QAROR DARAXTI YORDAMIDA AVTOMASHINA OQIMINI BASHORAT QILISH

Rasulmuxamedov  Maxamadaziz  Maxamadaminovich; Tashmetov Kamoliddin Shuxrat oʻgʻli; Tashmetov Timur  Shuxratovich

Ushbu ishda Toshkent shahri halqa yoʻlining Bogʻishamol koʻchasi bilan kesishgan chorrahada transport oqimini oʻrganishga qaratilgan. Tadqiqotning obyekti sifatida transport oqimi va uning dinamik koʻrsatkichlari, yaʼni intensivligi, zichligi va tezlik kabilar tadqiqot uchun oʻrganilgan va qayta ishlangan. Tadqiqotda qoʻyilgan asosiy masala, qaror daraxti yordamida transport oqimini bashorat qilish va buning asosida transport harakatini boshqarish masalalari olingan. Shu bilan birga ushbu ishda yoʻl harakatiga toʻsqinlik qiluvchi omillar tahlili va bu omillarni kamaytirish boʻyicha fikrlar keltirilgan. Tahlil natijalarida hozirgi kunda jadal rivojlanib kelayotgan yoʻnalishlarga alohida urgʻu berilib, bunda mashinani oʻrgatish, neyron tarmoqlari va intellektual transport tizimlari kabi texnologiyalarni transport sohasiga tobora kirib kelayotgani aniqlangan. Bu yoʻnalishlarning ichidan mashinani oʻqitish yoʻnalishining algoritmi, usuli va modellari tahlil qilingan. Qilingan tahlillar shuni koʻrsatdiki, qaror daraxti, tasodifiy oʻrmon va gradient boosting kabi modellar transport oqimini bashorat qilishda keng qoʻllanilishi maʼlum boʻldi. Ushbu ishda qaror daraxti yordamida ham Toshkent halqa yoʻli va Bogʻishamol koʻchasining yoʻllardagi transport oqimini bashorat qilish modeli yaxshi natijalarni koʻrsatdi. Bu koʻrsatkichni baholashda determinatsiya koeffitsiyenti qoʻllanildi va uning koʻrsatkichi 92% ni koʻrsatdi. Bu bashorat uchun yaxshi koʻrsatkich ekanligi aniqlandi.

Name of journalИнновацион технологиялар
Number of edition2024/2(54)
View count 93

Web Address

DOI

Date of creation in the UzSCI system 12-09-2024

Read count 93

Date of publication 27-06-2024

Main LanguageO'zbek

Pages46-53

Tags

model

algoritm

bashorat

transport oqimi

tirbandlik

qaror daraxti

mashinani oʻrgatish modellari

determinatsiya koeffitsiyenti

Ўзбек

Ushbu ishda Toshkent shahri halqa yoʻlining Bogʻishamol koʻchasi bilan kesishgan chorrahada transport oqimini oʻrganishga qaratilgan. Tadqiqotning obyekti sifatida transport oqimi va uning dinamik koʻrsatkichlari, yaʼni intensivligi, zichligi va tezlik kabilar tadqiqot uchun oʻrganilgan va qayta ishlangan. Tadqiqotda qoʻyilgan asosiy masala, qaror daraxti yordamida transport oqimini bashorat qilish va buning asosida transport harakatini boshqarish masalalari olingan. Shu bilan birga ushbu ishda yoʻl harakatiga toʻsqinlik qiluvchi omillar tahlili va bu omillarni kamaytirish boʻyicha fikrlar keltirilgan. Tahlil natijalarida hozirgi kunda jadal rivojlanib kelayotgan yoʻnalishlarga alohida urgʻu berilib, bunda mashinani oʻrgatish, neyron tarmoqlari va intellektual transport tizimlari kabi texnologiyalarni transport sohasiga tobora kirib kelayotgani aniqlangan. Bu yoʻnalishlarning ichidan mashinani oʻqitish yoʻnalishining algoritmi, usuli va modellari tahlil qilingan. Qilingan tahlillar shuni koʻrsatdiki, qaror daraxti, tasodifiy oʻrmon va gradient boosting kabi modellar transport oqimini bashorat qilishda keng qoʻllanilishi maʼlum boʻldi. Ushbu ishda qaror daraxti yordamida ham Toshkent halqa yoʻli va Bogʻishamol koʻchasining yoʻllardagi transport oqimini bashorat qilish modeli yaxshi natijalarni koʻrsatdi. Bu koʻrsatkichni baholashda determinatsiya koeffitsiyenti qoʻllanildi va uning koʻrsatkichi 92% ni koʻrsatdi. Bu bashorat uchun yaxshi koʻrsatkich ekanligi aniqlandi.

Tags

model

algoritm

bashorat

transport oqimi

tirbandlik

qaror daraxti

mashinani oʻrgatish modellari

determinatsiya koeffitsiyenti

№ Author name position Name of organisation

1 Rasulmuxamedov M.M. fizika-matematika fanlari nomzodi, dotsent Toshkent davlat transport universiteti

2 Tashmetov K.S. doktorant (PhD), Toshkent davlat texnika universitet

3 Tashmetov T. . assistent Toshkent davlat texnika universitet

№ Name of reference

1 Alajali, W., Zhou, W., & Wen, S. (2018). Traffic flow prediction for road intersection safety. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), (pp. 812-820).

2 Alajali, W., Zhou, W., Wen, S., & Wang, Y. (2018). Intersection traffic prediction using decision tree models. Symmetry, 386

3 Babaei, M., & Behzadi, S. (2023). Spatial Data-Driven Traffic Flow Prediction Using Geographical Information System. Journal of Soft Computing in Civil Engineering

4 Crosby, H., Davis, P., & Jarvis, S. A. . (2016). Spatially-intensive decision tree prediction of traffic flow across the entire UK road network. In 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), (pp. 116-119)

5 Hou, Y., Edara, P., & Sun, C. (2014). Traffic flow forecasting for urban work zones. IEEE transactions on intelligent transportation systems, 1761-1770.

6 Irawan, K., Yusuf, R., & Prihatmanto, A. S. (2020). A survey on traffic flow prediction methods. In 2020 6th International Conference on Interactive Digital Media (ICIDM), (pp. 14).

7 Leshem, G., & Ritov, Y. A. (2017). Traffic flow prediction using adaboost algorithm with random forests as a weak learner. International Journal of Mathematical and Computational Sciences, 1-6.

8 Liu, Y. &. (2017). Prediction of road traffic congestion based on random forest. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 361-364.

9 M. Rasulmukhamedov, K. Tashmetov, T. Tashmoetov. (2023). Method of dertermining traffic flow. Scientific and Technical Journal of NamIET, 208-216.

10 Meena, G., Sharma, D., & Mahrishi, M. (2020). Traffic prediction for intelligent transportation system using machine learning. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 145-148.

11 Prasad, K. S. N., & Ramakrishna, S. (2014). An efficient traffic forecasting system based on spatial data and decision trees. Int. Arab J. Inf. Technol., 186-194

12 Rasulmuhamedov M. M., Tashmetov K. Sh., Tashmetov T. Sh. (2023). Models used in the analysis of transport flows. Transportda resurs tejamkor texnologiyalar, (pp. 111-121). Toshkent, Uzbekiston.

13 Rasulmuhamedov M.M., Tashmetov K.Sh., Tashmetov T.Sh. (2024). Zamonaviy transport tizimlarida transport oqimlarini. Fan va texnologiyalar taraqqiyoti jurnali, 4-9.

14 Rasulmuxamedov Maxamadaziz Maxamadaminovich va boshqalar. (2024). Oʻzbekiston Dasturiy mahsulotga guvohnoma №. DGU 35986.

15 Rasulmuxamedov Maxamadaziz Maxamadaminovich va boshqalar. (2023). Oʻzbekiston Dasturiy mahsulotga guvohnoma No. DGU 32609.

16 Tamir, T. S., Xiong, G., Li, Z., Tao, H., Shen, Z., Hu, B., & Menkir, H. M. (2020). Traffic congestion prediction using decision tree, logistic regression and neural networks. IfacPapersOnline, 512-517.

17 Wang, Y. Z. (2020). Short term traffic flow prediction of urban road using time varying filtering based empirical mode decomposition. Applied Sciences, , 20-38.

18 Xia, Y., & Chen, J. (2017). Traffic flow forecasting method based on gradient boosting decision tree. In 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017), pp. 413-416.

19 Расулмухамедов М.М., Ташметов К.Ш. (2024). Модель машинного обучения для прогнозирования транспортных потоков: дерево решений. Вычислительные модели и технологии, (pp. 188-191). Ташкент.

Waiting

№	Author name	position	Name of organisation
1	Rasulmuxamedov M.M.	fizika-matematika fanlari nomzodi, dotsent	Toshkent davlat transport universiteti
2	Tashmetov K.S.	doktorant (PhD),	Toshkent davlat texnika universitet
3	Tashmetov T. .	assistent	Toshkent davlat texnika universitet

№	Name of reference
1	Alajali, W., Zhou, W., & Wen, S. (2018). Traffic flow prediction for road intersection safety. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), (pp. 812-820).
2	Alajali, W., Zhou, W., Wen, S., & Wang, Y. (2018). Intersection traffic prediction using decision tree models. Symmetry, 386
3	Babaei, M., & Behzadi, S. (2023). Spatial Data-Driven Traffic Flow Prediction Using Geographical Information System. Journal of Soft Computing in Civil Engineering
4	Crosby, H., Davis, P., & Jarvis, S. A. . (2016). Spatially-intensive decision tree prediction of traffic flow across the entire UK road network. In 2016 IEEE/ACM 20th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), (pp. 116-119)
5	Hou, Y., Edara, P., & Sun, C. (2014). Traffic flow forecasting for urban work zones. IEEE transactions on intelligent transportation systems, 1761-1770.
6	Irawan, K., Yusuf, R., & Prihatmanto, A. S. (2020). A survey on traffic flow prediction methods. In 2020 6th International Conference on Interactive Digital Media (ICIDM), (pp. 14).
7	Leshem, G., & Ritov, Y. A. (2017). Traffic flow prediction using adaboost algorithm with random forests as a weak learner. International Journal of Mathematical and Computational Sciences, 1-6.
8	Liu, Y. &. (2017). Prediction of road traffic congestion based on random forest. In 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 361-364.
9	M. Rasulmukhamedov, K. Tashmetov, T. Tashmoetov. (2023). Method of dertermining traffic flow. Scientific and Technical Journal of NamIET, 208-216.
10	Meena, G., Sharma, D., & Mahrishi, M. (2020). Traffic prediction for intelligent transportation system using machine learning. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 145-148.
11	Prasad, K. S. N., & Ramakrishna, S. (2014). An efficient traffic forecasting system based on spatial data and decision trees. Int. Arab J. Inf. Technol., 186-194
12	Rasulmuhamedov M. M., Tashmetov K. Sh., Tashmetov T. Sh. (2023). Models used in the analysis of transport flows. Transportda resurs tejamkor texnologiyalar, (pp. 111-121). Toshkent, Uzbekiston.
13	Rasulmuhamedov M.M., Tashmetov K.Sh., Tashmetov T.Sh. (2024). Zamonaviy transport tizimlarida transport oqimlarini. Fan va texnologiyalar taraqqiyoti jurnali, 4-9.
14	Rasulmuxamedov Maxamadaziz Maxamadaminovich va boshqalar. (2024). Oʻzbekiston Dasturiy mahsulotga guvohnoma №. DGU 35986.
15	Rasulmuxamedov Maxamadaziz Maxamadaminovich va boshqalar. (2023). Oʻzbekiston Dasturiy mahsulotga guvohnoma No. DGU 32609.
16	Tamir, T. S., Xiong, G., Li, Z., Tao, H., Shen, Z., Hu, B., & Menkir, H. M. (2020). Traffic congestion prediction using decision tree, logistic regression and neural networks. IfacPapersOnline, 512-517.
17	Wang, Y. Z. (2020). Short term traffic flow prediction of urban road using time varying filtering based empirical mode decomposition. Applied Sciences, , 20-38.
18	Xia, Y., & Chen, J. (2017). Traffic flow forecasting method based on gradient boosting decision tree. In 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017), pp. 413-416.
19	Расулмухамедов М.М., Ташметов К.Ш. (2024). Модель машинного обучения для прогнозирования транспортных потоков: дерево решений. Вычислительные модели и технологии, (pp. 188-191). Ташкент.