EcoLight+: a novel multi-modal data fusion for enhanced eco-friendly traffic signal control driven by urban traffic noise prediction


Ounoughi C., Ounoughi D., Ben Yahia S.

Knowledge and Information Systems, cilt.65, sa.12, ss.5309-5329, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 65 Sayı: 12
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s10115-023-01938-y
  • Dergi Adı: Knowledge and Information Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Sayfa Sayıları: ss.5309-5329
  • Anahtar Kelimeler: CO 2 emissions, Congestion, Data Fusion, Dueling DQN, Fuel consumption, SUMO Simulation, Traffic signal control, Urban noise
  • Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet

Özet

Urban traffic congestion is of utmost importance for modern societies due to population and economic growth. Thus, it contributes to environmental problems like increasing greenhouse gas emissions and noise pollution. Improved traffic flow in urban networks relies heavily on traffic signal control. Hence, optimizing cycle timing at many intersections is paramount to reducing congestion and increasing sustainability. This paper introduces an alternative to conventional traffic signal control, EcoLight+, which incorporates future noise predictions with the deep dueling Q-network reinforcement Learning algorithm to reduce noise levels, CO 2 emissions, and fuel consumption. An innovative data fusion approach is also proposed to improve our LSTM-based noise prediction model by integrating heterogeneous data from different sources. Our proposed solution allows the system to achieve higher efficiency than its competitors based on real-world data from Tallinn, Estonia.