Bibliographic Details
| Title: |
Izboljšanje učinkovitosti semaforiziranih križišč s simulacijami in strojnim učenjem. (Slovenian) |
| Alternate Title: |
Improving the efficiency of signalized intersections with simulations and machine learning. (English) |
| Authors: |
Hrnjičić, Šemso, Rajkovič, Uroš |
| Source: |
Uporabna Informatika; 2025, Vol. 33 Issue 3, p164-174, 11p |
| Subject Terms: |
SIGNALIZED intersections, MACHINE learning, TRAFFIC signs & signals, TRAFFIC flow, TRAFFIC engineering, ARTIFICIAL neural networks, SIMULATION software, CITY traffic |
| Abstract (English): |
The development of a simulation environment for the study of transport systems enables a deeper understanding and a better solution to traffic challenges. In the research, we focused on the optimization of a traffic light intersection with the aim of improving its efficiency and performance. Using state-of-the-art tools such as Unity and Blender, we have created a dynamic model that can simulate various traffic scenarios and respond to real-time changes in the traffic environment. The collection and analysis of simulation data allowed us to precisely adjust traffic light cycles and implement intelligent traffic control systems. By integrating advanced machine learning technologies, we have developed neural networks that optimize traffic signals and dramatically reduce waiting times. The results of our research show significant improvements in traffic flow and safety, proving that the approach using simulations and the implementation of simulated optimizations is crucial for future improvements in urban traffic planning. [ABSTRACT FROM AUTHOR] |
| Abstract (Slovenian): |
Razvoj simulacijskega okolja za proučevanje prometnih sistemov omogoča globlje razumevanje in boljše reševanje izzivov v prometu. V raziskavi smo se osredotočili na optimizacijo semaforiziranega križišča s ciljem izboljšanja njegove učinkovitosti in zmogljivosti. S pomočjo najsodobnejših orodij, kot sta Unity in Blender, smo ustvarili dinamičen model, ki lahko simulira različne prometne scenarije in se odziva na realno časovne spremembe v prometnem okolju. Zbiranje in analiza podatkov simulacije nam je omogočila, da natančno prilagodimo časovne cikle semaforjev in implementiramo inteligentne prometne nadzorne sisteme. Z integracijo naprednih tehnologij strojnega učenja smo razvili nevronske mreže, ki optimizirajo prometno signalizacijo in dramatično zmanjšajo čakalne čase. Rezultati naše raziskave kažejo občutne izboljšave v pretočnosti in varnosti prometa, kar dokazuje, da je pristop z uporabo simulacij in implementacijo simuliranih optimizacij ključen za prihodnje izboljšave v urbanem prometnem načrtovanju. [ABSTRACT FROM AUTHOR] |
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| Database: |
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