¼¼°èÀÇ Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀå
Intelligent Airways Transport Systems
»óǰÄÚµå : 1792966
¸®¼­Ä¡»ç : Global Industry Analysts, Inc.
¹ßÇàÀÏ : 2025³â 08¿ù
ÆäÀÌÁö Á¤º¸ : ¿µ¹® 369 Pages
 ¶óÀ̼±½º & °¡°Ý (ºÎ°¡¼¼ º°µµ)
US $ 5,850 £Ü 8,183,000
PDF & Excel (Single User License) help
PDF & Excel º¸°í¼­¸¦ 1¸í¸¸ ÀÌ¿ëÇÒ ¼ö ÀÖ´Â ¶óÀ̼±½ºÀÔ´Ï´Ù. ÆÄÀÏ ³» ÅØ½ºÆ®ÀÇ º¹»ç ¹× ºÙ¿©³Ö±â´Â °¡´ÉÇÏÁö¸¸, Ç¥/±×·¡ÇÁ µîÀº º¹»çÇÒ ¼ö ¾ø½À´Ï´Ù. Àμâ´Â 1ȸ °¡´ÉÇϸç, Àμ⹰ÀÇ ÀÌ¿ë¹üÀ§´Â ÆÄÀÏ ÀÌ¿ë¹üÀ§¿Í µ¿ÀÏÇÕ´Ï´Ù.
US $ 17,550 £Ü 24,550,000
PDF & Excel (Global License to Company and its Fully-owned Subsidiaries) help
PDF & Excel º¸°í¼­¸¦ µ¿ÀÏ ±â¾÷ ¹× 100% ÀÚȸ»çÀÇ ¸ðµç ºÐÀÌ ÀÌ¿ëÇÏ½Ç ¼ö ÀÖ´Â ¶óÀ̼±½ºÀÔ´Ï´Ù. Àμâ´Â 1Àδç 1ȸ °¡´ÉÇϸç, Àμ⹰ÀÇ ÀÌ¿ë¹üÀ§´Â ÆÄÀÏ ÀÌ¿ë¹üÀ§¿Í µ¿ÀÏÇÕ´Ï´Ù.


Çѱ۸ñÂ÷

Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ¼¼°è ½ÃÀåÀº 2030³â±îÁö 259¾ï ´Þ·¯¿¡ ´ÞÇÒ Àü¸Á

2024³â¿¡ 165¾ï ´Þ·¯·Î ÃßÁ¤µÇ´Â Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ¼¼°è ½ÃÀåÀº 2024³âºÎÅÍ 2030³â±îÁö CAGR 7.7%·Î ¼ºÀåÇÏ¿© 2030³â¿¡´Â 259¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ÀÌ º¸°í¼­¿¡¼­ ºÐ¼®ÇÑ ºÎ¹® Áß ÇϳªÀÎ Çϵå¿þ¾î ±¸¼º¿ä¼Ò´Â CAGR 6.8%¸¦ ±â·ÏÇÏ¸ç ºÐ¼® ±â°£ Á¾·á½Ã¿¡´Â 172¾ï ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ¼ÒÇÁÆ®¿þ¾î ±¸¼º¿ä¼Ò ºÐ¾ßÀÇ ¼ºÀå·üÀº ºÐ¼® ±â°£ µ¿¾È CAGR 9.9%·Î ÃßÁ¤µË´Ï´Ù.

¹Ì±¹ ½ÃÀåÀº 45¾ï ´Þ·¯, Áß±¹Àº CAGR 11.9%·Î ¼ºÀåÇÒ °ÍÀ¸·Î ¿¹Ãø

¹Ì±¹ÀÇ Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀåÀº 2024³â¿¡ 45¾ï ´Þ·¯·Î ÃßÁ¤µË´Ï´Ù. ¼¼°è 2À§ °æÁ¦ ´ë±¹ÀÎ Áß±¹Àº 2030³â±îÁö 55¾ï ´Þ·¯ÀÇ ½ÃÀå ±Ô¸ð¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹ÃøµÇ¸ç, ºÐ¼® ±â°£ÀÎ 2024-2030³â CAGRÀº 11.9%¸¦ ±â·ÏÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. ±âŸ ÁÖ¸ñÇÒ ¸¸ÇÑ Áö¿ªº° ½ÃÀåÀ¸·Î´Â ÀϺ»°ú ij³ª´Ù°¡ ÀÖ°í, ºÐ¼® ±â°£ µ¿¾È CAGRÀº °¢°¢ 4.0%¿Í 7.4%·Î ¿¹ÃøµË´Ï´Ù. À¯·´¿¡¼­´Â µ¶ÀÏÀÌ CAGR 5.1%·Î ¼ºÀåÇÒ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù.

¼¼°èÀÇ Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀå - ÁÖ¿ä µ¿Çâ°ú ÃËÁø¿äÀÎ Á¤¸®

Áö´ÉÇü ±³Åë ½Ã½ºÅÛÀÌ Çö´ë Ç×°ø¿¡ ¾ø¾î¼­´Â ¾È µÉ Á¸Àç°¡ µÇ°í ÀÖ´Â ÀÌÀ¯´Â ¹«¾ùÀϱî?

Àü ¼¼°è Ç×°ø ±³Åë·®ÀÌ Áö¼ÓÀûÀ¸·Î Áõ°¡Çϰí È¿À²¼º, ¾ÈÀü¼º, Áö¼Ó°¡´É¼º¿¡ ´ëÇÑ ¿ä±¸°¡ Àü·Ê ¾ø´Â ¼öÁØ¿¡ µµ´ÞÇÔ¿¡ µû¶ó Ç×°ø ºÐ¾ßÀÇ ±âº» ÇʼöǰÀ¸·Î ±ÞºÎ»óÇϰí ÀÖ´Â °ÍÀÌ ¹Ù·Î Áö´ÉÇü ±³Åë ½Ã½ºÅÛÀÔ´Ï´Ù. ÀÌ ½Ã½ºÅÛµéÀº Ç×°ø ±³Åë °üÁ¦ ¹× ºñÇà °æ·Î ÃÖÀûÈ­ºÎÅÍ °øÇ×ÀÇ Áö»ó ¼­ºñ½º ¹× ½Â°´ °æÇè¿¡ À̸£±â±îÁö ¸ðµç °ÍÀ» °ü¸®Çϴ ÷´Ü ±â¼úÀ» ÅëÇÕÇϰí ÀÖ½À´Ï´Ù. ¹Î°£ Ç×°ø ¿îÇ×, È­¹° ¾÷¹«, µµ½ÃÇü Ç×°ø ¸ðºô¸®Æ¼ ¼Ö·ç¼ÇÀÌ ±ÞÁõÇÔ¿¡ µû¶ó ÀüÅëÀûÀÎ Ç×°ø ¿î¼ÛÀÇ Æ²Àº ´õ ÀÌ»ó Çö´ë Ç×°ø ¾÷¹«ÀÇ º¹À⼺°ú ¾çÀ» °¨´çÇÒ ¼ö ¾ø°Ô µÇ¾ú½À´Ï´Ù. Áö´ÉÇü ½Ã½ºÅÛÀº ½Ç½Ã°£ µ¥ÀÌÅÍ ±³È¯, ¿¹Ãø ºÐ¼®, ÀÚµ¿È­ ±â´ÉÀ» Á¦°øÇÏ¿© »óȲ ÀνÄÀ» Å©°Ô °­È­Çϰí ÀÎÀû ¿À·ù¸¦ ÁÙÀ̸ç ÀÇ»ç°áÁ¤À» °£¼ÒÈ­ÇÕ´Ï´Ù. Ç×°ø»ç ÀÔÀå¿¡¼­´Â Á¤È®ÇÑ ¹ßÂø ½Ã°£, ¿¬·á ¼Òºñ ÃÖÀûÈ­, Ç×°ø±â ÀÌ¿ë·ü Çâ»óÀ¸·Î À̾îÁý´Ï´Ù. °øÇ×°ú ±ÔÁ¦ ´ç±¹Àº ´õ ³ªÀº ±³Åë È帧 °ü¸®, ¾ÈÀü ¸ð´ÏÅ͸µ °­È­, ±ä±Þ »óȲÀ̳ª È¥¶õ ½Ã ½Å¼ÓÇÑ ´ëÀÀÀÌ °¡´ÉÇØÁý´Ï´Ù. ½º¸¶Æ® ¼¾¼­, À§¼ºÇ×¹ý, AI¸¦ Ȱ¿ëÇÑ ¿¹Ãø ÅøÀ» Á¢¸ñÇÏ¿© ³¯¾¾ º¯È­, °ø¿ª È¥Àâµµ, ȰÁÖ·Î °¡¿ë¼º µî¿¡ µû¶ó °æ·Î¿Í ½ºÄÉÁÙÀ» µ¿ÀûÀ¸·Î Á¶Á¤ÇÒ ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, »ýüÀÎ½Ä Å¾½Â, ¼öÇϹ° ÃßÀû, ÀÚµ¿ üũÀÎ ½Ã½ºÅÛ µî ½Â°´ Áß½ÉÀÇ ¼Ö·ç¼ÇÀº Ç×°ø ¿î¼ÛÀÇ ¸ðµç Á¢Á¡¿¡ °ÉÄ£ ÀÎÅÚ¸®Àü½ºÀÇ Çʿ伺À» ´õ¿í °­Á¶Çϰí ÀÖ½À´Ï´Ù. Ç×°ø ÀÌÇØ°ü°èÀÚµéÀÌ È¯°æ¿¡ ¹ÌÄ¡´Â ¿µÇâÀ» ÃÖ¼ÒÈ­Çϸ鼭 ¿î¼Û ´É·ÂÀ» È®´ëÇÏ´Â µÎ °¡Áö °úÁ¦¸¦ ÇØ°áÇϱâ À§ÇØ ³ë·ÂÇϰí ÀÖ´Â °¡¿îµ¥, Áö´ÉÇü ±³Åë ½Ã½ºÅÛÀº Àü ¼¼°è Ç×°ø ¿î¿µÀÇ ¹Ì·¡¸¦ À籸¼ºÇÏ´Â µ¥ ÇʼöÀûÀÎ µµ±¸°¡ µÇ°í ÀÖ½À´Ï´Ù.

±â¼úÀº Áö´ÉÇü Ç×°ø ½Ã½ºÅÛ ¿î¿µÀ» ¾î¶»°Ô À籸¼ºÇϰí Àִ°¡?

±â¼úÀÇ ¹ßÀüÀº ÷´Ü µµ·Î ±³Åë ½Ã½ºÅÛÀÇ ÇÙ½ÉÀ̸ç, ÅëÇÕ, ÀÚµ¿È­, ½Ç½Ã°£ ¿¬°á¼ºÀ» ÅëÇØ Ç×°ø ¾÷¹«ÀÇ »óȲÀ» º¯È­½Ãų °ÍÀÔ´Ï´Ù. ÀΰøÁö´ÉÀº Ç×°ø°üÁ¦¿¡ Àû¿ëµÇ¾î ±³Åë È帧À» ¿¹ÃøÇϰí, ÀáÀçÀû Ãæµ¹À» °¨ÁöÇϰí, ÃÖÀûÀÇ ¿ìȸ Àü·«À» Á¦¾ÈÇÔÀ¸·Î½á °üÁ¦»ç°¡ ´õ ¸¹Àº Ç×°ø±â¸¦ µ¿½Ã¿¡ °ü¸®Çϸ鼭 ¾ÈÀü ¸¶ÁøÀ» °³¼±ÇÒ ¼ö ÀÖµµ·Ï µ½°í ÀÖ½À´Ï´Ù. ¸Ó½Å·¯´× ¾Ë°í¸®ÁòÀº ·¹ÀÌ´õ ½Ã½ºÅÛ, À§¼º, Ç×°ø±â ÅÚ·¹¸ÞÆ®¸®¿¡¼­ ¼öÁýµÈ ¹æ´ëÇÑ µ¥ÀÌÅͼ¼Æ®¸¦ ó¸®ÇÏ¿© ºñÇà °èȹÀÇ ¹Ì¼¼ Á¶Á¤, Áö¿¬ ¿¹Ãø, ¿¬·á È¿À²¼º Çâ»óÀ» ½ÇÇöÇÕ´Ï´Ù. »ç¹°ÀÎÅͳÝ(Internet of Things) ÀåÄ¡´Â °øÇ× ÀÎÇÁ¶ó ¹× Ç×°ø±â ½Ã½ºÅÛ¿¡ ³»ÀåµÇ¾î ÀåºñÀÇ ¼º´É, ½Â°´ÀÇ È帧, È­¹°ÀÇ ¿òÁ÷ÀÓÀ» Áö¼ÓÀûÀ¸·Î ¸ð´ÏÅ͸µÇÒ ¼ö ÀÖ½À´Ï´Ù. Ŭ¶ó¿ìµå ±â¹Ý Ç÷§ÆûÀº ÆÄÀÏ·µ°ú µð½ºÆÐó¿¡¼­ Ç×°ø °üÁ¦»ç ¹× Áö»ó ÀÛ¾÷ÀÚ¿¡ À̸£±â±îÁö ¸ðµç ÀÌÇØ°ü°èÀÚ °£¿¡ ÀÌ µ¥ÀÌÅͰ¡ ¾ÈÀüÇϰí Áï°¢ÀûÀ¸·Î °øÀ¯µÉ ¼ö ÀÖµµ·Ï º¸ÀåÇÕ´Ï´Ù. ÄÁÆ®·Ñ·¯-ÆÄÀÏ·µ-µ¥ÀÌÅ͸µÅ© Åë½Å(CPDLC)°ú °°Àº °í±Þ Åë½Å ÇÁ·ÎÅäÄÝÀº ¹«¼± È¥ÀâÀ» ¿ÏÈ­Çϰí Á¶Á¾¼®°ú °üÁ¦Å¾ °£ÀÇ º¸´Ù Á¤È®ÇÑ ÅØ½ºÆ® ±â¹Ý Åë½ÅÀ» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. Áõ°­Çö½ÇÀº À¯Áöº¸¼ö ¹× ÈÆ·Ã¿¡ Ȱ¿ëµÇ°í ÀÖÀ¸¸ç, Áö»ó ¿ä¿øÀÌ ´Ù¿îŸÀÓÀ» ÃÖ¼ÒÈ­Çϸ鼭 º¹ÀâÇÑ ½Ã½ºÅÛÀ» Áø´ÜÇÏ°í ¼ö¸®ÇÒ ¼ö ÀÖµµ·Ï Áö¿øÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, »çÀ̹ö º¸¾ÈÀÌ ÃÊÁ¡ÀÌ µÇ°í ÀÖÀ¸¸ç, Ç×°ø ´ç±¹°ú ±â¾÷µéÀº µðÁöÅÐ À§ÇùÀ¸·ÎºÎÅÍ »óÈ£¿¬°áµÈ ½Ã½ºÅÛÀ» º¸È£Çϱâ À§ÇØ Åº·ÂÀûÀÎ ¾ÆÅ°ÅØÃ³¿¡ ÅõÀÚÇϰí ÀÖ½À´Ï´Ù. µå·Ð°ú Àü±â ¼öÁ÷ÀÌÂø·ú±â(eVTOL)°¡ Ç×°ø »ýŰ迡 ÁøÀÔÇÔ¿¡ µû¶ó Àú°íµµ Æ®·¡ÇÈÀ» °ü¸®Çϰí ÀÌ·¯ÇÑ »õ·Î¿î Ç÷§ÆûÀ» ±âÁ¸ °ø¿ª¿¡ ¿øÈ°ÇÏ°Ô ÅëÇÕÇϱâ À§ÇØ Áö´ÉÇü ½Ã½ºÅÛÀÌ Àû¿ëµÇ°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ±â¼úÀÇ À¶ÇÕÀº Ç×°ø ¿î¼ÛÀ» ´õ¿í Áö´ÉÀûÀ¸·Î ¸¸µé »Ó¸¸ ¾Æ´Ï¶ó, ÁøÈ­ÇÏ´Â ¼ö¿ä¿¡ ´ëÇÑ ´ëÀÀ·Â, Áö¼Ó°¡´É¼º, º¹¿ø·ÂÀ» Çâ»ó½Ã۰í ÀÖ½À´Ï´Ù.

Ç×°ø »ýŰè Àü¹ÝÀÇ ÀÌÇØ°ü°èÀÚµéÀº Áö´ÉÇü ±³Åë Çõ½Å¿¡ ¾î¶»°Ô ÀûÀÀÇϰí Àִ°¡?

Ç×°ø±³Åë½Ã½ºÅÛÀÇ ¼º°øÀûÀÎ µµÀÔ ¿©ºÎ´Â ±¤¹üÀ§ÇÑ Ç×°ø ÀÌÇØ°ü°èÀÚµéÀÇ Çù·Â°ú ÀûÀÀ·Â¿¡ Å©°Ô Á¿ìµÇ¸ç, °¢ ÀÌÇØ°ü°èÀÚµéÀº »õ·Î¿î ±â¼ú ¿î¿µ°ú »õ·Î¿î Ç¥ÁØ¿¡ ´ëÀÀÇÏ´Â µ¥ Áß¿äÇÑ ¿ªÇÒÀ» ´ã´çÇϰí ÀÖ½À´Ï´Ù. Ç×°ø»ç´Â ½Ç½Ã°£ ¼º´É¿¡ ´ëÇÑ ÀλçÀÌÆ®, ³ë¼± ÃÖÀûÈ­, »çÀü ¿¹¹æÀû À¯Áöº¸¼ö ½ºÄÉÁÙ¸µ µîÀ» Á¦°øÇÏ´Â ½º¸¶Æ® Ç×°ø±â °ü¸® µµ±¸¿¡ ÅõÀÚÇÏ¿© ¼öÀͼºÀ» ³ôÀÌ°í ´Ù¿îŸÀÓÀ» ÁÙÀÌ´Â µ¥ µµ¿òÀ» ÁÖ°í ÀÖ½À´Ï´Ù. °øÇ× ´ç±¹Àº ÀÚµ¿È­ °ÔÀÌÆ®, AI¸¦ Ȱ¿ëÇÑ º¸¾È °Ë»ö, µ¿Àû ¿©°´ È帧 °ü¸® ½Ã½ºÅÛ µî Áö´ÉÇü ÀÎÇÁ¶ó ¼Ö·ç¼ÇÀ» µµÀÔÇÏ¿© 󸮷®°ú »ç¿ëÀÚ °æÇèÀ» Çâ»ó½Ã۰í ÀÖ½À´Ï´Ù. ±ÔÁ¦ ±â°üÀº ¾÷°è ´Üü¿Í Çù·ÂÇÏ¿© Áö´ÉÇü ±â¼úÀÇ ¾ÈÀüÇϰí Ç¥ÁØÈ­µÈ »ç¿ëÀ» Áö¿øÇÏ´Â ¿µ°ø Á¤Ã¥, ÀÎÁõ ÇÁ·¹ÀÓ¿öÅ©, µ¥ÀÌÅÍ °Å¹ö³Í½º ¸ðµ¨À» ¾÷µ¥ÀÌÆ®Çϰí ÀÖ½À´Ï´Ù. Ç×°ø Ç×¹ý ¼­ºñ½º Á¦°ø¾÷ü´Â ·¹°Å½Ã ½Ã½ºÅÛÀ» Çö´ëÈ­ÇÏ¿© ¿ªµ¿ÀûÀÎ °æ·Î ÇÒ´ç°ú À¯¿¬ÇÑ °ø¿ª °ü¸®¸¦ °¡´ÉÇÏ°Ô Çϴ ÷´Ü ¸ð´ÏÅ͸µ ¹× ÀÇ»ç°áÁ¤ Áö¿ø µµ±¸¸¦ ÅëÇÕÇϰí ÀÖ½À´Ï´Ù. Ç×°ø±â ¹× Ç×°øÀüÀÚ Á¦Á¶¾÷üµéÀº Â÷¼¼´ë ¸ðµ¨¿¡ Áö´ÉÇü ±â´ÉÀ» ÅëÇÕÇÏ¿© ¹Ì·¡ÀÇ Ç×°ø±â°¡ Á¡Á¡ ´õ ÀÚµ¿È­µÇ°í »óÈ£¿¬°áµÈ ȯ°æ¿¡ ´ëÀÀÇÒ ¼ö ÀÖµµ·Ï ³ë·ÂÇϰí ÀÖ½À´Ï´Ù. ½Â°´À» ¿ËÈ£ÇÏ´Â ´Üüµéµµ ½º¸¶Æ® ±â¼úÀÇ Åõ¸í¼º, ½Å·Ú¼º, Á¢±Ù¼ºÀ» Àå·ÁÇϰí, ½º¸¶Æ® ±â¼ú °³¹ß¿¡ ´ëÇÑ ¹ß¾ð±ÇÀ» °­È­Çϰí ÀÖ½À´Ï´Ù. ÀÌ¿Í º´ÇàÇÏ¿©, ÈÆ·Ã ±â°üÀº Á¶Á¾»ç, ¿£Áö´Ï¾î, °üÁ¦»ç°¡ Áö´ÉÇü »ýŰ迡¼­ Ȱµ¿ÇÒ ¼ö ÀÖµµ·Ï Ä¿¸®Å§·³À» ¾÷µ¥ÀÌÆ®ÇÏ°í µ¥ÀÌÅÍ ¸®ÅÍ·¯½Ã, ½Ã½ºÅÛ ÅëÇÕ, µðÁöÅÐ »óȲ ÀνĿ¡ ÁßÁ¡À» µÎ°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ Çù·ÂÀû ³ë·ÂÀº Áö´ÉÇü Ç×°ø·Î ½Ã½ºÅÛÀÇ ±â¼úÀû ¹æÇ⼺À» Çü¼ºÇÒ »Ó¸¸ ¾Æ´Ï¶ó Ç×°ø ¹ë·ùüÀο¡¼­ »õ·Î¿î ¿ªÇÒ°ú Ã¥ÀÓÀ» Á¤ÀÇÇϰí ÀÖ½À´Ï´Ù. »ýŰ谡 ÁøÈ­ÇÏ´Â °¡¿îµ¥ ¼º°øÀûÀ¸·Î ÀûÀÀÇÒ ¼ö ÀÖ´À³Ä ¾ø´À³Ä´Â Çõ½Å, Çù¾÷, Áö¼ÓÀûÀÎ °³¼±¿¡ ´ëÇÑ ¾à¼ÓÀ» °øÀ¯ÇÒ ¼ö ÀÖ´À³Ä ¾ø´À³Ä¿¡ ´Þ·Á ÀÖ½À´Ï´Ù.

Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀåÀÇ ±Þ¼ºÀå ¿øµ¿·ÂÀº?

Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀåÀÇ ¼ºÀåÀº ±â¼ú ¹ßÀü, ±ÔÁ¦ °³Çõ, ¿î¿µ È¿À²¼º Çâ»ó, ½Â°´ÀÇ ±â´ëÄ¡ »ó½Â µî ¿©·¯ °¡Áö »óÈ£ ¿¬°üµÈ ¿äÀο¡ ÀÇÇØ ÁÖµµµÇ°í ÀÖ½À´Ï´Ù. ƯÈ÷ Áß»êÃþÀÇ È®´ë¿Í Áö¿ªÀû ¿¬°á¼ºÀÌ ´õ ¸¹Àº Ç×°øÆí°ú ´õ ³ªÀº ¼­ºñ½º¿¡ ´ëÇÑ ¼ö¿ä¸¦ ÃËÁøÇÏ´Â ½ÅÈï ½ÃÀåÀÇ Ç×°ø ¿î¼Û·®ÀÇ ±Þ°ÝÇÑ Áõ°¡°¡ ÁÖ¿ä ¿äÀÎÀ¸·Î ÀÛ¿ëÇϰí ÀÖ½À´Ï´Ù. ¾ÈÀü°ú È¿À²¼ºÀ» Èñ»ýÇÏÁö ¾Ê°í ÀÌ·¯ÇÑ ¼ö¿ä¸¦ ÃæÁ·½Ã۱â À§ÇØ Ç×°ø»ç¿Í °øÇ×Àº Ç×°ø ¿î¼Û ÁÖ±â Àü¹Ý¿¡ °ÉÃÄ °¡½Ã¼º, ´ëÀÀ·Â, ÀÚµ¿È­¸¦ °­È­ÇÏ´Â Áö´ÉÇü ½Ã½ºÅÛÀ¸·Î ´«À» µ¹¸®°í ÀÖ½À´Ï´Ù. ¾÷°è´Â Ç×·Î ÃÖÀûÈ­, ¿¹Áöº¸Àü, ¿¡³ÊÁö È¿À²Àû °øÇ× ¿î¿µÀ» ÅëÇØ ÀÌ»êȭź¼Ò ¹èÃâ·®°ú ¿¬·á ¼Òºñ¸¦ ÁÙÀÌ´Â °ÍÀ» ¸ñÇ¥·Î Çϰí ÀÖ½À´Ï´Ù. ±ÔÁ¦ ´ç±¹µµ ƯÈ÷ À¯·´, ºÏ¹Ì, ¾Æ½Ã¾Æ ÀϺΠÁö¿ª¿¡¼­ µðÁöÅÐ Çõ½Å ÀÌ´Ï¼ÅÆ¼ºê¸¦ Àǹ«È­Çϰí Ç×°ø ±³Åë °ü¸® ¾÷±×·¹À̵忡 ÀÚ±ÝÀ» ÇÒ´çÇÔÀ¸·Î½á Çö´ëÈ­¸¦ °¡¼ÓÈ­Çϰí ÀÖ½À´Ï´Ù. Ç×°ø ¾÷°èÀÇ °æÀï ¿ªÇÐÀº ¿ì¼ö ¿îÇ×À» ÃËÁøÇÏ°í °í°´ Ãæ¼ºµµ¸¦ Çâ»ó½Ãų ¼ö ÀÖ´Â ±â¼úÀû Â÷º°È­ ¿ä¼Ò¸¦ ¸ð»öÇÏ´Â ±â¾÷µéÀÇ ±â¼ú Çõ½ÅÀ» ÃËÁøÇϰí ÀÖ½À´Ï´Ù. À§¼º Ç×¹ý, Ŭ¶ó¿ìµå ÄÄÇ»ÆÃ, 5G ¿¬°áÀÇ ¹ßÀüÀº ½Ç½Ã°£ ÀÇ»ç°áÁ¤°ú ÀûÀÀÇü ±³Åë °ü¸®¿¡ ÇʼöÀûÀÎ º¸´Ù °ß°íÇϰí Áö¿¬ÀÌ ÀûÀº Åë½Å ³×Æ®¿öÅ©¸¦ °¡´ÉÇÏ°Ô Çϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, ÆÒµ¥¹Í ÀÌÈÄ È¸º¹Àº ºñÁ¢ÃË½Ä Å¾½Â, µðÁöÅÐ °Ç°­ ¿©±Ç µî °Ç°­ ÅëÇÕ ¿î¼Û ±â¼ú¿¡ ´ëÇÑ ÅõÀÚ¸¦ ÃËÁøÇÏ¿© Áö´ÉÇü ½Ã½ºÅÛÀÇ ¹üÀ§¸¦ ´õ¿í ³ÐÈ÷°í ÀÖ½À´Ï´Ù. µµ½Ã Ç×°ø À̵¿°ú µå·Ð ¹°·ù´Â Áö´ÉÇü ½Ã½ºÅÛÀÌ µ¶ÀÚÀûÀ¸·Î ´ëóÇϱ⿡ ÀûÇÕÇÑ »õ·Î¿î ±³Åë °ü¸® ¹®Á¦¸¦ ¾ß±âÇϰí ÀÖ½À´Ï´Ù. Á¤ºÎ, ¹Î°£ ±â¾÷, ±¹Á¦Ç×°ø±â±¸°¡ º¸´Ù ½º¸¶Æ®ÇÏ°í ¾ÈÀüÇϸç È®À强ÀÌ ³ôÀº Ç×°ø ¿î¼Û ³×Æ®¿öÅ©¸¦ ±¸ÃàÇϱâ À§ÇØ ³ë·ÂÇϰí ÀÖ´Â °¡¿îµ¥, Áö´ÉÇü ±³Åë ½Ã½ºÅÛ ½ÃÀåÀº Áö¼ÓÀûÀ̰í Çõ½ÅÀûÀÎ ¼ºÀåÀ» ÀÌ·ê Áغñ°¡ µÇ¾î ÀÖ½À´Ï´Ù.

ºÎ¹®

±¸¼º¿ä¼Ò(Çϵå¿þ¾î ±¸¼º¿ä¼Ò, ¼ÒÇÁÆ®¿þ¾î ±¸¼º¿ä¼Ò), Àü°³(Ŭ¶ó¿ìµå Àü°³, ¿ÂÇÁ·¹¹Ì½º Àü°³), ±â¼ú(·Îº¿ ¹× ÀΰøÁö´É ±â¼ú, µ¥ÀÌÅÍ »çÀ̾𽺠±â¼ú, ±âŸ ±â¼ú)

Á¶»ç ´ë»ó ±â¾÷ »ç·Ê

AI ÅëÇÕ

¿ì¸®´Â °ËÁõµÈ Àü¹®°¡ ÄÁÅÙÃ÷¿Í AI ÅøÀ» ÅëÇØ ½ÃÀå°ú °æÀï Á¤º¸¸¦ Çõ½ÅÇϰí ÀÖ½À´Ï´Ù.

Global Industry Analysts´Â LLM ¹× ¾÷°è °íÀ¯ÀÇ SLMÀ» Á¶È¸ÇÏ´Â ÀϹÝÀûÀÎ ±Ô¹üÀ» µû¸£´Â ´ë½Å ºñµð¿À ±â·Ï, ºí·Î±×, °Ë»ö ¿£Áø Á¶»ç, ¹æ´ëÇÑ ¾çÀÇ ±â¾÷, Á¦Ç°/¼­ºñ½º, ½ÃÀå µ¥ÀÌÅÍ µî ¼¼°è Àü¹®°¡·ÎºÎÅÍ ¼öÁýÇÑ ÄÁÅÙÃ÷ ¸®Æ÷ÁöÅ丮¸¦ ±¸ÃàÇß½À´Ï´Ù.

°ü¼¼ ¿µÇâ °è¼ö

Global Industry Analysts´Â º»»ç ¼ÒÀçÁö, Á¦Á¶°ÅÁ¡, ¼öÃâÀÔ(¿ÏÁ¦Ç° ¹× OEM)À» ±âÁØÀ¸·Î ±â¾÷ÀÇ °æÀï·Â º¯È­¸¦ ¿¹ÃøÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ º¹ÀâÇÏ°í ´Ù¸éÀûÀÎ ½ÃÀå ¿ªÇÐÀº ¸ÅÃâ¿ø°¡(COGS) Áõ°¡, ¼öÀͼº Ç϶ô, °ø±Þ¸Á ÀçÆí µî ¹Ì½ÃÀû, °Å½ÃÀû ½ÃÀå ¿ªÇÐ Áß¿¡¼­µµ ƯÈ÷ °æÀï»çµé¿¡°Ô ¿µÇâÀ» ¹ÌÄ¥ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.

¸ñÂ÷

Á¦1Àå Á¶»ç ¹æ¹ý

Á¦2Àå ÁÖ¿ä ¿ä¾à

Á¦3Àå ½ÃÀå ºÐ¼®

Á¦4Àå °æÀï

KSM
¿µ¹® ¸ñÂ÷

¿µ¹®¸ñÂ÷

Global Intelligent Airways Transport Systems Market to Reach US$25.9 Billion by 2030

The global market for Intelligent Airways Transport Systems estimated at US$16.5 Billion in the year 2024, is expected to reach US$25.9 Billion by 2030, growing at a CAGR of 7.7% over the analysis period 2024-2030. Hardware Component, one of the segments analyzed in the report, is expected to record a 6.8% CAGR and reach US$17.2 Billion by the end of the analysis period. Growth in the Software Component segment is estimated at 9.9% CAGR over the analysis period.

The U.S. Market is Estimated at US$4.5 Billion While China is Forecast to Grow at 11.9% CAGR

The Intelligent Airways Transport Systems market in the U.S. is estimated at US$4.5 Billion in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$5.5 Billion by the year 2030 trailing a CAGR of 11.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 4.0% and 7.4% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 5.1% CAGR.

Global Intelligent Airways Transport Systems Market - Key Trends & Drivers Summarized

Why Are Intelligent Airways Transport Systems Becoming Essential in Modern Aviation?

Intelligent airways transport systems are rapidly emerging as a fundamental necessity in the aviation sector as global air traffic continues to increase and the demand for efficiency, safety, and sustainability reaches unprecedented levels. These systems integrate advanced technologies to manage everything from air traffic control and flight path optimization to airport ground services and passenger experience. With the surge in commercial flights, cargo operations, and urban air mobility solutions, traditional air transport frameworks are becoming inadequate to handle the complexity and volume of modern aviation. Intelligent systems offer real-time data exchange, predictive analytics, and automation capabilities that significantly enhance situational awareness, reduce human error, and streamline decision-making. For airlines, this translates into more punctual departures and arrivals, optimized fuel consumption, and improved aircraft utilization. For airports and regulators, it provides better traffic flow management, enhanced safety oversight, and faster response times in case of emergencies or disruptions. The incorporation of smart sensors, satellite navigation, and AI-driven forecasting tools allows for the dynamic adjustment of routes and schedules based on changing weather conditions, airspace congestion, or runway availability. Moreover, passenger-centric solutions such as biometric boarding, baggage tracking, and automated check-in systems further underscore the need for intelligence across all touchpoints in air transport. As aviation stakeholders strive to meet the dual challenge of expanding capacity while minimizing environmental impact, intelligent airways transport systems are becoming indispensable tools for reshaping the future of flight operations worldwide.

How Is Technology Reshaping the Operational Landscape of Intelligent Airways Systems?

Technological advancement is at the heart of intelligent airways transport systems, transforming the operational landscape of aviation through integration, automation, and real-time connectivity. Artificial intelligence is being applied in air traffic control to predict traffic flows, detect potential conflicts, and suggest optimal rerouting strategies, allowing controllers to manage more aircraft simultaneously with improved safety margins. Machine learning algorithms process massive datasets collected from radar systems, satellites, and aircraft telemetry to fine-tune flight plans, anticipate delays, and enhance fuel efficiency. Internet of Things devices are embedded in airport infrastructure and aircraft systems, enabling continuous monitoring of equipment performance, passenger flow, and cargo movement. Cloud-based platforms ensure that this data is shared securely and instantaneously among all stakeholders, from pilots and dispatchers to air traffic controllers and ground crews. Advanced communication protocols such as Controller Pilot Data Link Communications (CPDLC) are reducing radio congestion and enabling more precise, text-based exchanges between cockpit and control towers. Augmented reality is being used in maintenance and training to assist ground personnel in diagnosing and repairing complex systems with minimal downtime. Additionally, cybersecurity has become a focal point, with aviation authorities and companies investing in resilient architectures to protect interconnected systems from digital threats. As drones and electric vertical takeoff and landing aircraft (eVTOLs) enter the aviation ecosystem, intelligent systems are being adapted to manage low-altitude traffic and integrate these new platforms seamlessly into existing airspaces. The convergence of these technologies is not only making air transport more intelligent but also more responsive, sustainable, and resilient in the face of evolving demands.

How Are Stakeholders Across Aviation Ecosystems Adapting to Intelligent Transport Innovations?

The successful implementation of intelligent airways transport systems depends heavily on the coordination and adaptability of a broad spectrum of aviation stakeholders, each of whom plays a critical role in operationalizing new technologies and aligning with emerging standards. Airlines are investing in smart fleet management tools that provide real-time performance insights, route optimization, and proactive maintenance scheduling, all of which enhance profitability and reduce downtime. Airport authorities are deploying intelligent infrastructure solutions such as automated gates, AI-powered security screening, and dynamic passenger flow management systems to improve throughput and user experience. Regulatory bodies are collaborating with industry groups to update airspace policies, certification frameworks, and data governance models that support safe and standardized use of intelligent technologies. Air navigation service providers are modernizing legacy systems to incorporate advanced surveillance and decision-support tools that allow for dynamic route allocation and flexible airspace management. Manufacturers of aircraft and avionics are integrating intelligent capabilities into next-generation models, ensuring that future fleets are equipped to handle increasingly automated and interconnected environments. Passenger advocacy groups are also becoming more vocal, encouraging transparency, reliability, and accessibility in the deployment of smart technologies. In parallel, training institutions are updating curricula to prepare pilots, engineers, and controllers to operate within intelligent ecosystems, emphasizing data literacy, systems integration, and digital situational awareness. These coordinated efforts are not only shaping the technical direction of intelligent airways systems but are also defining new roles and responsibilities within the aviation value chain. As the ecosystem evolves, successful adaptation will depend on a shared commitment to innovation, collaboration, and continuous improvement.

What Is Fueling the Rapid Growth of the Intelligent Airways Transport Systems Market?

The growth in the intelligent airways transport systems market is driven by several interrelated factors tied to technological advancement, regulatory reform, operational efficiency, and rising passenger expectations. A primary driver is the exponential increase in air traffic volume, particularly in emerging markets where middle-class expansion and regional connectivity are spurring demand for more flights and better service. To meet this demand without sacrificing safety or efficiency, airlines and airports are turning to intelligent systems that enhance visibility, responsiveness, and automation throughout the air transport cycle. Environmental sustainability is another major influence, as industry players seek to reduce carbon emissions and fuel consumption through route optimization, predictive maintenance, and energy-efficient airport operations. Regulatory bodies are also accelerating modernization by mandating digital transformation initiatives and allocating funding for air traffic management upgrades, especially in Europe, North America, and parts of Asia. The competitive dynamics of the aviation industry are encouraging innovation as companies look for technological differentiators that can drive operational excellence and improve customer loyalty. Advances in satellite navigation, cloud computing, and 5G connectivity are enabling more robust and low-latency communication networks, which are essential for real-time decision-making and adaptive traffic management. Additionally, the post-pandemic recovery has catalyzed investments in health-integrated transport technologies, such as contactless boarding and digital health passports, further broadening the scope of intelligent systems. Urban air mobility and drone logistics are introducing new traffic management challenges that intelligent systems are uniquely suited to address. As governments, private sector firms, and international aviation organizations align their efforts to build smarter, safer, and more scalable air transport networks, the intelligent airways transport systems market is poised for sustained and transformative growth.

SCOPE OF STUDY:

The report analyzes the Intelligent Airways Transport Systems market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:

Component (Hardware Component, Software Component); Deployment (On Cloud Deployment, On-Premise Deployment); Technology (Robotic & Artificial Intelligence Technology, Data Science Technology, Other Technologies)

Geographic Regions/Countries:

World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Select Competitors (Total 42 Featured) -

AI INTEGRATIONS

We're transforming market and competitive intelligence with validated expert content and AI tools.

Instead of following the general norm of querying LLMs and Industry-specific SLMs, we built repositories of content curated from domain experts worldwide including video transcripts, blogs, search engines research, and massive amounts of enterprise, product/service, and market data.

TARIFF IMPACT FACTOR

Our new release incorporates impact of tariffs on geographical markets as we predict a shift in competitiveness of companies based on HQ country, manufacturing base, exports and imports (finished goods and OEM). This intricate and multifaceted market reality will impact competitors by increasing the Cost of Goods Sold (COGS), reducing profitability, reconfiguring supply chains, amongst other micro and macro market dynamics.

TABLE OF CONTENTS

I. METHODOLOGY

II. EXECUTIVE SUMMARY

III. MARKET ANALYSIS

IV. COMPETITION

(ÁÖ)±Û·Î¹úÀÎÆ÷¸ÞÀÌ¼Ç 02-2025-2992 kr-info@giikorea.co.kr
¨Ï Copyright Global Information, Inc. All rights reserved.
PC¹öÀü º¸±â