ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
Часть–6_ Мая –2025
358
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"SUN'IY INTELLEKT YORDAMIDA GEOINFORMATSION
TIZIMLARDA YO‘L TARMOQLARINI OPTIMALLASHTIRISH"
Nomozali Uzaqov Hamdamovich
Qarshi davlat texnika universiteti o‘qituvchisi.
G’ayratova Asila Zayniddin qizi
Qarshi Davlat Texnika universiteti talabasi
Tel:+998952780615
Sulaymonova Salomatxon Hamza qizi
Qarshi Davlat Texnika universiteti talabasi
Tel:+998940301426
Anotatsiya
. Ushbu maqolada sun’iy intellekt (SI) texnologiyalarining
geoinformatsion tizimlar (GIT) bilan integratsiyasi orqali yo‘l tarmoqlarini
optimallashtirish imkoniyatlari tahlil qilinadi. Zamonaviy shaharlarda transport
oqimini samarali boshqarish va yo‘l infratuzilmasini takomillashtirish muhim
ahamiyat kasb etadi. SI asosidagi algoritmlar, jumladan neyron tarmoqlar, genetik
algoritmlar va mashinaviy o‘rganish metodlari GIT orqali yig‘ilgan ma’lumotlarni
tahlil qilishda yuqori aniqlik va samaradorlikni ta’minlaydi. Tadqiqot davomida real
vaqtda monitoring, tirbandliklarni bashorat qilish, eng maqbul yo‘nalishlarni aniqlash
kabi vazifalar bo‘yicha SI yondashuvlarining afzalliklari yoritiladi. Shuningdek, GIT
va SI integratsiyasining amaliy misollari va ularning yo‘l tarmog‘i rejalashtirishidagi
roli ko‘rib chiqiladi.
Kalit so‘zlar:
Sun'iy intellect, geoinformatsion tizimlar (GIT), yo‘l tarmoqlari,
transport optimallashtirish, mashinaviy o‘rganish, neyron tarmoqlar.
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
Часть–6_ Мая –2025
359
2181-3187
Annotation
. This article analyzes the possibilities of optimizing road networks
through the integration of artificial intelligence (AI) technologies with geographic
information systems (GIS). Effective traffic flow management and road infrastructure
improvement are of great importance in modern cities. AI-based algorithms, including
neural networks, genetic algorithms, and machine learning methods, provide high
accuracy and efficiency in analyzing data collected through GIS. The study highlights
the advantages of GIS approaches for tasks such as real-time monitoring, traffic jam
prediction, and determining the most optimal routes. Practical examples of GIS and AI
integration and their role in road network planning are also considered.
Keywords
: Artificial intelligence, geographic information systems (GIS), road
networks, transportation optimization, machine learning, neural networks.
Kirish.
Zamonaviy dunyoda shaharlar tez sur’atlarda rivojlanib, transport tizimlari
murakkablashmoqda. Yo‘l tarmoqlarining samarali ishlashi va transport oqimining
optimallashtirilishi – har bir shaharsozlik va yo‘l qurilishi sohasida muhim vazifa
hisoblanadi. Aholining ko‘payishi va transport vositalarining sonining oshishi bilan
yo‘l infratuzilmasida tirbandliklar paydo bo‘lmoqda, bu esa nafaqat vaqt va
resurslarning behuda sarflanishiga, balki atrof-muhitning zarar ko‘rishiga ham olib
keladi. Shu sababli yo‘l tarmoqlarini samarali boshqarish va optimallashtirish
texnologiyalari dolzarb ahamiyat kasb etmoqda.
Geoinformatsion tizimlar (GIT) – geografik ma’lumotlarni yig‘ish, saqlash, tahlil
qilish va tasvirlashga imkon beruvchi ilg‘or axborot texnologiyalari majmui
hisoblanadi. GIT yordamida shaharlarning yo‘l tarmoqlari, transport oqimlari va
boshqa geografik ob’ektlar haqidagi ma’lumotlar real vaqtda kuzatiladi va qayta
ishlanadi. Shu bilan birga, katta hajmdagi ma’lumotlarni samarali tahlil qilish va
ulardan maqsadli natijalar chiqarish ko‘pincha murakkab hisoblanadi.
Sun’iy intellekt (SI) esa ma’lumotlarni avtomatik ravishda tahlil qilish, o‘rganish
va qarorlar qabul qilish imkoniyatini beradi. Mashinaviy o‘rganish, neyron tarmoqlar,
genetik algoritmlar kabi SI metodlari katta hajmdagi ma’lumotlar asosida transport
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
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oqimini bashorat qilish, tirbandliklarni aniqlash va eng maqbul yo‘l tarmoqlarini
belgilashda yuqori samaradorlik ko‘rsatmoqda. SI texnologiyalari geoinformatsion
tizimlar bilan birlashganda, shaharsozlik va transport tizimlarini boshqarishning yangi
avlodi shakllanmoqda.
Ushbu maqolada sun’iy intellekt yordamida geoinformatsion tizimlarda yo‘l
tarmoqlarini optimallashtirishga oid metodlar, ularning afzalliklari va amaliy
qo‘llanilish yo‘nalishlari ko‘rib chiqiladi. Tadqiqot real vaqtda transport oqimini
monitoring qilish, tirbandliklarni bashorat qilish va yo‘l qurilishi hamda rekonstruksiya
jarayonlarini rejalashtirishda SI integratsiyasining ahamiyatini ta’kidlaydi.
Kamchiliklar va ularning ahamiyati
Sun’iy intellekt va geoinformatsion tizimlarning integratsiyasi transport va yo‘l
tarmoqlarini optimallashtirishda katta imkoniyatlar yaratgan bo‘lsa-da, ushbu
yondashuvning ayrim kamchiliklari ham mavjud. Ularga e’tibor qaratish tadqiqotning
yanada samarali rivojlanishi uchun muhimdir.
Birinchidan, SI tizimlari yuqori sifatli va katta hajmdagi ma’lumotlarga tayanadi.
Ammo geoinformatsion ma’lumotlarning to‘liqligi va ishonchliligi ko‘pincha
cheklangan bo‘lishi mumkin. Noaniq, eskirgan yoki yetarlicha yangilanmagan
ma’lumotlar natijalar sifatiga salbiy ta’sir ko‘rsatadi. Shu bois ma’lumotlar yig‘ish va
tozalash jarayoni murakkab va vaqt talab qiluvchi bo‘lishi mumkin.
Ikkinchidan, sun’iy intellekt modellarining murakkabligi va ularning ishlashini
tushunish qiyinligi mavjud. Ko‘p hollarda «qora quti» deb ataluvchi tizimlar
natijalarining ichki mantiqini aniqlash mushkul bo‘ladi, bu esa qaror qabul qilish
jarayonida shaffoflikni kamaytiradi. Ayniqsa, yo‘l tarmoqlarini rejalashtirish kabi
strategik sohalarda bu kamchilik muhimdir.
Uchinchidan, SI tizimlarining real vaqtda ishlashi uchun yuqori hisoblash
quvvatlari va zamonaviy texnologiyalar talab etiladi. Bu esa infrastruktura xarajatlarini
oshiradi va kichik shahar yoki kam resursli hududlarda ushbu texnologiyalarni joriy
qilishni qiyinlashtiradi.
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
Часть–6_ Мая –2025
361
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To‘rtinchidan, inson omilining kamayishi ham muhim kamchilik sifatida
ko‘riladi. Sun’iy intellekt asosida qabul qilingan qarorlar inson nazorati va ekspert
bahosidan mahrum bo‘lsa, noto‘g‘ri yoki kontekstga mos kelmaydigan natijalar yuzaga
kelishi mumkin.
Shu bilan birga, ushbu kamchiliklarga qaramay, SI va GIT integratsiyasi yo‘l
tarmoqlarini boshqarish va optimallashtirish sohasida yangi imkoniyatlar ochib beradi.
Ularning samarali qo‘llanilishi uchun kamchiliklarni bartaraf etish yo‘llarini izlash va
texnologiyalarni rivojlantirish zarur.
Introduction.
In the modern world, cities are developing rapidly, and transport systems are
becoming more complex. The efficient operation of road networks and the optimization
of traffic flow are important tasks in every field of urban planning and road
construction. With the increase in population and the number of vehicles, traffic jams
are occurring in the road infrastructure, which not only leads to a waste of time and
resources, but also to environmental damage. Therefore, technologies for effective
management and optimization of road networks are gaining urgent importance.
Geographic information systems (GIS) are a set of advanced information
technologies that allow collecting, storing, analyzing and visualizing geographic data.
With the help of GIS, information about road networks, traffic flows and other
geographic objects of cities is monitored and processed in real time. At the same time,
it is often difficult to effectively analyze large amounts of data and derive targeted
results from them.
Artificial intelligence (AI) provides the ability to automatically analyze, learn, and
make decisions from data. AI methods such as machine learning, neural networks, and
genetic algorithms are highly effective in predicting traffic flows, identifying traffic
jams, and determining the most optimal road networks based on large amounts of data.
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
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When AI technologies are combined with geographic information systems, a new
generation of urban planning and transport system management is being formed.
This article reviews methods for optimizing road networks in geographic
information systems using artificial intelligence, their advantages, and practical
applications. The study emphasizes the importance of AI integration in real-time traffic
flow monitoring, traffic jam prediction, and planning road construction and
reconstruction processes.
Disadvantages and their significance
Although the integration of artificial intelligence and geographic information
systems has created great opportunities for optimizing transport and road networks,
this approach also has some disadvantages. Paying attention to them is important for
the more effective development of research.
First, SI systems rely on high-quality and large-scale data. However, the
completeness and reliability of geoinformation data can often be limited. Inaccurate,
outdated or insufficiently updated data negatively affects the quality of the results.
Therefore, the process of collecting and cleaning data can be complex and time-
consuming.
Second, there is the complexity of artificial intelligence models and the difficulty
of understanding their operation. In many cases, it is difficult to determine the internal
logic of the results of the so-called “black box” systems, which reduces transparency
in the decision-making process. This drawback is especially important in strategic areas
such as road network planning.
Third, high computing power and modern technologies are required for real-time
operation of SI systems. This increases infrastructure costs and makes it difficult to
implement these technologies in small cities or areas with limited resources.
Fourth, the reduction of the human factor is also seen as a significant drawback.
Decisions based on artificial intelligence, when deprived of human control and expert
judgment, can lead to incorrect or out-of-context results.
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
Часть–6_ Мая –2025
363
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However, despite these shortcomings, the integration of SI and GIT opens up new
opportunities in the field of management and optimization of road networks. For their
effective application, it is necessary to find ways to eliminate shortcomings and
develop technologies
Xulosa
Sun’iy intellekt va geoinformatsion tizimlarning integratsiyasi yo‘l tarmoqlarini
optimallashtirish sohasida yangi imkoniyatlar yaratmoqda. SI metodlari yordamida
transport oqimini tahlil qilish, tirbandliklarni bashorat qilish va eng maqbul
yo‘nalishlarni aniqlash an’anaviy usullarga nisbatan samaraliroq bo‘lib, shaharsozlik
va yo‘l qurilishi jarayonlarini sezilarli darajada yaxshilashi mumkin. Biroq, yuqori
sifatli ma’lumotlarga ehtiyoj, modellar murakkabligi, hisoblash quvvati talablarining
yuqoriligi va inson omilining kamayishi kabi ayrim muammolar ham mavjud. Ushbu
kamchiliklarni bartaraf etish va texnologiyalarni yanada rivojlantirish orqali sun’iy
intellekt va geoinformatsion tizimlarning yo‘l tarmoqlarini boshqarishdagi ahamiyati
yanada oshadi. Natijada, bu yondashuv transport tizimlarining samaradorligini oshirish
va shaharlarning barqaror rivojlanishiga xizmat qiladi.
Conclusion
The integration of artificial intelligence and geographic information systems is
creating new opportunities in the field of road network optimization. Using SI methods,
traffic flow analysis, traffic jam prediction, and determination of optimal routes are
more effective than traditional methods and can significantly improve urban planning
and road construction processes. However, there are also some problems, such as the
need for high-quality data, model complexity, high computational power requirements,
and reduced human factor. By eliminating these shortcomings and further developing
technologies, the importance of artificial intelligence and geographic information
systems in road network management will increase. As a result, this approach will
ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ
https://scientific-jl.org/obr
Выпуск журнала №-69
Часть–6_ Мая –2025
364
2181-3187
serve to increase the efficiency of transport systems and the sustainable development
of cities.
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Bishop, C. M. (2006).
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3.
Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015).
Geographic Information Systems and Science
. Wiley.
4.
Batty, M. (2013).
The New Science of Cities
. MIT Press.
5.
Zhang, J., & Wang, F. (2018). “Applications of Machine Learning in Urban
Traffic Flow Prediction: A Survey.”
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, 18(1), 10–25.
