机器学习_US AIRPORT STATISTICS(美国机场统计数据)

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机器学习算法在航空安全领域的应用

机器学习算法在航空安全领域的应用近年来，机器学习算法越来越成为科技领域的热门话题。

机器学习是人工智能（AI）的一种分支，它通过对数据的分析和学习来实现智能化的解决方案。

机器学习技术在机器人、医药、金融、教育、自然语言处理等领域得到了广泛应用，而在航空安全领域中，机器学习技术也愈发重要。

机器学习在航空安全领域的应用需要处理大量的数据。

这些数据包括从班次表、飞机工程师检查、机组人员的培训纪录和维护人员的报告，到飞行数据、气象观测数据和交通控制数据。

机器学习算法能够分析这些数据，从中发现规律和模式，以提高航空安全。

机器学习算法的一个应用是预测飞行员的疲劳水平和工作负荷。

疲劳是导致空难的因素之一。

疲劳飞行员可能会出现判断能力和反应速度的减慢，增加了出现错误和事故的风险。

机器学习系统可以通过智能分析飞行员的数据，如飞行时间和时区差异，对疲劳飞行员进行预测。

这种预测有助于监控和管理飞行员的疲劳和工作负荷。

机器学习算法还可以优化航空公司的航班调度。

航班调度是复杂的决策过程，需要考虑许多参数，例如航班时刻、航班起降时间、飞行时间和燃油使用量等。

机器学习算法能够分析过去的数据和预测的趋势，从而预测未来的旅客流量和运输的需求。

根据这些预测结果，系统可以进行智能化的航班调度决策，以满足客户需求和航空公司的效益。

机器学习算法在故障预测、故障检测和故障诊断方面也有着广泛的应用。

故障预测是通过机器学习算法在飞行数据中寻找异常模式或信号，根据这些模式和信号来预测机械部件是否会出现故障，从而提供改进方案。

故障检测则是通过计算机感知技术自动监控飞机部件的状态和功能，如发动机和液压系统，以及实时监测系统的可靠性和安全性。

故障诊断是在飞机出现故障时诊断出故障原因并提供改进方案的过程。

在航空安全领域中，机器学习的应用还包括对天气条件和气象情况的分析，帮助船只和飞机规避风暴和其他天气恶劣的条件。

在机场的航行过程中，机器学习算法还可以用于加强飞机的自动导航系统，提高飞机的自动驾驶性能和导航准确度。

美国空管大数据应用研究

美国空管大数据应用研究作者：张晨凌帆来源：《创新科技》2017年第06期[摘要] 空管作为民航重要数据保障中枢，其海量、多元和异构的数据信息资源蕴含丰富价值，大数据开发一直是美国空管技术研究的重点方向。

本文概述美国空管大数据应用的基本情况，可为国内空管大数据安全共享机制的建立，关键数据信息垄断的破除，相关部门加快信息化建设提供思路。

[关键词] 空中航行服务；大数据；应用[中图分类号] V355.1 [文献标识码] A [文章编号] 1671-0037（2017）6-94-3Research of the Big Data Applications of the American ATMZhang Chen Ling Fan（Strategic Development Department， Huadong Air Traffic Management Bureau CAAC，Shanghai 200335）Abstract： Air traffic management system （ATM）， as the significant data guarantee center of civil aviation system， owns massive， diversified， heterogeneous and valuable data assets. Therefore， the R&D in terms of big data used to be the important technique research field of American ATM. The paper summarized the current application situations in terms of big data of American ATM， which would provideview points for setting up the ATM big-data safely sharing mechanism， breaking the monopoly of key data information， and speeding up the informatization of relevant departments in China.Key words： ANSP； big data； application为应对航空运输系统快速增长挑战，提高空管安全性水平，摆脱航班延误等困境，美国在ICAO公约附件4、10、14和15的框架下，20世纪90年代开始研发空管大数据处理与分析系统。

机器学习算法在航班延误预测中的使用方法与准确度评价

机器学习算法在航班延误预测中的使用方法与准确度评价机器学习算法是一种利用数据来训练计算机模型，使其能够通过实例经验进行学习和预测的方法。

在航空业中，航班延误是一个严重的问题，对乘客和航空公司都有很大的影响。

因此，使用机器学习算法来预测航班延误变得越来越重要。

一、机器学习算法在航班延误预测中的使用方法1. 数据收集和预处理：航空公司可以使用自己的数据或者第三方提供的数据集。

这些数据包括航班的起飞时间、到达时间、机场信息、天气情况、航空公司等等。

在预处理阶段，数据需要进行清洗、标准化和特征选择等操作。

2. 特征工程：在机器学习模型中，特征选择是非常重要的一步。

特征工程的目标是提取和构建能够反映航班延误的特征。

例如，可以将起飞时间和到达时间转化为具体的时间段或者小时数，以捕捉到不同时间段的延误情况。

3. 训练模型：根据特征工程的结果，选择合适的机器学习算法来训练模型。

常用的算法包括决策树、支持向量机、神经网络和随机森林等。

这些算法根据已有的航班数据进行学习，从而得到一个可以预测航班延误的模型。

4. 模型评估与调优：在训练模型后，需要对模型进行评估和调优。

常用的评估指标包括准确率、召回率、F1值等。

可以使用交叉验证的方法来评估模型的性能，并利用网格搜索等技术选择最优的参数。

通过这些步骤，可以得到一个较为准确的航班延误预测模型。

二、机器学习算法在航班延误预测中的准确度评价1. 准确率：准确率是机器学习算法评估模型预测结果的主要指标之一。

它可以简单地表示为预测正确的样本数除以总样本数。

在航班延误预测中，准确率可以用来衡量模型对延误和非延误航班的预测能力。

2. 召回率：召回率是指模型正确预测出的延误航班在所有实际延误航班中所占的比例。

在航班延误预测中，召回率可以用来衡量模型对延误航班的敏感度。

较高的召回率意味着模型能够更准确地识别出真正发生延误的航班。

3. F1值：F1值是准确率和召回率的调和平均值，用来综合评估模型的性能。

基于机器学习的航班延误预测模型

基于机器学习的航班延误预测模型随着人们生活水平的提高和旅游业的兴起，航空旅行已成为大家出行的重要方式之一，但是航班延误一直是影响出行的一个困扰。

这不仅对旅客带来了不便，也加大了航空公司运营的成本和压力。

因此，建立一种有效的航班延误预测模型，对于提高旅行体验和航空公司的运营效率具有重要意义。

机器学习是一种基于数据的科学，通过构建模型并利用数据，以实现自动化决策和预测的目的。

这种技术的应用可以大大提高我们对航班延误的预测能力。

一、数据预处理首先，我们需要对航班数据进行预处理，以便于机器学习的算法进行学习。

航班数据包括航班号、航班起降时间、出发城市、到达城市、航班状态（正常/延迟）、天气等因素，这些数据需要进行清洗和转换。

例如，航班起降时间需要被转换成数字类型，城市名称需要进行编码或者One-hot编码等操作。

同时，我们需要利用现有的航空数据集，添加额外的特征值，包括节假日、天气状况、运算时间、机场服务质量等，以提高我们模型的精度。

二、模型选择航班延误预测模型可以采用多种机器学习算法进行建模，包括回归算法、分类算法、深度学习等。

其中，回归算法通常用于预测连续值，例如航班的起降时间、飞行距离等。

而分类算法则通常用于预测离散值，例如航班是否延误。

深度学习是一种基于神经网络的机器学习技术，通常用于处理大规模数据以及非线性和复杂的问题。

在处理航班数据时，我们可以使用适当的深度学习模型，例如卷积神经网络、循环神经网络等。

三、模型训练训练机器学习模型的过程需要将数据分为训练集和测试集。

训练集用于训练模型，测试集用于检验模型的精度和泛化能力。

在训练模型时，我们需要选择合适的损失函数和优化器，以及调整模型的超参数，例如学习率、激活函数、层数等。

四、模型评估模型评估是衡量机器学习模型预测能力的重要指标。

我们可以使用多种评估指标来衡量模型的性能，例如平均绝对误差、均方误差、准确率等。

此外，我们还可以通过绘制ROC曲线、PR曲线等评估模型的表现，并进行模型调整和优化。

基于机器学习的航班延误预测模型研究

04 模型评估与优化
模型评估方法
准确率评估
通过对比预测结果与实际结果，计算预测正确的比例，评估模型的预测精度。
召回率与查准率
通过计算预测为正例的样本中有多少是真正的正例，以及预测为正例的样本中有多少是真正的负例，评估模型的预测效果。
ROC曲线和AUC值
绘制ROC曲线并计算AUC值，全面评估模型在不同阈值下的性能。
利用机器学习算法，如支持向量机、神经网络等，对历史航班数据进行训练和学习，建立预测模型。
现有研究的不足与挑战
01
数据质量不高
航班延误数据存在噪声和异常值，影响预测模型的准确性和稳定性。
02
影响因素复杂
03
预测精度有待提高
航班延误受多种因素影响，如天气、航空管制、机械故障等，难以全面考虑所有影响因素。
研究不足与展望
01
本研究仅考虑了部分影响航班延误的因素，未来可进一步拓展数据源和考虑其他影响因素。
02
目前预测模型主要基于历史数据，未来可尝试引入实时数据，提高预测准确率。
03
可进一步研究不同地区、不同航空公司和不同航线上的航班延误规律，为具体实践提供指导。
1.谢谢聆听
现有预测模型在某些情况下预测精度不够高，需要进一步优化和改进。
基于机器学习的航班延误预测
03
模型
机器学习基本概念
数据集
机器学习需要大量的数据集进行训练和验证，航班延误预测的数据集通常包括航班起飞和降落时间、天气状况、机场交通状况等。
训练与测试
在机器学习中，通常会将数据集分为训练集和测试集，训练集用于训练模型，测试集用于评估模型的准确性和泛化能力。
基于机器学习的航班延误预测模型研究

机器学习_Airline Dataset(航空公司数据集)

Airline Dataset(航空公司数据集)数据摘要：An airline provides air transport services for passengers or freight. Airlines lease or own their aircraft with which to supply these services and may form partnerships or alliances with other airlines for mutual benefit. Generally, airline companies are recognized with an air operating certificate or license issued by a governmental aviation body.Airlines vary from those with a single aircraft carrying mail or cargo, through full-service international airlines operating hundreds of aircraft. Airline services can be categorized as being intercontinental,intra-continental, domestic, or international, and may be operated as scheduled services or charters.中文关键词：航空,数据集,机器学习,分类,英文关键词：Airline,dataset,Machine Learning,Classification,数据格式：TEXT数据用途：Information Processing Classification数据详细介绍：AirlineAn airline provides air transport services for passengers or freight. Airlines lease or own their aircraft with which to supply these services and may form partnerships or alliances with other airlines for mutual benefit. Generally, airline companies are recognized with an air operating certificate or license issued by a governmental aviation body.Airlines vary from those with a single aircraft carrying mail or cargo, through full-service international airlines operating hundreds of aircraft. Airline services can be categorized as being intercontinental, intra-continental, domestic, or international, and may be operated as scheduled services or charters.History This section does not cite any references or sources.Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (July 2008)[edit] The first airlinesFailed attempt at an airline before DELAGAmericans, such as Rufus Porter and Frederick Marriott, attempted to start airlines using airships in the mid-19th century, focusing on the New York–California route. Those attempts floundered due to such mishaps as the airships catching fire and the aircraft being ripped apart by spectators. DELAG, DeutscheLuftschiffahrts-Aktiengesellschaft was the world's first airline.[3] It was founded on November 16, 1909 with government assistance, and operated airships manufactured by The Zeppelin Corporation. Its headquarters were in Frankfurt. The four oldest non-dirigible airlines that still exist are Netherlands' KLM, Colombia's Avianca, Australia's Qantas, and the Czech Republic's Czech Airlines. KLM first flew in May 1920, while Qantas (which stands for Queensland and Northern Territory Aerial Services Limited) was founded in Queensland, Australia, in late 1920.[edit] U.S. airline industry[edit] Early developmentTWA Douglas DC-3 in 1940. The DC-3, often regarded as one of the most influential aircraft in the history of commercial aviation, revolutionized the aviation industry.[4]Tony Jannus conducted the United State's first scheduled commercial airline flight on 1 January 1914 for the St. Petersburg-Tampa Airboat Line.[5] The 23-minute flight traveled between St. Petersburg, Florida and Tampa, Florida, passing some 50 feet (15 m) above Tampa Bay in Jannus' Benoist XIV biplane flying boat. Chalk's International Airlines began service between Miami and Bimini in the Bahamas in February 1919. Based in Ft. Lauderdale, Chalk's claimed to be the oldest continuously operating airline in the United States until its closure in 2008.[6]Following World War I, the United States found itself swamped with aviators. Many decided to take their war-surplus aircraft on barnstorming campaigns, performing acrobatic maneuvers to woo crowds. In 1918, the United States Postal Service won the financial backing of Congress to begin experimenting with air mail service, initially using Curtiss Jenny aircraft that had been procured by the United States Army for reconnaissance missions on the Western Front. Private operators were the first to fly the mail but due to numerous accidents the US Army was tasked with mail delivery. During the course of the Army's involvement they proved to be too unreliable and lost their air mail duties. By the mid-1920s, the Postal Service had developed its own air mail network, based on a transcontinental backbone between New York and San Francisco. To supplant this service, they offered twelve contracts for spur routes to independent bidders. Some of the carriers that won these routes would, through time and mergers, evolve intoPan Am, Delta Air Lines, Braniff Airways, American Airlines, United Airlines (originally a division of Boeing), Trans World Airlines, Northwest Airlines, and Eastern Air Lines.Service during the early 1920s was sporadic: most airlines at the time were focused on carrying bags of mail. In 1925, however, the Ford Motor Company bought out the Stout Aircraft Company and began construction of the all-metal Ford Trimotor, which became the first successful American airliner. With a 12-passenger capacity, the Trimotor made passenger service potentially profitable. Air service was seen as a supplement to rail service in the American transportation network.At the same time, Juan Trippe began a crusade to create an air network that would link America to the world, and he achieved this goal through his airline, Pan American World Airways, with a fleet of flying boats that linked Los Angeles to Shanghai and Boston to London. Pan Am and Northwest Airways (which began flights to Canada in the 1920s) were the only U.S. airlines to go international before the 1940s.With the introduction of the Boeing 247 and Douglas DC-3 in the 1930s, the U.S. airline industry was generally profitable, even during the Great Depression. This trend continued until the beginning of World War II.[edit] Development since 1945In October 1945, the American Export Airlines became the first airline to offer regular commercial flights between North America and Europe.[7] Shown here is Am Ex Boeing 377 Stratocruiser in 1949.As governments met to set the standards and scope for an emergent civil air industry toward the end of the war, the U.S. took a position of maximum operating freedom; U.S. airline companies were not as hard-hit as European and the few Asian ones had been. This preference for "open skies" operating regimes continues, within limitations, to this day.[citation needed]World War II, like World War I, brought new life to the airline industry. Many airlines in the Allied countries were flush from lease contracts to the military, and foresaw a futureexplosive demand for civil air transport, for both passengers and cargo. They were eager to invest in the newly emerging flagships of air travel such as the Boeing Stratocruiser, Lockheed Constellation, and Douglas DC-6. Most of these new aircraft were based on American bombers such as the B-29, which had spearheaded research into new technologies such as pressurization. Most offered increased efficiency from both added speed and greater payload.In the 1950s, the De Havilland Comet, Boeing 707, Douglas DC-8, and Sud Aviation Caravelle became the first flagships of the Jet Age in the West, while the Soviet Union bloc had Tupolev Tu-104 and Tupolev Tu-124 in the fleets of state-owned carriers such as Czechoslovak ČSA, Soviet Aeroflot and East-German Interflug. The Vickers Viscount and Lockheed L-188 Electra inaugurated turboprop transport.The next big boost for the airlines would come in the 1970s, when the Boeing 747, McDonnell Douglas DC-10, and Lockheed L-1011 inaugurated widebody ("jumbo jet") service, which is still the standard in international travel. The Tupolev Tu-144 and its Western counterpart, Concorde, made supersonic travel a reality. Concorde first flew in 1969 and operated through 2003. In 1972, Airbus began producing Europe's most commercially successful line of airliners to date. The added efficiencies for these aircraft were often not in speed, but in passenger capacity, payload, and range. Airbus also features modern electronic cockpits that were common across their aircraft to enable pilots to fly multiple models with minimal cross-training.Pan Am Boeing 747 Clipper Neptune's Car in 1985. The deregulation of the American airline industry increased the financial troubles of the iconic airline which ultimately filed for bankruptcy in December 1991.[8]1978's U.S. airline industry deregulation lowered barriers for new airlines just as a downturn occurred. New start-ups entered during the downturn, during which time they found aircraft and funding, contracted hangar and maintenance services, trained new employees, and recruited laid off staff from other airlines.As the business cycle returned to normalcy, major airlines dominated their routes through aggressive pricing and additional capacity offerings, often swamping new startups. Only America West Airlines (which has since merged with US Airways) remained a significant survivor from this new entrant era, as dozens, even hundreds, have gone under.In many ways, the biggest winner in the deregulated environment was the air passenger. Indeed, the U.S. witnessed an explosive growth in demand for air travel, as many millions who had never or rarely flown before became regular fliers, even joining frequent flyer loyalty programs and receiving free flights and other benefits from their flying. New services and higher frequencies meant that business fliers could fly to another city, do business, and return the same day, for almost any point in the country. Air travel's advantages put intercity bus lines under pressure, and most have withered away.By the 1980s, almost half of the total flying in the world took place in the U.S., and today the domestic industry operates over 10,000 daily departures nationwide.Toward the end of the century, a new style of low cost airline emerged, offering a no-frills product at a lower price. Southwest Airlines, JetBlue, AirTran Airways, Skybus Airlines and other low-cost carriers began to represent a serious challenge to the so-called "legacy airlines", as did their low-cost counterparts in many other countries. Their commercial viability represented a serious competitive threat to the legacy carriers. However, of these, ATA and Skybus have since ceased operations.Increasingly since 1978, US airlines have been reincorporated and spun off by newly created and interally led manangement companies, and thus becoming nothing more than operating units and subsidiaries with limited finanically decisive control. Among some of these holding companies and parent companies that are the relatively well known, are the UAL Corporation, along with the AMR Corporation, among a long list of airline holding companies sometime recognized world wide. Less recognized are the private equity firms which often seize managerial, financial, and board of directors control of distressed airlinecompanies by temporarily investing large sums of capital in air carriers, so as to rescheme an airlines assets into a profitable organization or liquidating an air carrier of their profitable and worthwhile routes and business operations.Thus the last 50 years of the airline industry have varied from reasonably profitable, to devastatingly depressed. As the first major market to deregulate the industry in 1978, U.S. airlines have experienced more turbulence than almost any other country or region. Today, American Airlines is the only U.S. legacy carrier to survive bankruptcy-free.[edit] The Airline Industry BailoutCongress passed the Air Transportation Safety and System Stabilization Act (P.L. 107-42) in response to a severe liquidity crisis facing the already-troubled airline industry in the aftermath of the September 11th terrorist attacks. Congress sought to provide cash infusions to carriers for both the cost of the four-day federal shutdown of the airlines and the incremental losses incurred through December 31, 2001 as a result of the terrorist attacks. This resulted in the first government bailout of the 21st century.[9]In recognition of the essential national economic role of a healthy aviation system, Congress authorized partial compensation of up to $5 billion in cash subject to review by the Department of Transportation and up to $10 billion in loan guarantees subject to review by a newly created Air Transportation Stabilization Board (ATSB). The applications to DOT for reimbursements were subjected to rigorous multi-year reviews not only by DOT program personnel but also by the Government Accountability Office [10] and the DOT Inspector General.[11][12]Ultimately, the federal government provided $4.6 billion in one-time, subject-to-income-tax cash payments to 427 U.S. air carriers, with no provision for repayment, essentially a gift from the taxpayers. (Passenger carriers operating scheduled service received approximately $4 billion, subject to tax.) [13] In addition, the ATSB approved loan guarantees to six airlines totaling approximately $1.6 billion. Data from the Treasury Department show that the government recouped the $1.6 billion and a profit of $339million from the fees, interest and purchase of discounted airline stock associated with loan guarantees.[14][edit] European airline industryThe Imperial Airways Empire Terminal, Victoria, London. Trains ran from here to flying boats in Southampton, and to Croydon Airport.The first countries in Europe to embrace air transport were Austria, Belgium, Finland, France, Germany, the Netherlands and the United Kingdom.Austria initiated the first regularly scheduled airmail service on March 31, 1918 in the midst of World War I. The route provided airmail service spanning Vienna to Krakow (now in Poland) to Lviv (now in Ukraine), as was often also extended to Kiev andOdessa.[15][16]KLM, the oldest carrier still operating under its original name, was founded in 1919. The first flight (operated on behalf of KLM by Aircraft Transport and Travel) transported two English passengers to Schiphol, Amsterdam from London in 1920. Like other major European airlines of the time (see France and the UK below), KLM's early growth depended heavily on the needs to service links with far-flung colonial possessions (Dutch Indies). It is only after the loss of the Dutch Empire that KLM found itself based at a small country with few potential passengers, depending heavily on transfer traffic, and was one of the first to introduce the hub-system to facilitate easy connections.France began an air mail service to Morocco in 1919 that was bought out in 1927, renamed Aéropostale, and injected with capital to become a major international carrier. In 1933, Aéropostale went bankrupt, was nationalized and merged with several other airlines into what became Air France.In Finland, the charter establishing Aero O/Y (now Finnair) was signed in the city of Helsinki on September 12, 1923. Junkers F 13 D-335 became the first aircraft of the company, when Aero took delivery of it on March 14, 1924. The first flight was betweenHelsinki and Tallinn, capital of Estonia, and it took place on March 20, 1924, one week later.Germany's Lufthansa began in 1926. Lufthansa, unlike most other airlines at the time, became a major investor in airlines outside of Europe, providing capital to Varig and Avianca. German airliners built by Junkers, Dornier, and Fokker were the most advanced in the world at the time. In 1931, the airship Graf Zeppelin began offering regular scheduled passenger service between Germany and South America, usually every two weeks, which continued until 1937.[17] In 1936, the airship Hindenburg entered passenger service and successfully crossed the Atlantic 36 times before crashing at Lakehurst, New Jersey on May 6, 1937.[18]The British company Aircraft Transport and Travel commenced a London to Paris service on August 25, 1919, this was the world's first regular international flight. The United Kingdom's flag carrier during this period was Imperial Airways, which became BOAC (British Overseas Airways Co.) in 1939. Imperial Airways used huge Handley-Page biplanes for routes between London, the Middle East, and India: images of Imperial aircraft in the middle of the Rub'al Khali, being maintained by Bedouins, are among the most famous pictures from the heyday of the British Empire.In Soviet Union the Chief Administration of the Civil Air Fleet was established in 1921. One of its first acts was to help found Deutsch-Russische Luftverkehrs A.G. (Deruluft), a German-Russian joint venture to provide air transport from Russia to the West. Domestic air service began around the same time, when Dobrolyot started operations on 15 July 1923 between Moscow and Nizhni Novgorod. Since 1932 all operations had been carried under the name Aeroflot. By the end of the 1930s Aeroflot had become the world's largest airline, employing more than 4,000 pilots and 60,000 other service personnel and operating around 3,000 aircraft (of which 75% were considered obsolete by its own standards). During the Soviet era Aeroflot was synonymous with Russian civil aviation, as it was the only air carrier. It became the first airline in the world to operate sustained regular jet services on 15 September 1956 with the Tupolev Tu-104.[edit] DeregulationDeregulation of the European Union airspace in the early 1990s has had substantial effect on structure of the industry there. The shift towards 'budget' airlines on shorter routes has been significant. Airlines such as EasyJet and Ryanair have grown at the expense of the traditional national airlines.There has also been a trend for these national airlines themselves to be privatised such as has occurred for Aer Lingus and British Airways. Other national airlines, includingItaly's Alitalia, have suffered - particularly with the rapid increase of oil prices in early 2008.[edit] Asian airline industryAlthough Philippine Airlines (PAL) was officially founded on February 26, 1941, its license to operate as an airliner was derived from merged Philippine Aerial Taxi Company (PATCO) established by mining magnate Emmanuel N. Bachrach on December 3, 1930, making it Asia's oldest scheduled carrier still in operation.[19] Commercial air service commenced three weeks later from Manila to Baguio, making it Asia's first airline route. Bachrach's death in 1937 paved the way for its eventual merger with Philippine Airlines in March 1941 and made it Asia's oldest airline. It is also the oldest airline in Asia still operating under its current name.[20] Bachrach's majority share in PATCO was bought by beer magnate Andres R. Soriano in 1939 upon the advice of General Douglas McArthur and later merged with newly formed Philippine Airlines with PAL as the surviving entity. Soriano has controlling interest in both airlines before the merger. PAL restarted service on March 15, 1941 with a single Beech Model 18 NPC-54 aircraft, which started its daily services between Manila (from Nielson Field) and Baguio, later to expand with larger aircraft such as the DC-3 and Vickers Viscount.India was also one of the first countries to embrace civil aviation.[21] One of the first West Asian airline companies was Air India, which had its beginning as Tata Airlines in 1932, a division of Tata Sons Ltd. (now Tata Group). The airline was founded by India's leading industrialist, JRD Tata. On October 15, 1932, J. R. D. Tata himself flew a single engined De Havilland Puss Moth carrying air mail (postal mail of Imperial Airways) from Karachi toMumbai via Ahmedabad. The aircraft continued to Madras via Bellary piloted by Royal Air Force pilot Nevill Vintcent . Tata Airlines was also one of the world's first major airlines which began its operations without any support from the Government.[22]With the outbreak of World War II, the airline presence in Asia came to a relative halt, with many new flag carriers donating their aircraft for military aid and other uses. Following the end of the war in 1945, regular commercial service was restored in India and Tata Airlines became a public limited company on July 29, 1946 under the name Air India. After the independence of India, 49% of the airline was acquired by the Government of India. In return, the airline was granted status to operate international services from India as the designated flag carrier under the name Air India International.On July 31, 1946, a chartered Philippine Airlines (PAL) DC-4 ferried 40 American servicemen to Oakland, California from Nielson Airport in Makati City with stops in Guam, Wake Island, Johnston Atoll and Honolulu, Hawaii, making PAL the first Asian airline to cross the Pacific Ocean. A regular service between Manila and San Francisco was started in December. It was during this year that the airline was designated as the flag carrier of Philippines.During the era of decolonization, newly-born Asian countries started to embrace air transport. Among the first Asian carriers during the era were Cathay Pacific of Hong Kong (founded in September 1946), Orient Airways (later Pakistan International Airlines; founded in October 1946), Malayan Airlines (later Singapore and Malaysia Airlines; founded in 1947), El Al in Israel in 1948, Garuda Indonesia in 1949, Japan Airlines in 1951, and Korean Air in 1962.[edit] Latin American airline industryTAM Airlines is the largest airline in Latin America in terms of number of annual passengers flown.[23]Among the first countries to have regular airlines in Latin America were Colombia with Avianca, Brazil with Varig, Chile with LAN Chile (today LAN Airlines), Dominican Republic with Dominicana de Aviación, Mexico with Mexicana de Aviación,and TACA as a brand of several airlines of Central American countries (Honduras, El Salvador, Costa Rica, Guatemala and Nicaragua). All the previous airlines started regular operations before World War II.The air travel market has evolved rapidly over recent years in Latin America. Some industry estimations over 2000 new aircraft will begin service over the next five years in this region.[citation needed]These airlines serve domestic flights within their countries, as well as connections within Latin America and also overseas flights to North America, Europe, Australia, Africa and Asia.Just three airlines: LAN (Latin American Networks), Oceanair and TAM Airlines have international subsidiaries with Chile as the central operation along with Peru, Ecuador, Argentina and some operations in the Dominican Republic and TAM with TAM Mercosur have a base in Asuncion, Paraguay. Avianca have the control of Oceanair, VIP Airlines and also have an estrategic alliance with TACA.The three main hubs in Latin America are Mexico City in Mexico, São Paulo in Brazil and Santiago in Chile.[edit] Regulatory considerations[edit] NationalGaruda Indonesia Boeing 747-400 parked at Narita International Airport. This Indonesian Flag carrier is wholly owned by the Indonesian GovernmentMany countries have national airlines that the government owns and operates. Fully private airlines are subject to a great deal of government regulation for economic, political, and safety concerns. For instance, governments often intervene to halt airline labor actions in order to protect the free flow of people, communications, and goods between different regions without compromising safety.The United States, Australia, and to a lesser extent Brazil, Mexico, India, the United Kingdom and Japan have "deregulated" their airlines. In the past, these governments dictated airfares, route networks, and other operational requirements for each airline. Since deregulation, airlines have been largely free to negotiate their own operating arrangements with different airports, enter and exit routes easily, and to levy airfares and supply flights according to market demand.Cyprus Airways national airline of CyprusThe entry barriers for new airlines are lower in a deregulated market, and so the U.S. has seen hundreds of airlines start up (sometimes for only a brief operating period). This has produced far greater competition than before deregulation in most markets, and average fares tend to drop 20% or more. The added competition, together with pricing freedom, means that new entrants often take market share with highly reduced rates that, to a limited degree, full service airlines must match. This is a major constraint on profitability for established carriers, which tend to have a higher cost base.As a result, profitability in a deregulated market is uneven for most airlines. These forces have caused some major airlines to go out of business, in addition to most of the poorly established new entrants.[edit] InternationalSingapore Airlines Airbus A380 lands at Changi Airport. Singapore Airlines was the first international airline to operate the A380, the world's largest passenger airliner.[24]Groups such as the International Civil Aviation Organization establish worldwide standards for safety and other vital concerns. Most international air traffic is regulated by bilateral agreements between countries, which designate specific carriers to operate on specific routes. The model of such an agreement was the Bermuda Agreement between the US and UK following World War II, which designated airports to be used for transatlantic flights and gave each government the authority to nominate carriers to operate routes.Bilateral agreements are based on the "freedoms of the air", a group of generalized traffic rights ranging from the freedom to overfly a country to the freedom to provide domestic flights within a country (a very rarely granted right known as cabotage). Most agreements permit airlines to fly from their home country to designated airports in the other country: some also extend the freedom to provide continuing service to a third country, or to another destination in the other country while carrying passengers from overseas.In the 1990s, "open skies" agreements became more common. These agreements take many of these regulatory powers from state governments and open up international routes to further competition. Open skies agreements have met some criticism, particularly within the European Union, whose airlines would be at a comparative disadvantage with the United States' because of cabotage restrictions.[edit] Economic considerationsJuan Trippe, the founder of Pan American World Airways, surveying his globe. The collapse of Pan Am, an airline often credited for shaping the international airline industry, in December 1991 highlighted the financial complexities faced by major airline companies.Historically, air travel has survived largely through state support, whether in the form of equity or subsidies. The airline industry as a whole has made a cumulative loss during its 100-year history, once the costs include subsidies for aircraft development and airport construction.[25][26]One argument is that positive externalities, such as higher growth due to global mobility, outweigh the microeconomic losses and justify continuing government intervention. A historically high level of government intervention in the airline industry can be seen as part of a wider political consensus on strategic forms of transport, such as highways and railways, both of which receive public funding in most parts of the world. Profitability is likely to improve in the future as privatization continues and more competitive low-cost carriers proliferate.[citation needed]Although many countries continue to operate state-owned or parastatal airlines, many large airlines today are privately owned and are therefore governed by microeconomic principles in order to maximize shareholder profit.[edit] Ticket revenueAirlines assign prices to their services in an attempt to maximize profitability. The pricing of airline tickets has become increasingly complicated over the years and is now largely determined by computerized yield management systems.Because of the complications in scheduling flights and maintaining profitability, airlines have many loopholes that can be used by the knowledgeable traveler. Many of these airfare secrets are becoming more and more known to the general public, so airlines are forced to make constant adjustments.Most airlines use differentiated pricing, a form of price discrimination, in order to sell air services at varying prices simultaneously to different segments. Factors influencing the price include the days remaining until departure, the booked load factor, the forecast of total demand by price point, competitive pricing in force, and variations by day of week of departure and by time of day. Carriers often accomplish this by dividing each cabin of the aircraft (first, business and economy) into a number of travel classes for pricing purposes.A complicating factor is that of origin-destination control ("O&D control"). Someone purchasing a ticket from Melbourne to Sydney (as an example) for AU$200 is competing with someone else who wants to fly Melbourne to Los Angeles through Sydney on the same flight, and who is willing to pay AU$1400. Should the airline prefer the $1400 passenger, or the $200 passenger plus a possible Sydney-Los Angeles passenger willing to pay $1300? Airlines have to make hundreds of thousands of similar pricing decisions daily.Lufthansa Boeing 747-400.The advent of advanced computerized reservations systems in the late 1970s, most notably Sabre, allowed airlines to easily perform cost-benefit。

机器学习在航空客流预测中的应用

机器学习在航空客流预测中的应用随着人们生活水平的提高和交通出行的便利化，人们对于航空客流的需求也越来越大。

而随着机器学习技术的发展，越来越多的航空公司开始利用这种技术对航空客流进行预测，以提高客服质量、优化航班安排等方面的工作效率。

本文将探讨机器学习在航空客流预测中的应用及其带来的好处。

一、机器学习算法在航空客流预测中的应用机器学习算法是一种通过学习经验数据来识别和预测模式的方法。

在航空客流预测中，机器学习算法可以通过收集历史的航班数据、天气数据、经济和事件数据等，利用这些数据建立统计模型。

模型可以对未来的天气情况、经济情况等进行预测，从而推断出未来航班的客流情况，做出适当的安排。

机器学习算法主要包括以下几类：1.决策树决策树是一种用于分类、回归和其他任务的树形结构学习模型。

决策树可以通过分析历史数据，找到影响客流量的主要因素，从而预测未来的客流情况。

2.朴素贝叶斯分类器朴素贝叶斯分类器是一种基于概率统计的分类器。

它通过分析历史数据，计算出各个变量对应的概率值，并通过这些概率值来预测未来的客流情况。

3.线性回归线性回归是一种用于预测连续值的机器学习模型。

它通过分析历史数据中的变化趋势，预测未来的客流量，从而做出相应的安排。

二、机器学习算法在航空客流预测中的优势机器学习算法在航空客流预测中具有以下几个优势：1.提高准确性机器学习可以在短时间内处理大量数据，能够将历史数据中的规律和模式准确地应用到未来的客流预测中，从而提高预测的准确性。

2.适应性强随着社会经济、环境和季节的变化，航空客流量也会发生变化。

机器学习可以实现实时数据处理和分析，随时调整预测模型，保证预测结果的准确性。

3.大幅提高工作效率传统的客流预测是通过人工查询资料、统计，预测结果受限于人工处理能力，效率低下。

机器学习算法可以自动处理大量的数据，缩短了人工处理的时间，从而大幅提高了工作效率。

4.有较低的成本传统的客流预测需要大量的时间和人力，难以进行大规模的统计和分析。

基于机器学习的航空航班延误预测与优化

基于机器学习的航空航班延误预测与优化航空航班延误是造成旅客不便和航空公司经济损失的重要问题。

随着航空业的快速发展和旅客需求的增加，航空航班延误对整个航空系统的影响愈发显著。

因此，基于机器学习的航空航班延误预测与优化成为航空公司和相关机构关注的热点问题。

一、航空航班延误预测航空航班延误预测是根据历史航班数据、天气数据、航空公司数据等因素，运用机器学习模型预测航班是否有延误发生，以提前做好调度和安排。

在这个过程中，可以利用以下几种机器学习算法：1.1. 逻辑回归逻辑回归模型是一种经典的机器学习方法，适用于二分类问题。

针对航班延误预测问题，可以将航班延误与不延误分别作为两个类别，通过训练逻辑回归模型，得到航班延误的概率预测结果。

该模型基于历史航班数据和相关特征，可以较好地捕捉影响航班延误的因素。

1.2. 随机森林随机森林是一种集成学习方法，通过构建多个决策树进行预测，并通过投票或平均获得最终结果。

对于航班延误预测问题，可以利用随机森林模型进行训练和预测。

该模型能够处理复杂的非线性关系，并能够自动筛选和组合特征，提高预测准确性。

1.3. 深度学习模型近年来，深度学习模型在许多领域取得了显著的成果。

对于航空航班延误预测问题，深度学习模型如多层感知机（MLP）、长短期记忆网络（LSTM）等可以被应用。

这些模型具有较强的能力来学习数据中的复杂模式和时序关系，从而提高航班延误预测的准确性。

二、航空航班延误优化除了预测延误，航空公司还需要在延误发生后采取相应的优化措施，以减少延误对旅客和公司造成的影响。

2.1. 调整航班计划航空公司可以根据航班延误预测结果，提前调整航班计划。

例如，在预测到某个航班延误的概率较高时，可以安排备用飞机和备用机组人员，以应对可能的延误情况。

这种灵活的航班调整策略可以减少延误对其他航班的连锁影响，最大程度地降低延误的程度和持续时间。

2.2. 优化航班排班和资源调度通过机器学习模型对航班延误进行预测，航空公司可以根据预测结果进行航班排班和资源调度的优化。

1. 统计机器学习简介

增强学习(Reinforcement Learning) 多任务学习(Multi-task learning)
24
有监督学习
标定的训练数据训练过程：根据目标输出与实际输出的误差信号来调节参数典型方法
全局：BN, NN,SVM, Decision Tree 局部：KNN、CBR(Case-base reasoning)
m
attributes
Output ---C1 ---C2 ---… X X …
A11,A12,…,A1m A21,A22,…,A2m … … An1,An2,…,Anm
n instance
---… … Task X ---Cn
示例：聚类
26
半监督学习
结合（少量的）标定训练数据和（大量的）未标定数据来进行学习典型方法 Co-training、EM、Latent variables….
argmin R
*

In the case of equal risk, it becomes to minimize the error ratio. 损失函数 loss function (L, Q)：the error of a given function on a given example L : x, y, f L y, f x,
L ( p ( x, w)) -log p ( x, w)
23
统计学习的基本方法有监督/无监督学习
有监督(Supervised)：分类、回归无监督(Unsupervised)：概率密度估计、聚类、降维半监督(Semi-supervised)：EM、Cotraining

基于大数据的航班准点率预测与分析研究

基于大数据的航班准点率预测与分析研究随着航空业的迅猛发展，准时起降的航班成为旅客选择航空交通方式的重要标准之一。

然而，航班的准点率受到很多因素的影响，如天气状况、机场运营能力、航空公司管理水平等。

因此，基于大数据的航班准点率预测与分析研究具有重要的现实意义和价值。

本文将从数据挖掘的角度，使用大数据技术和机器学习算法，对航班准点率进行预测和分析。

一、数据收集与预处理在进行航班准点率预测与分析之前，首先需要收集关于航班的大数据。

这些数据可以包括航班的起降时间、航线距离、飞行速度、载客量、机型等信息。

此外，还可以收集关于机场的数据，如机场运营时间、跑道数量、航站楼容量等。

另外，还可以考虑收集天气预报数据、历史航班数据和航空公司运营数据等。

在收集到数据后，需要进行数据清洗和预处理工作，包括数据去重、缺失值处理、异常值处理等。

二、特征提取与选择在数据预处理完成后，需要进行特征提取与选择，以提取数据中的有用特征。

可以使用特征工程的方法，将原始数据转化为能够用于建模的特征。

比如，可以从航班起降时间中提取小时、分钟等时间信息。

同时，可以计算机场的运行能力指标，如每小时起降航班数、平均等待时间等。

此外，还可以根据天气预报数据计算天气状况对航班准点率的影响程度。

三、模型建立与训练在进行航班准点率预测与分析的过程中，需要选择合适的机器学习算法来建立模型并进行训练。

可以考虑使用监督学习算法，如决策树、随机森林、支持向量机等。

同时，可以使用时间序列分析方法，如ARIMA模型、神经网络等。

这些模型可以根据所选特征和标签数据进行训练，并根据评价指标进行模型的优化和选择。

四、模型评估与优化在模型建立与训练完成后，需要对模型进行评估和优化。

可以使用交叉验证方法，将数据集划分为训练集和测试集，在测试集上进行模型评估。

常用的评价指标包括均方误差（MSE）、平均绝对误差（MAE）、准确率等。

根据评估结果，对模型进行优化和调整，以提高模型的预测准确性和稳定性。

基于机器学习的航班时间预测研究

基于机器学习的航班时间预测研究随着人们生活水平的提高和旅游产业的发展，航空业的发展也越来越快速。

然而，每年依旧要面对延误、取消等航班问题。

航空公司、旅客、机场管理人员和其他相关人员都需要准确可靠的飞行信息来做出相应的决策。

因此，正确预测航班时间对所有人而言都是至关重要的。

在此背景下，基于机器学习的航班时间预测技术应运而生，成为航空业普遍采取的方法。

一、机器学习的原理机器学习是一种人工智能的分支，目的是使机器能够从数据中学习并进行预测。

机器学习中最重要的一个概念是模型，模型是根据训练数据产生的知识和经验的数学表示。

机器学习使用不同的算法和技术来构建模型，例如决策树、神经网络、随机森林等。

当模型被训练后，可以使用新的数据进行测试和预测。

二、航班时间预测的特点相比其他预测问题，航班时间预测有一些特殊的特点和难点。

首先，航班时间预测需要考虑许多外部因素，例如天气、航班流量、机场管理等。

这些因素影响复杂且多变，因此要预测航班时间需要使用大量的数据和复杂的算法。

其次，航班时间预测模型需要考虑不同阶段的航班，例如起飞时间、飞行时间、降落时间。

这些不同阶段的预测需要采用不同的模型和算法，并需要进行综合考虑和优化。

最后，航班时间预测需要考虑不同类型的航班，例如国际航班、国内航班和区域航班等。

这些不同类型的航班存在明显的差异和特点，需要采用不同的预测模型和算法。

三、航班时间预测的方法目前，航班时间预测主要采用以下几种方法：1. 基于统计方法的预测：该方法使用历史数据进行统计分析和预测，例如平均时间、平均延误时间等。

这种方法简单易行，但只能提供简单的预测结果，不适用于复杂的情况。

2. 基于规则引擎的预测：该方法使用规则引擎来处理各种规则和条件，例如天气、航班流量等。

这种方法具有较高的准确性和可靠性，但需要人工编写规则和条件。

3. 基于神经网络的预测：该方法利用神经网络算法来建立模型，通过学习历史数据来进行预测。

神经网络具有很强的适应性和泛化能力，能够适应不同条件和情况。

基于机器学习算法的航班延误预测

基于机器学习算法的航班延误预测摘要：针对航班延误这一航空运输业亟待解决问题，通过对航班延误影响指标的深入分析，提出基于机器学习算法解决航班延误问题。

根据对大量历史数据的训练，并将机器学习分类算法与时间序列模型结合应用到数据训练，便于模型构建。

结合所采集时间点的实际航班延误数对比分析评估预测模型。

实验结果表明，该方法有效、精准地实现航班延误数量时间段预测及特定航班延误时间等级预测这两个业务功能，有利于机场及时进行政策方案调控，以便解决延误问题。

关键词：时间序列航班延误预测分析机器学习1 航班延误业务场景介绍航班延误是国际国内民航业亟待解决的问题，改善延误状况迫在眉睫。

繁忙的枢纽机场作为多数航班的转乘点是航班延误的高频发生地，因此本文通过搭建航班延误预测模型，做到航班信息提前预测，进而实现对航班延误程度的全局掌握，有利于机场及时进行政策方案调控，以便解决延误问题。

本文将贝叶斯[8]及决策树等机器学习分类算法用到航班延误的预测领域。

面向某千万级机场航班延误业务场景，利用机器学习算法处理时间序列数据，采用数据挖掘等为其建模，对其中一定时间段内的延误航班数量进行预测（功能1），为机场发布延误等级[9]预警提供信息；利用集成算法结合贝叶斯学习器、决策树算法这两个子学习器完成基于集成学习算法的航班延误预测模型的建立。

应用航班预测模型对特定航班的延误时间进行预测（功能2），并辅以概率表示，机场后续可结合本功能加以颜色辅助，面向航空公司与乘客发布延误提示。

2 航班延误时间段预测2.1 传统方法实现为预测未来航班延误情况，首先对数据进行处理，选取2016年5-6月的记录作为实验数据，将日期作为自变量，航班延误数量、平均延误时间、延误率分别作为3个因变量，逐个进行建模预测。

为消除时间序列中的不规则变动，选取时间预测模型中的移动平均法，通过将本期观察值与前一期指数平滑值的加权平均完成时间序列预测模型的搭建。

移动平均法：测试数据集预测结果分析图1- 时间序列预测效果图根据SPSS运行训练5-6月数据结果可知，模型统计量平稳的R方为0.168，显著性sig值为0.039<0.05，因此可判断时间序列模型比较适合，但由结果可知，传统方法拟合效果一般，且未达到很高准确度。

机器学习实例--预测美国人口收入状况

机器学习实例--预测美国⼈⼝收⼊状况⼀．问题描述每个⼈都希望⾃⼰能获得更⾼的收⼊，⽽影响收⼊⾼低的因素有很多，能否通过⼤数据分析来找出对收⼊影响相对较⼤的因素？⼆．研究意义如果我们知道对收⼊⾼低起决定性的作⽤，或者哪些因素组合在⼀起也能增⼤收⼊的可能性，那可以帮助很多⼈少⾛弯路，朝着正确的⽅向努⼒，早⽇达到⽬标。

三．数据预处理1. 选取数据集本报告选取“adult”数据集，由美国⼈⼝普查数据集库抽取⽽来。

该数据集类变量为年收⼊是否超过50k，属性变量包含年龄，⼯种，学历，职业，⼈种等14个属性变量，其中有7个类别型变量。

共有30000多条数据。

2. 预处理由于capital-gain、capital-loss属性缺失70%以上的数据，所以选择删去这两个属性。

在其他类变量中，有缺少或异常属性400多条，占总数据⽐重较⼩，也选择删去。

四．数据可视化1.workclasscation3.race4.sex5.marital-status五．算法选取与实现本次报告中选⽤决策树算法。

决策树是⼀种依托决策⽽建⽴起来的⼀种树。

在机器学习中，决策树是⼀种预测模型，代表的是⼀种对象属性与对象值之间的⼀种映射关系，每⼀个节点代表某个对象，树中的每⼀个分叉路径代表某个可能的属性值，⽽每⼀个叶⼦节点则对应从根节点到该叶⼦节点所经历的路径所表⽰的对象的值。

决策树仅有单⼀输出，如果有多个输出，可以分别建⽴独⽴的决策树以处理不同的输出。

由于数据量过⼤，普通决策树不能达到预期效果，所以再⽤预剪枝进⾏处理。

预剪枝是在决策树⽣成过程中，在划分节点时，若该节点的划分没有提⾼其在训练集上的准确率，则不进⾏划分。

下⾯是预剪枝决策树程序1. 计算数据集的基尼系数def calcGini(dataSet):numEntries=len(dataSet)labelCounts={}#给所有可能分类创建字典for featVec in dataSet:currentLabel=featVec[-1]if currentLabel not in labelCounts.keys():labelCounts[currentLabel]=0labelCounts[currentLabel]+=1Gini=1.0#以2为底数计算⾹农熵for key in labelCounts:prob = float(labelCounts[key])/numEntriesGini-=prob*probreturn Gini2. 对离散变量划分数据集，取出该特征取值为value的所有样本def splitDataSet(dataSet,axis,value):retDataSet=[]for featVec in dataSet:if featVec[axis]==value:reducedFeatVec=featVec[:axis]reducedFeatVec.extend(featVec[axis+1:])retDataSet.append(reducedFeatVec)return retDataSet3. 对连续变量划分数据集，direction规定划分的⽅向，决定是划分出⼩于value的数据样本还是⼤于value的数据样本集def splitContinuousDataSet(dataSet,axis,value,direction):retDataSet=[]for featVec in dataSet:if direction==0:if featVec[axis]>value:reducedFeatVec=featVec[:axis]reducedFeatVec.extend(featVec[axis+1:])retDataSet.append(reducedFeatVec)else:if featVec[axis]<=value:reducedFeatVec=featVec[:axis]reducedFeatVec.extend(featVec[axis+1:])retDataSet.append(reducedFeatVec)return retDataSet4. 选择最好的数据集划分⽅式def chooseBestFeatureToSplit(dataSet,labels):numFeatures=len(dataSet[0])-1bestGiniIndex=100000.0bestFeature=-1bestSplitDict={}for i in range(numFeatures):featList=[example[i] for example in dataSet]#对连续型特征进⾏处理if type(featList[0]).__name__=='float'or type(featList[0]).__name__=='int':#产⽣n-1个候选划分点sortfeatList=sorted(featList)splitList=[]for j in range(len(sortfeatList)-1):splitList.append((sortfeatList[j]+sortfeatList[j+1])/2.0)bestSplitGini=10000slen=len(splitList)#求⽤第j个候选划分点划分时，得到的信息熵，并记录最佳划分点for j in range(slen):value=splitList[j]newGiniIndex=0.0subDataSet0=splitContinuousDataSet(dataSet,i,value,0)subDataSet1=splitContinuousDataSet(dataSet,i,value,1)prob0=len(subDataSet0)/float(len(dataSet))newGiniIndex+=prob0*calcGini(subDataSet0)prob1=len(subDataSet1)/float(len(dataSet))newGiniIndex+=prob1*calcGini(subDataSet1)if newGiniIndex<bestSplitGini:bestSplitGini=newGiniIndexbestSplit=j#⽤字典记录当前特征的最佳划分点bestSplitDict[labels[i]]=splitList[bestSplit]GiniIndex=bestSplitGini#对离散型特征进⾏处理else:uniqueVals=set(featList)newGiniIndex=0.0#计算该特征下每种划分的信息熵for value in uniqueVals:subDataSet=splitDataSet(dataSet,i,value)prob=len(subDataSet)/float(len(dataSet))newGiniIndex+=prob*calcGini(subDataSet)GiniIndex=newGiniIndexif GiniIndex<bestGiniIndex:bestGiniIndex=GiniIndexbestFeature=i#若当前节点的最佳划分特征为连续特征，则将其以之前记录的划分点为界进⾏⼆值化处理#即是否⼩于等于bestSplitValue#并将特征名改为 name<=value的格式if type(dataSet[0][bestFeature]).__name__=='float'or type(dataSet[0][bestFeature]).__name__=='int':bestSplitValue=bestSplitDict[labels[bestFeature]]labels[bestFeature]=labels[bestFeature]+'<='+str(bestSplitValue)for i in range(shape(dataSet)[0]):if dataSet[i][bestFeature]<=bestSplitValue:dataSet[i][bestFeature]=1else:dataSet[i][bestFeature]=0return bestFeature5. 特征若已经划分完，节点下的样本还没有统⼀取值，则需要进⾏投票def majorityCnt(classList):classCount={}for vote in classList:if vote not in classCount.keys():classCount[vote]=0classCount[vote]+=1return max(classCount)6.由于在Tree中，连续值特征的名称以及改为了 feature<=value的形式，因此对于这类特征，需要利⽤正则表达式进⾏分割，获得特征名以及分割阈值def classify(inputTree,featLabels,testVec):firstStr=list(inputTree.keys())[0]if'<='in firstStr:featvalue=float(pile("(<=.+)").search(firstStr).group()[2:])featkey=pile("(.+<=)").search(firstStr).group()[:-2]secondDict=inputTree[firstStr]featIndex=featLabels.index(featkey)if testVec[featIndex]<=featvalue:judge=1else:judge=0for key in secondDict.keys():if judge==int(key):if type(secondDict[key]).__name__=='dict':classLabel=classify(secondDict[key],featLabels,testVec)else:classLabel=secondDict[key]else:secondDict=inputTree[firstStr]featIndex=featLabels.index(firstStr)for key in secondDict.keys():if testVec[featIndex]==key:if type(secondDict[key]).__name__=='dict':classLabel=classify(secondDict[key],featLabels,testVec)else:classLabel=secondDict[key]return classLabeldef testing(myTree,data_test,labels):error=0.0for i in range(len(data_test)):if classify(myTree,labels,data_test[i])!=data_test[i][-1]:error+=1print ('myTree %d' %error)return float(error)def testingMajor(major,data_test):error=0.0for i in range(len(data_test)):if major!=data_test[i][-1]:error+=1print ('major %d' %error)return float(error)7.主程序，递归产⽣决策树def createTree(dataSet,labels,data_full,labels_full,data_test):classList=[example[-1] for example in dataSet]if classList.count(classList[0])==len(classList):return classList[0]if len(dataSet[0])==1:return majorityCnt(classList)temp_labels=copy.deepcopy(labels)bestFeat=chooseBestFeatureToSplit(dataSet,labels)bestFeatLabel=labels[bestFeat]myTree={bestFeatLabel:{}}if type(dataSet[0][bestFeat]).__name__=='str':currentlabel=labels_full.index(labels[bestFeat])featValuesFull=[example[currentlabel] for example in data_full]uniqueValsFull=set(featValuesFull)featValues=[example[bestFeat] for example in dataSet]uniqueVals=set(featValues)del(labels[bestFeat])#针对bestFeat的每个取值，划分出⼀个⼦树。

美国飞机场的数目-最权威的数据来了

美国飞机场的数⽬-最权威的数据来了根据美国NPIAS的数据,美国机场数⽬如下(截⾄到2018/5):
(国家综合机场系统(NPIAS)是美国航空基础设施资产的清单。

NPIAS由联邦航空管理局(FAA)开发并维护。

它确定了对美国国家航空运输具有重要意义的现有和未来机场)
美国现有机场数⽬统计
总结⼀句就是，美国截⽌到2018/5总共有19627个飞机场，其中私⼈机场14528个，公⽤机场5099个。

下图是美国飞机场的分布图。

⽽其中主要的公⽤飞机场有380个，分布图如下。

这380个主要飞机场运载了99%的飞机乘客。

其中⼤型枢纽机场30个(⼤型枢纽机场定义为可以运载超过1%飞机乘客的机场.30个⼤型枢纽机场可以运载72%的飞机乘客); 中型飞机场31个(中型飞机场定义为可以运载0.25%-1%飞机乘客的机场.31个中型飞机场可以运载16%的飞机乘客);⼩型飞机场72个(⼩型飞机场定义为可以运载0.05%-0.25%飞机乘客的机场.72个中型飞机场可以运载8%的飞机乘客)；微型飞机场247个(微型主要飞机场定义为运载⼒⼩于0.05%每年乘客⼤于1万⼈的机场，247个微型机场运载了3%的飞机乘客)。

所以如果要对⽐中美航空运输的运载⼒，应该采⽤380这个数据，否则是很有误解性的。

我之前也犯了⼀些错误，论述不太严谨。

但是美国私⼈飞机异常活跃，这是中美飞机航空现状的另⼀个巨⼤区别。

基于机器学习的航空数据分析与预测

基于机器学习的航空数据分析与预测机器学习是一种应用于航空领域的重要技术，它能够处理大量的航空数据，并从中获取有价值的信息。

本文将探讨如何基于机器学习进行航空数据分析与预测，以及其在航空领域中的应用。

首先，我们来了解一下航空数据的种类和来源。

航空数据可以分为三大类：航空运输数据、航空交通管理数据和航空安全数据。

航空运输数据包括航班时刻表、航班调度和航班运营等方面的数据；航空交通管理数据包括飞行计划、飞行监控和空中交通流量等方面的数据；而航空安全数据包括事故数据、违规事件和风险评估等方面的数据。

这些数据主要来源于航空公司、航空交通管制中心、机场和政府监管机构等。

在进行机器学习的航空数据分析和预测之前，我们需要对数据进行清洗和预处理。

数据清洗包括去除噪声、处理缺失值和异常值等。

数据预处理包括数据归一化、特征选择和特征工程等。

通过清洗和预处理，我们可以得到更加干净和有用的数据，以用于机器学习算法的训练和预测。

接下来，我们将讨论几种常用的机器学习算法在航空数据分析与预测中的应用。

第一种算法是决策树算法。

决策树是一种基于树状图的分类和回归算法。

在航空数据分析中，我们可以使用决策树算法来进行航班延误预测。

通过分析历史航班数据和各种因素，比如天气、空中交通流量和机场状况等，我们可以建立一个决策树模型，来预测航班是否会延误。

这个模型可以帮助航空公司和乘客提前做出相应的调整和安排。

第二种算法是支持向量机（Support Vector Machine，简称SVM）算法。

SVM是一种常用的分类和回归算法，在航空数据分析中有广泛的应用。

例如，我们可以使用SVM算法来预测乘客的购票倾向。

通过分析乘客的历史购票记录和个人信息，比如年龄、性别和航空公司偏好等，我们可以建立一个SVM模型，来预测乘客是否会购买机票。

这个模型可以帮助航空公司进行市场营销和客户关系管理。

第三种算法是神经网络算法。

神经网络是一种模仿人脑神经元网络结构的机器学习算法。

机器学习技术在航班延误预测中的应用

机器学习技术在航班延误预测中的应用引言：航班延误是航空运输领域面临的一个重要问题。

航班延误不仅给乘客带来不便，也对航空公司的运营效率和利润产生负面影响。

因此，准确预测航班延误成为了航空业务中的一个关键挑战。

然而，航班延误预测是一个复杂的问题，受到众多因素的影响。

近年来，机器学习技术的快速发展为航班延误预测提供了新的解决方案。

本文将介绍机器学习技术在航班延误预测中的应用，并探讨其优势和挑战。

一、机器学习技术的基本原理机器学习是一种基于数据的方法，通过从大量数据中学习规律和模式，并用于预测和决策。

在航班延误预测中，机器学习技术可以分为监督学习和无监督学习两种类型。

监督学习通常使用已有的航班数据作为样本，包括出发地、目的地、航班日期、天气等因素作为特征，同时记录航班是否延误作为标签。

无监督学习则是通过对数据进行聚类、关联和降维等处理，自动发现数据中的模式和结构。

二、机器学习技术在航班延误预测中的应用1. 特征工程机器学习模型的性能很大程度上取决于特征的选择和提取。

而在航班延误预测中，选择合适的特征对模型的准确性至关重要。

特征工程的目标是通过对航班数据进行处理和转换，提取出对延误预测有意义的特征。

例如，可以通过提取航班的历史准点率、起飞前是否有天气警报、航空器机型等特征，来增加模型的预测准确性。

2. 监督学习模型监督学习模型是航班延误预测中最常用的方法。

常见的监督学习算法包括决策树、随机森林、支持向量机和神经网络等。

这些模型可以根据已有的航班数据进行训练，从而学习出一个预测模型，用于预测未来航班是否会延误。

这些模型通常具有较高的准确性和可解释性，能够给出特定特征对航班延误的影响程度。

3. 无监督学习模型与监督学习模型不同，无监督学习模型不需要事先准备好的标签数据。

无监督学习可以通过聚类算法对航班数据进行自动分类，将相似的航班归为一组。

这样的分类结果可以为航班延误预测提供新的视角，帮助发现数据中的潜在模式和规律。

机器学习技术在航空安全保障中的使用技巧总结

机器学习技术在航空安全保障中的使用技巧总结引言航空安全保障对于保障乘客生命安全和航空业的可持续发展至关重要。

随着航空业的不断发展和技术的进步，机器学习技术正成为航空安全保障中的重要工具。

本文将总结机器学习技术在航空安全保障中的使用技巧，包括异常检测、数据分析和预测分析等方面。

一、异常检测异常检测是机器学习技术在航空安全保障中的重要应用之一。

通过对航空数据进行异常检测，可以及时发现和识别可能存在的安全隐患，以便采取相应的安全措施。

以下是一些在航空安全保障中使用机器学习技术进行异常检测的具体技巧：1. 数据预处理：在进行异常检测之前，需要对航空数据进行预处理，包括去噪、归一化和特征选择等。

这样可以减少噪声对异常检测结果的干扰，提高异常检测的准确性和可靠性。

2. 特征工程：选择适当的特征对异常进行描述和判定是异常检测的关键。

对于航空数据来说，可以考虑使用飞行参数、航班计划和传感器数据等进行特征提取。

通过合理选择和设计特征，可以更好地反映航空安全情况，并提高异常检测的效果。

3. 异常检测算法：选择合适的异常检测算法是保证异常检测效果的重要因素。

常用的异常检测算法包括基于统计学的方法、聚类方法和机器学习方法等。

根据不同的数据特点和异常检测需求，选择最合适的算法进行异常检测，可以提高检测效果。

二、数据分析数据分析是航空安全保障中常用的技术手段之一。

通过对航空数据进行分析和挖掘，可以发现潜在的安全隐患和规律，提供决策支持和改进方案。

以下是一些在航空安全保障中使用机器学习技术进行数据分析的具体技巧：1. 大数据处理：航空数据量庞大，对于大规模数据的处理是数据分析的挑战之一。

机器学习技术可以帮助有效地处理大数据，并提取有用的信息和模式。

使用分布式计算和并行化技术，可以加速数据分析过程，提高处理效率。

2. 数据可视化：将航空数据进行可视化分析，可以更直观地展示数据间的关系和趋势。

通过使用机器学习技术进行数据可视化，可以提高数据分析的效果和可理解性。

机器学习算法在航空安全中的应用效果比较

机器学习算法在航空安全中的应用效果比较航空安全一直是航空业最重要的关注领域之一。

随着科技的不断发展，机器学习算法为提高航空安全水平提供了新的可能性。

机器学习算法可以通过分析大量的数据，识别潜在的安全风险，并提供更有效的安全措施。

然而，不同的机器学习算法在航空安全中的应用效果可能存在差异。

本文将比较几种常见的机器学习算法在航空安全中的应用效果。

首先，支持向量机（Support Vector Machine，SVM）是一种常见的机器学习算法，被广泛应用于航空安全过程中的异常检测和故障诊断。

SVM通过构建分离超平面的方式，将不同类别的数据分开。

对于航空安全来说，SVM可以识别出潜在的安全威胁，如异常飞行行为和故障指示。

然而，SVM算法在处理大规模数据集时可能会面临一些挑战，因为其计算复杂度较高，可能需要更多的计算资源和时间。

另一种常见的机器学习算法是决策树（Decision Tree）。

决策树可以通过对事先定义的属性进行划分，生成一系列决策规则。

在航空安全中，决策树可以用于识别潜在的风险因素，并提供相关的应对措施。

与其他机器学习算法相比，决策树的优势在于可解释性强，易于理解和解释生成的决策规则。

然而，决策树算法容易受到训练数据的影响，可能出现过拟合或欠拟合的情况。

随机森林（Random Forest）是一种集成学习算法，通过建立多个决策树模型并对其进行平均，提高了模型的准确性和稳定性。

在航空安全中，随机森林算法可以通过集成多个决策树的判断，提供更可靠的风险评估和预测。

与其他机器学习算法相比，随机森林算法通常具有较高的准确性和鲁棒性。

然而，随机森林算法的训练和构建需要更多的计算资源和时间，尤其是在处理大规模数据集时。

此外，神经网络（Neural Network）也是一种常见的机器学习算法，可以模拟人脑的神经网络结构。

神经网络在航空安全中的应用包括飞行数据的异常检测和故障预测等。

神经网络算法具有强大的学习能力和适应能力，可以从复杂的数据中提取特征并进行预测。

机器学习_UsStateDataset（美国各洲数据集）

机器学习_us state dataset(美国各洲数据集) us state dataset(美国各洲数据集) 数据摘要: this dataset consists of a collection of infoboxes on the topic of us state. this dataset comes fromwikipedia for information processing and classification. 中文关键词: 洲,美国,信息框,收集, 英文关键词: state,us,infoboxes,collection, 数据格式: text 数据用途: information processing classification 数据详细介绍: us state this dataset consists of a collection of infoboxes from wikipedia on the topic of us state. tags: state price: free package size: about 16.7 kb categories: collection: wikipedia infoboxes sources: /doc/9817190310.html dbpedia license: infochimps (tm) is copyright infochimps, inc. ? 2010 数据预览: 点此下载完整et（美国各洲数据集）
Us State Dataset(美国各洲数据集) 数据摘要： This dataset consists of a collection of Infoboxes on the topic of Us State. This dataset comes fromWikipedia for Information Processing and Classification. 中文关键词：洲,美国,信息框,收集, 英文关键词： State,US,Infoboxes,collection, 数据格式： TEXT 数据用途： Information Processing Classification 数据详细介绍： Us State This dataset consists of a collection of Infoboxes from Wikipedia on the topic of Us State. Tags: State Price: Free Package size: about 16.7 KB Categories: Collection: Wikipedia Infoboxes Sources: /doc/9e61582a647d27284b7351eb.html DBpedia License: Infochimps (TM) is copyright Infochimps, Inc. ? 2010 数据预览：