGESTED HOURS BASED ON IMMUNE CLONAL SELECTION ALGORITHM

In order to ease congestion and ground delays in major hub airports, an aircraft taxiing scheduling optimization model is proposed with schedule time as the object function. In the new model, the idea of a classical job shop-schedule problem is adopted and three types of special aircraft-taxi conflicts are considered in the constraints. To solve such nondeterministic polynomial time-complex problems, the immune clonal selection algorithm（ICSA） is introduced. The simulation results in a congested hour of Beijing Capital International Airport show that, compared with the first-come-first-served（FCFS） strategy, the optimization-planning strategy reduces the total scheduling time by 13.6 min and the taxiing time per aircraft by 45.3 s, which improves the capacity of the runway and the efficiency of airport operations.

Control Research for a Small Fixed-Wing UAV DuringGround Taxiing

Ground taxiing is the key process of take-off and landing for a tricycle-undercarriage unmanned aerial vehicle( UAV). Nonlinear model of a sample UAV is established based on stiffness and damping model of landing gears and tires taken into account. Then lateral nonlinear model is linearized and state space equations are deduced by using nose wheel and ruder as inputs and lateral states as outputs. Adaptive internal model control( AIMC) is proposed and applied to lateral control based on decoupled and linearized dynamic model during ground taxiing process. Different control strategies are analyzed and compared by simulations,and then a combined control strategy of nose wheel steering with holding and rudder control is given. Hardware in loop simulations( HILS) proves the validity of the controller designed.

Green Aircraft Taxiing Strategy Based on Multi-scenario Joint Optimization

The issue of green aircraft taxiing under various taxi scenarios is studied to improve the efficiency of aircraft surface operations and reduce environmental pollution around the airport from aircraft emissions.A green aircraft taxi programming model based on multi-scenario joint optimization is built according to airport surface network topology modeling by analyzing the characteristics of aircraft operations under three different taxiing scenarios:all-engine taxi,single-engine taxi,and electronic taxi.A genetic algorithm is also used in the model to minimize fuel consumption and pollutant emissions.The Shanghai Pudong International Airport is selected as a typical example to conduct a verification analysis.Compared with actual operational data,the amount of aircraft fuel consumption and gas emissions after optimization are reduced significantly through applying the model.Under an electronic taxiing scenario,fuel consumption can be lowered by 45.3%,and hydrocarbon(HC)and carbon dioxide(CO)emissions are decreased by 80%.The results show that a green aircraft taxiing strategy that integrates taxiway optimization and electronic taxiing can effectively improve the efficiency of airport operations and reduce aircraft pollution levels in an airport′s peripheral environment.

4-D Trajectory Prediction and Dynamic Planning of Aircraft Taxiing Considering Time and Fuel

Most of the traditional taxi path planning studies assume that the aircraft is in uniform speed,and the optimization goal is the shortest taxi time.Although it is easy to solve,it does not consider the changes in the speed profile of the aircraft when turning,and the shortest taxi time does not necessarily bring the best taxi fuel consumption.In this paper,the number of turns is considered,and the improved A*algorithm is used to obtain the P static paths with the shortest sum of the straight-line distance and the turning distance of the aircraft as the feasible taxi paths.By balancing taxi time and fuel consumption,a set of Pareto optimal speed profiles are generated for each preselected path to predict the 4-D trajectory of the aircraft.Based on the 4-D trajectory prediction results,the conflict by the occupied time window in the taxiing area is detected.For the conflict aircraft,based on the priority comparison,the waiting or changing path is selected to solve the taxiing conflict.Finally,the conflict free aircraft taxiing path is generated and the area occupation time window on the path is updated.The experimental results show that the total taxi distance and turn time of the aircraft are reduced,and the fuel consumption is reduced.The proposed method has high practical application value and is expected to be applied in real-time air traffic control decision-making in the future.

Methods for determining unimpeded aircraft taxiing time and evaluating airport taxiing performance

The objective of this study is to improve the methods of determining unimpeded（nominal） taxiing time,which is the reference time used for estimating taxiing delay,a widely accepted performance indicator of airport surface movement.After reviewing existing methods used widely by different air navigation service providers（ANSP）,new methods relying on computer software and statistical tools,and econometrics regression models are proposed.Regression models are highly recommended because they require less detailed data and can serve the needs of general performance analysis of airport surface operations.The proposed econometrics model outperforms existing ones by introducing more explanatory variables,especially taking aircraft passing and over-passing into the considering of queue length calculation and including runway configuration,ground delay program,and weather factors.The length of the aircraft queue in the taxiway system and the interaction between queues are major contributors to long taxi-out times.The proposed method provides a consistent and more accurate method of calculating taxiing delay and it can be used for ATM-related performance analysis and international comparison.

A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

Taxi origin and destination demand prediction based on deep learning:a review

Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.

木聚糖酶TAXI型抑制蛋白的研究进展

木聚糖酶是一种重要的半纤维素酶,广泛用于饲料工业。近年来在小麦中发现一类能抑制木聚糖酶活性的蛋白,这类抑制蛋白可能会对木聚糖酶的应用效果产生影响。目前发现在不同的谷物中共存在2种类型的木聚糖酶抑制蛋白,根据其结构的不同,分为TAXI型(triticum aestivum xylanase inhibitor)和XIP型(xylanase inhibi-tor protein)。本文主要简述了TAXI型抑制蛋白的分子结构、抑制特性和活性影响因素等方面的研究进展,对于深入研究木聚糖酶及其活性影响因子,充分开发利用我国饲料资源和促进畜牧业生产可持续发展具有重要现实意义。

Research on development of urban taxi supply based on influence factors classification

In order to determine the regulations of the development of taxi supply under entry regulations in Chinese cities, an improved neural network model is applied to find the particular years when the government artificially puts new taxis into the market, and then extract the political influence from the taxi supply. The model is also utilized to study the relationships between the adjusted taxi supply and non-policy factors. A case study of Nanjing city is conducted. The results show that 2001 and 2007 are the particular years that the Nanjing government artificially put new taxis into its taxi market, which is in accordance with the five-year plan of China and the local development plans. The results also show that the improved neural network model has a good performance in expositing the evolution of adjusted taxi supply related to non-policy factors.

Safety analysis of wheel brake system based on STAMP/STPA and Monte Carlo simulation

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

Real-Time Four-Dimensional Trajectory Generation Based on Gain-Scheduling Control and a High-Fidelity Aircraft Model

Aircraft ground movement plays a key role in improving airport efficiency,as it acts as a link to all other ground operations.Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations.Moreover,autonomous taxiing is envisioned as a key component in future digitalized airports.However,state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases.The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions.This paper proposes a new approach for generating efficient four-dimensional trajectories(4DTs)on the basis of a high-fidelity aircraft model and gainscheduling control strategy.Working in conjunction with a routing and scheduling algorithm that determines the taxi route,waypoints,and time deadlines,the proposed approach generates fuel-efficient 4DTs in real time,while respecting operational constraints.The proposed approach can be used in two contexts:①as a reactive decision support tool to generate new trajectories that can resolve unprecedented events;and②as an autopilot system for both partial and fully autonomous taxiing.The proposed methodology is realistic and simple to implement.Moreover,simulation studies show that the proposed approach is capable of providing an up to 11%reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.

木聚糖酶TAXI型抑制剂及基因工程研究

谷物中存在2种类型的木聚糖酶抑制剂,根据其结构的不同可分为TAXI型和XIP型。本文主要简述了木聚糖酶TAXI型抑制剂对木聚糖酶的抑制特性及其基因工程研究。

Structure identification for compound I separated and purified from taxoids-produced endophytic fungi (Alternaria. alternata var. taxi 1011)

Endophytic fungi are widely found in almost all kinds of plants. Many endophytic fungi can produce some physio-logical active compounds, which are same to or analog to those isolated from their hosts. Producing physiological active com-pounds through microbial fermentation can give a new way to resolve resource limitation and to find out alternative source. Through the methods of organic solvent extraction, thin layer chromatography (TLC) and column chromatography, compound I was isolated, purified from the liquid fermentation metabolites of the taxoids-produced endophytic fungi (Alternaria. alternata var. taxi 1011 Y. Xiang et LU An-guo) that was screened from the bark of Taxus. cuspidata Sieb.et Zucc.. Compound I was identified as one kind of taxoids type III, based on the analyzing results by using the methods of ultraviolet spectroscopy (UV), infrared spectroscopy (IR), mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR). This study provides a com-pleted method for separation and purification of the endophytic fungi as well as structure identification of its fermentation me-tabolite

语义数据模型及其建模的探讨

本文首先介绍ＴＡＸＩＳ语言的主要特性，然后阐述ＴＡＸＩＳ建模的方法及ＴＡＸＩＳ系统的特点。

木聚糖酶抑制蛋白的研究进展

简要介绍了三类木聚糖酶抑制蛋白的分子结构、抑制特性及其在谷物中的抗病防御作用。

抗抑制活性木聚糖酶研究进展

麦类饲料含非淀粉多糖(NSP)抗营养因子,严重影响麦类饲料的饲喂价值。添加木聚糖酶能减轻饲料中NSP的抗营养作用,但是麦类饲料中的木聚糖酶抑制蛋白会抑制外源添加的微生物来源木聚糖酶的活性,抗抑制性木聚糖酶的使用具有明显优势。文章综述谷物中存在的木聚糖酶抑制蛋白对外源添加木聚糖酶的抑制作用、抗抑制性木聚糖酶的潜在应用价值、研究现状及应用前景,以期麦类饲料能更好地在畜禽日粮中添加应用。

Prediction of Departure Aircraft Taxi Time Based on Deep Learning

With the continuous increase in the number of flights,the use of airport collaborative decision-making(ACDM)systems has been more and more widely spread.The accuracy of the taxi time prediction has an important effect on the A-CDM calculation of the departure aircraft’s take-off queue and the accurate time for the aircraft blockout.The spatial-temporal-environment deep learning(STEDL)model is presented to improve the prediction accuracy of departure aircraft taxi-out time.The model is composed of time-flow sub-model(airport capacity,number of taxiing aircraft,and different time periods),spatial sub-model(taxiing distance)and environmental sub-model(weather,air traffic control,runway configuration,and aircraft category).The STEDL model is used to predict the taxi time of departure aircraft at Hong Kong Airport and the results show that the STEDL method has a prediction accuracy of 95.4%.The proposed model also greatly reduces the prediction error rate compared with the other machine learning methods.

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model： A Case Study in Nanjing, China

With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

Analyzing Cross-domain Transportation Big Data of New York City with Semi-supervised and Active Learning

The majority of big data analytics applied to transportation datasets suffer from being too domain-specific,that is,they draw conclusions for a dataset based on analytics on the same dataset.This makes models trained from one domain(e.g.taxi data)applies badly to a different domain(e.g.Uber data).To achieve accurate analyses on a new domain,substantial amounts of data must be available,which limits practical applications.To remedy this,we propose to use semi-supervised and active learning of big data to accomplish the domain adaptation task:Selectively choosing a small amount of datapoints from a new domain while achieving comparable performances to using all the datapoints.We choose the New York City(NYC)transportation data of taxi and Uber as our dataset,simulating different domains with 90%as the source data domain for training and the remaining 10%as the target data domain for evaluation.We propose semi-supervised and active learning strategies and apply it to the source domain for selecting datapoints.Experimental results show that our adaptation achieves a comparable performance of using all datapoints while using only a fraction of them,substantially reducing the amount of data required.Our approach has two major advantages:It can make accurate analytics and predictions when big datasets are not available,and even if big datasets are available,our approach chooses the most informative datapoints out of the dataset,making the process much more efficient without having to process huge amounts of data.