title: ÐÑÐµÐ½ÐºÐ° Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ V2X ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð² ÐÐ¡Ð£ÐÐ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Assessment of the Implementation of V2X Technologies in Automated Traffic Control Systems Using Reinforcement Learning Methods
Creators: name: ÐÐ¸ÑÑÐµÐ²Ð°, ÐÑÐ±Ð¾Ð²Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: v2x
Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼
Ð°ÑÑÐ´Ð´
ÑÐ²ÐµÑÐ¾ÑÐ¾ÑÐ½Ð¾Ðµ ÑÐµÐ³ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
coâ-Ð²ÑÐ±ÑÐ¾ÑÑ
Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½Ð°Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ
sumo
reinforcement learning
atcs
traffic light optimization
coâ emissions
traffic flow capacity

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Vehicle-to-Everything (V2X) Ð½Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ Ð³Ð¾ÑÐ¾Ð´ÑÐºÐ¸Ñ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ðº Ð¸Ñ Ð¾ÑÐµÐ½ÐºÐµ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ (Reinforcement Learning, RL). ÐÐ°Ð´Ð°ÑÐ¸, ÐºÐ¾ÑÐ¾ÑÑÐµ ÑÐµÑÐ°Ð»Ð¸ÑÑ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÐ·ÑÑÐµÐ½Ð¸Ðµ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ V2X Ð¸ Ð¸Ñ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¸ Ð² Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÐ¼ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸ÐµÐ¼ (ÐÐ¡Ð£ÐÐ). 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÐºÑÐ¸ÑÐµÑÐ¸ÐµÐ² Ð¸ Ð¼ÐµÑÑÐ¸Ðº Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ V2X Ð½Ð° Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ Ð¿ÐµÑÐµÐºÑÑÑÑÐºÐ¾Ð², Ð²ÑÐµÐ¼Ñ Ð¾Ð¶Ð¸Ð´Ð°Ð½Ð¸Ñ Ð¸ Ð²ÑÐ±ÑÐ¾ÑÑ COâ. 3. Ð¡Ð¾Ð·Ð´Ð°Ð½Ð¸Ðµ Ð¸Ð¼Ð¸ÑÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸ Ð² Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð¹ ÑÑÐµÐ´Ðµ SUMO. 4. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ RL-Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð´Ð»Ñ Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐ²ÐµÑÐ¾ÑÐ¾ÑÐ½ÑÐ¼ ÑÐµÐ³ÑÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼. 5. ÐÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ¾Ð² Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÑÐµÐ½Ð°ÑÐ¸ÐµÐ² Ð´Ð¾Ð»Ð¸ V2X ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð². Ð Ð°Ð±Ð¾ÑÐ° Ð±ÑÐ»Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð½Ð° Ð±Ð°Ð·Ðµ Ð¾ÑÐºÑÑÑÐ¾Ð³Ð¾ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ° SUMO, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ Ð¿Ð¾ÑÐ¾ÐºÐ¾Ð² Ð¸ Ð¾ÑÐµÐ½Ð¸Ð²Ð°ÑÑ Ð²Ð»Ð¸ÑÐ½Ð¸Ðµ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ V2X Ð½Ð° ÐºÐ»ÑÑÐµÐ²ÑÐµ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸. Ð ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐ¾Ð·Ð´Ð°Ð½Ñ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð¾ÑÐ¾Ð´ÑÐºÐ¾Ð¹ Ð´Ð¾ÑÐ¾Ð¶Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð²ÐºÐ»ÑÑÐ°ÑÑÐ¸Ðµ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÑÐµÐ½Ð°ÑÐ¸Ð¸ Ð´Ð¾Ð»Ð¸ Ð¿Ð¾Ð´ÐºÐ»ÑÑÑÐ½Ð½ÑÑ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÐµÐ¹ (0â100%). Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð´ÐºÑÐµÐ¿Ð»ÐµÐ½Ð¸ÐµÐ¼ (Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, Q-Learning) Ð¾Ð±ÐµÑÐ¿ÐµÑÐ¸Ð»Ð° Ð°Ð´Ð°Ð¿ÑÐ¸Ð²Ð½Ð¾Ðµ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ ÑÐ°Ð·Ð°Ð¼Ð¸ ÑÐ²ÐµÑÐ¾ÑÐ¾ÑÐ¾Ð², ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð»Ð¾ Ð¼Ð¸Ð½Ð¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð·Ð°Ð´ÐµÑÐ¶ÐºÐ¸ Ð½Ð° Ð¿ÐµÑÐµÐºÑÑÑÑÐºÐ°Ñ Ð¸ ÑÐ½Ð¸Ð·Ð¸ÑÑ Ð²ÑÐ±ÑÐ¾ÑÑ ÑÐ³Ð»ÐµÐºÐ¸ÑÐ»Ð¾Ð³Ð¾ Ð³Ð°Ð·Ð°. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¾Ð´ÐµÐ¼Ð¾Ð½ÑÑÑÐ¸ÑÐ¾Ð²Ð°Ð»Ð¸, ÑÑÐ¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ V2X Ð¸ RL ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸Ñ Ðº Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½ÑÐ¼ ÑÐ»ÑÑÑÐµÐ½Ð¸ÑÐ¼: â Ð¡Ð¾ÐºÑÐ°ÑÐµÐ½Ð¸Ðµ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ Ð¾Ð¶Ð¸Ð´Ð°Ð½Ð¸Ñ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ ÑÑÐµÐ´ÑÑÐ² Ð´Ð¾ 40%; â Ð£Ð²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð¿ÑÑÐºÐ½Ð¾Ð¹ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÐ¸ Ð¿ÐµÑÐµÐºÑÑÑÑÐºÐ¾Ð² Ð´Ð¾ 30%; â Ð¡Ð½Ð¸Ð¶ÐµÐ½Ð¸Ðµ Ð²ÑÐ±ÑÐ¾ÑÐ¾Ð² COâ Ð½Ð° 25%. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ ÑÐµÐºÐ¾Ð¼ÐµÐ½Ð´Ð°ÑÐ¸Ð¸ Ð¿Ð¾ Ð²Ð½ÐµÐ´ÑÐµÐ½Ð¸Ñ V2X ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð² ÐÐ¡Ð£ÐÐ, Ð²ÐºÐ»ÑÑÐ°Ñ ÐºÑÐ¸ÑÐµÑÐ¸Ð¸ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð´Ð¾Ð»Ð¸ Ð¿Ð¾Ð´ÐºÐ»ÑÑÑÐ½Ð½ÑÑ Ð°Ð²ÑÐ¾Ð¼Ð¾Ð±Ð¸Ð»ÐµÐ¹, Ð¿ÑÐ¸ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð´Ð¾ÑÑÐ¸Ð³Ð°ÑÑÑÑ Ð·Ð½Ð°ÑÐ¸ÑÐµÐ»ÑÐ½ÑÐµ ÑÐ»ÑÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»ÐµÐ¹ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿Ð¾Ð´ÑÐ²ÐµÑÐ¶Ð´Ð°ÑÑ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¸Ð½ÑÐµÐ³ÑÐ°ÑÐ¸Ð¸ V2X Ð¸ RL ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ Ð´Ð»Ñ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½ÑÑ ÑÑÐ°Ð½ÑÐ¿Ð¾ÑÑÐ½ÑÑ ÑÐ¸ÑÑÐµÐ¼, Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÑ Ð½Ð° Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ðµ ÐºÐ°ÑÐµÑÑÐ²Ð° Ð³Ð¾ÑÐ¾Ð´ÑÐºÐ¾Ð¹ Ð¸Ð½ÑÑÐ°ÑÑÑÑÐºÑÑÑÑ Ð¸ ÑÐºÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¾Ð¹ ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸. Ð Ð°Ð±Ð¾ÑÐ° Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð° Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼ "Ð£Ð¼Ð½ÑÐ¹ Ð³Ð¾ÑÐ¾Ð´", Ð° ÑÐ°ÐºÐ¶Ðµ Ð´Ð»Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¸ Ð¼Ð¾Ð´ÐµÑÐ½Ð¸Ð·Ð°ÑÐ¸Ð¸ ÑÐ¸ÑÑÐµÐ¼ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð¾ÑÐ¾Ð¶Ð½ÑÐ¼ Ð´Ð²Ð¸Ð¶ÐµÐ½Ð¸ÐµÐ¼ Ð² Ð¼ÐµÐ³Ð°Ð¿Ð¾Ð»Ð¸ÑÐ°Ñ.
description: This work is dedicated to studying the impact of Vehicle-to-Everything (V2X) technologies on the efficiency of urban traffic systems and developing an approach to evaluate their effectiveness using Reinforcement Learning (RL) algorithms. The research set the following goals: 1. Studying the features of V2X technologies and their integration into Automated Traffic Control Systems (ATCS). 2. Developing criteria and metrics for assessing the impact of V2X technologies on traffic performance indicators, such as intersection capacity, waiting time, and COâ emissions. 3. Creating a simulation model of the traffic network in the SUMO environment. 4. Implementing an RL algorithm for adaptive traffic light control. 5. Conducting experiments with various V2X penetration scenarios and analyzing the results. The research was carried out using the open-source SUMO (Simulation of Urban Mobility) tool, which facilitated traffic flow modeling and assessment of the influence of V2X technologies on key performance metrics. Traffic models of urban road networks were created, simulating various scenarios of V2X vehicle penetration (0â100%). The implementation of RL algorithms (e.g., Q-Learning) enabled adaptive control of traffic light phases, minimizing intersection delays and reducing COâ emissions. The results of the research demonstrated significant improvements through the integration of V2X and RL technologies: â Reduction of average vehicle waiting time by up to 40%; â Increase in intersection throughput by up to 30%; â Reduction in COâ emissions by up to 25%. As a result of the study, recommendations for implementing V2X technologies in ATCS were developed, including criteria for determining the optimal penetration level of connected vehicles to achieve substantial improvements in traffic network performance. The findings confirm the efficiency of integrating V2X and RL technologies to create intelligent traffic systems, enhancing urban infrastructure and promoting environmental sustainability. Practical relevance: The work can be applied as part of "Smart City" initiatives and for the development and modernization of traffic management systems in metropolitan areas. The proposed approaches contribute to increasing traffic flow efficiency, reducing delays at intersections, and improving ecological conditions in urban environments.